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AI in E-Learning: How to Track and Prove Training Effectiveness

2 October 2025

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Imagine an organization where every employee knows exactly how to grow their skills, and training is no longer seen as a cost but as an investment that drives the entire business forward. Today, this vision is possible thanks to AI-powered tools. These solutions make it easier than ever to connect corporate strategy with everyday learning and development needs.

In this article, you’ll discover how AI can help diagnose skill gaps, design tailored development programs, and act as a strategic advisor to the board by clearly demonstrating how training impacts business results – from cost reduction to increased innovation.

1. AI as a Breakthrough in Measuring Training Effectiveness

1.1 Why Course Completion Rates Are No Longer Enough

Just a few years ago, the success of training programs was measured by simple metrics: how many employees completed a course and how they rated it in a survey. At first glance, those tables full of “checked-off” results gave leaders a sense of control. But today, that picture is far too flat. Boards are no longer satisfied with completion clicks. They want proof that training drives real change – higher revenues, lower costs, faster onboarding, or greater readiness to embrace innovation.

The e-learning function cannot operate in isolation from the company’s strategy – its effectiveness depends on close collaboration with the board. This is what shifts training from being a “nice-to-have” to a strategic growth tool. When priorities are set together, development programs focus on the skills that truly matter – entering new markets, supporting digital transformation, or boosting innovation. This collaboration also enables faster responses to business needs and provides stronger budget justification by showing ROI in hard numbers. Even more, integrating learning data with analytics tools makes it possible to report measurable outcomes – from reducing operational errors to increasing sales – positioning training as a genuine investment in the company’s future.

1.2 How AI and Power BI Enable Real-Time Reporting

Artificial intelligence opens a new chapter. AI tools now automate course creation and, when connected with e-learning platforms, enable reporting almost in real time.

This is exactly how AI4E-learning works – a dedicated solution that automates and streamlines the entire course creation process, from analyzing source materials to generating ready-to-use e-learning modules.

With AI4E-learning, training that once took weeks can now be created in hours or days. What’s more, it immediately delivers performance data – such as completion rates, time spent on tasks, and areas needing further improvement.

When integrated with platforms like Power BI, AI4E-learning allows CLOs to present data through clear dashboards and link training activity with any business KPI. By synchronizing information from LMS, CRM, and HR systems, organizations gain a full picture of how development programs impact company performance. And because AI4E-learning accelerates course design, it also helps organizations quickly adapt to shifting business priorities.

2. The Strategic Role of the CLO in AI-Enhanced Learning

2.1 The CLO as a Transformation Leader

The Chief Learning Officer is no longer simply responsible for delivering training. Today, the CLO is a transformation leader who leverages AI to monitor, predict, and optimize the impact of development initiatives.

The example of L’Oréal illustrates how this role is evolving. Nicolas Pauthier implements a learning strategy built on cohort-based learning and precise skills mapping. As CLO, he doesn’t just organize training – he advises the board strategically. His focus is on creating experiences that emotionally engage employees, motivating them to learn, while also reporting the business value of training programs – from increased sales to cost reductions. This shows that an effective CLO bridges the gap between people development and strategic business goals – and AI-driven analytics are invaluable in achieving this.

2.2 Linking Training to Business Priorities

When training is directly tied to company priorities, employee development stops being a cost and becomes an investment that truly drives business growth. That’s when learning starts working toward strategic goals – and the results are visible in practice.

Imagine a company entering a new market. Without preparation, this could mean months of chaos and costly mistakes. But with prior training on local regulations, customer service, or language skills, employees are ready from day one, making expansion faster and safer. The same applies to cost reduction: when production teams complete safety training on new procedures, workplace accidents and downtime decrease, delivering immediate savings.

In digital transformation, training also bridges the gap between investing in new technologies and actually using them. A company that equips employees with AI and automation skills will see a faster return on investment than one that expects staff to “figure it out themselves.” Similarly, strategically developed skills – such as customer service excellence or agile methodologies – are hard to replicate and become a unique competitive asset.

And finally, there’s the human factor. Employees who see that training is not “for show” but genuinely helps them in their daily work and supports organizational goals feel a stronger sense of purpose. This boosts motivation, increases engagement, and ultimately reduces turnover and recruitment costs.

3. Key Business Metrics Measured Through E-Learning

E-learning opens entirely new possibilities for measuring effectiveness, allowing organizations to track indicators that were practically impossible to capture in traditional training.

Learning Management Systems (LMS) record every step of the learning journey – from logins and activity on the platform to test results. When combined with analytics tools and artificial intelligence, this data goes far beyond completion rates. It becomes a valuable source of insight into skill development and its impact on overall business performance.

So, what do learning leaders in large organizations measure today?

3.1 Revenue Growth Prediction – Linking Training to Sales

This metric predicts how specific training programs can directly influence company revenue growth.

AI-powered tools analyze data from LMS platforms and sales systems to identify correlations between employee training participation and business results. For example: after a product training, the sales team may achieve a higher conversion rate or increase average deal size. AI not only identifies these relationships retroactively but can also forecast how much revenue will grow if a given group of employees completes the course.

This measurement helps set training priorities – highlighting which programs have the greatest impact on sales and business growth. It also enables companies to predict which skills will be most critical for financial performance in the near future.

3.2 Cost Reduction Analysis – Fewer Errors and Downtime

Another measurable benefit of AI-driven e-learning is cost savings. This analysis shows to what extent training helps reduce both operational and strategic costs.

In practice, this could mean fewer production errors after quality training, fewer customer complaints following service courses, or reduced downtime thanks to better-prepared technical teams. AI compares LMS data with inputs from operational, financial, and HR systems to clearly demonstrate where training has lowered costs.

This approach allows CLOs to speak the board’s language: instead of reporting how many employees completed a course, they can show that customer complaints dropped by 15% – translating into hundreds of thousands of dollars saved annually. Training thus becomes a tangible element of cost optimization and organizational efficiency.

3.3 Time-to-Competency – Faster Path to Full Productivity

Time-to-Competency measures how long it takes an employee to reach full productivity after training. Traditionally, this was difficult to capture – organizations often didn’t know exactly when a new hire became fully effective.

With e-learning, especially AI-enhanced tools, this process is measurable. LMS platforms track how quickly employees absorb knowledge, complete assignments, and pass assessments. AI then compares these results with job performance data – such as projects delivered, customers handled, or sales closed. CLOs can therefore precisely determine how long it takes to move from training to peak performance.

Shortening Time-to-Competency brings measurable benefits: faster onboarding, less disruption in operations, and reduced costs of adaptation.

3.4 Sentiment Analysis – The Learner’s Voice as a Data Source

With natural language processing (NLP), organizations can analyze comments, surveys, ratings, and even communication patterns to understand learners’ satisfaction and engagement levels.

Traditional training relied on simple surveys like “Rate the course from 1 to 5.” Sentiment analysis goes much further – capturing nuances and distinguishing between polite ratings and genuine enthusiasm (or frustration). AI can, for example, reveal that employees respond positively to interactive modules and practical exercises but react negatively to long, monotonous video content.

This measurement is extremely valuable, not only for improving training programs but also for linking learner satisfaction to broader metrics – such as talent retention and organizational culture. In effect, sentiment analysis provides a window into how training influences workplace climate, employee motivation, and the team’s readiness for future growth.

3.5 Innovation Readiness Score – Preparing for Innovation

This metric answers a crucial question: are our employees ready to adopt and co-create innovation, or do they still need additional support?

AI evaluates not only e-learning course data but also the pace of acquiring new skills, engagement in project tasks, and openness to new technologies. This helps determine the extent to which a team is prepared for the implementation of AI tools, new sales processes, or digital production solutions.

The metric is highly practical because it reflects not only current skill levels but also the organization’s innovation potential. A high score signals that the company can confidently invest in new technologies or business models, while a low score highlights the need to strengthen training programs and foster a culture that embraces change.

4. From AI Data to Strategic Insights for the Board

4.1 Reports that Speak the Language of Business

Data gathered from AI tools only gains real value when translated into insights that executives can act upon. Raw statistics – such as logins, course completions, or average learning time – don’t reveal whether training investments truly support business growth. Only well-prepared reports allow CLOs to highlight clear connections: faster onboarding of new hires, reduced operational costs, or increased sales following product training. In this way, training becomes part of strategic discussions, not just an operational activity of the L&D department, and executives receive concrete proof that people development drives both financial results and competitiveness.

In practice, one of the most effective ways to report training outcomes to the board is through interactive dashboards. With tools like Power BI, organizations can build visualizations that clearly show how learning initiatives impact business performance. For example, a dashboard might display course completion rates alongside sales results, making it easy to see how product training improves sales team effectiveness. Another visualization could compare the number of errors or operational downtimes before and after training, providing evidence of cost savings.

Equally valuable for executives is tracking Time-to-Competency – the average time it takes new employees to reach full productivity. For companies focused on innovation, a dedicated panel displaying the Innovation Readiness Score adds another dimension, showing the organization’s readiness to adopt new technologies and business models. Dashboards like these help structure complex data and enable more informed business decisions based on facts, figures, and forecasts.

4.2 Predictive Analytics as a Driver of Smarter Planning

Predictive analytics is more than just a buzzword – it’s a powerful tool that is changing the way business decisions are made. Its strength lies in the ability to forecast the future based on data, rather than only analyzing the past. In the context of e-learning, this means CLOs and L&D teams don’t have to wait until skill gaps emerge – they can proactively design development programs in the areas where demand will grow in one, two, or three years.

For example, if a company is introducing process automation in customer service, predictive analytics will show that the demand will shift away from routine operational skills – soon to be handled by AI – and toward soft skills such as problem-solving, abstract thinking, relationship building, and empathy. These are precisely the qualities that artificial intelligence has yet to master, and they are becoming increasingly valuable in modern organizations.

As AI automates repetitive tasks, the focus of human work moves to more complex and creative areas. For employees, this means developing new capabilities – analyzing data instead of manually entering it, designing solutions rather than just following instructions, or engaging in conversations with clients in challenging, emotional situations where empathy and emotional intelligence are crucial. For CLOs, this represents both a challenge and an opportunity: well-designed training programs can prepare the organization for a future where competitive advantage is defined not by the quantity of work done, but by its quality and adaptability.

In other words, predictive analytics powered by AI helps not only forecast which skills will be needed in the future but also build development programs around the capabilities that AI will not replace anytime soon – abstract thinking, creativity, empathy, and decision-making under uncertainty.

In the e-learning context, predictive analytics provides CLOs and L&D teams with the ability to:

Forecast skill demand – anticipate which competencies will be critical in 2–3 years due to expansion plans or the introduction of new technologies.
Identify skill gaps before they become problems – AI can highlight which departments will need additional training to meet future challenges.
Predict the business impact of training – estimate outcomes such as increased sales after launching a targeted development program.
Optimize training investments – identify which programs deliver the highest ROI and which have only a marginal impact.

5. AI-Based Measurement Challenges – and How to Overcome Them

5.1 System integration

One of the biggest challenges in implementing AI-driven solutions is the lack of integration between systems. The key to overcoming this lies in having a technology partner who not only understands integration but also the business context and the specifics of different organizational areas. This is exactly how TTMS operates – combining expertise in AI implementation with practical knowledge in HR, sales, and e-learning. Our developers work hand in hand with domain experts, ensuring that solutions address real business needs.

This approach is particularly valuable for companies without specialized in-house teams. By partnering with TTMS, they gain immediate access to proven practices from large organizations, regardless of their own resource scale.

5.2 Data security and compliance

Adhering to data security standards and ensuring ethical data use are fundamental in today’s unstable geopolitical climate. Cyberattacks are increasing every year, and data leaks are no longer a movie plotline but a real and serious threat to businesses.

That’s why it is essential to implement modern cybersecurity measures and ensure full compliance with regulations such as the AI Act and ISO standards. Collaborating with a partner who can embed cybersecurity into every stage of software implementation is the safest path forward.

5.3 New analytical competencies for L&D teams

To fully unlock the potential of AI, L&D teams need to strengthen their ability to interpret data and apply it in a business context. Modern e-learning programs collect and integrate large volumes of information from LMS platforms, which requires developing new analytical skills, including:

Data literacy – the ability to read, interpret, and draw conclusions from reports and dashboards.
Learning analytics – identifying participation trends, measuring engagement, and evaluating training effectiveness.
Data storytelling – translating raw numbers into clear narratives for managers and executives (e.g., ROI of training, impact on business KPIs).
Predictive analytics – using AI models and statistics to forecast training needs, knowledge gaps, and future competency demands.
Data governance and compliance – understanding legal frameworks (e.g., GDPR, AI Act) and applying ethical, secure data management practices.
Connecting HR and business data – integrating learning metrics with workforce turnover, performance, and team outcomes.
Experimentation and A/B testing – designing and analyzing training format experiments to optimize L&D programs.

Fortunately, many of these areas can already be supported by AI-powered tools. AI can:

Automate data analysis – process large data sets quickly and uncover hidden patterns.
Generate predictions – anticipate which employees may struggle to complete courses or which competencies will be in shortage in the future.
Deliver actionable insights – e.g., “sales teams learn faster with video content than with e-books.”
Personalize learning experiences – adapt training to individual learner profiles and preferences.
Support data storytelling – automatically create summaries that make training results more accessible to decision-makers.

6. Strategic Recommendations for CLOs and Executive Boards

6.1 Designing AI-Ready KPIs

Designing KPIs with AI-powered tools in mind should begin as early as the program development stage. Clearly defining business goals and performance indicators allows organizations to measure training effectiveness with precision later on. Modern e-learning platforms provide data that significantly enrich analysis – from tracking participant engagement in detail (e.g., where learners pause during video modules or which quizzes they find most challenging) to assessing learning speed and preferred learning styles (visual vs. text-based), as well as measuring knowledge transfer into practice by integrating training outcomes with corporate systems.

As a result, KPIs can be designed to capture real training effectiveness, not just user activity. Examples include developmental indicators such as tracking skill progression over time or predictive KPIs that use AI algorithms to forecast whether an employee will reach the required knowledge level within a defined timeframe.

When building KPIs, it is important to avoid focusing solely on quantitative data – for instance, the number of LMS logins does not reflect training effectiveness. A dynamic approach is essential: KPIs should be reviewed and adjusted during training programs. Equally important is combining data from multiple systems – LMS, CRM, and HRIS – to provide a holistic view of training impact on the organization.

In practice, AI-powered e-learning KPIs can be divided into several categories:

Cost-efficiency KPIs – measuring training ROI, e.g., cost per employee vs. performance improvement or reduced onboarding time.
Adaptive KPIs – focusing on organizational readiness for market changes, such as reskilling and upskilling speed or time to adopt new tools and processes.
Business KPIs – directly tied to company results, such as increased sales after training or improved customer service quality.
Strategic KPIs – measuring competitive positioning, e.g., response time to industry shifts or the percentage of critical competencies covered by AI-driven learning paths.

6.2 Quarterly Reporting Cycles

Quarterly reporting provides the optimal balance between strategic and practical perspectives for executive boards. A three-month cycle is long enough to capture the real effects of both training and business initiatives, yet short enough to allow for timely adjustments when results diverge from the intended strategy.

Quarterly reports avoid the information overload often caused by monthly reporting, focusing instead on what matters most to executives: trends, patterns, and the impact of initiatives on business goals. This reporting rhythm also aligns naturally with corporate budgeting and financial cycles, making it easier to compare learning KPIs with operational and financial outcomes.

In the training context, quarterly summaries offer an additional advantage – they allow enough time to gather reliable data, observe how knowledge is applied in practice, and analyze results through AI-powered tools. Regular quarterly reporting also strengthens organizational accountability and transparency by creating a consistent rhythm in which every initiative is not only launched but also evaluated and continuously improved based on actionable insights.

7. Conclusion – AI as a Lever for Strategic Growth

Artificial intelligence not only streamlines the course creation process but also empowers Chief Learning Officers (CLOs) to report training effectiveness in a way that is accurate, predictive, and aligned with executive expectations.

Transition Technologies MS (TTMS) supports learning leaders in measuring the impact of development initiatives by delivering solutions that combine data analytics, AI tools, and seamless integration with enterprise systems. With deep expertise in designing and implementing digital platforms, TTMS enables organizations not just to capture learner activity but to translate it into concrete business metrics.

By integrating e-learning platforms with CRM, HRIS, and ERP systems, TTMS helps link training outcomes directly to measurable results such as revenue growth, improved customer service quality, or faster onboarding of new employees. The company also provides support in creating dedicated dashboards and quarterly reports that clearly present the effectiveness of L&D initiatives and the ROI of workforce development to executive boards.

As a result, e-learning teams gain tools that not only simplify performance monitoring but also demonstrate the strategic value of training for the entire organization.

And if managing e-learning courses and organizational knowledge feels like a challenge, make sure to visit our page – LMS Administration Services | TTMS.

Explore our dedicated tool for rapid online course creation – AI4E-learning.

Check out our full range of AI solutions for business.

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Salesforce and OpenAI Partnership — A New Era of Intelligent Organisations

The enterprise AI landscape has just witnessed a groundbreaking shift. At Dreamforce 2025, Salesforce and OpenAI unveiled a major expansion of their strategic partnership that promises to fundamentally change how businesses work, sell, and serve customers. This isn’t just another integration announcement—it’s a vision for the “agentic enterprise,” where artificial intelligence and human expertise converge in natural, conversational interfaces that live directly inside the tools people already use every day. 1. Dreamforce 2025 Conference: Announcing a New Era of Artificial Intelligence in Business The collaboration between Salesforce and OpenAI represents a seismic shift in how enterprise technology operates. Instead of forcing employees to switch between multiple applications, dashboards, and interfaces, this partnership brings powerful AI capabilities directly into ChatGPT, Slack, and the Salesforce platform itself. 1.1 Deep OpenAI–Salesforce Integration — Revolutionary AI Integration in CRM Systems The partnership introduces several transformative capabilities that bridge the gap between frontier AI models and enterprise data. Salesforce customers can now leverage OpenAI’s latest models, including the advanced GPT-5 system, to build intelligent agents and prompts directly within the Salesforce Platform. GPT-5 represents a unified AI system that intelligently decides when to respond quickly and when to engage in deeper reasoning to provide expert-level responses. But the real innovation goes beyond just model access. This partnership also encompasses collaborations with Stripe to create the Agentic Commerce Protocol, with Anthropic to serve regulated industries, and with Google to integrate Gemini models into the Agentforce 360 ecosystem. Together, these partnerships position Salesforce as a central hub for enterprise AI, giving customers unprecedented choice and flexibility. 1.2 Agentforce 360 in the ChatGPT environment — full CRM and AI integration One of the most striking announcements is that Salesforce’s Agentforce 360 platform will be accessible directly within ChatGPT. This means that users can query sales records, review customer conversations, and even build sophisticated Tableau visualizations simply by typing natural language questions into ChatGPT. Imagine a sales manager asking, “Show me my top five opportunities closing this quarter,” and instantly receiving not just data, but actionable insights and visualizations—all without leaving the chat interface. This represents a fundamental reimagining of how work gets done, moving from application-centric workflows to conversation-driven productivity. 2. Salesforce and OpenAI Are Changing How We Work with CRM Systems The partnership fundamentally transforms the employee experience by making enterprise data and workflows conversational, accessible, and intuitive. 2.1 From Prompt to Decision — How AI Streamlines Everyday Work Traditional business intelligence requires navigating complex interfaces, running reports, and manually assembling insights. The Salesforce-OpenAI integration changes this entirely. Employees can now have natural conversations with their business data, asking questions in plain language and receiving immediate, contextual responses grounded in their CRM, analytics, and operational systems. This conversational approach dramatically reduces the time between question and action. A manager preparing for a quarterly review no longer needs to log into multiple systems, export data, and create presentations manually. Instead, they can simply ask for what they need, and the AI assembles it in real time. 2.2 AI Agents in Slack, Tableau, and CRM The integration extends deeply into Slack, which Salesforce positions as the “Agentic Operating System” for the modern enterprise. ChatGPT is now available directly within Slack, enabling teams to draft content, summarize lengthy conversation threads, search across organizational knowledge, and connect with internal tools—all without leaving their collaboration environment. Additionally, OpenAI’s Codex agent comes to Slack, allowing developers to delegate coding tasks using natural language commands. This means engineers can describe what they need built, and the AI can generate, test, and refine code directly within Slack threads. The partnership also brings voice and multimodal capabilities to the Agentforce 360 Platform, enabling richer, more intuitive interactions across every customer touchpoint. 3. Agentic Commerce — Lightning-Fast Shopping and More Perhaps the most consumer-facing innovation is Agentforce Commerce, which transforms how people discover and purchase products online. 3.1 Agentforce Commerce — Shopping Directly in ChatGPT Through the new integration, merchants using Salesforce’s Agentforce Commerce can now surface their product catalogs directly within ChatGPT, reaching hundreds of millions of potential customers where they already spend time. When a user expresses interest in a product during a ChatGPT conversation, they can complete the entire purchase without ever leaving the chat interface. This isn’t just about convenience—it’s about capturing demand at the exact moment of discovery.Research from Salesforce reveals that 48% of shoppers who already use AI are open to having an AI agent make purchases on their behalf. The Agentforce Commerce integration makes this future a reality today. 3.2 Secure Transactions with Stripe and the Agentic Commerce Protocol Security and trust are paramount in any commerce transaction. That’s why Salesforce partnered with Stripe and OpenAI to develop the Agentic Commerce Protocol (ACP)—an open-source framework that standardizes how businesses interact with consumers through AI agents while maintaining full control over customer relationships, data, and fulfillment. The protocol ensures that payment information remains secure, merchants retain the direct customer relationship throughout the purchase flow, and businesses can accept or decline orders based on their own risk assessment. Stripe’s robust financial infrastructure handles the payment processing, including support for Link and multiple payment methods, while merchants maintain complete ownership of the post-purchase experience. This three-way collaboration between Salesforce, Stripe, and OpenAI creates a complete, end-to-end solution that empowers merchants to drive revenue growth and build deeper customer loyalty directly within platforms where shoppers already reside. 4. What Impact Will the Salesforce and ChatGPT Partnership Have on Businesses and Customers? The partnership delivers tangible benefits for both employees and customers, fundamentally changing how organizations operate and engage with their markets. 4.1 AI Support for Sales Teams For employees, the integration eliminates the cognitive overhead of switching between applications and remembering complex query syntax or navigation paths. Sales representatives can access CRM insights conversationally, support agents can retrieve knowledge articles and customer history through natural language, and analysts can generate visualizations without mastering business intelligence tools. Early adopters are already seeing remarkable results.Reddit deployed Agentforce to handle advertiser support inquiries, achieving 46% case deflection and reducing resolution times by 84%—from an average of 8.9 minutes down to just 1.4 minutes. This efficiency improvement allowed Reddit to boost advertiser satisfaction by 20% while freeing human representatives from repetitive questions. 4.2 New Customer Engagement Channels — The Same Quality of Service For customers, the partnership creates seamless experiences across their preferred channels. Whether they’re chatting with an AI agent in ChatGPT, speaking with a voice-enabled agent over the phone, or shopping directly through conversational interfaces, the experience is consistent, personalized, and grounded in their complete customer history. Agentforce Voice, a key component of the Agentforce 360 Platform, delivers natural, real-time voice conversations with ultra-low latency that feels genuinely human. These voice agents can update CRM records, trigger workflows, call APIs, and execute meaningful actions—all while maintaining a conversation that flows naturally and reflects the brand’s unique tone and personality. 5. Trustworthy AI — Secure Solutions for Business Enterprise adoption of AI hinges on trust, security, and compliance—areas where Salesforce has built a comprehensive framework. 5.1 GPT-5, Anthropic Claude — Combining the Power of Models with Salesforce Security Salesforce gives customers unprecedented choice in AI models by integrating multiple frontier providers. Beyond OpenAI’s GPT-5, the partnership with Anthropic makes Claude a preferred model for regulated industries including financial services, healthcare, cybersecurity, and life sciences. Anthropic represents the first LLM vendor to be fully integrated within Salesforce’s trust boundary, meaning all Claude traffic remains contained within Salesforce’s virtual private cloud. The partnership with Google brings Gemini models into the Atlas Reasoning Engine, the intelligence layer behind Agentforce 360. This hybrid reasoning approach combines the creativity and flexibility of large language models with the reliability and predictability of structured business processes. All of these models operate within the Einstein Trust Layer—Salesforce’s secure AI architecture built directly into the platform. The Trust Layer provides multiple security guardrails including secure data retrieval that respects existing user permissions, data masking that identifies and protects sensitive information before it reaches external models, zero data retention agreements with all LLM providers, toxicity detection on generated content, and complete audit trails. 5.2 AI That Meets the Highest Standards of Regulated Industries For organizations in regulated sectors, compliance isn’t optional—it’s existential. The expanded Anthropic partnership specifically addresses this need by making Claude available through Salesforce’s secure cloud environment, allowing companies to leverage frontier AI capabilities while maintaining the appropriate safeguards for sensitive data and workloads. The partnership also includes plans to co-develop industry-specific AI solutions for regulated sectors, beginning with financial services, that address unique regulatory, privacy, and workflow demands. 6. The Era of Conversational AI: A New Chapter for Enterprises The announcements at Dreamforce 2025 are just the beginning of a longer transformation journey. 6.1 Roadmap for Agentforce 360 and OpenAI Integrations OpenAI frontier models are already live within Agentforce, allowing customers to begin building agents and prompts immediately. ChatGPT and Codex features in Slack are also available as of the announcement. Detailed rollout schedules for Agentforce 360 apps and Agentforce Commerce within ChatGPT will be announced in the coming months as the integrations move from preview to general availability. This phased approach allows Salesforce and OpenAI to refine the experience based on early customer feedback before scaling to millions of users globally. The Data 360 platform, formerly known as Data Cloud, now serves as the unified data layer that provides context and trusted information to every AI agent across the ecosystem. New capabilities like Intelligent Context connect structured data from CRM records with unstructured sources like emails, PDFs, and call transcripts, while Tableau Semantics ensures consistent business definitions across all applications. Feature/Integration Description Platform(s) Availability Agentforce 360 in ChatGPT Query CRM, visualizations, workflows via chat ChatGPT Preview (details TBA) OpenAI models in Salesforce Build agents/prompts, access GPT-5, multimodal/voice features Salesforce Platform Live Instant Checkout Commerce and payments natively in ChatGPT ChatGPT Preview ChatGPT in Slack Draft, summarize, search, connect internal tools Slack Live Codex in Slack Delegate coding tasks using natural language Slack Live Privacy-compliant commerce Secure, embedded transactions, customer control ChatGPT, Stripe Preview 6.2 Competitive Advantage in the Era of AI-Driven Workflows As Marc Benioff emphasized during the Dreamforce keynote, this partnership creates “the trusted foundation for companies to become Agentic Enterprises”. Sam Altman echoed this vision, stating that the collaboration aims to make everyday tools “work better together, so work feels more natural and connected”. The competitive advantage lies not just in having access to powerful AI models, but in how those models are embedded within existing workflows, grounded in trusted enterprise data, and governed by robust security frameworks. Organizations that embrace this conversational, agent-driven approach to work will be able to move faster, make better decisions, and deliver superior customer experiences compared to competitors still operating with traditional, application-centric paradigms. 7. TTMS Insights — Prepare Your Organization for the Era of AI Agents The Salesforce-OpenAI partnership represents more than technological innovation—it signals a fundamental shift in how enterprise software is designed, deployed, and experienced. As businesses evaluate how to leverage these new capabilities, several strategic considerations emerge. First, organizations need to assess their data readiness. The power of conversational AI depends entirely on having clean, accessible, well-governed data that agents can use to provide accurate, contextual responses. Second, companies should identify high-value use cases where conversational interfaces can deliver immediate impact. Customer support, sales enablement, and marketing represent natural starting points where the technology is proven and the ROI is clear. Third, organizations must develop governance frameworks that balance innovation with risk management. This includes establishing clear policies around when AI agents can act autonomously versus when human oversight is required, how sensitive data is protected, and how agent behavior is monitored and audited. 8. How TTMS Helps Companies Build Intelligent Enterprises with Salesforce and OpenAI At TTMS, we specialize in helping organizations navigate complex technology transformations. Our expertise spans Salesforce implementation projects, outsourcing and managed services, and AI integration across Sales Cloud, Service Cloud, Marketing Cloud, Experience Cloud, and Nonprofit Cloud platforms. The convergence of Salesforce’s enterprise CRM platform with OpenAI’s frontier models creates unprecedented opportunities for businesses ready to embrace the agentic enterprise vision. Whether you’re looking to deploy Agentforce agents for customer support, implement Agentforce Commerce to reach new customers through ChatGPT, or integrate voice AI to transform your contact center, TTMS can guide you through every step of the journey. The future of work is conversational, intelligent, and embedded directly in the tools your teams use every day. The question isn’t whether to adopt these technologies—it’s how quickly you can leverage them to gain competitive advantage. With the right strategy, implementation partner, and commitment to data quality and governance, your organization can become an agentic enterprise that operates faster, smarter, and more efficiently than ever before. Contact us now!

The 2025 Guide to Salesforce Marketing: Platforms, Editions, and What’s New

In 2025, Salesforce continues to redefine how organizations connect with customers across every stage of the journey — from lead generation to personalized engagement and long-term loyalty. Its marketing ecosystem, built around the powerful Marketing Cloud platform and Data Cloud foundation, now offers a unified approach for both B2B and B2C marketers. With new editions, smarter automation, and AI-driven orchestration, Salesforce enables marketing teams to move beyond one-size-fits-all campaigns toward real-time, data-powered experiences. Whether you’re exploring Marketing Cloud for the first time or planning to upgrade your current setup, understanding the latest updates is key to making informed decisions. This guide breaks down Salesforce’s marketing platforms and editions, explains what’s new in 2025 — including the rise of AI agents and Marketing Cloud Next — and helps you identify which solution best aligns with your organization’s goals and marketing maturity. 1. Exploring Marketing Cloud Editions – What Each Edition Really Means For Marketing Managers? Choosing the right Salesforce Marketing Cloud edition can be a challenge. Each version offers different capabilities that directly impact how marketing managers plan campaigns, engage customers, and measure results. Below, we break down the main editions and what they actually mean for day-to-day marketing operations. 1.1 Marketing Cloud Growth Edition The Growth Edition is designed for organizations that want to get started with scalable, data-driven marketing. It provides the essentials to manage customer data, run targeted campaigns, and track performance without overwhelming complexity. Unified customer profiles for better audience segmentation Multi-channel campaign execution (email, SMS, push notifications) Automation tools for recurring campaigns Pre-built reporting dashboards For marketing managers, Growth Edition is the right choice when the priority is building a strong foundation and achieving measurable results quickly. 1.2 Marketing Cloud Advanced Edition The Advanced Edition builds on Growth by offering deeper personalization and AI-driven insights. It’s tailored for teams that already have experience with digital marketing and want to scale up sophistication. AI recommendations for personalization at scale Advanced analytics with deeper segmentation options Expanded automation journeys across multiple channels Integration with CRM and third-party apps for connected workflows For managers, this edition means more control over audience targeting, richer insights, and the ability to deliver highly individualized customer experiences. 1.3 Marketing Cloud Engagement Marketing Cloud Engagement focuses on email marketing at scale but extends well beyond simple campaigns. It’s ideal for global organisations who want to orchestrate customer journeys with precision. Advanced email design, testing, and personalization Journey Builder for cross-channel automation Real-time behavioral triggers (abandoned cart, product views, etc.) Deep analytics on engagement and conversions This edition is a fit for organizations where email is still the primary revenue-driving channel but needs to be complemented by journey-based experiences. 1.4 Marketing Cloud Account Engagement (formerly Pardot) Account Engagement is Salesforce’s B2B marketing automation solution. Unlike Engagement, it focuses on lead generation, nurturing, and aligning marketing with sales. Lead scoring and grading to prioritize prospects Automated nurture campaigns based on buyer stage Sales-marketing alignment with CRM integration Analytics on campaign ROI and pipeline contribution For marketing managers in B2B organizations, Account Engagement means shorter sales cycles, more qualified leads, and measurable impact on revenue. 1.5 Marketing Cloud Engagement Plus (2025 upgrade) The Engagement Plus edition, introduced in 2025, brings together the best of Engagement and Advanced capabilities. It’s designed for marketing teams ready to move toward real-time, AI-driven engagement. Hyper-personalization using AI models across all channels Real-time data activation for instant campaign adjustments Expanded capacity for automation and segmentation Deeper integrations with Commerce Cloud and Service Cloud For managers, this upgrade means moving from campaign planning to always-on engagement — campaigns that adapt automatically to customer behavior, increasing both efficiency and ROI. 2. How to Choose the Right Edition Picking the right edition is about fit — not feature envy. Use this quick decision flow as a guide: Are you B2B or B2C? B2B → Account Engagement (MCAE) or Growth if SMB B2B. B2C → Engagement or Engagement Plus for enterprise scale. How advanced are your marketing tools and data systems? Minimal data maturity → Growth Edition (fast setup + Data Cloud starter). Solid CDP and analytics → Advanced or Engagement Plus for maximum leverage. Do you need AI to run campaigns or to augment people? Tactical help (content ideas, personalization) → Growth/Advanced. Autonomous orchestration (agents acting across channels) → Marketing Cloud Next / Engagement Plus features. Budget & staffing constraints: factor in implementation costs, deliverability and creative resources, and ongoing platform admin. Growth reduces upfront lift; Advanced/Plus require more skilled operations. If you’re still unsure about which edition to pick, contact us and schedule a free consultation where we’ll discuss your business needs. 3. The Future: Introducing Marketing Cloud Next In June 2025 Salesforce unveiled Marketing Cloud Next — a reimagined, agentic marketing platform that aims to unify B2B and B2C marketing under a single, AI-native architecture. Built on the core Salesforce platform and Data Cloud, Next reframes marketing from campaign-driven playbooks to agentic marketing — where intelligent agents (Agentforce) autonomously plan, execute, and optimize outreach while preserving human oversight. The goal is two-fold: scale personalization in real time, and reduce the operational friction of multichannel orchestration. Marketing Cloud Next’s tight coupling with Data Cloud removes many historical data silos. A single customer (or account) profile is accessible across B2B and B2C workflows, enabling unified identity, improved measurement, and faster experimentation. Practically, this reduces integration complexity and lets marketers move from “one campaign at a time” to continuous, cross-channel relationship-building. 3.1 AI and Agentforce Agentforce is a set of AI agents that can draft campaign briefs, create journey templates, recommend audience segments, generate creative prompts, and — in some configurations — trigger and optimize campaign execution. For marketing managers this promises dramatic productivity gains: routine work gets automated, insights are surfaced continuously, and campaigns can adapt based on live data signals. 4. Strategic Impact: Why These Changes Matter Salesforce’s unification and AI push are not just product updates — they change how marketing teams organize, measure, and scale. From where we sit, the strategic impacts fall into five practical categories: 4.1 Operational efficiency and cost-to-serve Consolidating tooling reduces integration maintenance, duplicated data models, and the engineering cycles needed to keep disconnected systems speaking. For many clients we work with, this lowers ongoing TCO and frees engineering bandwidth for new features rather than housekeeping. 4.2 Faster time-to-market and campaign velocity Pre-built journeys, templates, and agentic assistants compress the time required to launch tests and campaigns. This speed is especially valuable in retail, travel, or finance where promotional windows are short and reactive campaigns drive material revenue. 4.3 Measurable revenue alignment When marketing systems natively understand accounts and opportunities (B2B) or unified customer lifetime value (B2C), it becomes easier to tie marketing activities to revenue metrics. That shifts marketing from “cost center” reporting to demonstrating direct ROI and influencing budgets. 4.4 Personalization at scale — and the supplier of complexity The ability to personalize at scale increases relevance and LTV, but it also increases creative volume and governance needs. Organizations that succeed are those that pair personalization with content modularity and clear KPIs per segment. 4.5 Data governance, privacy, and regulatory compliance Unified Data Cloud capabilities improve identity resolution but also centralize risk: a single customer profile used across channels must comply with GDPR, ePrivacy, CCPA, and sector-specific rules (finance, health, pharma). 5. Conclusion Salesforce Marketing Cloud is entering a new era — one defined by data unification, AI-driven engagement, and continuous personalization. As features evolve and editions expand, the challenge for organizations is no longer access to technology, but using it strategically: choosing the right edition, connecting systems, and translating automation into measurable business outcomes. At TTMS, we help companies navigate this complexity with a structured, value-first approach. Whether you’re starting with Marketing Cloud Growth Edition or preparing to move into AI-powered Engagement Plus or Next, our experts ensure that every step — from design to adoption — is aligned with your goals and delivers lasting ROI. 6. How TTMS Helps You Choose and Implement the Right Salesforce Solution Selecting the right Salesforce edition is more than a licensing decision — it’s about matching your business goals with the right technology stack. At TTMS, we guide organizations through every stage of this process, ensuring that the chosen Salesforce Marketing Cloud solution delivers measurable business impact. Why work with TTMS? Proven expertise – With years of experience in Salesforce consulting and implementation, we know how to tailor the platform to unique business needs. End-to-end support – From the first workshop to go-live and beyond, we support our clients with implementation, integrations, training, and managed services. Cross-industry knowledge – Our teams have delivered Salesforce solutions for industries such as retail, life sciences, finance, and non-profit, adapting best practices to different business models. Certified consultants – Our Salesforce experts are certified across multiple clouds, including Marketing Cloud, Sales Cloud, Service Cloud, and Experience Cloud. Scalable solutions – Whether your team is just starting with Growth Edition or planning to adopt Engagement Plus, we design roadmaps that evolve with your organization. Our approach We begin with an assessment of your marketing and sales processes to understand current challenges and long-term goals. Based on that, we recommend the most suitable Salesforce edition and define a clear implementation roadmap. Once in place, we integrate Salesforce with your existing ecosystem, ensure adoption across teams, and provide ongoing support to maximize ROI. With TTMS as your Salesforce Consulting Partner, you don’t just get a platform — you get a strategic solution aligned with your growth plans. Let’s talk about how we can help your organization unlock the full potential of Salesforce. Contact us today to schedule a consultation with our Salesforce team.

How to Create Business Apps – 2025 Guide

Creating a mobile app for business is no longer just a nice-to-have. It’s become essential. As digital transformation gains momentum across industries, companies that embrace mobile technologies are ahead of the competition. Whether you want to streamline your team’s workflow or better connect with your customers, learning to build a business app requires strategic thinking, technical expertise, and careful implementation. 1. Why your business needs a mobile app, current trends in the mobile application market The world of mobile apps continues to explode with growth. The global mobile app market reached $252.9 billion in 2023 and is expected to reach $626.4 billion by 2030. This massive growth is fundamentally changing the way businesses connect with customers and conduct business. Mobile devices dominate digital interactions today. Companies that utilize mobile apps gain greater brand visibility, stronger customer relationships, and a real competitive advantage. Interestingly, no-code and low-code platforms have made app development accessible to companies of all sizes. Industry experts predict that by 2025, as many as 70% of new projects will be based on these solutions. App development leaders also emphasize that AI-based predictive analytics are becoming standard in business applications. It’s no longer the exclusive domain of tech giants. This allows companies to deliver highly personalized user experiences, offering recommendations and interfaces that significantly increase engagement and keep users coming back. Another important trend is Progressive Web Apps. They combine the accessibility of websites with the functionality of native apps, a particularly clever solution. This hybrid approach allows companies to reach broader audiences while still providing users with the user experience of apps. On-demand applications are also an extremely strong growth category, with users spending almost $58 billion annually in this sector. 2. Types of business apps you can create Understanding how to build a business app begins with understanding the different types available. Customer-facing apps include e-commerce platforms, appointment booking systems, delivery tracking, and feedback tools. These apps have a direct impact on revenue and customer satisfaction. Internal applications focus on streamlining processes, such as team management platforms, workflow automation tools, and communication systems. There are also industry-specific solutions that address specific needs, such as restaurant ordering systems, real estate listing platforms, medical forms, and event registration tools. Modern application development is flexible enough to create solutions tailored to your processes or niche markets. A simple information application can evolve into a complex platform with payment processing, inventory management, and extensive reporting. 3. Planning a business application strategy 3.1 Defining the purpose and assumptions of the application Learning how to create an app idea begins with a clear understanding of its purpose. Your app should solve specific problems or provide real value to users. Setting measurable goals provides a roadmap for feature development and benchmarks for tracking success. Opar is a good example. This company successfully launched a social app by focusing on user-centric design and advanced matching algorithms that connect people based on location and interests. Ensure your app’s goals align with your broader business strategy. This ensures your app supports your business’s growth, rather than operating in isolation. Ask yourself: is your top priority customer engagement, revenue generation, process improvement, or brand enhancement? A clear answer will shape every decision you make during the development process. 3.2 Target group identification You need to thoroughly understand the demographics, behaviors, and pain points of your audience. This is the foundation of effective app development. Research reveals who will benefit most from your solution and helps prioritize features. A good example is the fitness app of a major sportswear brand. Through data analysis and user research, they discovered that easy navigation and personalized content were key. The result? A 40% increase in user retention and a 60% increase in active engagement. Creating detailed user profiles supports marketing and communication strategies. This research step protects against costly mistakes and ensures your app meets the needs of the right audience. Be sure to include both primary and secondary users, as different types of people may use your app differently. 4. Conducting market research and competitive analysis In-depth market research validates your app idea and demonstrates real demand. Competitive analysis reveals industry standards, popular features, and opportunities for differentiation. Understanding existing solutions allows you to leverage best practices and better understand user expectations in your market segment. Analyzing failed apps provides valuable insights into common mistakes and poor decisions. This knowledge helps you make smarter development choices and avoid repeating the mistakes of others. Market research also reveals effective pricing strategies, monetization models, and user acquisition methods in your industry. 5. Creating user personas and usage scenarios Developing detailed user personas helps you anticipate needs and design features that actually serve them. These extensive profiles represent your ideal audience, taking into account their goals, frustrations, and behavioral patterns. Usage scenario mapping clarifies how different types of users will use your app in real-world situations. This process ensures the application remains intuitive and addresses the problems users actually face. Usage scenarios provide guidance in developing functional requirements and designing user journeys, creating a roadmap to seamless experiences. Well-defined personas and scenarios provide a reference point at every stage of development, keeping the team focused on real user needs. 6. Choosing the right approach to app development 6.1 Native app development 6.1.1 Native iOS App Development Native iOS apps are built using Apple’s development tools and programming languages like Swift and Objective-C. This approach ensures superior performance and seamless integration with the iOS ecosystem. However, apps must meet Apple’s stringent guidelines and undergo the App Store’s review process. Native iOS development provides access to the latest Apple features and maintains consistency with the platform’s design standards. However, it requires specialized knowledge of the operating system and allows for the development of apps exclusively for Apple devices. 6.1.2 Native Android app development Native Android apps are developed in Java or Kotlin within Android Studio. This approach leverages the diversity of Android devices and their customization capabilities. A more flexible distribution model allows apps to be made available not only through the Google Play Store but also through other channels. Native Android development works well with a variety of Android hardware and provides deep integration with Google services. Similar to iOS, it requires platform-specific knowledge and allows for the development of single-system solutions. 6.2 Advantages and disadvantages of native applications Native development provides superior performance, full access to device features, and a refined user experience that fits naturally into the platform. Such apps typically load faster, run more smoothly, and integrate seamlessly with device features like the camera, GPS, and sensors. The main disadvantages are longer development time and higher costs, as a separate application must be created for each platform. Native development also requires specialized knowledge of each operating system, which can mean doubling resources and extending the project timeline. 7. Progressive web applications (PWA) 7.1 When to choose PWA for business PWAs are ideal for situations where companies want broad availability without the need for publishing to app stores. This approach is ideal for businesses that require rapid updates, SEO benefits, and compatibility with various devices. PWAs are a perfect fit for content-rich apps or services that require frequent updates. PWAs are a good choice when your users value convenience over advanced functionality. They’re a great solution for companies that want to test market demand before investing in full native development, or for those that support users across devices and platforms. 7.2 Benefits of PWA development PWAs provide a native app-like experience through a web browser while maintaining web accessibility. They work offline, update automatically, and eliminate app store fees and approval processes. Users can use PWAs immediately without downloading them, lowering the barrier to entry. Such solutions are built on a single codebase, reducing maintenance complexity. PWAs remain visible in search engines, offering SEO advantages that traditional apps lack. This is a particularly cost-effective solution for companies that prioritize reach over advanced hardware integration. 8. Creating cross-platform applications 8.1 React Native and Flutter options Cross-platform frameworks like React Native and Flutter enable the creation of iOS and Android apps from a single codebase. CTOs and digital strategy leaders regularly recommend these solutions for their code reuse, fast and cost-effective development cycles, and consistent user experiences across platforms. This approach reduces development time and costs compared to separate native development. React Native uses JavaScript, a language familiar to many developers, while Flutter uses Dart, enabling the creation of highly flexible interfaces. Both frameworks enjoy strong community support and regular updates from major tech companies. 8.2 Hybrid solutions Hybrid application development combines web technologies with native containers, allowing for rapid application deployment across platforms. This approach is effective for moderately complex applications that don’t require full native performance. Hybrid solutions often enable faster time-to-market, which is crucial for companies prioritizing time-to-market over maximum performance. Modern hybrid frameworks have significantly reduced the performance gap compared to native applications. They are particularly suitable for content-driven applications or business tools where user interface consistency is more important than intensive computing capabilities. 9. No-Code and Low-Code Platforms 9.1 The Best No-Code App Builders for Business No-code platforms offer application development using drag-and-drop interfaces and pre-built templates. Industry experts emphasize that low-code/no-code solutions enable even those without programming experience to create applications for rapid prototyping and increased business agility. These tools allow companies to build functional applications without any programming knowledge, making them ideal for prototypes, MVPs, and simple business applications. Popular no-code solutions offer industry-specific templates, integrated databases, and publishing features. They are especially valuable for small businesses or departments that want to test concepts before committing to a dedicated solution. Many platforms also offer analytics, user management, and basic e-commerce features. 9.2 Limitations and Considerations No-code and low-code platforms have limitations in terms of customization, scalability, and access to advanced features. They are best suited for simple applications or as a starting point before moving on to dedicated development. Complex business logic or unique project requirements may exceed the capabilities of these tools. When choosing no-code solutions, consider long-term development plans. While they allow for a quick start and lower initial costs, you may eventually need dedicated development as your requirements grow. Check the platform provider’s stability and data export options to avoid future migration issues. 10. Power Apps in practice Power Apps is not just a platform for rapid application development, but a way to truly transform organizational operations. The following examples demonstrate how companies are using TTMS solutions based on Power Apps to automate processes, save time, and improve team efficiency. 10.1 Leave Manager – quick leave reporting and approval In many organizations, the leave request process is inefficient and opaque. Leave Manager automates the entire process—from request submission to approval. Employees can submit leave requests in just a few clicks, and managers gain real-time visibility into team availability. The application ensures complete transparency, shortens response times, and eliminates errors resulting from manual processing. 10.2 Smart Office Supply – Shopping App Daily office operations often suffer from chaotic reporting of faults or material shortages. Smart Office Supply centralizes this process, enabling quick reporting of needs—from missing coffee to equipment failures. The application integrates with Microsoft 365, sends email and Teams notifications to the appropriate people, and all requests are archived in one place. The result? Time savings, greater transparency, and a modern office image. 10.3 Benefit Manager – digital management of Social Benefits Fund benefits Paper applications, emails, and manual filing are a thing of the past. Benefit Manager completely digitizes the Company Social Benefits Fund (ZFŚS) process. Employees submit applications online, and the system automatically routes them to the appropriate person. Integration with Microsoft 365 makes the process fully GDPR-compliant, transparent, and measurable. HR saves time, and employees gain a convenient digital experience. 10.4 Device Manager – company hardware management Device Manager streamlines the management of IT assets—computers, phones, and corporate devices. Administrators can assign devices to users, track their status and service history, and log repairs and maintenance. The application automates hardware replacement and failure reporting processes, minimizing the risk of device loss and increasing control over IT resources. 10.5 Safety Check – workplace safety In factories and production plants, rapid response to threats is crucial. Safety Check is a Power App for occupational health and safety inspectors that enables immediate risk reporting using photos and location. Users can track the progress of corrective actions, generate reports, and confirm hazard removal. The solution increases safety, supports regulatory compliance, and improves communication within production teams. Each of the above applications demonstrates that Power Apps is a tool that allows you to quickly translate business needs into working solutions. Combining a simple interface with Power Automate and Power BI integration, the platform supports digital transformation in practice – from the office to the production floor. 11. Step-by-step application development process 11.1 Step 1: Wireframe and Prototyping Wireframes establish the structural foundation of an app, defining key navigation and user flow before visual design begins. They can be compared to architectural plans that define the layout of rooms before interior design. This stage focuses on functionality and optimizing the user journey, rather than aesthetics. Prototyping brings wireframes to life, creating interactive models that showcase user experiences. Early prototypes reveal usability issues and allow you to gather stakeholder feedback before making larger development investments. Iterative refinement during the prototyping phase saves significant time and resources in later development phases. 11.2 Step 2: UI/UX Design for Business Applications User interface and experience design transforms functional wireframes into engaging, intuitive applications. Effective business app design balances simplicity with functionality while maintaining brand consistency. Design choices should ensure easy navigation, fast loading, and enjoyable interactions that encourage regular use. Digital transformation experts emphasize that AR integration delivers high ROI in sectors like retail, education, and healthcare, enabling interactive, real-world experiences. For example, IKEA, which uses furniture visualization to reduce returns and increase conversions, is a key example. When designing business applications, consider the user context. Internal tools may prioritize efficiency and data density, while customer-facing applications prioritize visual appeal and ease of use. Considering accessibility requirements ensures that the application will be usable by people with diverse needs and abilities. 11.3 Step 3: Selecting the technology The technology stack determines an application’s capabilities, performance, and future scalability. Enterprise IT strategists consistently recommend cloud infrastructure because it supports scalability and innovation, enables easy global deployment, flexible scaling, and a usage-based cost model. The technology choice influences development speed, maintenance requirements, and specialist availability. Factors such as team expertise, project timeline, budget constraints, and scalability needs must be considered. Popular technology stacks offer extensive documentation and integrations with external solutions, while newer technologies can offer performance advantages, although they often have smaller support communities. 11.4 Step 4: Backend and Database Configuration Backend systems are responsible for data storage, user authentication, business logic, and API connections that drive application functionality. Much like a restaurant kitchen, the backend remains invisible to users, yet it determines the quality and reliability of the service. A robust backend architecture ensures secure and scalable performance under variable load conditions. Database selection impacts data retrieval speed, storage costs, and scalability. Data types, query patterns, and growth projections should be considered when deciding between relational and NoSQL databases. Cloud solutions often offer better scalability and lower maintenance costs than self-hosted options. 11.5 Step 5: Frontend and User Interface The front-end transforms design mockups into interactive user interfaces that interface with back-end systems. This stage requires careful attention to responsive design to ensure consistent experiences across screens and devices. Performance optimization is crucial because front-end code directly impacts users’ perception of the application’s speed and reliability. Integration between frontend and backend must be seamless to ensure a seamless user experience. API connections, data synchronization, and error handling require thorough testing to avoid user frustration and data inconsistency. 11.6 Step 6: Integrating APIs and External Services API integrations expand an application’s capabilities by connecting it to external services such as payment systems, maps, social media platforms, and business tools. Such solutions accelerate development and provide professional functionality that would be costly to develop internally. When selecting external services, ensure APIs are reliable and secure. It’s important to prepare contingency plans for critical integrations and monitor service availability to maintain application stability. Documenting API dependencies facilitates future maintenance and updates. 11.7 Step 7: Testing and quality control Comprehensive testing helps detect bugs, usability issues, and performance bottlenecks before users encounter them. Testing should encompass functionality across devices, operating system versions, and network conditions. Security testing is particularly important for business applications handling sensitive data or financial transactions. Automated testing tools can streamline iterative testing, while manual testing can catch subtle usability issues that might escape automation. Beta testing with real users provides valuable feedback on actual app usage patterns and audience preferences. 12. Key features of business applications 12.1 Basic functional requirements The most important features must be directly linked to the application’s primary purpose and user needs. Prioritizing core functionality ensures immediate value while avoiding unnecessary complexity that could discourage users or increase development costs. Core features provide the foundation upon which subsequent application elements can be built. Clearly defining priorities helps manage project scope and budget constraints. It’s important to consider which features are absolutely essential for launching the app and which can be added in later updates. This approach allows you to get your app to market faster while maintaining a focus on user value. 12.2 User authentication and security Secure login protects user data and builds trust in the business application. Implementation should balance security requirements with ease of use, avoiding overly complex processes that could discourage use. Multi-factor authentication, strong password requirements, and session management are the foundations of security. Regular security audits and updates protect against new threats and support compliance with industry regulations. Business applications often process sensitive data, so security should be a priority, impacting both user adoption and regulatory compliance. 12.3 Push notifications and messaging systems Well-thought-out push notifications engage users by providing them with timely, relevant information about new products, offers, and important reminders. An effective notification strategy should deliver value without being intrusive or overwhelming. Users should be able to manage their preferences themselves to maintain a positive experience. In-app messaging features can support customer service, user interactions, or internal communication between business teams. Such solutions extend the value of the app by reducing the need for external tools and keeping all interactions within a single platform. 12.4 Analytics and reporting tools Built-in analytics provide insights into user behavior, feature usage, and app key performance indicators. This data supports business decisions, guides feature development, and allows you to measure return on investment. Analytics helps pinpoint features that are performing best and areas for improvement. Reporting tools should present data in formats that enable quick decision-making. It’s important to determine which metrics are most relevant to your business goals and design reports to clearly highlight key KPIs. 12.5 Payment integration Secure payment processing is essential for business applications that process transactions. Integration with trusted payment providers builds user trust and supports compliance with financial regulations. Providing a variety of payment methods addresses diverse user preferences and can increase conversion rates. The reliability of your payment system directly impacts revenue and customer trust. Choose providers with a proven track record of security, good customer service, and transparent costs. Thoroughly test your payment processes in various scenarios and across multiple devices. 12.6 Offline functionality The ability to use an application offline increases its reliability and user satisfaction, especially in environments with limited network access. Key features should remain accessible without an internet connection, and data synchronization should occur automatically when an internet connection is restored. This functionality can distinguish your application from the competition. Determine which features are most important offline and design appropriate data caching strategies. Users should be clearly informed when they are offline and how this impacts app performance. 12.7 Customer support features Integrated support options like chat, FAQs, and contact forms improve user satisfaction and reduce support costs. Easy access to support builds trust and allows for quick resolution of issues before they escalate into negative reviews or app abandonment. Self-service options often allow users to quickly resolve basic issues while reducing the burden on support teams. Help functions should be easily accessible and offer clear paths to resolution for different types of users. 13. Budget and timeline for app development 13.1 Cost breakdown by development method App development costs vary significantly depending on the chosen approach, level of complexity, and required features. Recent industry data shows that business mobile app development costs range from $40,000 to over $400,000, depending on complexity. Simple apps typically cost between $40,000 and $100,000, medium-complexity apps between $100,000 and $200,000, and advanced apps can reach $200,000–$400,000 or more. Cross-platform development using frameworks like Flutter or React Native can reduce costs compared to building standalone native apps. Development rates average between $25 and $49 per hour, varying by region, developer experience, and platform complexity. No-code platforms offer the lowest upfront costs but can generate higher long-term expenses due to monthly subscriptions and limited customization options. For example, a comprehensive marketplace app with reservations, payments, and reviews required around $300,000 or more for full platform development, while apps with IoT integration typically start at $60,000, depending on the complexity of the devices supported. 13.2 Hidden costs to consider Beyond initial development costs, ongoing costs must be considered, which significantly impact the budget. Annual maintenance costs average around 20% of the initial application development cost, including updates, bug fixes, and improvements. Marketing is a significant investment, with annual costs ranging from 50% to 100% of the initial development budget. Additional expenses include integrations with external services ($5,000–$20,000 per year), backend infrastructure ($20,000–$100,000), app store fees, server hosting, and ongoing support resources. It’s worth planning these recurring costs in advance to avoid budget surprises that could impact app quality or business stability. 13.3 Estimated timeline for different application types Application development time varies depending on the level of complexity and the approach taken. Simple applications require 3 to 6 months of work, medium-complexity applications 6 to 9 months, and complex enterprise-class solutions can take anywhere from 9 to 18 months or longer. Real-world examples demonstrate how these timelines play out: the social app Opar was developed in about 4–6 months, while the comprehensive marketplace platform required over 9 months. It’s also worth factoring in the time it takes for apps to be approved in marketplaces, which can take several weeks and require rework. 13.4 Financing options for app development Funding for an app project can come from a variety of sources, such as self-funding, crowdfunding, angel investors, or venture capital funds. Each option comes with its own set of requirements, timelines, and implications for business control and future strategic decisions. Preparing a compelling investment presentation with a clearly defined value proposition, market analysis, and financial forecasts increases your chances of securing financing. It’s also worth considering how different funding sources align with your business goals and growth plans before making a commitment. 14. Business application testing 14.1 User Acceptance Testing (UAT) User acceptance testing (UAT) confirms that an application meets business requirements and user expectations before its public release. This is a crucial step in which real users perform common tasks to identify usability issues or missing features. UAT feedback often reveals discrepancies between developer assumptions and actual user needs. The success of a major sportswear brand’s fitness app demonstrates the importance of comprehensive user research—surveys and focus groups—which indicated that easy navigation and personalized content are key. The UAT phase should be well-planned, with clearly defined test scenarios, success criteria, and feedback collection methods. 14.2 Performance and load testing Performance tests verify the stability, speed, and responsiveness of an application under various usage conditions. Load tests simulate periods of peak traffic to identify potential bottlenecks or system failures. These tests ensure the application runs smoothly even under heavy traffic, preventing crashes that undermine user confidence. Testing should span devices, network conditions, and operating system versions to ensure consistent performance. In the fitness app example, performance optimization resulted in a 25% drop in bounce rate, demonstrating the real-world impact of thorough testing on business outcomes. 14.3 Safety testing and regulatory compliance Security testing identifies vulnerabilities that could threaten user data or business operations. This process is crucial for applications processing sensitive data, financial transactions, or regulated information. Regular security audits help maintain protection against new threats. Compliance requirements vary by industry and location, impacting aspects such as data storage and user consent processes. It’s important to understand applicable regulations early in the planning process to avoid costly rework or legal issues after the app’s launch. 14.4 Beta testing with real users Beta testing programs allow select users to use an app before its official release, allowing them to gather valuable feedback on functionality, usability, and appeal. Beta testers often uncover edge cases and unusual usage patterns that may have been missed during internal testing, leading to a more polished final product. Recruit beta testers who represent your target audience and provide them with clear channels for feedback. It’s important to balance the length of your beta testing with your launch schedule to ensure you have enough time to fix key bugs without losing development momentum. 15. Application maintenance and updating 15.1 Regular updates and feature improvements Continuous updates allow for bug fixes, performance improvements, and new features that keep users engaged. A well-known sportswear brand’s fitness app achieved impressive results thanks to strategic updates, increasing downloads by 50% and referral traffic by 70% after performance optimizations and new features. It’s important to plan your update schedule to balance new feature development with stability improvements. Changes should be clearly communicated to users, highlighting the benefits and improvements they will experience after the update. The frequency of new releases should align with user expectations and competitive market pressures. 15.2 Integration of user feedback Actively collecting and analyzing user feedback helps set development priorities and demonstrates a commitment to customer satisfaction. Feedback channels should be easily accessible and encourage honest sharing of experiences and suggestions for improvement. It’s worth developing a systematic process for reviewing, categorizing, and prioritizing feedback. While not all suggestions can be implemented, simply acknowledging them and explaining the decisions made builds brand loyalty and trust. 15.3 Performance monitoring and data analysis Continuous performance monitoring allows you to track usage patterns, identify technical issues, and measure key business success metrics. Analytics support fact-based decisions about feature development, user experience optimization, and business strategy adjustments. Monitor both technical performance indicators and business KPIs to understand how application performance impacts business results. It’s also important to set up alerts for critical issues that require immediate attention to maintain high user satisfaction. 15.4 Long-term application development strategy Planning for future development ensures that the application can adapt to changing business needs, technological advancements, and market conditions. An evolution strategy should consider scalability requirements, new platform capabilities, and changes in the competitive landscape. Create roadmaps that balance innovation and stability—so that new features enhance the user experience, not complicate it. Regular strategy reviews allow you to adjust your plans based on market feedback and business performance data. 16. The most common traps and how to avoid them 16.1 Technical challenges and how to solve them Technical issues such as platform fragmentation, complex integrations, or limited scalability can disrupt application development or cause long-term operational challenges. Proactive planning, proper technology stack selection, and comprehensive testing significantly mitigate these risks. Complex, feature-rich, or highly secure enterprise applications generate the highest costs and longest timelines due to requirements for a dedicated backend, regulatory compliance (e.g., HIPAA, GDPR), and advanced integrations. Partnering with experienced developers or partners specializing in these solutions, such as TTMS, helps overcome these challenges with expertise in AI implementation, system integration, and process automation. 16.2 User Experience (UX) Errors Poor design, unintuitive navigation, or slow app performance can discourage users, regardless of its functionality. Prioritizing intuitive interfaces, responsive design, and fast loading significantly improves user retention and satisfaction. A case study of a fitness app shows that improving user experience can significantly increase engagement levels. Regular usability testing during development helps detect user experience issues before they impact real-world users. Simple, clear design solutions often prove more effective than complex interfaces that try to do too much at once. 16.3 Security and compliance issues Inadequate security measures can lead to data leaks, legal consequences, and lasting damage to a company’s reputation. Implementing best security practices, conducting regular audits, and monitoring regulatory changes are key investments in business protection. Security issues should be considered at every stage of application development, not treated as an afterthought. The cost of properly implementing security measures is small compared to the potential losses resulting from their absence. 16.4 Budget overruns and schedule delays Underestimating project complexity, scope creep, and hidden costs are common causes of application implementation problems. Realistic budget planning with a financial reserve, a clearly defined project scope, and monitoring progress based on milestones help maintain implementation control. It’s also worth remembering that application maintenance can cost from 20% to as much as 100% of the initial project cost annually—incorporating this into the budget prevents financial surprises. Regular project reviews enable early detection of potential issues and course corrections before they become serious. Good communication between all stakeholders helps manage expectations and prevent misunderstandings that could lead to costly changes. 17. Summary Building effective business apps in 2025 requires strategic planning, sound technology choices, and a consistent commitment to user satisfaction. Whether you choose native, cross-platform, or no-code development, effective business app development is about finding the right balance between user needs, technological capabilities, and business goals. The key to successful app development is thorough preparation, thoughtful execution, and continuous improvement based on user feedback and analytical data. With the dynamic growth of the global mobile app market, the ROI potential for well-designed business apps remains high. Companies such as TTMS provide expert knowledge in AI solutions, process automation and system integration, which allows you to increase application functionality while ensuring reliable and scalable implementations tailored to business needs. It’s important to remember that launching an app is just the beginning of a longer journey that includes maintenance, updates, and development in response to changing market needs. Success requires treating app development as a continuous investment in digital transformation, not a one-off project – so that your mobile strategy delivers value for many years. If you are interested contact us now!

TOP 10 AEM partners in 2025

Ranking the Best AEM Companies: Meet the Top 10 Partners for Your 2025 Projects The market for Adobe Experience Manager (AEM) implementations continues to expand as brands seek unified content management and customer‑centric digital experiences. Organisations that partner with AEM implementation partners gain access to deep technical expertise, accelerators and strategic guidance that help them move faster than competitors. Below are ten leading AEM development companies in 2025, ranked by market presence, breadth of services and overall experience. TTMS tops the list of the best Adobe Experience Manager Consulting Partners thanks to its comprehensive services, experienced consultants and innovative use of AI for content delivery. 1. Transition Technologies MS (TTMS) TTMS is a Bronze Adobe Solution Partner with one of the largest AEM competence centres in Poland and top AEM experts. The company’s philosophy emphasises personalisation and customer‑centric design: it provides end‑to‑end services covering architecture, development, maintenance and performance optimisation, and its 90‑plus consultants ensure deep expertise across all AEM modules. TTMS integrates AEM with marketing automation platforms such as Marketo, Adobe Campaign and Analytics, as well as Salesforce and customer identity systems, enabling seamless omnichannel experiences. The firm also leverages generative AI to automate tagging, translation and metadata generation, offers AI‑powered search and chatbots, and uses accelerators to reduce time‑to‑market, giving clients significant competitive advantage. Beyond core implementation, TTMS specialises in product catalogue and PIM integration. Its AEM development teams integrate existing product data into AEM’s DAM and authoring tools to eliminate manual entry errors and ensure consistent product information across channels. They also build secure customer portals on AEM that provide personalised experiences and HIPAA‑compliant document management. For organisations moving to AEM as a Cloud Service, TTMS handles performance testing, environment set‑up, integrated marketing workflows and training. Consulting services include platform audits, tailored onboarding, optimisation of legacy implementations, custom integrations and training for internal teams. Thanks to this comprehensive offering, TTMS stands out as a trusted AEM implementation partner that delivers strategic advice and innovative solutions. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/aem/ Headquarters: Warsaw, Poland Main services / focus: AEM consulting & development, AI integration, PIM & product catalogue integration, customer portals, cloud migration, marketing automation integration, training and support 2. Vaimo Headquartered in Stockholm, Vaimo is a global commerce solution provider known for implementing AEM alongside Magento. The company’s strength lies in combining strategy, design and technology to build unified digital commerce platforms. Vaimo integrates AEM with e‑commerce systems and marketing automation tools, enabling brands to manage content and product data across multiple channels. Its expertise in user experience, technical architecture and performance optimisation positions Vaimo as a reliable AEM implementation partner for retailers seeking personalised shopping experiences. Vaimo: company snapshot Revenues in 2024: Undisclosed Number of employees: 500+ Website: www.vaimo.com Headquarters: Stockholm, Sweden Main services / focus: AEM & Magento integration, digital commerce platforms, design & strategy, omnichannel experiences 3. Appnovation Appnovation is a full‑service digital consultancy with offices in North America, Europe and Asia. The firm combines digital strategy, experience design and technology to deliver enterprise‑grade AEM solutions. Appnovation’s multidisciplinary teams develop multi‑channel content architectures, integrate analytics and marketing automation tools, and provide managed services to optimise clients’ AEM platforms. Its global presence and user‑centric design approach make Appnovation a popular AEM development company for organisations pursuing large‑scale digital transformation. Appnovation: company snapshot Revenues in 2024: Undisclosed Number of employees: 600+ Website: www.appnovation.com Headquarters: Vancouver, Canada Main services / focus: AEM implementation, user‑experience design, digital strategy, cloud‑native development, managed services 4. Magneto IT Solutions Magneto IT Solutions specialises in building e‑commerce platforms and digital experiences for retail brands. It leverages Adobe Experience Manager to create scalable, content‑driven websites and integrates AEM with Magento, Shopify and other commerce platforms. The company’s strong focus on design and conversion optimisation helps clients deliver seamless shopping experiences. Magneto’s ability to customise AEM for specific retail verticals positions it among the top AEM implementation partners for online stores. Magneto IT Solutions: company snapshot Revenues in 2024: Undisclosed Number of employees: 200+ Website: www.magnetoitsolutions.com Headquarters: Ahmedabad, India Main services / focus: AEM development for retail, e‑commerce integration, UX/UI design, digital marketing 5. Akeneo Akeneo is recognised for its product information management (PIM) platform and its synergy with AEM. The company enables brands to centralise and enrich product data, then syndicate it to AEM to ensure consistency across digital channels. By integrating AEM with its PIM tool, Akeneo helps organisations streamline product catalogue management, reduce manual entry and improve data accuracy. This focus on product data integrity makes Akeneo an important partner for companies using AEM in commerce and manufacturing. Akeneo: company snapshot Revenues in 2024: Undisclosed Number of employees: 400+ Website: www.akeneo.com Headquarters: Nantes, France Main services / focus: Product information management, AEM & PIM integration, digital commerce solutions 6. Codal Codal is a design‑driven digital agency that combines user experience research with robust engineering. The firm adopts a user‑centric approach to AEM implementations, ensuring that information architecture, component design and content workflows meet both customer and business needs. Codal’s teams also integrate data analytics and marketing automation platforms with AEM, enabling clients to make informed decisions and deliver personalised experiences. This design‑first ethos makes Codal a top choice for brands looking to align aesthetics and technology. Codal: company snapshot Revenues in 2024: Undisclosed Number of employees: 250+ Website: www.codal.com Headquarters: Chicago, USA Main services / focus: AEM implementation, UX/UI design, data analytics, integration services 7. Synecore Synecore is a UK‑based digital marketing agency that blends inbound marketing strategies with AEM’s powerful content management capabilities. It helps clients craft inbound campaigns, develop content strategies and integrate marketing automation tools with AEM. Synecore’s team ensures that content, design and technical implementations support lead generation and customer engagement. Its expertise in inbound marketing and content strategy positions Synecore as a valuable AEM development company for organisations seeking to combine marketing and technology. Synecore: company snapshot Revenues in 2024: Undisclosed Number of employees: 50+ Website: www.synecore.co.uk Headquarters: London, UK Main services / focus: Inbound marketing, content strategy, AEM implementation, marketing automation integration 8. Mageworx Mageworx is best known for its Magento extensions, but the company also offers AEM integration services for e‑commerce sites. By connecting AEM with Magento and other e‑commerce platforms, Mageworx enables brands to manage product information and content in one environment. The company develops custom modules, optimises website performance and provides SEO and analytics integration to drive online sales. For merchants looking to leverage AEM within a Magento ecosystem, Mageworx is a solid partner. Mageworx: company snapshot Revenues in 2024: Undisclosed Number of employees: 100+ Website: www.mageworx.com Headquarters: Minneapolis, USA Main services / focus: Magento extensions, AEM integration, performance optimisation, SEO & analytics 9. Spargo Spargo is a Polish digital transformation firm focusing on commerce, content and marketing technologies. It uses AEM to deliver integrated digital experiences for clients in retail, finance and media. Spargo combines product information management, marketing automation and e‑commerce integrations to help brands operate efficiently across multiple channels. With its cross‑platform expertise and agile methodology, Spargo stands out among regional AEM implementation partners. Spargo: company snapshot Revenues in 2024: Undisclosed Number of employees: 100+ Website: www.spargo.pl Headquarters: Warsaw, Poland Main services / focus: Digital commerce solutions, AEM development, PIM integration, marketing automation 10. Divante Divante is an e‑commerce software house and innovation partner based in Poland. It has strong expertise in Magento, Pimcore and AEM, and builds headless commerce architectures that allow clients to deliver content across multiple devices and channels. Divante’s teams focus on open‑source technologies, API‑first approaches and custom integrations, enabling rapid experimentation and scalability. The company’s community‑driven culture and technical depth make it a trusted partner for enterprises looking to modernise their digital commerce stack. Divante: company snapshot Revenues in 2024: Undisclosed Number of employees: 300+ Website: www.divante.com Headquarters: Wrocław, Poland Main services / focus: Headless commerce, AEM development, open‑source platforms, custom integrations Our AEM Case Studies: Proven Expertise in Action At TTMS, we believe that real results speak louder than promises. Below you will find selected case studies that illustrate how our team successfully delivers AEM consulting, migrations, integrations, and AI-driven optimizations for global clients across various industries Migrating to Adobe EDS – We successfully migrated a complex ecosystem into Adobe EDS, ensuring seamless data flow and robust scalability. The project minimized downtime and prepared the client for future growth. Adobe Analytics Integration with AEM – TTMS integrated Adobe Analytics with AEM to deliver actionable insights for marketing and content teams. This improved customer experience tracking and enabled data-driven decision-making. Integration of PingOne and Adobe AEM – We implemented secure identity management by integrating PingOne with AEM. The solution strengthened authentication and improved user experience across digital platforms. AI SEO Meta Optimization – By applying AI-driven SEO optimization in AEM, we boosted the client’s search visibility and organic traffic. The approach delivered measurable improvements in engagement and rankings. AEM Cloud Migration for a Watch Manufacturer – TTMS migrated a luxury watch brand’s digital ecosystem into AEM Cloud. The move improved performance, reduced costs, and enabled long-term scalability. Migration from Adobe LiveCycle to AEM Forms – We replaced legacy Adobe LiveCycle with modern AEM Forms, improving usability and efficiency. This allowed the client to streamline processes and reduce operational risks. Headless CMS Architecture for Multi-App Delivery – TTMS designed a headless CMS approach for seamless content delivery across multiple apps. The solution increased flexibility and accelerated time-to-market. Pharma Design System & Template Unification – We developed a unified design system for a global pharma leader. It improved brand consistency and reduced development costs across international teams. Accelerating Adobe Delivery through Expert Intervention – Our experts accelerated stalled Adobe projects, delivering results faster and more efficiently. The intervention saved resources and increased project success rates. Comprehensive Digital Audit for Strategic Clarity – TTMS conducted an in-depth digital audit that revealed key optimization areas. The client gained actionable insights and a roadmap for long-term success. Expert-Guided Content Migration – We supported a smooth transition to a new platform through structured content migration. This minimized risks and ensured business continuity during change. Global Patient Portal Improvement – TTMS enhanced a global medical portal by simplifying medical terminology for patients. The upgrade improved accessibility, patient satisfaction, and global adoption. If you want to learn how we can bring the same success to your AEM projects, our team is ready to help. Get in touch with TTMS today and let’s discuss how we can accelerate your digital transformation journey together. What makes a good AEM implementation partner in 2025? A good AEM implementation partner in 2025 is not only a company with certified Adobe Experience Manager expertise, but also one that can combine consulting, cloud migration, integration, and AI-driven solutions. The best partners deliver both technical precision and business alignment, ensuring that the implementation supports digital transformation goals. What really distinguishes the top firms today is their ability to integrate AEM with analytics, identity management, and personalization engines. This creates a scalable, secure, and customer-focused digital platform that drives measurable business value. How do I compare different AEM development companies? How to compare the best Adobe AEM implementation companies? When comparing AEM development companies, it is essential to look beyond price and consider factors such as their proven track record, the number of certified AEM developers, and the industries they serve. A reliable partner will provide transparency about previous projects, case studies, and long-term support models. It is also worth checking if the company is experienced in AEM Cloud Services, as many enterprises are migrating away from on-premises solutions. Finally, cultural fit and communication style play a huge role in successful collaborations, especially for global organizations. Is it worth choosing a local AEM consulting partner over a global provider? The decision between a local and a global AEM consulting partner depends on your organization’s priorities. A local partner may offer closer cultural alignment, time zone convenience, and faster on-site support. On the other hand, global providers often bring broader expertise, larger teams, and experience with complex multinational implementations. Many businesses in 2025 follow a hybrid approach, where they choose a mid-sized international AEM company that combines the flexibility of local service with the scalability of a global player. How much does it cost to implement AEM with a professional partner? The cost of implementing Adobe Experience Manager with a professional partner varies significantly depending on the project’s scale, complexity, and integrations required. For smaller projects, costs may start from tens of thousands of euros, while large-scale enterprise implementations can easily exceed several hundred thousand euros. What matters most is the return on investment – a skilled AEM partner will optimize content workflows, personalization, and data-driven marketing, generating long-term business value that outweighs the initial spend. Choosing the right partner ensures predictable timelines and reduced risk of costly delays. What are the latest trends in AEM implementations in 2025? In 2025, the hottest trends in AEM implementations revolve around AI integration, headless CMS architectures, and cloud-native deployments. Companies increasingly expect their AEM platforms to be fully compatible with AI-powered personalization, predictive analytics, and automated SEO optimization. Headless CMS setups are gaining momentum because they allow content to be delivered seamlessly across web, mobile, and IoT applications. At the same time, more organizations are moving to AEM Cloud Services, reducing infrastructure overhead while ensuring continuous updates and scalability. These trends highlight the need for AEM implementation partners who can innovate while maintaining enterprise-grade stability.

Microsoft’s In-House AI Move: MAI-1 and MAI-Voice-1 Signal a Shift from OpenAI

Microsoft’s In-House AI Move: MAI-1 and MAI-Voice-1 Signal a Shift from OpenAI August 2025 – Microsoft has unveiled two internally developed AI models – MAI-1 (a new large language model) and MAI-Voice-1 (a speech generation model) – marking a strategic pivot toward technological independence from OpenAI. After years of leaning on OpenAI’s models (and investing around $13 billion in that partnership since 2019), Microsoft’s AI division is now striking out on its own with homegrown AI capabilities. This move signals that despite its deep ties to OpenAI, Microsoft is positioning itself to have more direct control over the AI technology powering its products – a development with big implications for the industry. A Strategic Pivot Away from OpenAI Microsoft’s announcement of MAI-1 and MAI-Voice-1 – made in late August 2025 – is widely seen as a bid for greater self-reliance in AI. Industry observers note that this “proprietary” turn represents a pivot away from dependence on OpenAI. For years, OpenAI’s GPT-series models (like GPT-4) have been the brains behind many Microsoft products (from Azure OpenAI services to GitHub Copilot and Bing’s chat). However, tensions have emerged in the collaboration. OpenAI has grown into a more independent (and highly valued) entity, and Microsoft reportedly “openly criticized” OpenAI’s GPT-4 as “too expensive and slow” for certain consumer needs. Microsoft even quietly began testing other AI models for its Copilot services, signaling concern about over-reliance on a single partner. In early 2024, Microsoft hired Mustafa Suleyman (co-founder of DeepMind and former Inflection AI CEO) to lead a new internal AI team – a clear sign it intended to develop its own models. Suleyman has since emphasized “optionality” in Microsoft’s AI strategy: the company will use the best models available – whether from OpenAI, open-source, or its own lab – routing tasks to whichever model is most capable. The launch of MAI-1 and MAI-Voice-1 puts substance behind that strategy. It gives Microsoft a viable in-house alternative to OpenAI’s tech, even as the two remain partners. In fact, Microsoft’s AI leadership describes these models as augmenting (not immediately replacing) OpenAI’s – for now. But the long-term trajectory is evident: Microsoft is preparing for a post-OpenAI future in which it isn’t beholden to an external supplier for core AI innovations. As one Computerworld analysis put it, Microsoft didn’t hire a visionary AI team “simply to augment someone else’s product” – it’s laying groundwork to eventually have its own AI foundation. Meet MAI-1 and MAI-Voice-1: Microsoft’s New AI Models MAI-Voice-1 is Microsoft’s first high-performance speech generation model. The company says it can generate a full minute of natural-sounding audio in under one second on a single GPU, making it “one of the most efficient speech systems” available. In practical terms, MAI-Voice-1 gives Microsoft a fast, expressive text-to-speech engine under its own roof. It’s already powering user-facing features: for example, the new Copilot Daily service has an AI news host that reads top stories to users in a natural voice, and a Copilot Podcasts feature can create on-the-fly podcast dialogues from text prompts – both driven by MAI-Voice-1’s capabilities. Microsoft touts the model’s high fidelity and expressiveness across single- and multi-speaker scenarios. In an era where voice interfaces are rising, Microsoft clearly views this as strategic tech (the company even said “voice is the interface of the future” for AI companions). Notably, OpenAI’s own foray into audio has been Whisper, a model for speech-to-text transcription – but OpenAI hasn’t productized a comparable text-to-speech model. With MAI-Voice-1, Microsoft is filling that gap by offering AI that can speak to users with human-like intonation and speed, without relying on a third-party engine. MAI-1 (Preview) is Microsoft’s new large language model (LLM) for text, and it represents the company’s first internally trained foundation model. Under the hood, MAI-1 uses a mixture-of-experts architecture and was trained (and post-trained) on roughly 15,000 NVIDIA H100 GPUs. (For context, that is a substantial computing effort, though still more modest than the 100,000+ GPU clusters reportedly used to train some rival frontier models.) The model is designed to excel at instruction-following and helpful responses to everyday queries – essentially, the kind of general-purpose assistant tasks that GPT-4 and similar models handle. Microsoft has begun publicly testing MAI-1 in the wild: it was released as MAI-1-preview on LMArena, a community benchmarking platform where AI models can be compared head-to-head by users. This allows Microsoft to transparently gauge MAI-1’s performance against other AI models (competitors and open models alike) and iterate quickly. According to Microsoft, MAI-1 is already showing “a glimpse of future offerings inside Copilot” – and the company is rolling it out selectively into Copilot (Microsoft’s AI assistant suite across Windows, Office, and more) for tasks like text generation. In coming weeks, certain Copilot features will start using MAI-1 for handling user queries, with Microsoft collecting feedback to improve the model. In short, MAI-1 is not yet replacing OpenAI’s GPT-4 within Microsoft’s products, but it’s on a path to eventually play a major role. It gives Microsoft the ability to tailor and optimize an LLM specifically for its ecosystem of “Copilot” assistants. How do these models stack up against OpenAI’s? In terms of capabilities, OpenAI’s GPT-4 (and the newly released GPT-5) still set the bar in many domains, from advanced reasoning to code generation. Microsoft’s MAI-1 is a first-generation effort by comparison, and Microsoft itself acknowledges it is taking an “off-frontier” approach – aiming to be a close second rather than the absolute cutting edge. “It’s cheaper to give a specific answer once you’ve waited for the frontier to go first… that’s our strategy, to play a very tight second,” Suleyman said of Microsoft’s model efforts. The architecture choices also differ: OpenAI has not disclosed GPT-4’s architecture, but it is believed to be a giant transformer model utilizing massive compute resources. Microsoft’s MAI-1 explicitly uses a mixture-of-experts design, which can be more compute-efficient by activating different “experts” for different queries. This design, plus the somewhat smaller training footprint, suggests Microsoft may be aiming for a more efficient, cost-effective model – even if it’s not (yet) the absolute strongest model on the market. Indeed, one motivation for MAI-1 was likely cost/control: Microsoft found that using GPT-4 at scale was expensive and sometimes slow, impeding consumer-facing uses. By owning a model, Microsoft can optimize it for latency and cost on its own infrastructure. On the voice side, OpenAI’s Whisper model handles speech recognition (transcribing audio to text), whereas Microsoft’s MAI-Voice-1 is all about speech generation (producing spoken audio from text). This means Microsoft now has an in-house solution for giving its AI a “voice” – an area where it previously relied on third-party text-to-speech services or less flexible solutions. MAI-Voice-1’s standout feature is its speed and efficiency (near real-time audio generation), which is crucial for interactive voice assistants or reading long content aloud. The quality is described as high fidelity and expressive, aiming to surpass the often monotone or robotic outputs of older-generation TTS systems. In essence, Microsoft is assembling its own full-stack AI toolkit: MAI-1 for text intelligence, and MAI-Voice-1 for spoken interaction. These will inevitably be compared to OpenAI’s GPT-4 (text) and the various voice AI offerings in the market – but Microsoft now has the advantage of deeply integrating these models into its products and tuning them as it sees fit. Implications for Control, Data, and Compliance Beyond technical specs, Microsoft’s in-house AI push is about control – over the technology’s evolution, data, and alignment with company goals. By developing its own models, Microsoft gains a level of ownership that was impossible when it solely depended on OpenAI’s API. As one industry briefing noted, “Owning the model means owning the data pipeline, compliance approach, and product roadmap.” In other words, Microsoft can now decide how and where data flows in the AI system, set its own rules for governance and regulatory compliance, and evolve the AI functionality according to its own product timeline, not someone else’s. This has several tangible implications: Data governance and privacy: With an in-house model, sensitive user data can be processed within Microsoft’s own cloud boundaries, rather than being sent to an external provider. Enterprises using Microsoft’s AI services may take comfort that their data is handled under Microsoft’s stringent enterprise agreements, without third-party exposure. Microsoft can also more easily audit and document how data is used to train or prompt the model, aiding compliance with data protection regulations. This is especially relevant as new AI laws (like the EU’s AI Act) demand transparency and risk controls – having the AI “in-house” could simplify compliance reporting since Microsoft has end-to-end visibility into the model’s operation. Product customization and differentiation: Microsoft’s products can now get bespoke AI enhancements that a generic OpenAI model might not offer. Because Microsoft controls MAI-1’s training and tuning, it can infuse the model with proprietary knowledge (for example, training on Windows user support data to make a better helpdesk assistant) or optimize it for specific scenarios that matter to its customers. The Copilot suite can evolve with features that leverage unique model capabilities Microsoft builds (for instance, deeper integration with Microsoft 365 data or fine-tuned industry versions of the model for enterprise customers). This flexibility in shaping the roadmap is a competitive differentiator – Microsoft isn’t limited by OpenAI’s release schedule or feature set. As Launch Consulting emphasized to enterprise leaders, relying on off-the-shelf AI means your capabilities are roughly the same as your competitors’; owning the model opens the door to unique features and faster iterations. Compliance and risk management: By controlling the AI models, Microsoft can more directly enforce compliance with ethical AI guidelines and industry regulations. It can build in whatever content filters or guardrails it deems necessary (and adjust them promptly as laws change or issues arise), rather than being subject to a third party’s policies. For enterprises in regulated sectors (finance, healthcare, government), this control is vital – they need to ensure AI systems comply with sector-specific rules. Microsoft’s move could eventually allow it to offer versions of its AI that are certified for compliance, since it has full oversight. Moreover, any concerns about how AI decisions are made (transparency, bias mitigation, etc.) can be addressed by Microsoft’s own AI safety teams, potentially in a more customized way than OpenAI’s one-size-fits-all approach. In short, Microsoft owning the AI stack could translate to greater trust and reliability for enterprise customers who must answer to regulators and risk officers. It’s worth noting that Microsoft is initially applying MAI-1 and MAI-Voice-1 in consumer-facing contexts (Windows, Office 365 Copilot for end-users) and not immediately replacing the AI inside enterprise products. Suleyman himself commented that the first goal was to make something that works extremely well for consumers – leveraging Microsoft’s rich consumer telemetry and data – essentially using the broad consumer usage to train and refine the models. However, the implications for enterprise clients are on the horizon. We can expect that as these models mature, Microsoft will integrate them into its Azure AI offerings and enterprise Copilot products, offering clients the option of Microsoft’s “first-party” models in addition to OpenAI’s. For enterprise decision-makers, Microsoft’s pivot sends a clear message: AI is becoming core intellectual property, and owning or selectively controlling that IP can confer advantages in data governance, customization, and compliance that might be hard to achieve with third-party AI alone. Build Your Own or Buy? Lessons for Businesses Microsoft’s bold move raises a key question for other companies: Should you develop your own AI models, or continue relying on foundation models from providers like OpenAI or Anthropic? The answer will differ for each organization, but Microsoft’s experience offers some valuable considerations for any business crafting its AI strategy: Strategic control vs. dependence: Microsoft’s case illustrates the risk of over-dependence on an external AI provider. Despite a close partnership, Microsoft and OpenAI had diverging interests (even reportedly clashing over what Microsoft gets out of its big investment). If an AI capability is mission-critical to your business or product, relying solely on an outside vendor means your fate is tied to their decisions, pricing, and roadmap changes. Building your own model (or acquiring the talent to) gives you strategic independence. You can prioritize the features and values important to you without negotiating with a third party. However, it also means shouldering all the responsibility for keeping that model state-of-the-art. Resources and expertise required: On the flip side, few companies have the deep pockets and AI research muscle that Microsoft does. Training cutting-edge models is extremely expensive – Microsoft’s MAI-1 used 15,000 high-end GPUs just for its preview model, and the leading frontier models use even larger compute budgets. Beyond hardware, you need scarce AI research talent and large-scale data to train a competitive model. For most enterprises, it’s simply not feasible to replicate what OpenAI, Google, or Microsoft are doing at the very high end. If you don’t have the scale to invest in tens of millions (or more likely, hundreds of millions) of dollars in AI R&D, leveraging a pre-built foundation model might yield a far better ROI. Essentially, build if AI is a core differentiator you can substantially improve – but buy if AI is a means to an end and others can provide it more cheaply. Privacy, security, and compliance needs: A major driver for some companies to consider “rolling their own” AI is data sensitivity and compliance. If you operate in a field with strict data governance (say, patient health data, or confidential financial info), sending data to a third-party AI API – even with promises of privacy – might be a non-starter. An in-house model that you can deploy in a secure environment (or at least a model from a vendor willing to isolate your data) could be worth the investment. Microsoft’s move shows an example of prioritizing data control: by handling AI internally, they keep the whole data pipeline under their policies. Other firms, too, may decide that owning the model (or using an open-source model locally) is the safer path for compliance. That said, many AI providers are addressing this by offering on-premises or dedicated instances – so explore those options as well. Need for customization and differentiation: If the available off-the-shelf AI models don’t meet your specific needs or if using the same model as everyone else diminishes your competitive edge, building your own can be attractive. Microsoft clearly wanted AI tuned for its Copilot use cases and product ecosystem – something it can do more freely with in-house models. Likewise, other companies might have domain-specific data or use cases (e.g. a legal AI assistant, or an industrial AI for engineering data) where a general model underperforms. In such cases, investing in a proprietary model or at least a fine-tuned version of an open-source model could yield superior results for your niche. We’ve seen examples like Bloomberg GPT – a financial domain LLM trained on finance data – which a company built to get better finance-specific performance than generic models. Those successes hint that if your data or use case is unique enough, a custom model can provide real differentiation. Hybrid approaches – combine the best of both: Importantly, choosing “build” versus “buy” isn’t all-or-nothing. Microsoft itself is not abandoning OpenAI entirely; the company says it will “continue to use the very best models from [its] team, [its] partners, and the latest innovations from the open-source community” to power different features. In practice, Microsoft is adopting a hybrid model – using its own AI where it adds value, but also orchestrating third-party models where they excel, thereby delivering the best outcomes across millions of interactions. Other enterprises can adopt a similar strategy. For example, you might use a general model like OpenAI’s for most tasks, but switch to a privately fine-tuned model when handling proprietary data or domain-specific queries. There are even emerging tools to help route requests to different models dynamically (the way Microsoft’s “orchestrator” does). This approach allows you to leverage the immense investment big AI providers have made, while still maintaining options to plug in your own specialty models for particular needs. Bottom line: Microsoft’s foray into building MAI-1 and MAI-Voice-1 underscores that AI has become a strategic asset worth investing in – but it also demonstrates the importance of balancing innovation with practical business needs. Companies should re-evaluate their build-vs-buy AI strategy, especially if control, privacy, or differentiation are key drivers. Not every organization will choose to build a giant AI model from scratch (and most shouldn’t). Yet every organization should consider how dependent it wants to be on external AI providers and whether owning certain AI capabilities could unlock more value or mitigate risks. Microsoft’s example shows that with sufficient scale and strategic need, developing one’s own AI is not only possible but potentially transformative. For others, the lesson may be to negotiate harder on data and compliance terms with AI vendors, or to invest in smaller-scale bespoke models that complement the big players. In the end, Microsoft’s announcement is a landmark in the AI landscape: a reminder that the AI ecosystem is evolving from a few foundation-model providers toward a more heterogeneous field. For business leaders, it’s a prompt to think of AI not just as a service you consume, but as a capability you cultivate. Whether that means training your own models, fine-tuning open-source ones, or smartly leveraging vendor models, the goal is the same – align your AI strategy with your business’s unique needs for agility, trust, and competitive advantage in the AI era. Supporting Your AI Journey: Full-Spectrum AI Solutions from TTMS As the AI ecosystem evolves, TTMS offers AI Solutions for Business – a comprehensive service line that guides organizations through every stage of their AI strategy, from deploying pre-built models to developing proprietary ones. Whether you’re integrating AI into existing workflows, automating document-heavy processes, or building large-scale language or voice models, TTMS has capabilities to support you. For law firms, our AI4Legal specialization helps automate repetitive tasks like contract drafting, court transcript analysis, and document summarizations—all while maintaining data security and compliance. For customer-facing and sales-driven sectors, our Salesforce AI Integration service embeds generative AI, predictive insights, and automation directly into your CRM, helping improve user experience, reduce manual workload, and maintain control over data. If Microsoft’s move to build its own models signals one thing, it’s this: the future belongs to organizations that can both buy and build intelligently – and TTMS is ready to partner with you on that path. Why is Microsoft creating its own AI models when it already partners with OpenAI? Microsoft values the access it has to OpenAI’s cutting-edge models, but building MAI-1 and MAI-Voice-1 internally gives it more control over costs, product integration, and regulatory compliance. By owning the technology, Microsoft can optimize for speed and efficiency, protect sensitive data within its own infrastructure, and develop features tailored specifically to its ecosystem. This reduces dependence on a single provider and strengthens Microsoft’s long-term strategic position. How do Microsoft’s MAI-1 and MAI-Voice-1 compare with OpenAI’s models? MAI-1 is a large language model designed to rival GPT-4 in text-based tasks, but Microsoft emphasizes efficiency and integration rather than pushing absolute frontier performance. MAI-Voice-1 focuses on ultra-fast, natural-sounding speech generation, which complements OpenAI’s Whisper (speech-to-text) rather than duplicating it. While OpenAI still leads in some benchmarks, Microsoft’s models give it flexibility to innovate and align development closely with its own products. What are the risks for businesses in relying solely on third-party AI providers? Total dependence on external AI vendors creates exposure to pricing changes, roadmap shifts, or availability issues outside a company’s control. It can also complicate compliance when sensitive data must flow through a third party’s systems. Businesses risk losing differentiation if they rely on the same model that competitors use. Microsoft’s decision highlights these risks and shows why strategic independence in AI can be valuable. hat lessons can other enterprises take from Microsoft’s pivot? Not every company can afford to train a model on thousands of GPUs, but the principle is scalable. Organizations should assess which AI capabilities are core to their competitive advantage and consider building or fine-tuning models in those areas. For most, a hybrid approach – combining foundation models from providers with domain-specific custom models – strikes the right balance between speed, cost, and control. Microsoft demonstrates that owning at least part of the AI stack can pay dividends in trust, compliance, and differentiation. Will Microsoft continue to use OpenAI’s technology after launching its own models? Yes. Microsoft has been clear that it will use the best model for the task, whether from OpenAI, the open-source community, or its internal MAI family. The launch of MAI-1 and MAI-Voice-1 doesn’t replace OpenAI overnight; it creates options. This “multi-model” strategy allows Microsoft to route workloads dynamically, ensuring it can balance performance, cost, and compliance. For business leaders, it’s a reminder that AI strategies don’t need to be all-or-nothing – flexibility is a strength.

The Cyber Resilience Act in the energy sector – obligations, risks, and how to prepare for 2025?

The EU’s Cyber Resilience Act (CRA) marks a turning point in the way digital products are secured across Europe. By 2027, all software will need to comply with CRA requirements, and as early as next year, companies will face mandatory cybersecurity incident reporting. This issue is particularly critical for the energy sector, where outdated and poorly secured systems are still in use. A lack of proper safeguards can lead to severe consequences – not only financial but also operational and social. CRA applies to all software in the EU starting in 2027. For the energy sector, this means obligations such as SBOM, secure-by-design, and incident reporting. TTMS supports companies in preparing for and implementing CRA requirements. Ignoring the regulation may result in fines, market exclusion, and exposure to real cyberattacks. 1. Why is the energy sector especially vulnerable? The energy sector is the backbone of modern society – the economy, public administration, and daily life all depend on its stability. As critical infrastructure, electricity supply must be uninterrupted. Any disruption can cause serious social and economic fallout – from halting transport and communications to crippling hospitals or emergency services. Yet, this infrastructure relies on complex control systems such as SCADA, RTU, EMS, or HMI. Many of them were designed in an era when cybersecurity was not a top design priority. Built primarily for performance and reliability, they are often ill-equipped to withstand today’s digital threats. The challenge intensifies with the convergence of OT and IT systems. More elements of physical infrastructure are now connected to corporate networks, increasing the attack surface and complicating risk management. Cybercriminals no longer need physical access to a power plant or substation – a single vulnerability in a remote-control system may be enough. Adding to the risk is technological legacy. Many organisations still rely on outdated operating systems and applications deeply embedded in technological processes. These cannot be easily updated or replaced, making them an easy target for cyberattacks. 1.1 The threat is not theoretical – real incidents prove it. In 2017, a cyberattack targeted the German company Netcom BW, a telecommunications network operator owned by EnBW, one of Germany’s largest energy providers. The attacker was a Russian national and a member of Berserk Bear, a group linked to Russia’s FSB intelligence service. The goal was to infiltrate communication infrastructure used not only by Netcom BW but also by energy system operators. While the companies assured that the core energy infrastructure remained intact, the attack exposed vulnerabilities in the supply chain and the dependencies between IT systems and critical energy assets. This is a warning that cannot be ignored. Incidents like this highlight that cybersecurity cannot stop at the boundaries of a power plant or transmission grid – it must extend to technology suppliers, communication systems, and all interconnected digital components. This is precisely why the implementation of the EU’s Cyber Resilience Act is not only a legal requirement but also a strategic step towards building a resilient energy sector for the future. 2. CRA – What Does It Mean for Energy Companies and How Can TTMS Help? The new EU regulation introduced by the Cyber Resilience Act (CRA) imposes binding cybersecurity obligations on software providers across the energy sector. For many organisations, this means reorganising development processes, implementing new tools, and ensuring both formal and technical compliance. This is where Transition Technologies MS steps in, offering both advisory and technological support. 2.1 Mandatory SBOMs (Software Bill of Materials) CRA requires every company delivering software to maintain a complete list of components, libraries, and dependencies used in their product. How TTMS helps: We implement tools that automate the creation and updating of SBOMs in popular formats (e.g. SPDX, CycloneDX), integrating them with CI/CD pipelines. We also support risk analysis of open-source components and help establish dependency management policies. 2.2 Secure-by-Design Development CRA enforces the obligation to embed security into products from the very first design stage. How TTMS helps: We provide threat modelling workshops, application architecture security audits, and the implementation of secure DevSecOps practices. Our support also includes penetration testing and code reviews at every stage of the product lifecycle. 2.3 Vulnerability Management The regulation requires organisations to detect, classify, and patch vulnerabilities quickly – not only in their own code but also in third-party components. How TTMS helps: We build and integrate vulnerability management processes – from static scanning (SAST) and dynamic testing (DAST) to real-time vulnerability monitoring systems. We help implement procedures aligned with best practices (e.g. CVSS, CVD). 2.4 Incident Reporting Every major security incident must be reported to ENISA or the local CSIRT within 24 hours. How TTMS helps: We create incident response plans (IRPs), implement detection and automated reporting systems, and train IT and OT teams in CRA-compliant procedures. TTMS can also act as an external cyber emergency response partner. 2.5 EU Declaration of Conformity Software providers must deliver a formal document confirming compliance with CRA requirements – this is not only a declaration but also a legal responsibility. How TTMS helps: We support companies in creating and maintaining CRA-required documentation, including declarations of conformity, security policies, and technical support plans. We provide pre-implementation audits and assistance in preparing for regulatory inspections. 2.6 Additional Support and Parallel Development Implementing CRA requirements does not have to mean halting other development projects. At TTMS, we provide additional resources in a staff augmentation model, enabling organisations to continue software development in parallel with the process of adapting applications to new regulations. This way, energy companies can maintain their pace of innovation while effectively meeting legal requirements. Moreover, we offer comprehensive cybersecurity testing support across three key areas: Infrastructure audits and penetration testing Application audits and penetration testing Source code audits All these services are delivered by TTMS in cooperation with Transition Technologies Software (TTSW), ensuring complete security both at the system and application level. Why Work with TTMS? Proven experience in the energy sector – deep knowledge of SCADA, EMS, DMS, and OT/IT environments. Dedicated Quality and Cybersecurity experts – supporting organisations throughout the entire CRA compliance cycle. Ready-to-use solutions and tools – from SBOM management to incident response and risk analysis. Security-as-a-Service – flexible support models tailored to client needs. 3. Ignoring CRA Could Cost More Than You Think Non-compliance with the Cyber Resilience Act is not just a formal issue – it is a real risk to business continuity and market presence in the EU. CRA foresees severe financial penalties – up to €15 million or 2.5% of global annual turnover – for failing to meet software security requirements. In addition, non-compliant products may be completely excluded from the EU market, which for many companies – especially those in critical infrastructure – could mean the loss of key contracts. Neglecting security also increases the risk of real cyberattacks that may paralyse systems, leak sensitive data, and cause massive financial and reputational losses. A notable example is the ransomware attack on the Norwegian company Norsk Hydro in March 2019. The global aluminium producer and energy provider had its IT systems worldwide shut down, forcing plants to switch to manual operations. The direct and indirect costs exceeded $70 million, and the company struggled for weeks to restore operations and rebuild market trust. Although this case dates back a few years, the number of similar attacks has been rising steadily amid Europe’s ongoing hybrid warfare. In 2025, Poland reported two major cybersecurity incidents in public institutions – one involving a personal data breach caused by an email system intrusion, and another targeting industrial control systems. Cases like these show that failing to act proactively on cybersecurity can cost far more than investing in CRA compliance. It is not only a legal obligation but also a condition for maintaining competitiveness and business resilience in the digital era. 4. Cyber Resilience Act – Consequences of Non-Compliance and Real Risks of Cyberattacks Failure to comply with CRA can result in: Financial penalties of up to €15 million or 2.5% of global annual turnover Exclusion from the EU market Increased risk of cyberattacks leading to system paralysis and massive financial losses 4.1 When Should You Start Acting? The Clock Is Ticking The Cyber Resilience Act was adopted in October 2024. While full compliance will not be required until December 2027, one of the key obligations – reporting security incidents within 24 hours – will already apply from September 2026. This means that companies – especially those in critical infrastructure sectors such as energy – have less than a year to prepare procedures, train teams, implement the right tools, and test their systems. Implementing CRA is not about a single document – it requires a comprehensive change in how software is developed and maintained, covering security, documentation, vulnerability management, and formal compliance. Leaving compliance until the last minute is a recipe for errors, system gaps, and costly consequences. Organisations that start preparing now will gain not only a time advantage but also a strategic one, demonstrating to partners and customers that they take cybersecurity seriously – before being forced to. This is precisely where Transition Technologies MS (TTMS) can make the difference. Our expert teams support organisations at every stage of CRA readiness – from analysing current processes and conducting security audits, to implementing SBOM and vulnerability management tools, developing incident reporting procedures, and preparing formal compliance documentation. TTMS does more than advise – we implement real technical solutions, deliver training, and provide ongoing support as part of a long-term partnership. If your organisation operates in the energy sector, do not delay CRA compliance – the consequences of inaction can be severe both operationally and financially. Talk to one of our cybersecurity experts and discover how TTMS can help you navigate this process smoothly and effectively. Visit ttms.pl/energy to learn more about the software and solutions we build for energy companies. Looking for a quick summary? Check out our FAQ section, where we have gathered the most important questions and answers from this article. When does the Cyber Resilience Act (CRA) come into force and what is the timeline? The Cyber Resilience Act was officially adopted in October 2024. Full compliance with its provisions will be mandatory from December 2027. However, from September 2026, companies will already be required to report security incidents within 24 hours. This leaves limited time for organisations to analyse, prepare, and implement the necessary processes – especially in the energy sector, where action must be both fast and methodical. Which products and systems in the energy sector are covered by CRA? The regulation applies to all “products with digital elements,” meaning both physical devices and software that can connect to a network. In practice, this includes critical energy management and control systems such as SCADA, RTU, EMS, DMS, and HMI – the backbone of digital energy infrastructure. If your software operates in this environment, CRA directly affects your organisation. What specific obligations does CRA impose on energy companies? Energy companies must introduce Software Bills of Materials (SBOMs), design systems with a secure-by-design approach, manage and patch vulnerabilities quickly, report major incidents to relevant institutions within strict deadlines, and prepare an EU Declaration of Conformity for their products. These are not mere formalities – they have a tangible impact on the security and resilience of entire energy systems. What are the risks for companies that ignore CRA requirements? Non-compliance may result in fines of up to €15 million or 2.5% of a company’s global annual turnover – whichever is higher. In addition, non-compliant products may be removed from the EU market entirely. Beyond financial penalties, ignoring CRA also exposes companies to real cyber risks, such as ransomware attacks. The Norsk Hydro case showed how a single incident can cause operational paralysis, data loss, and reputational damage with long-term consequences. Does every company have to report incidents, even if there was no service disruption? Yes. CRA requires reporting of any major security incident or actively exploited vulnerability within 24 hours of detection. A follow-up report must then be submitted within 72 hours, and a final summary within 14 days. This applies not only to incidents that cause outages but also to those that could potentially affect product or user security. The aim is to ensure early transparency and rapid mitigation across the entire EU market.

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TTMS has really helped us thorough the years in the field of configuration and management of protection relays with the use of various technologies. I do confirm, that the services provided by TTMS are implemented in a timely manner, in accordance with the agreement and duly.

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