Managed Services – A Strategic IT Delivery Model for Large Companies
In today’s fast-paced business environment, large enterprises need IT solutions that are not only cost-effective but also reliable and scalable in the long run. One model of IT outsourcing that fulfills these needs is the Managed Services model. Under a Managed Services arrangement, a company partners with an IT provider to take over full responsibility for a defined set of IT services or operations, usually on an ongoing basis with clear Service Level Agreements (SLAs). This is more than just contracting tech talent – it’s about entrusting an external team to manage and deliver an entire IT function (from system analysis and development to maintenance and support) as a strategic long-term partner. Managed Services is often considered “the most technologically advanced form of IT outsourcing services” and is increasingly preferred by the world’s largest corporations for its ability to ensure stability and continuous improvement in IT delivery. What is the Managed Services Model in IT? In a Managed Services model, the service provider takes full ownership of an IT area on behalf of the client. This means the provider supplies a dedicated team (or teams) of specialists and manages the day-to-day operations, maintenance, and enhancements of the systems or processes in scope. Unlike one-off projects or simple staff augmentation, the provider is accountable for end-to-end outcomes – they monitor performance, proactively address issues, and guarantee certain results as defined by the contract (for example, system uptime, response times, or delivery of new features). The client, in turn, benefits from hands-off management of that IT function, focusing instead on core business activities while the Managed Services partner handles the technical work. Key characteristics of Managed Services: Long-term engagement: Managed Services are typically structured as multi-year contracts or ongoing engagements, rather than short-term assignments. The provider becomes a long-term partner who deeply understands the client’s systems and business goals. This fosters a relationship built on consistent service and continuous improvement over time. Defined scope and SLAs: Both parties agree on the scope of services (e.g. managing a cloud infrastructure, supporting an enterprise application, running an outsourced operations center) and specific performance metrics or Service Levels. The provider is then responsible for meeting those targets (such as 99.9% uptime or resolving support tickets within X hours), ensuring a predictable quality of service. Provider-managed team: Unlike models where the client manages day-to-day tasks, in Managed Services the vendor handles team leadership, processes, and delivery. The external team might work remotely or on-site, but they operate under the provider’s management structure and best practices. The client receives updates and reports, but doesn’t need to micromanage the technicians. Comprehensive services: A Managed Services contract often spans a range of activities – from initial analysis and design to ongoing support and maintenance. For instance, the provider might not only develop a software platform, but also maintain it, apply updates, monitor its performance 24/7, and support end-users. In many cases, the provider also handles things like capacity planning, security patching, and continual optimizations as part of the service. Flexible and scalable delivery: While the engagement is long-term, Managed Services can scale resources up or down as needed. If the client’s needs grow, the provider can add more specialists or introduce new skill sets quickly; if needs decrease, the team can be optimized accordingly. This is done under the umbrella of the service agreement, without the client having to recruit or lay off staff. In essence, Managed Services is about outsourcing an outcome rather than just people. The provider commits to delivering a functioning service or system, and it’s up to them to ensure they have the right people, processes, and tools to meet that commitment. Benefits of Managed Services for Large Enterprises For large companies, choosing a Managed Services model can offer numerous strategic benefits. By entrusting critical IT operations to a specialist partner, enterprises can achieve greater continuity and efficiency in their IT delivery. Below are some of the key advantages of Managed Services and how they address the needs of enterprise IT environments: Long-Term Reliability and Partnership: Managed Services engender a stable, long-term working relationship. The provider’s deep familiarity with the client’s IT landscape and business processes means fewer surprises and more reliability over time. Knowledge retention is higher because the same partner has been managing the system for years. For example, TTMS’s managed services engagements often turn into multi-year partnerships – in one case, a global energy management company has collaborated with TTMS since 2010, relying on a dedicated team to continuously develop and support its critical software ecosystem. Such longevity translates into reliability; the client can count on consistent service and trust that the provider will support future needs as well. Operational Continuity and Risk Mitigation: With Managed Services, enterprises gain 24/7 operational coverage and robust risk management for their IT systems. The provider is responsible for keeping the lights on at all times, often with proactive monitoring and a standby support team to quickly resolve any issues before they impact the business. This ensures high availability of systems and minimal downtime. Moreover, the provider handles personnel risks like staff turnover – if an engineer leaves, it’s the provider’s duty to replace and train a new one without disrupting the service. For the client, this means business continuity is assured. One TTMS specialization is providing such continuity: backed by the resources of a large IT group, TTMS can smoothly manage attrition and knowledge transfer so that service is never interrupted. In short, the Managed Services partner absorbs the operational risks, allowing the enterprise to run without worrying about IT breakdowns or staffing gaps. Cost Control and Predictability: Managed Services can be financially advantageous through better cost predictability and optimization. Typically, the engagement is billed as a steady monthly fee or as per an agreed budget, which makes IT costs more predictable compared to ad-hoc projects. Enterprises avoid large upfront investments and can often convert fixed costs into variable costs. Additionally, providers leverage economies of scale and efficient processes to reduce the overall cost of ownership. Importantly, clients pay for outcomes rather than hours – if the provider can accomplish the work with fewer resources or automate tasks, those efficiency gains benefit the client. The Managed Services model also helps prevent the hidden costs of downtime or failures by actively maintaining systems. Over time, many clients see cost savings from optimized operations and not having to expand their internal IT headcount for these functions. The flexibility of scaling the service up or down to match real needs (and budget) further ensures cost-effectiveness. Scalability and Flexibility: A key benefit of Managed Services is the ease of scaling. As a large enterprise grows or enters new markets, its IT needs can spike accordingly – more users to support, more data to manage, new features required, etc. With a Managed Services partner, scaling up is straightforward: you simply renegotiate the service scope and the provider will add more specialists or teams to handle the increased workload. Conversely, if certain operations become less intensive, the provider can scale down the team, avoiding unnecessary cost. This elasticity is particularly valuable for large organizations that may go through dynamic changes (mergers, acquisitions, seasonal peaks, etc.). The Managed Services model, especially with a provider like TTMS that has a broad talent pool, allows enterprises to quickly adjust capacity without the delays of hiring or the pain of layoffs. In short, you get “fast scaling-up [or down], with a ready supply of qualified experts” to meet your current demands. This flexibility extends to technology as well – need to adopt a new tech stack or tool? Your managed service partner can introduce the right experts or training to do so. Access to Specialized Skills and Innovation: When partnering via Managed Services, enterprises gain ongoing access to a wide range of specialized IT skills that might be scarce or expensive to maintain in-house. The provider brings in a team with diverse expertise – for example, cloud architects, security experts, database administrators, and more – all under one service umbrella. This means the enterprise can tap into this expertise whenever needed without having to hire each role internally. Moreover, a good Managed Services provider will keep innovating and improving the service, bringing in industry best practices and new solutions to benefit the client. They often have experience across multiple clients and industries, which allows them to introduce fresh ideas and avoid stagnation. For instance, TTMS leverages its broad experience with world-leading companies to continuously optimize its services; the company’s long-term engagements have shown that quality and competence improvements by the provider directly translate into better IT outcomes for the client. In practice, this might mean the Managed Services team suggests a performance optimization, implements an automation tool, or ensures the systems are always using up-to-date, secure technology – all as part of their service. The client gains the benefit of these innovations without having to chase them independently. In summary, Managed Services provide a steady, scalable, and expert-driven IT delivery capability. Large enterprises choose this model to ensure their IT operations are in safe hands for the long haul – with predictable costs, assured performance, and the agility to evolve as the business grows. When to Use Managed Services: Ideal Scenarios Managed Services is a powerful model, but it shines the most in particular scenarios and needs. Large companies should consider a Managed Services approach in situations where long-term support and strategic value outweigh the need for short-term flexibility. Here are some common situations where Managed Services is most effective: Ongoing Platform Support and Maintenance: If your organization has a critical software platform or enterprise application that requires continuous support, regular updates, and user assistance, a Managed Service is often the best fit. Rather than treating each update or issue as a separate project, you can establish a dedicated team to own the platform’s health and improvements over time. This is ideal for systems that have to run 24/7 (such as e-commerce sites, banking systems, or internal tools used daily by thousands of employees) where you cannot afford downtime. For example, a pharmaceutical company’s vendor management system initially built in 2008 was later handed over to TTMS under a Managed Services arrangement; TTMS took over the system’s ongoing maintenance in 2018 and continued to enhance its capabilities. Such a transition ensured the platform stayed up-to-date and performant without burdening the client’s own staff. If you have a similar long-lived application that is core to your operations, a Managed Service can provide steady maintenance, user support, and incremental development as needed. Complex, Multi-Year IT Programs: Large-scale IT initiatives – like digital transformation programs, global system rollouts, or large application ecosystems – often span many years and phases. In these cases, maintaining continuity is crucial. A Managed Services model can supply a stable core team throughout the program’s life. Even as projects within the program evolve, the provider maintains context and knowledge accumulated from phase to phase. This avoids the “restart” costs of constantly onboarding new vendors or teams. For instance, in the energy sector, a leading energy management enterprise engaged TTMS as a nearshore partner to develop and maintain a suite of applications from 2010 onward. Over time, separate applications were consolidated into a unified platform, and TTMS provided around 60 specialists to support this evolution – handling development, maintenance, and innovations as an integrated service. Such continuity over a multi-year program ensured that the software ecosystem kept improving without interruption as the client’s strategy evolved. Operations Centers and 24/7 Support Needs: If your business requires an outsourced operations center, network monitoring center, or a 24/7 helpdesk, the Managed Services model is an excellent choice. These scenarios demand constant vigilance and a team working in shifts to cover all hours – something that’s hard and costly to maintain internally. A Managed Services provider can set up a dedicated Operations Center with round-the-clock staff to monitor your infrastructure, respond to incidents, and support users at any time of day. Because the provider manages scheduling, training, and scaling of that team, you get continuous service without the HR headaches. This is particularly useful for industries like finance, telecom, or online services, where downtime outside “business hours” is not an option. Under a managed contract, the provider will ensure that night or weekend support is built into the agreement, giving you peace of mind that experts are always on call. In essence, whenever you need “always-on” IT support or monitoring, managed services can deliver a turnkey team to handle it. Need for Strict Service Levels and Compliance: There are situations where not meeting an IT performance target can have serious consequences (financial penalties, customer churn, regulatory issues). Examples include meeting a certain transaction processing time in banking, or ensuring quick recovery from any outage in healthcare systems. In such cases, the accountability and structure of Managed Services are very valuable. You can formalize strict SLAs (e.g., incident response times, resolution times, security compliance levels) in the contract, and the provider is contractually bound to meet them. Providers that specialize in managed IT services often have mature processes (ITIL practices, etc.) and certified quality standards to consistently hit these targets. If your enterprise operates in a highly regulated or mission-critical environment, using a Managed Services partner can actually improve your compliance and reliability posture, since the provider’s entire delivery framework is tuned to meet predefined standards. The managed team will handle audits, documentation, and continuity plans as part of their service, which can be a huge relief for your internal compliance officers. Situations Lacking Internal Expertise or Resources: Perhaps your company is adopting a new technology (say, a move to the cloud, or implementing a sophisticated ERP module) and you don’t have the in-house experts to manage it long term. Or maybe your IT team is stretched thin and cannot take on the support of another system. These are prime opportunities to bring in a Managed Services provider. Instead of attempting a big internal hiring and training effort, you can outsource the whole function to specialists who already know what to do. Managed Services is effective here because it’s not just a one-time consulting engagement – it ensures that after initial implementation, the experts remain in place to run and optimize the solution continuously. This was the case for a certain global company that needed a new Salesforce ecosystem managed: they opted for TTMS’s Managed Services, which provided “full management of their Salesforce platform, including user support and system optimization, so the company didn’t need an in-house Salesforce team”. In general, whenever your organization faces an IT need that is important but outside your core competencies, Managed Services can fill that gap effectively and sustainably. In summary, Managed Services work best for IT functions that are ongoing, critical to business performance, and prone to change or growth over time. If you foresee that an area of IT will require continuous attention and evolution, that’s a strong sign that a Managed Service model could be the right approach. On the other hand, for very short-term projects or extremely well-defined one-off tasks, a simpler outsourcing model might suffice. The value of Managed Services grows the more you need strategic, ongoing collaboration rather than a quick fix. How Managed Services Differs from Time & Material or Staff Augmentation It’s important to distinguish Managed Services from other popular IT outsourcing models like Time & Material (T&M) contracts or Staff Augmentation (also known as “Body Leasing”). All three models involve external IT providers, but the responsibilities, control, and risk distribution are very different in each: Managed Services vs. Time & Material: In a Time & Material model, the client pays for the actual hours and materials the provider uses on a project. It’s a flexible, often short-term engagement where the client typically still guides what needs to be done, and the scope can evolve as needed. Control and direction generally remain with the client in T&M – the provider supplies people and expertise to do tasks under the client’s oversight. In contrast, Managed Services shifts more responsibility to the provider. The provider is not just billing hours; they are bound to deliver a result or maintain a service over time. The scope in Managed Services is defined in terms of outcomes (e.g., keep System X running smoothly and updated), and it’s the provider’s job to figure out how to allocate and manage resources to meet that goal. You can think of T&M as pay-as-you-go development or support, whereas Managed Services is all-inclusive maintenance of an IT capability. For example, if developing a new feature were a T&M project, the client might prioritize features and accept or reject work in sprints; but if that software is under Managed Services, the provider’s team might independently schedule improvements, perform maintenance, and only report back periodically on progress and KPIs. Risk and accountability are also different: in T&M, if something takes longer, the client generally pays more; in Managed Services, the provider often eats the cost of overruns (unless out of scope) because they’ve committed to an outcome or fixed fee. T&M is great for flexibility and evolving projects, while Managed Services is great for assured continuity and meeting established service benchmarks. Managed Services vs. Staff Augmentation: Staff augmentation is essentially hiring external IT personnel to extend your internal team. In that model, if you need, say, five extra developers or a UX designer for a period of time, an outsourcing company provides those individuals, but you integrate them into your own projects and manage them directly. The augmented staff follow your processes, use your tools, and take day-to-day direction from your managers, just as if they were your employees (except payroll and HR are handled by the vendor). The key difference with Managed Services is the management aspect: in Managed Services, the provider supplies an outcome, not individual people. You don’t tell the managed service team members what to do each day – their own team lead (employed by the provider) handles that. As TTMS’s CEO describes, in managed IT services “not only experts and their work are delivered, but the service provider is responsible for the entire development of teams and projects”. This means the provider builds and nurtures the team, plans the work, and ensures delivery – a scope far beyond staff augmentation. Another difference is scope of work: staff aug typically fills specific skill gaps on projects you control, whereas managed services covers a whole function or system (often encompassing multiple roles). From a client’s perspective, staff augmentation gives you extra hands (but your responsibility doesn’t lessen), while managed services gives you a fully managed solution. If an augmented staff member goes on leave, that’s for you to handle; if a managed service team member leaves, the provider will replace them behind the scenes and keep the service on track without troubling you. Staff augmentation is often easier for short-term or uncertain needs, but it won’t provide the strategic guidance or full accountability that a Managed Service does. In summary, choosing between these models comes down to what you want to manage yourself versus outsource. If you simply need additional capacity and want to stay in control, staff augmentation or T&M might suffice. But if you want an entire outcome managed for you – with the provider taking charge of talent management, quality control, and delivery – then Managed Services is the distinct choice. It offers a higher level of service wherein the provider acts as an ongoing stakeholder in your success, not just a contractor. That’s why many large enterprises engage in all three models for different needs: for instance, using staff augmentation to temporarily fill a role, T&M for an exploratory pilot project, and Managed Services for established products or infrastructure that require dependable, long-term oversight. TTMS Case Studies: Managed Services in Action To illustrate the Managed Services model, here are a couple of real-life examples of projects delivered by TTMS under long-term service arrangements. These cases demonstrate how Managed Services work in practice and the tangible benefits they provide to large organizations: Energy Sector – 13+ Year Ongoing Development & Support Partnership: One of TTMS’s flagship Managed Services engagements is with a global leader in energy management and automation (a Fortune 500 company in the electrical industry). Initially, this client sought a nearshore development partner back in 2010 to help build several applications for configuring protective relay devices. What started as a project-based collaboration soon transitioned into a fully managed service as the client decided to consolidate multiple tools into a single integrated platform. TTMS took on the responsibility not only to develop the unified application but also to maintain and continuously improve it thereafter. Currently, TTMS provides around 60 specialists across four agile teams to this client, delivering ongoing development, maintenance, and technical support for the entire software ecosystem. The engagement operates under defined service terms, ensuring the client’s platform is always up-to-date, secure, and aligned with evolving business needs. The results have been impressive: the consolidation led to major efficiency gains and cost savings for the client, and TTMS has become a trusted long-term partner in the client’s digital transformation journey. Over 13 years of successful collaboration, this Managed Services model has guaranteed operational continuity for the client’s critical systems and provided the scalability to tackle new projects on demand (the TTMS teams have delivered multiple major software projects for the client over the years, all under the managed umbrella). This case shows how a well-executed Managed Service can evolve into a strategic partnership — the client can rely on TTMS as an extension of their own IT department, delivering value continuously rather than in one-off spurts. Healthcare Sector – Outsourced Platform Maintenance and Enhancement: In the healthcare industry, TTMS has a Managed Services success story with a client that operates a global IT services center for a pharmaceutical company. This client had a custom Contractor and Vendor Management System developed in-house in 2008 to handle the complex process of managing external IT vendors and contractors across many countries. By 2018, the system had become critical but also needed new features and more rigorous support to meet evolving compliance demands. The client made a strategic decision to outsource the platform’s management to TTMS under a Managed Services contract. TTMS stepped in as the dedicated service provider, taking over full responsibility for the application. This included setting up a permanent team to understand the old codebase, start modernizing the platform, provide user support, and ensure all regulatory compliance features (like tax and legal requirements in various regions) were up to date. The Managed Services team delivered continuous improvements to the system – indeed, after TTMS took charge, the platform’s capabilities were further enhanced beyond what it originally offeredttms.com. Importantly, the client no longer needed to allocate their own developers to this tool; TTMS handled all enhancements, bug fixes, and maintenance as an ongoing service. This arrangement freed the client’s internal team to focus on new strategic projects while TTMS ensured the vendor management operations ran smoothly. The outcome has been very positive: the platform remains robust and compliant with international standards, and the client enjoys peace of mind knowing that a skilled partner is always watching over this critical system. This is a great example of how Managed Services can take an existing, business-critical platform and provide it a new life, with sustained support and improvements delivered year after year. (These are just two examples; TTMS’s portfolio includes many similar long-term engagements in different domains – from running outsourced support centers for global enterprises, to managing entire Salesforce ecosystems as a service. In each case, the common theme is a lasting partnership that delivers continuous value. Most TTMS case studies ultimately tell a story of ongoing cooperation, which is the essence of the Managed Services approach.) Conclusion: Leverage Managed Services for Long-Term IT Success For large companies looking to achieve strategic IT objectives at scale, the Managed Services model offers a proven pathway. By embracing Managed Services, enterprises secure not just a vendor, but a strategic partner dedicated to keeping their IT operations running optimally and evolving to meet future challenges. The benefits – from long-term reliability and operational continuity to flexible scaling and access to specialized expertise – directly address the complexities of enterprise IT environments. Unlike short-term contracts, a Managed Service builds a foundation of trust and deep collaboration. As seen in TTMS’s real-world cases, this model can lead to decades-long partnerships where the provider essentially becomes an extension of the client’s organization. When comparing cooperation models, it’s clear that Managed Services occupies a special place for initiatives where sustained performance and continuous improvement are non-negotiable. It differs from Time & Material or staff augmentation by delivering outcomes, not just effort. For companies that want to focus on their core business while ensuring their IT backbone is expertly managed, this model is often the ideal choice. It allows you to offload the complexity of day-to-day IT operations to a partner like TTMS who has the processes, people, and experience to handle it efficiently and proactively. Now is the time to consider Managed Services as part of your IT strategy. If your organization is seeking long-term stability, better cost control, and the agility to scale IT operations seamlessly, partnering with a Managed Services provider can be a game-changer. TTMS has been supporting the world’s largest corporations in this model for years, building a track record of success through reliability, innovation, and a partnership approach. We invite you to explore what this could mean for your business. Contact TTMS to discuss how a Managed Services partnership can be tailored to your needs and to start a conversation about driving your IT operations to new heights of efficiency and performance. Let’s talk about creating a Managed Services solution that powers your long-term success. What is the difference between Managed Services and traditional IT outsourcing? Traditional IT outsourcing typically means hiring external professionals to perform tasks under the client’s supervision – for example, through staff augmentation or Time & Material models. In contrast, Managed Services shift the responsibility for delivering results to the service provider. The provider not only supplies the experts but also manages them, oversees the workflows, and ensures that agreed outcomes are met. This model is about outsourcing an entire function with measurable service levels, rather than just supplementing internal capacity. When should a company consider using the Managed Services model? The Managed Services model is ideal when your business needs long-term, stable support for critical IT systems or operations. It’s particularly effective for managing enterprise platforms, supporting legacy systems, maintaining high availability environments, or delivering 24/7 helpdesk services. Companies should consider this model when internal teams are stretched, when they need guaranteed performance levels, or when they want to focus on core business functions while a trusted partner ensures the IT backbone remains operational and optimized. What are the main business benefits of Managed Services for large enterprises? Large organizations can achieve multiple strategic benefits through Managed Services. These include improved operational continuity, reduced IT risk, better cost predictability, and ongoing access to a broad range of specialized skills. Instead of handling recruitment, training, or service management internally, enterprises can rely on a provider to take full ownership of delivery. Managed Services contracts are also built for continuous improvement, enabling innovation and process optimization over time – something that one-off projects or staff augmentation cannot guarantee. Does the Managed Services model allow for flexible scaling of IT resources? Yes, flexibility and scalability are among the biggest strengths of the Managed Services model. The provider can increase or reduce the size and composition of the team based on your current business needs – without the delays and costs associated with hiring or downsizing internal staff. This is especially valuable during growth phases, seasonal peaks, or digital transformations. Additionally, the provider can quickly bring in experts with new skill sets if a technology change occurs, ensuring your IT capabilities evolve seamlessly. What does a typical Managed Services contract include? A Managed Services contract outlines the scope of work (such as platform maintenance, application development, or 24/7 monitoring), key performance indicators (like uptime percentages or response times), and pricing structure (often a fixed monthly fee or scalable model). It also defines roles, responsibilities, and escalation procedures. These contracts ensure accountability, reduce uncertainty, and provide transparency, allowing enterprises to trust that the provider will deliver consistent service without the need for constant oversight or micromanagement.
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AI in Digital Transformation Strategy 2025: 6 Key Trends for Large Companies
First, some statistics… Digital transformation is gaining momentum – in 2025, as many as 94% of organizations are conducting various types of digital initiatives. Artificial intelligence (AI) is increasingly at the center of these activities. Over three-quarters of companies already use AI in at least one area of their operations, and 83% of enterprises consider AI to be a strategic priority. AI is not a futuristic curiosity, but a key factor of competitive advantage. What AI trends should be included in the strategy of organizations planning development after 2025? Below we present the most important of them, especially important for leaders of digital transformation in large companies. Global AI software revenues are growing exponentially, signaling massive business investment in AI. The rapid growth of the AI market is accompanied by a rapidly growing number of implementations in companies – according to McKinsey research, 78% of organizations use AI in at least one business function. For management, this means that AI must be included in long-term strategies to stay ahead of the competition. More and more leaders are recognizing this fact – almost half declare that AI is already fully integrated into the strategic plans of their business. A strategic approach to AI, based on current trends, is therefore becoming a condition for successful digital transformation after 2025. 1. Process automation (hyperautomation) Business process automation using AI is one of the pillars of digital transformation. In the era of striving for operational excellence, companies reach for the so-called hyperautomation – combining many technologies (AI, machine learning, RPA) to automate everything possible. According to Gartner, hyperautomation is a priority for 90% of large enterprises, which shows how important it has become to streamline processes using AI. Both routine back-office tasks (e.g. document processing, reporting) and customer interactions (chatbots, voicebots) can be automated. For example, AI algorithms can analyze documents and extract data from them in a matter of seconds – something that used to take employees hours to do manually. RPA systems combined with AI can independently handle financial, HR, and logistics processes, learning from data and improving their operation over time. 70% of organizations indicate simplifying workflow and eliminating manual activities as a top priority in their digital strategy, and AI fits perfectly into these goals. What’s more, it is estimated that by 2026, 30% of enterprises will automate more than half of their network processes (up from <10% in 2023) – proof that the scale of automation is growing rapidly. Companies investing in AI-driven automation note tangible benefits: reduced operating costs, faster task execution, and relieving employees of tedious duties (allowing them to focus on creative tasks). As a result, digital transformation accelerated by automation is becoming a fact, giving organizations greater agility and productivity. 2. Predictive analytics and data-driven decision making Predictive analytics is another key area that should be part of every large company’s AI strategy. By using machine learning to analyze historical data, organizations can predict future trends, events, and demand with unprecedented accuracy. Instead of relying solely on reports describing the past, companies using predictive analytics can predict, for example, an increase in product demand, the risk of customer churn, or a production machine failure before it happens. This type of AI in business translates into better decisions—proactive, based on data, not intuition. The market for predictive analytics solutions is growing rapidly (around 21% per year) and is expected to almost double in value from USD 9.5 billion in 2022 to around USD 17 billion in 2025. No wonder – companies implementing predictive AI models are seeing significant benefits. In one study, 64% of companies indicated improved efficiency and productivity as the main advantage of using predictive analytics. For example, retail chains using AI to forecast demand can better manage inventory (avoiding shortages and surpluses), while banks that predict which customers may have difficulty repaying their loans are able to take remedial action earlier. Predictive analytics is used in every industry – from industry (maintenance of traffic based on predicting machine failures), through logistics (optimization of the supply chain based on forecasts), to marketing (predicting customer behavior and personalizing the offer). For management, this means the ability to make better decisions faster. AI solutions for business in the area of prediction are therefore becoming an essential element of the strategy of companies that want to be data-driven and stay ahead of market changes instead of just reacting to them. 3. AI integration with CRM/ERP systems Another trend shaping AI 2025 is the penetration of AI into key business systems, such as CRM (customer relationship management) and ERP (enterprise resource planning). Instead of treating AI as a separate experiment on the sidelines, leaders are focusing on integrating AI with existing platforms—so that machine intelligence supports sales, customer service, finance, and operations processes within existing tools. Business software vendors are recognizing this need and are increasingly offering built-in AI modules. Microsoft, for example, has introduced GPT-4-based Dynamics 365 Copilot into its ERP/CRM system, and SAP is developing the AI assistant “Joule” in its business applications. The benefits of such integration are enormous. In AI-powered CRM systems, salespeople receive suggestions on which lead is the most promising (AI scoring), which products to recommend to the customer, and even ready-made drafts of offer emails generated by the language model. AI support also means automatic logging of customer interactions or analysis of the sentiment of the customer’s statements (are they satisfied or irritated?). In turn, in ERP systems, AI helps to optimize the supply chain (better demand and inventory level forecasts), detect financial anomalies, improve production planning or automatically compare supplier offers. According to analyses, more than half of companies have already implemented AI-enhanced CRM systems – what’s more, these companies are 83% more likely to exceed their sales goals thanks to better use of customer data. This shows the real impact of AI on the core of the business. Integrating AI with CRM/ERP systems often requires a professional approach – identifying the right points where AI will add the most value, adapting models to company data and ensuring smooth cooperation of the new “intelligence” with existing processes. An example of a successful implementation is a project where TTMS introduced an AI system integrated with Salesforce CRM, automatically analyzing requests for proposals (RFP) and assessing key criteria. This solution significantly improved the bidding process – AI accelerated decision-making and allocation of resources needed to prepare the offer. This is real proof that well-integrated AI can relieve employees (here: the sales department) from time-consuming document analyses and allows them to focus on building relationships with the customer. Similar AI implementations are becoming a part of an increasing number of companies – they integrate, for example, AI-based chatbots with customer service systems, machine learning modules with inventory management systems or AI in finance, connecting with ERP to automatically classify expenses. As a result, an AI strategy should closely intertwine AI with a company’s core IT infrastructure, so that AI permeates end-to-end processes rather than operating in isolation from them. 4. Generative AI – from ChatGPT to custom models Generative AI has gained a lot of publicity in 2023-2024 thanks to models like GPT-4 (ChatGPT), DALL-E and other systems capable of creating new content – texts, images, code – at a level close to human. For large companies, generative AI opens up completely new possibilities, which is why it should become an important element of the strategy for the coming years. The applications are very wide: automation of creating marketing content, generating personalized offers for customers, creating chatbots that can conduct natural dialogue, supporting R&D departments (e.g. generating and testing new product concepts), and even assistance in programming (an “artificial programmer” suggesting code). Today, 71% of organizations declare regular use of generative AI in at least one area of activity (up from 65% at the beginning of 2024). This means that generative models have very quickly moved from the phase of curiosity to practical implementations in business. For leaders of digital transformation, generative AI is a double challenge: on the one hand, a huge opportunity for innovation, and on the other – the need for caution and ethics (more on that in a moment). Trends indicate that in the coming years, companies will build their own generative models specialized in their domain (e.g. a model that will generate a financial report based on company data or an assistant to handle internal corporate knowledge). GenAI-as-a-Service solutions are already being created in the cloud, which allow models to be trained on their own data while ensuring confidentiality. Generative AI is also changing the rules of the game in the area of customer service – a new generation chatbot can solve much more complex customer problems, while connecting to the company’s internal systems. Another important trend is the use of generative AI in work tools – for example, GPT-based assistants appear in office suites, facilitating the creation of summaries, presentations and analyses. This affects employee efficiency, in a way “doubling” human resources: PwC predicts that the use of AI agents can give an effect equivalent to doubling the size of the team thanks to the automation of routine tasks. An example of the use of generative AI in a large company can be the TTMS case study from the automotive industry, where a PoC was developed using Azure OpenAI (GPT-4) to automatically process vehicle parameter queries and calculate discounts. Such an intelligent application is able to generate an optimal price offer in a few seconds based on the description of the car configuration – something that previously required manual analysis of price lists and discount tables. This shows that generative AI can support sales and pricing in real time, increasing the pace of business operations. In summary, generative AI is a trend that large companies cannot ignore. The AI strategy for 2025+ should include pilot implementations of generative tools where they can bring the fastest return (e.g. content marketing, customer service, developer support). At the same time, it is necessary to take care of the framework for managing such models – from quality control of generated content to protection against the generation of unwanted data. Those who learn to use generative AI effectively in their business first will gain an innovator’s advantage and significantly accelerate their digital transformation. 5. AI Ethics and Responsibility The integration of AI into business strategy on a large scale requires an equally large attention to ethical issues and responsible AI development. The more algorithms decide on important matters (e.g. granting credit, medical diagnosis, CV selection of candidates), the louder the questions are asked: does AI make fair and non-exclusive decisions? Is it transparent and explainable? Is customer data adequately protected? Leaders of large companies must ensure that AI operates in accordance with ethical principles, otherwise they expose the organization to legal (upcoming regulations, such as the EU AI Act), reputational and business risks. The concept of Responsible AI is gaining in importance – a set of practices and principles that are supposed to ensure that the developed models are free from undesirable biases, and their operation is transparent and compliant with regulations. The ROI from AI depends on the adoption of the principles of Responsible AI – PwC experts note. In other words, investments in AI will bring full benefits only if customers and partners trust these systems. Meanwhile, there is a lot to be done here – although 75% of executives consider AI ethical issues to be very important, at the same time only 40% of customers and citizens trust companies to use AI responsibly. We see a clear gap between intentions and social perception. Organizations must fill this gap through specific actions: creating AI codes of ethics, establishing algorithm oversight committees, training on unconscious data biases, implementing AI Governance principles and monitoring models in terms of their decisions. Fortunately, the trend is positive – awareness of the problems is growing. As many as 90% of companies admitted that they had encountered an ethical “slip” of AI in their operations (e.g. biased indications of the recruitment system), which encourages the development of better practices. Awareness of specific issues has increased: for example, 78% of managers are already aware of the importance of AI explainability (compared to 32% a year earlier). The AI strategy for 2025 and beyond should therefore include the AI ethics by design component – from the outset, implementations should be planned so that they are transparent, fair and legal. This also applies to the use of data: AI should not violate privacy or information security principles. Companies that choose responsible AI will not only minimize risk, but will also gain an advantage – they will build greater customer trust, and their brand will be distinguished by credibility. All this translates into a long-term AI strategy consistent with business values and sustainable development. 6. Scalability of AI implementations across the organization The last but absolutely crucial trend (and challenge) is scaling AI solutions across the entire organization. Many large companies have successful AI pilot implementations behind them – prototypes of models or limited rollouts, e.g. in one department. However, for AI to truly change business, it cannot remain an isolated experiment. The AI strategy should include a plan to move from PoC (proof of concept) to production use on a large scale, in all places where the technology brings value. And this can be a problem – as IDC research shows, as many as 88% of AI projects get stuck at the pilot stage and do not go into production on a company-wide scale. In other words, statistically only 4 out of 33 AI initiatives manage to successfully develop globally. The reasons can be various: lack of clear business goals for the project, insufficient data or infrastructure quality, difficulties in integrating the solution with existing systems, as well as a shortage of talent (lack of MLOps, data science experts). In 2025, large organizations are therefore focusing on AI scalability and maintenance. Concepts such as MLOps (Machine Learning Operations) are gaining popularity – they mean a set of practices and tools that allow you to manage the life cycle of models (from prototype, through testing, to implementation and monitoring) similarly to software management. IT leaders realize that the right resources are needed: cloud AI platforms that will allow for a rapid increase in computing power for model training, repositories of functions and models for reuse in various projects, mechanisms for automatic scaling of AI applications as the number of users or data grows. Companies that have managed to build such an “AI factory” note a much higher return on investment – they achieve the scale effect: if one model saves PLN 1 million, then implementing similar models in 10 areas will already give PLN 10 million in benefits. McKinsey research confirms that AI implementation leaders use AI in an average of 3 business functions, while the rest are limited to single applications. In practice, this means that these companies are able to replicate successes – for example, an AI model tested in the sales department can be more easily adapted later in the after-sales service department, etc. Scalability also means changing the organizational culture – for AI to permeate the company, employees must be trained and convinced to work with AI, cross-departmental teams should jointly implement projects (business + IT + analysts), and the board should actively patronize AI initiatives. As McKinsey points out, the CEO’s involvement in overseeing AI projects strongly correlates with achieving a higher AI impact on the company’s results. In other words, scaling AI is a strategic task, not just a technical one – it requires vision, investment, and coordination across the entire organization. The strategy for 2025+ should therefore include: a plan for building infrastructure and competencies for scaling AI, selecting appropriate platforms (e.g. tools for automating model implementations), establishing success metrics (KPIs) for AI projects and a process for evaluating them before expansion. Companies that do this will turn individual AI implementations into a lasting advantage – AI will become part of their organizational “DNA”, not just an add-on. As a result, digital transformation will be driven at all levels by AI solutions for business – from operations, through analytics, to customer interactions. Ready for AI Strategy 2025? The future of large organizations will undoubtedly be shaped by the above AI trends: from widespread process automation, through predictive data approach, AI integration in systems, generative innovation, to the emphasis on ethics and scaling solutions. Each of these elements should be reflected in your AI strategy for the coming years. Putting them into practice will allow you to streamline the digital transformation of your business and maintain a competitive advantage in the world after 2025. Contact us – TTMS experts will help you translate these trends into specific actions. Together we will develop an effective AI strategy for your company and implement AI tailored to its needs. With the support of an experienced partner, you will maximize the potential of artificial intelligence, ensuring your organization’s growth and innovation in the digital era. What is hyperautomation and how does it differ from traditional automation? Hyperautomation is an advanced approach to process automation that combines technologies such as AI, machine learning, robotic process automation (RPA), and intelligent workflows to automate as many business processes as possible. Unlike traditional automation, which typically focuses on repetitive tasks, hyperautomation integrates multiple systems and data sources to optimize entire end-to-end processes, allowing for continuous improvement and greater scalability. What exactly is generative AI and how can businesses use it? Generative AI refers to AI models capable of creating new content — such as text, images, or code — based on training data. Examples include ChatGPT and DALL·E. Businesses use generative AI to automate content creation, personalize customer communication, support product development, and assist software engineering. It enables faster innovation and improves efficiency across marketing, sales, and customer support functions. What does MLOps mean and why is it important? MLOps, short for Machine Learning Operations, is a set of practices that aims to streamline the development, deployment, monitoring, and management of machine learning models. Similar to DevOps in software engineering, MLOps ensures that AI models are continuously integrated, tested, and updated in a scalable and secure way. It is essential for organizations that want to move from pilot AI projects to large-scale, production-ready implementations across departments. Why is explainability in AI so important? Explainability in AI refers to the ability to understand how and why an AI system made a specific decision. This is crucial in regulated industries like finance or healthcare, where transparency and accountability are required. Explainable AI builds trust among users and stakeholders and helps ensure that models are fair, reliable, and compliant with ethical and legal standards. What are the risks of implementing AI, and how can they be mitigated? AI implementation comes with risks such as data bias, lack of transparency, data privacy concerns, and unintended consequences in decision-making. These risks can be mitigated through responsible AI practices — including clear governance frameworks, continuous monitoring, ethical guidelines, and user education. Involving multidisciplinary teams and ensuring human oversight are also key strategies to maintain control over AI-driven processes.
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TTMS Aligns with SBTi Goals: Setting the Bar Higher for Climate Responsibility
Transition Technologies MS (TTMS) is proud to announce our alignment with the Science Based Targets initiative (SBTi) – a globally recognized framework that helps companies define clear, science-based emissions reduction targets aligned with the goals of the Paris Agreement. Currently, together with other companies – including Bank Ochrony Środowiska, CCC, Comarch, Fideltronik Poland, Młyny Kapka, Mokate, Polenergia, Przedsiębiorstwo “Rol-Ryz”, Stock Spirits Group, Tele-Fonika Kable, and ZUP Emiter – Transition Technologies MS is awaiting official verification of its climate targets by SBTi. A recent report from ESGinfo highlights that Japan leads in SBTi adoption, while Poland remains at the bottom of the list. As a Polish-founded IT company with a growing global footprint, TTMS believes it’s time to change that narrative. By embracing SBTi standards, we are: Committing to measurable climate goals Increasing transparency in how we track and report emissions Setting an example for responsible innovation in the tech sector For us, sustainability is not just a checkbox — it’s a direction. SBTi compliance means holding ourselves accountable to the highest standards and actively contributing to a greener, more resilient global economy. This move follows our ongoing ESG commitments, including ISO 14001 certification, energy-saving initiatives across our offices, and responsible project lifecycle management for clients worldwide. In an era where climate risk is business risk, TTMS chooses to lead. We encourage more Polish companies to follow suit — because it’s the right thing to do.
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What Is a Temporary Chat in ChatGPT? Everything You Need to Know
What Is a Temporary Chat in ChatGPT? Everything You Need to Know As AI tools like ChatGPT become increasingly popular, users seek more control over their data and interactions. One useful feature that supports privacy-conscious and casual usage is the Temporary Chat. But what exactly is a Temporary Chat in ChatGPT, and how does it work? In this article, we’ll explain its purpose, benefits, limitations, and availability—helping you decide if it’s the right option for your needs. What Is a Temporary Chat? A Temporary Chat in ChatGPT is a conversation that isn’t saved to your chat history. Unlike regular chats, these sessions do not appear in your chat sidebar, and won’t be used to train OpenAI’s models (unless you opt in to share feedback). Temporary Chats are ideal for short, one-time interactions where you don’t want to store any context or personal information. Think of it as ChatGPT’s “incognito mode.” Benefits of Using a Temporary Chat Here are some key advantages of using Temporary Chat: 1. Enhanced Privacy Temporary Chats are not stored in your account history. This means you can ask questions without worrying that the conversation will be saved or referenced later. 2. No Impact on Training Data OpenAI does not use Temporary Chat conversations to train its models by default, which adds another layer of data privacy. 3. Clean Slate Every Time Each Temporary Chat starts fresh. ChatGPT has no memory of past messages, which is ideal for users who want unbiased or unlinked answers. 4. Quick and Simple You don’t need to manage or delete history—everything disappears automatically after the session ends. Who Should Use Temporary Chats? Temporary Chats are useful for: Privacy-conscious users who prefer not to leave digital footprints. New users testing the tool without committing to an account or long-term interaction. Professionals handling sensitive or confidential questions. Students and researchers conducting quick fact-checks or one-off tasks. Developers experimenting with prompts in isolation. Where to Find the Temporary Chat Option To start a Temporary Chat in ChatGPT: Open ChatGPT and log into your account. Click on the “+ New Chat” button. On the left side at the top, look for the “Temporary Chat” option. Start chatting—the session will not be saved to history. You can also access Temporary Chat via direct links or when using ChatGPT without an active login in some cases. Limitations of Temporary Chats While useful, Temporary Chats come with some limitations: No memory or continuity: The model does not remember previous messages after the session ends. Limited personalization: Since the chat is stateless, you don’t get customized replies based on past interactions. Unavailable features: Some advanced features tied to memory or custom instructions may not be accessible. No chat history recovery: Once closed, the conversation cannot be retrieved. Which Plans Include Temporary Chat? Temporary Chat is available on all plans, including: ✅ Free Plan (GPT-3.5) – fully accessible. ✅ ChatGPT Plus (GPT-4) – available alongside advanced model access. Note: While all users can start Temporary Chats, access to GPT-4 and other premium tools depends on your subscription. Final Thoughts Temporary Chat is a powerful and flexible feature that gives users more control over their data and privacy. Whether you’re handling sensitive topics or just exploring AI without commitment, this feature ensures a secure and distraction-free experience. Looking for a private, no-strings-attached chat? Temporary Chat is your go-to solution. 💡 Pro Tip: Want to keep your chat data private and benefit from memory features when needed? You can toggle memory on or off per chat in your settings. Want to Go Beyond Temporary Chat? While Temporary Chat is a great starting point for secure and casual conversations, the true potential of ChatGPT and other AI tools lies in their ability to transform how businesses operate. Whether you’re exploring AI-powered automation, customer support, or data-driven decision-making, we can help you unlock that potential. At Transition Technologies MS (TTMS), we specialize in creating tailored AI solutions for businesses—from prototypes and pilots to enterprise-scale integrations using tools like ChatGPT, Azure OpenAI, and more. Discover how we can help your business grow with AI →
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Seeing More Than the Human Eye – AI as a Battlefield Analyst
The modern battlefield is not only a physical space but also a dynamic digital environment where data and its interpretation play a crucial role. With the growing number of sensors, drones, cameras, and radar systems, the military now has access to an unprecedented volume of information. The challenge is no longer data scarcity but effective analysis. This is where Artificial Intelligence (AI) steps in, transforming reconnaissance and real-time decision-making. AI as a Digital Scout Traditional methods of intelligence data analysis are time-consuming and prone to human error. AI changes the rules of engagement by enabling: automatic object recognition in satellite and video imagery, detection of anomalies in troop movements and activity, identification of enemy behaviour patterns based on historical data, real-time analysis of audio, visual, and sensor data, classification and prioritisation of threats using risk models. Thanks to machine learning (ML) and deep learning (DL), AI systems can not only identify vehicles, weapons, or military infrastructure but also distinguish between civilian and military objects with high accuracy. Image analysis algorithms can rapidly compare current data with historical records to detect changes that may indicate military activity. For example, an AI system can detect a newly established missile site by analysing differences in satellite imagery over time. AI Supports Decisions, It Doesn’t Replace Commanders Artificial Intelligence does not replace commanders – it provides ready-to-use analysis and recommendations that support fast and accurate decisions. So-called “intelligent command dashboards” integrated with AI systems enable: analysis of projectile trajectories and prediction of impact points, risk assessment for specific units and areas of operation, generation of dynamic situational maps that reflect enemy movement, correlation of data from multiple sources, including: Radar: provides real-time movement tracking, SIGINT (Signals Intelligence): analyses intercepted electronic signals, e.g., enemy radio communication, HUMINT (Human Intelligence): includes data from agents, soldiers, and local informants, OSINT (Open Source Intelligence): utilises publicly available data from social media, news, and live feeds. AI also supports mission planning by analysing “what if” scenarios. For example: what happens if the enemy moves 10 km west – will our forces maintain the advantage? These tools significantly increase situational awareness, which is crucial during rapid conflict escalation. Examples of AI Use in Global Defence Project Maven (USA): A U.S. Department of Defense initiative that uses AI to automatically analyse drone video footage, detecting objects and suspicious behaviour without human analysts. NATO Allied Command Transformation: Using AI systems to support decision-making across multi-domain environments (land, air, sea, cyber, space). Israel: The Israeli military uses AI to merge real-time intelligence from multiple sources, enabling precision strikes within minutes of identifying a target. TTMS and AI Projects for the Defence Sector Transition Technologies MS (TTMS) delivers solutions in data analytics, image processing, and Artificial Intelligence, supporting defence institutions. Our experience includes: designing and implementing AI models tailored to military needs (e.g., object classification, change detection, predictive analytics), integrating with existing IT and hardware infrastructure, ensuring compliance with security standards and regulations (including NIS2), building applications that analyse data from radars, drones, optical and acoustic sensors. The systems we develop enable faster and more precise data processing, which on the battlefield can translate into real operational advantage, shorter response time, and fewer losses. The Future: Predicting Enemy Actions and Autonomous Operations The most advanced AI systems not only analyse current events but also predict future scenarios based on past patterns and live data. Predictive models, based on deep learning and multifactor analysis, can support: detection of offensive preparations, prediction of enemy troop movements, assessment of enemy combat readiness, automation of defensive responses, e.g., via C-RAM (Counter Rocket, Artillery, and Mortar) systems – these are automated defence platforms that detect, track, and neutralise incoming rockets, artillery shells, and mortars before impact. C-RAM systems use a combination of radar, tracking software, and rapid-fire weapons (such as the Phalanx system), while AI enhances threat detection, classification, and timing of countermeasures. In the near future, AI will also become the backbone of autonomous combat units – land, air, and sea-based vehicles capable of independently analysing their surroundings and executing missions in highly uncertain environments. Artificial Intelligence is no longer a futuristic concept but a real tool enhancing national security. TTMS, as a technology partner, is actively shaping this transformation by offering proven, defence-tailored solutions. Want to learn how AI can support your institution? Contact us! What is the Phalanx system? The Phalanx system is an automated Close-In Weapon System (CIWS) primarily used on naval ships and in some land-based versions. It neutralizes incoming threats such as missiles, artillery, or mortars before they strike. It includes radar and a rapid-fire 20mm Gatling gun that automatically tracks and eliminates targets. It’s a key component of C-RAM defense layers. How does the Israeli army use AI to integrate real-time intelligence? The Israeli military integrates intelligence from various sources (SIGINT, HUMINT, drones, satellites, cameras) using AI-powered systems. These algorithms analyze real-time data to identify threats and targets, allowing for precise strikes within minutes of detection. What is NIS2? NIS2 is the updated EU directive on network and information system security, replacing NIS1. It expands cybersecurity responsibilities for essential service operators (including defense) and digital service providers. It includes risk management, incident reporting, and supply chain evaluation requirements. What are C-RAM systems? C-RAM (Counter Rocket, Artillery, and Mortar) systems detect, track, and neutralize incoming projectiles before they reach their targets. They use advanced radar, optics, and weapons like the Phalanx CIWS. AI supports these systems by automating threat detection and engagement decisions. What is SIGINT? SIGINT (Signals Intelligence) involves intercepting and analyzing electromagnetic signals, including communications (e.g., radio) and non-communications (e.g., radar). AI can analyze massive volumes of SIGINT data to detect military activity patterns and anomalies. What is HUMINT? HUMINT (Human Intelligence) is based on information gathered from human sources – agents, soldiers, and local informants. While harder to automate, AI helps assess report consistency, translate languages, and cross-reference with other intelligence. What is OSINT? OSINT (Open Source Intelligence) refers to intelligence from publicly available sources – social media, news outlets, livestreams, and open satellite imagery. AI plays a key role in filtering and identifying relevant insights in real-time from vast data pools.
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AI and Copilot in Power BI – How Artificial Intelligence Transforms Data Analysis
The development of artificial intelligence (AI) has significantly transformed how businesses analyse and present data. Microsoft Copilot in Power BI is an advanced AI-powered tool that automates report creation, data interpretation, and anomaly detection, making data analysis more intuitive and accessible for all users—regardless of their technical expertise. 1. What is Microsoft Copilot in Power BI? Microsoft Copilot is an advanced AI assistant that is part of the Microsoft ecosystem and is used in many applications, including Power BI. In the context of Power BI, Copilot acts as a tool supporting users in data analysis, report generation, and interpretation of results without the need to manually create queries or configure visualisations. It allows users to communicate with data in a natural way – by entering questions in English – and then automatically generates appropriate reports and insights. With it, you can build dashboards, analyse trends, and quickly respond to market changes without needing to know DAX or M coding. Microsoft has chosen to integrate Copilot with Power BI in response to the needs of companies aiming to automate and simplify data analysis. The tool is designed to accelerate business processes, reduce human error, and support strategic, data-driven decisions. 2. How to Access Copilot in Power BI? Copilot in Power BI is available to users with a Power BI Premium or Power BI Pro licence and access to Microsoft Fabric. To activate Copilot, your organisation’s administrator must enable it in the Microsoft Fabric settings. Copilot is being rolled out in preview across regions, so some users may not yet have access to it. 2.1 How to Enable Copilot in Power BI? Log in to the Power BI Service as an administrator. Navigate to Admin Settings. Locate the Copilot option under the Microsoft Fabric section. Enable Copilot for the organisation and assign access to users. 3. What are the Features of Copilot in Power BI? Microsoft Copilot in Power BI offers a wide range of functionalities that enhance data analysis, reporting, and business decision-making. Its main advantage lies in the use of artificial intelligence to automate analytical processes, removing the need for manual report preparation or the analysis of complex queries. Copilot integrates with the Power BI interface, allowing users to interact using natural language. Here are the key features that make Copilot a powerful analytical tool: 3.1 Report Generation Using Natural Language Queries Copilot enables users to create reports without needing to manually define data sources, choose visualisations, or configure filters. Simply enter a question, such as “Show me sales by region for the last three months,” and Copilot will automatically generate the relevant report and adjust the data formatting. Users can also edit reports with simple text commands, such as “Add a line chart to the report” or “Change the X-axis to sales dates.” 3.2 Automated Narrative Generation and Insights Interpretation Copilot not only creates visualisations but also provides descriptive summaries of key insights from the analysis. This feature helps users to quickly understand trends and anomalies in the data without having to conduct in-depth analysis. For example, if a report shows a sudden increase in sales in one region, Copilot might generate a comment such as, “Sales in the North region increased by 15% last quarter, mainly due to increased orders from B2B customers.” 3.3 Visualisation Recommendations Copilot assists users in selecting the most suitable method for visualising data by analysing the report’s structure and the nature of the dataset. If a user is unsure how best to present their data, Copilot can suggest various types of charts and tables. For instance, when analysing sales trends, it may recommend a line chart or column chart, while for demographic data, a heat map or pie chart might be more appropriate. 3.4 Trend and Anomaly Detection Copilot applies AI algorithms to identify unusual patterns and deviations in the data. This allows users to automatically pinpoint areas that require attention, such as sudden drops in revenue, rising operational costs, or irregular sales figures. Copilot not only highlights these anomalies but also suggests possible causes and actions to explain or address them. 3.5 Automatic Correlation Analysis Between Data Sets Using AI, Copilot can analyse relationships between different variables within a dataset and identify correlations that may impact business outcomes. For example, Copilot might reveal that an increase in website visits corresponds with a rise in order volume over a specific period. This enables companies to adjust their marketing and sales strategies based on real evidence. 3.6 Predictive Analytics Support Although Copilot is not a full substitute for advanced machine learning tools, it does offer some predictive analytics features. For instance, Copilot can use historical sales data to forecast future buying trends and identify potential risks linked to demand fluctuations. Finance teams can leverage this feature for budgeting and inventory planning. 3.7 Integration with Microsoft Fabric and Other Services Copilot is fully integrated with the Microsoft Fabric ecosystem, enabling it to draw from multiple data sources such as Azure Data Lake, OneLake, and Microsoft Dataverse. This gives users a more comprehensive view of the organisation and allows them to create reports using data from various systems. 3.8 Team Collaboration and Interactive Analytics Sessions Copilot supports teamwork by allowing real-time collaborative editing of reports and sharing of insights. Users can ask questions in an interactive session and dynamically adjust reports to suit the team’s needs. This enhances report creation efficiency and speeds up decision-making. 3.9 Personalised Results and User Preferences Copilot learns from user behaviour, gradually improving the precision of its suggestions and analysis. Users can personalise report generation by defining preferences for formatting, the depth of analysis, and the presentation of data. 3.10 Advanced Query Handling and Data Filtering Copilot allows users to pose more complex questions, including those with advanced filtering criteria. For example, a user could ask, “Show me sales only to customers in the UK technology sector who placed an order in the past six months and whose order value exceeded £10,000.” Copilot will instantly generate a report showing only the relevant data. These features make Copilot in Power BI an indispensable tool for companies seeking to maximise the value of their data and make informed decisions based on solid analysis. Its versatility makes it useful for both data scientists and business managers who need fast access to critical insights. Microsoft Copilot in Power BI offers a wide range of functionalities that make working with data easier: Reporting – Users can type queries in natural language, and Copilot generates visualisations and recommendations. Automatic narrative generation – Copilot analyses data and presents key findings in a narrative format. Identifying trends and anomalies – AI scans data and detects unusual patterns. Visualisation suggestions – Suggests the best ways to present data. Interactive dataset queries – Users can ask questions without having to write DAX code. 4. What are the Limitations of Copilot in the Basic Version? The preview version of Copilot in Power BI has several limitations: Supports English only. Can generate reports for specific data types only. Requires activation by an administrator. Available in selected regions only. Does not support all complex data models. 5. Example Prompts for Copilot in Power BI Users can ask Copilot questions such as: “Create a sales report for the last three months by region.” “Show me a revenue trend chart for this year.” “What were the biggest changes in financial results last quarter?” “Find anomalies in last month’s sales data.” 6. How Much Does Copilot in Power BI Cost? Copilot in Power BI is included in Power BI Premium and Power BI Pro licences. Currently, it is available in a preview version, and pricing details may change as new features are introduced. Microsoft may introduce additional licensing options in the future for more advanced users. 7. Examples of AI and Copilot Applications in Business 7.1 Power BI and Copilot in Marketing Copilot in Power BI enables marketing companies to analyse the performance of advertising campaigns in real time. This allows them to identify which channels are performing best, which customer segments are converting most effectively, and where marketing budgets are being used least efficiently. For example, an e-commerce company can use Copilot to track advertising performance across platforms, automatically generating comparative reports that help optimise budgets. 7.2 Power BI and Copilot in Finance Finance departments can use Copilot to create budget forecasts and analyse cash flows. The tool can automatically detect anomalies in financial data, such as unexpected increases in expenses or irregular cash inflows. In the banking sector, Copilot can support the analysis of credit indicators and generate reports on the financial stability of customers, which speeds up the credit decision-making process. 7.3 Power BI and Copilot in Sales Sales teams can use Copilot to monitor sales performance and optimise sales strategies. The system allows for quick reporting on top- and bottom-selling products, customer purchasing trends, and sales seasonality. This enables sales managers to make more informed decisions about pricing and inventory planning. 8. Power BI Solutions from TTMS At Transition Technologies MS (TTMS), we specialise in delivering comprehensive analytics solutions based on Power BI. Our services include designing, implementing, and optimising reports and dashboards tailored to your organisation’s needs. By working with our experts, y ou can fully leverage AI-powered tools like Microsoft Copilot to enhance business efficiency and make data-driven decisions faster. Find out more at https://ttms.com/uk/power-bi/ Can Copilot in Power BI be used for real-time data analysis? Yes, Copilot can process and analyze near real-time data, provided the dataset is connected to a live data source. However, response times may depend on the complexity of queries and the refresh rate of the data source. Is Copilot in Power BI available on mobile devices? Copilot functionalities are primarily designed for the desktop and web versions of Power BI. While you can view and interact with reports on mobile devices, full Copilot capabilities may not yet be fully supported. Can Copilot generate DAX formulas automatically? Yes, Copilot can assist in generating DAX formulas based on natural language queries. It helps users create complex calculations without deep knowledge of DAX, improving efficiency in report development. How does Copilot ensure data security when processing reports? Copilot adheres to Microsoft’s enterprise security standards, ensuring that all processed data remains within the organization’s security framework. It does not store or share sensitive data outside of the Power BI environment. Can Copilot be customized to specific business needs? While Copilot operates on general AI principles, it adapts to user interactions over time, improving recommendations. Future updates may include more customization options to align with specific business processes and reporting standards. What is Microsoft Fabric? Microsoft Fabric is a comprehensive cloud-based analytics platform designed to integrate, process, and analyze data within a unified environment. It combines various Microsoft data services, such as Azure Data Factory, Power BI, Synapse Analytics, and Data Lake, providing businesses with a flexible and scalable data management solution. Key Features of Microsoft Fabric: Lakehouse Architecture – Enables storing and analyzing large datasets in a Data Lake without the need for data movement. Power BI Integration – Simplifies the creation of interactive reports and analytics based on data stored in Fabric. Built-in AI Capabilities – Supports predictive analytics, automated data processing, and anomaly detection. OneLake – A central data repository that eliminates duplication and provides unified data access. Support for ETL and ELT – Facilitates efficient data processing and transformation for advanced analytics. Security and Compliance – Advanced data protection mechanisms compliant with corporate standards and legal regulations. With Microsoft Fabric, businesses can collect, process, analyze, and visualize data within a single ecosystem, enabling data-driven decision-making and accelerating digital transformation.
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