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.
Read
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.
Read
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 →
Read
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 amount of information. The challenge is no longer data scarcity but effective analysis. This is where Artificial Intelligence (AI) steps in, revolutionizing reconnaissance and real-time decision-making. AI as a Digital Scout Traditional intelligence data analysis methods 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 behavior patterns based on historical data, real-time analysis of audio, visual, and sensor data, classification and prioritization 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 analyzing 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): analyzes intercepted electronic signals, e.g., enemy radio communication, HUMINT (Human Intelligence): includes data from agents, soldiers, and local informants, OSINT (Open Source Intelligence): utilizes publicly available data from social media, news, and live feeds. AI also supports mission planning by analyzing “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 Defense Project Maven (USA): A U.S. Department of Defense initiative that uses AI to automatically analyze drone video footage, detecting objects and suspicious behavior 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 Defense Sector Transition Technologies MS (TTMS) delivers solutions in data analytics, image processing, and Artificial Intelligence, supporting defense 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 analyze 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 analyze 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 defense platforms that detect, track, and neutralize 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 analyzing 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, defense-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.
Read
AI and Copilot in Power BI – How Artificial Intelligence Transforms Data Analysis
The development of artificial intelligence (AI) has significantly influenced how businesses analyze 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. 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 visualizations. It allows users to communicate with data in a natural way – by entering questions in English – and then automatically generates appropriate reports and conclusions. Thanks to it, you can create dashboards, analyze trends and quickly respond to market changes without having to know DAX or M coding. Microsoft has chosen to integrate Copilot with Power BI in response to the needs of companies that seek to automate and simplify data analysis. The tool is designed to accelerate business processes, eliminate human error, and facilitate strategic, data-driven decisions. How to Access Copilot in Power BI? Copilot in Power BI is available to users with a Power BI Premium or Power BI Pro license and access to Microsoft Fabric. To activate Copilot, your organization’s administrator must enable it in Microsoft Fabric settings. Copilot is being rolled out in preview across regions, so some users may not have access to it yet. How to Enable Copilot in Power BI? Log in to Power BI Service as an administrator. Navigate to Admin Settings. Locate the Copilot option under the Microsoft Fabric section. Enable Copilot for the organization and assign access to users. What are the Requirements for Copilot in Power BI? To use Copilot, users must meet the following requirements: Power BI Pro or Power BI Premium license Microsoft Entra ID account (formerly Azure AD) Administrator permissions to enable Copilot in Power BI Service Access to Microsoft Fabric The latest version of Power BI Desktop What are the Features of Copilot in Power BI? Microsoft Copilot in Power BI offers a wide range of functionalities that improve data analysis, reporting, and business decision-making. Its main advantage is the use of artificial intelligence to automate analytical processes, which eliminates the need for manual report preparation or analyzing 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: 1. Report Generation Using Natural Language Queries Copilot enables users to create reports without having to manually define data sources, select visualizations, or configure filters. Simply enter a question, such as “Show me sales by region for the last three months,” and Copilot automatically generates the appropriate report and adjusts 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.” 2. Automated Narrative Generation and Insights Interpretation Copilot not only creates visualizations, but also provides descriptive summaries of key insights from the analysis. This feature allows users to quickly understand trends and anomalies in the data without having to perform detailed analysis. For example, if a report shows a sudden increase in sales in one region, Copilot can generate a comment like, “Sales in the North region increased by 15% last quarter, mainly due to increased orders from B2B customers.” 3. Visualization Recommendations Copilot helps users choose the best method for visualizing data by analyzing the structure of the report and the nature of the data. If a user is unsure about how to best present the data, Copilot can suggest different types of charts and tables. For example, if the data is about sales trends, Copilot might suggest a line chart or column chart, while for demographic data, it might suggest a heat map or pie chart. 4. Trend and Anomaly Detection Copilot uses AI algorithms to detect unusual patterns and deviations in data. This allows users to automatically identify areas that require attention, such as sudden drops in revenue, increases in operating costs, or irregularities in sales results. Copilot not only highlights these anomalies, but also suggests possible causes and actions that can be taken to explain or mitigate them. 5. Automatic Correlation Analysis Between Data Sets With AI, Copilot can analyze the relationships between different variables in a data set and pinpoint correlations that could impact business outcomes. For example, Copilot can show that an increase in visits to a company’s website directly translates into more orders over a given period. This allows companies to adjust their marketing and sales strategies based on real data. 6. Predictive Analytics Support While Copilot is not a complete replacement for advanced machine learning solutions, it does offer some predictive analytics capabilities. For example, Copilot can use historical sales data to predict future purchasing trends and identify potential risks related to demand fluctuations. Finance departments can use this feature for budget planning and inventory management. 7. Integration with Microsoft Fabric and Other Services Copilot is fully integrated with the Microsoft Fabric ecosystem, meaning it can leverage data from multiple sources, such as Azure Data Lake, OneLake, and Microsoft Dataverse. This gives users a more complete picture of the organization and allows them to create reports that include data from multiple systems. 8. Team Collaboration and Interactive Analytics Sessions Copilot supports teamwork by enabling collaborative editing of reports and sharing of analyses in real time. Users can ask questions in an interactive analysis session and dynamically adjust reports to the needs of the team. This makes working on reports more efficient and decision-making faster. 9. Personalized Results and User Preferences Copilot learns from user interactions, meaning it becomes more precise in its suggestions and analysis over time. Users can customize how reports are generated, specifying preferences for formatting, level of analysis detail, and how data is presented. 10. Advanced Query Handling and Data Filtering Copilot lets you ask more sophisticated questions, including advanced filtering conditions. For example, a user can ask, “Show me sales only to customers in the U.S. technology sector who placed an order in the last 6 months and whose order value exceeded $10,000.” Copilot will instantly generate a report that includes only the relevant data. These features make Copilot in Power BI an invaluable tool for companies that want to get the most out of their data and make informed decisions based on solid analytics. Its versatility makes it useful for both data scientists and business managers who need quick access to key information. 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 visualizations and recommendations. • Automatic narrative generation – Copilot analyzes data and presents key findings in a narrative format. • Identifying trends and anomalies – AI scans data and detects unusual patterns. • Visualization suggestions – Suggests the best ways to present data. • Interactive dataset queries – Users can ask questions without having to write DAX code. What are the Limitations of Copilot in the Basic Version? The preview version of Copilot in Power BI has several limitations: Supports only English. Can generate reports only for specific data types. Requires activation by an administrator. Available only in selected regions. Does not support all complex data models. 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.” How Much Does Copilot in Power BI Cost? Copilot in Power BI is included in Power BI Premium and Power BI Pro licenses. 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. Examples of AI and Copilot applications in business Power BI and Copilot in Marketing Copilot in Power BI enables marketing companies to analyze the performance of advertising campaigns in real time. This allows them to identify which channels are performing best, which customer segments are converting the most, and where marketing budgets are being used the least efficiently. For example, an e-commerce company can use Copilot to track advertising performance across platforms, automatically generating comparative reports that help optimize budgets. Power BI and Copilot in Finances Finance departments can use Copilot to create budget forecasts and analyze 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. Power BI and Copilot in Sales Sales teams can use Copilot to monitor sales performance and optimize sales strategies. The system allows for quick reporting on top and bottom-selling products, customer purchasing trends, and sales seasonality. This allows sales managers to make more informed decisions about pricing and inventory planning. Power BI Solutions from TTMS At Transition Technologies MS (TTMS), we specialize in delivering comprehensive analytics solutions based on Power BI. Our services include designing, implementing, and optimizing reports and dashboards tailored to your organization’s needs. By working with our experts, you 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/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.
Read
A flexible Time & Material model designed for complex IT projects in large companies
Time & Material (T&M) is a model of cooperation in which billing is based on the actual time worked by specialists and the resources used. Unlike the rigid Fixed Price model, where the scope and cost are defined upfront, T&M ensures flexibility – the scope of work can evolve during the project, and the client pays for the actual tasks performed. This model is gaining popularity among companies undergoing digital transformation, who need quick access to competencies and the ability to adapt to changes. Below, we explain why T&M is the preferred model for digital transformation leaders, in which situations it works best, and provide examples (including the cooperation between TTMS Software Sdn Bhd and ADA). Finally, we invite you to talk about how T&M can support your project. 1. What is the Time & Material model in IT? The Time & Material model means that the client pays for the hours worked and the tools used to complete the IT project. There is no fixed total cost or fully frozen scope – the project is carried out iteratively, and details can be refined during the work. This model is fully compatible with Agile methodologies and the iterative approach to software development. The project team logs work hours, reports progress, and settlements are made periodically (e.g., monthly or per stage). The client gains full transparency – they know exactly what they are paying for and can continuously adjust the direction of the work. In practice, the T&M contract sets the rates (e.g., hourly or daily) for specific roles in the project (developer, tester, analyst, etc.) and general rules of cooperation. But it leaves space for scope changes. If new requirements or changes arise during the project, there is no need to renegotiate the contract – the team simply continues the work, and the client pays for the additional time based on the agreed rates. This significantly shortens the project launch time and reduces the risk of underestimating or omitting important elements. In T&M, both the IT provider and the client act as partners sharing responsibility for the project’s success. 2. Flexibility above all – why leaders choose T&M Today’s business environment is extremely dynamic. Companies that are leaders in digital transformation know that changes are the norm in ambitious IT projects – new ideas appear, user expectations change, and technology constantly evolves. Traditional settlement models (e.g., fixed-price projects) often turn out to be too inflexible in such conditions. That’s why leading organizations increasingly choose Time & Material to ensure the ability to respond quickly and keep up with innovation. The T&M model offers a number of benefits for large enterprises and digital transformation programs: Quick project start and delivery in stages: No need to wait for a perfectly refined scope – work can start fast, and solutions are delivered in short iterations. This allows early business value realization and continuous verification. Flexibility in implementing changes: When new challenges arise or new ideas appear, the team can immediately adjust the scope of work. There is no need to amend the contract for each change – the plan evolves within the agreed framework. Cost transparency: At every stage, it is clear how much time has been worked and what the budget is spent on. The client receives regular reports, knows exactly what they are paying for, and can control the budget throughout the project. Full control and involvement on the client side: The client is actively involved in the project – can prioritize tasks, decide on the order of implementation, and quickly change direction if necessary. Access to needed competencies exactly when they are needed: In the T&M model, the team can be scaled flexibly – increased in size or supplemented with new experts when the project enters a new phase. Higher quality through continuous improvements: As the project is run iteratively, the final product can be of better quality – continuous testing, feedback, and improvements increase value step by step. It is worth noting that the T&M model eliminates the need to pay for “extra” assumptions. In a fixed-price model, providers often add a risk buffer – so the client pays in advance, even for unforeseen difficulties. In T&M, you pay only for the actual work. If some tasks turn out to be unnecessary or simplified, the budget can be shifted to other priorities. 3. When does the T&M model work best? The Time & Material model is not a cure-all – there are situations where it works perfectly and others where a fixed-price model might be better. Below are typical scenarios where T&M works best: Long-term, complex projects – if the initiative is extended over time and consists of many phases, it is obvious that it’s hard to predict all requirements at the start. T&M allows scope adjustment according to current needs. Unclear requirements at the start – when the client has a general vision but not a detailed list of functionalities. This often occurs in innovative projects. T&M allows starting with MVP and then iterative development. Dynamic business or technology environment – in industries like fintech, e-commerce, or telecom, change is constant. If user needs evolve quickly, regulations change, or there’s competitive pressure, fixed contracts can slow you down. T&M allows flexibility and speed. Budget control during the project – paradoxically, although T&M doesn’t specify the final amount upfront, it allows strict budget control. You can monitor ROI and decide on funding further stages based on previous outcomes. Outsourcing and need for specific know-how – if you’re using IT outsourcing or staff augmentation, T&M is a natural choice. You can get the expert you need without long hiring processes. Of course, the T&M model requires trust and maturity on both sides – the client must be ready to collaborate and supervise, and the provider must ensure transparency. Experienced partners like TTMS introduce control mechanisms (hour tracking, budget checkpoints, milestones) to protect the project. 4. Example: TTMS and ADA – partnership in T&M model A real example of T&M flexibility is the recent cooperation between TTMS Software Sdn Bhd (TTMS branch in Malaysia) and ADA, a leading digital transformation company in Southeast Asia. ADA specializes in data analytics, AI, and digital marketing, serves over 1,500 clients in 12 markets, and is backed by investors like SoftBank and Axiata Group. The partnership began in the Time & Material model, with TTMS providing a Salesforce Administrator for three months. This form enabled ADA to use TTMS experience exactly when needed and created a foundation for further cooperation. Read more in the press release: TTMS Software Sdn Bhd starts cooperation with ADA 5. Other examples of T&M at TTMS At TTMS, we have been delivering projects in the Time & Material or similar flexible models for years. Most of our case studies are stories of long-term cooperation, iterative system improvement, and partnership approach – that’s what T&M enables. For example: In the energy sector, we created a scalable application that integrated many systems. In the pharmaceutical sector, we supported an international company in building a CRM system with a growing scope. For Schneider Electric, we are a long-term outsourcing partner – we provide specialists in the T&M model. 6. T&M in Asia – a growing trend We observe growing interest in flexible contracts in Asia. Companies in this region, known for dynamic growth, often point to the T&M model as key to successful transformation. For example: A telecom operator in Southeast Asia chose T&M for a new digital platform, which allowed them to adapt the roadmap in real time. In e-commerce, a platform was iteratively adapted to user needs through a T&M-based cooperation with an external team. These examples show that flexibility = effectiveness. 7. Choose the right model Time & Material is a proven way to run an IT project when speed, adaptability, and access to talent matter. Leaders choose it because it lets them focus on business goals instead of renegotiating contracts. Properly applied, T&M gives: Freedom of action Transparent costs Quality and results If your company is planning a new system or wants to improve an existing one and needs a flexible and experienced IT partner, T&M may be the right choice. TTMS has been supporting clients in this model for years – providing top experts and teams, building long-term relationships based on trust and shared goals. Let’s talk – we’ll tailor the cooperation model to your project. Contact us. What is the difference between Time & Material and Staff Augmentation? While both offer flexibility, Time & Material refers to billing for work completed over time, often in a project context. Staff Augmentation focuses on providing personnel to extend internal teams. T&M may include team delivery, project milestones, and shared goals—beyond just supplying resources. Is the Time & Material model more expensive than Fixed Price? Not necessarily. Although T&M lacks a fixed upfront budget, it often avoids overpayment by billing only for actual work done. Fixed Price contracts may include large risk buffers, while T&M enables better cost control if well-managed. How do you control scope and costs in a Time & Material project? T&M requires strong project governance—typically involving time tracking, regular reporting, sprint reviews, and clear communication. Clients remain actively involved, adjusting priorities and validating outcomes in real time. Is Time & Material suitable for regulated industries like pharma or finance? Yes. When combined with proper documentation, validation, and quality controls, T&M can meet industry compliance needs. It’s especially useful in complex environments where detailed requirements evolve during the project lifecycle. Can we start with Time & Material and switch to Fixed Price later? Absolutely. Many companies begin with T&M for discovery, MVPs, or early development. Once scope stabilizes, transitioning to a Fixed Price or hybrid model is common—ensuring flexibility early on and predictability later.
Read