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AI Avatars in E-Learning: Boost Engagement in 2026
Online learning has amotivationproblem.Courses get built, learners enroll, and thena significant portionquietlystopshowing up. The content may be excellent, but without a human presence to guide and engage, it canfeel like readinga manual alone.AI avatars in e-learning are changing the learning experience by making online training feel more engaging, interactive, and easier to remember than traditional course formats. 1. Why AI Avatars Are Changing the Way People Learn Online AI avatars work because they make online training feel less like clicking through slides and more like being guided by a real instructor. A face, voice, and consistent on-screen presence help learners follow the material, stay focused, and complete the course. In many traditional e-learning modules, attention drops after the first few screens. Learners start to skim, click through, or lose context. AI avatars can reduce this fatigue by turning passive content into a more guided experience. Instead of leaving employees alone with blocks of text, the avatar introduces topics, explains key points, and keeps the pace clear and consistent. For organizations training people at scale, this matters even more. When hundreds of employees go through onboarding, compliance training, or product updates, avatars help deliver the same message with the same tone, energy, and clarity across locations, languages, and time zones. 2. What AI Avatars in E-Learning Actually Are AI avatars in e-learning are digital characters powered by artificial intelligence that simulate human instruction within a course environment. They use technologies like natural language processing, text-to-speech synthesis, and adaptive learning logic to interact with learners in real time. What separates an AI avatar from a simple talking head video is interactivity. A talking head delivers a script. An AI avatar can respond to learner inputs, adjust pace based on performance data, offer feedback, and guide learners down different paths depending on their choices. 2.1 AI-Powered Avatars vs.Traditional Video Instruction Recorded video works well for straightforward content delivery, but it has a fixed ceiling. Once recorded, it cannot adapt or respond. An AI avatar changes that relationship entirely, bringing presence and responsiveness without requiring a live instructor. It can detect when a learner is struggling andofferan alternative explanation, or prompt reflection with a question rather than simply presenting answers. 2.2 Types of AI Avatars and Their Roles Instructor avatarsserve as theprimary guide through course content, presentinginformationand keeping learners oriented. A well-designedinstructoravatar carries authority without feeling distant, striking a tone that feels like a knowledgeable colleague rather than a textbook. Peerand coach avatarsaddress one of online learning’s most persistent challenges: isolation. Peer avatars simulate the social dimension of learning, encouragingreflectionand creating a sense of learning alongside someone. Coach avatars motivate, check in on progress, and celebrate milestones. Scenario-based character avatarsappear within simulated situations. A customer service course might feature a challenging customer the learner must respond to; a leadership course might include a team member presenting a workplace conflict. These let learners practice in realistic, low-stakes environments before the real thing. 3. Key Benefits of Using AI Avatars in E-Learning 3.1 Personalized Learning at Scale AI avatars analyze how each learner responds to content andadjustdelivery accordingly. A learner who breezes through foundational material can move faster, while someone needing reinforcement getsadditionalexplanation before advancing. This kind of adaptive instruction was once reserved for one-on-one tutoring. Withavatars, itscales tothousands of learners simultaneously. 3.2 Higher Learner Engagement and Completion Rates One of the biggest challenges in e-learning is keeping learners engaged until the end of a course. When training feels impersonal or repetitive, attention naturally starts to fade. AI avatars help create a more engaging learning experience by presenting information in a way that feels conversational rather than static. They can explain concepts, guide learners through scenarios, andmaintaina consistent presence throughout the course. As a result, employees are more likely to stay focused, complete the training, and remember what they have learned. 3.3 Faster Production and Lower Costs Traditional training videos are expensive to produce and difficult to update. They require recording sessions, presenters, editing, and often another round of production whenever the content changes. AI avatars make this process faster. Instead of recording a new video from scratch, teams can update the script, choose a digital presenter, and generatea new versionof the module much more quickly. This is especially useful for onboarding, compliance training, product updates, and other materials that need to stay current. For L&D teams, the main benefit is not only lower productioncost. It is the ability to refresh training content without restarting the whole video production process every time something changes. 3.4 Consistent Multilingual Delivery Global organizations face a recurring challenge: training that feels equally strong across languages and regions. AI avatars can speak dozens of languages fluently,maintainingconsistent tone and quality throughout. A learner in São Paulo and one inSingapore both receive instruction that feels native and natural, without multiplying production costs. 4. High-Impact Use Cases for Avatar-Based Training 4.1 Employee Onboarding and Orientation First impressions shape long-term retention. An avatar-guided onboarding journey delivers a structured introduction to company culture, processes, and expectations in a format new employees can engage with at their own pace. Rewe Group tookthis astep further with “goRobert,” a hyper-realistic digital twin of a management member that new hires can query both in person and via Microsoft Teams.The system lets employees ask sensitive or practical questions without fear of judgment, improving psychological safety and information access during onboarding. 4.2 Compliance and Mandatory Training An AI avatar changes the delivery of compliance content without changing the substance. It can present complex regulations clearly, check comprehension with interactive questions, and keep the experience from feeling punitive. The result is better retention andcompletionrecords that hold up in audits. 4.3 Sales, Product, and Customer Service Training AI avatar courses can simulate realistic customer conversations, allowing sales and service teams to rehearse objections and handle difficult interactions beforeencounteringthem live. Research on AI avatars in hospitality employee training found that avatar-led instruction improved learning outcomes and engagement compared to static e-learning while also reducingreliance on live facilitators. This scenario-driven approach builds both skill and confidence, with real-world performance improving as a direct result. 4.4 Soft Skills and Leadership Practice Teaching soft skills through traditional e-learning has always beenhard. Avatar simulations create situations where learners must respond, make decisions, and experience consequences. A manager in a leadership course might face a difficult performance conversation with an AI avatar playing a resistant employee. That emotional realism makes the learning stick in ways a lecture cannot. 5. How to Create and Deploy AI Avatars for Your Courses 5.1 Choose the Right AI Avatar Tool Platforms range from template-based avatars to fully customizable digital humans, so evaluating options requires a clear framework. Four criteria matter most for corporate training contexts: Check whether the platform supports SCORM orxAPIstandards for reliable integration and learner data tracking. Assess interactivity depth. Some platforms support branching scenarios and adaptive pathways; others offer only linear delivery. Consider language coverage and how naturally the synthetic voices perform in each language your teamsactually use. Evaluate avatar customization. Some platforms let you reflect your brand andlearnerdemographics; others lock you into templates. Aligning the platform’s strengths with your specific training goals, whether that’s compliance delivery, onboarding, or sales simulation, makes a meaningful difference in outcomes. TTMS has direct experience evaluating and integrating avatar platforms intoexisting learning environments, which helps organizations avoid costly mismatches between tool capabilities and training needs. 5.2 Design Avatar Appearance and Persona Visual design choices, including gender presentation, age, style, and cultural representation, shape how learners perceive and relate to the avatar. For global programs, building a diverse set of avatars ensures more learners see themselves reflected in the instruction. The persona matters equally: a compliance avatar might project calm authority, while an onboarding avatar might lean warmer. Whenpersonamatches context, the experience feels intentional rather than generic. 5.3 Script and Integrate Avatars into Your LMS Good avatar scripting reads naturally when spoken, avoids passive constructions, andbuilds innatural pauses and branching points where learner input changes the direction of instruction. Once the content is ready, integration into your LMS ensures learner progress is tracked, completion is recorded, and data flows into reporting dashboards. 6. Best Practices for Effective Avatar-Based Learning A strong AI avatar program requires more than choosing the right tool. Before designing any interaction, start with a clear answer to one question: what does this learner need to be able to do, and how does this avatar help them get there? When the purpose is clear, the experience feels cohesive. Whenit’svague, learners notice and disengage. Consistency matters just as much. If an AI avatar shifts tone or appearance between modules without explanation,learnertrust erodes. Maintaining visual and persona consistency across a course reinforces the mental model learners build early on and reflects organizational culture in corporate training contexts. Accessibility and cultural inclusivityaren’toptional extras. Caption options, visual contrast, and avatar personas that reflect the diversity of the learner population all ensure the course functions for everyone. Treatlaunchas the beginning of an iterative cycle, not the finish line. Completion data, quiz performance, and learner feedback reveal where the experience breaks down and where it earns the most engagement. 7. Frequently Asked Questions About AI Avatars in E-Learning What makes an AI avatar different from a simple animated character? An AI avatar uses artificial intelligence to generate speech, adapt responses, and interact with learner inputs in real time. A simple animated character is scripted and static. The intelligence layer is what enables personalization, real-time feedback, and adaptive learning pathways. Can AI avatars work across different languages and regions? Yes. Modern platforms support dozens of languages, and avatars can be localized not just linguistically but culturally, adapting tone and examples to suit regional audiences. How much does it cost to build avatar-based e-learning? Costs vary by platform and interactivity complexity. In general, avatar-based production is significantly faster and less expensive than traditional video, particularly for content that needs regular updates. Do learners actually respond well to AI avatars? Research and real-world deployments consistently show stronger engagement with avatar-guided content than with text-only or static video formats. The key is designing avatars that feel genuine, with strong scripts, clear purpose, and a persona appropriate to the subject matter. How does TTMS support organizations adopting avatar-based learning? TTMS provides end-to-end e-learning services covering course development, avatar integration, LMS administration, and performance analytics. As a partner with hands-on experience in both AI implementation and learning system integration, TTMS helps organizations build AI avatar training programs that are practical, scalable, and tied to measurable business outcomes.
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What is reporting in business intelligence and how it can help your organization
In most companies today, data is everywhere: in CRM, ERP, financial systems or marketing tools. The problem is usually not the lack of them, but the fact that it is difficult to quickly answer a simple question: “what actually happens in business?”. However, access to data alone is not enough to make the right decisions. The biggest challenge is to translate them into concrete conclusions and actions. This is where Business Intelligence (BI) reporting helps. BI reporting has ceased to be the domain of IT departments only and has become one of the key competencies of modern organizations. Whether you’re a CFO analyzing quarterly performance or a marketing manager evaluating campaign performance, BI reports provide a structured, transparent, and actionable view of your data. With clear visualizations and analytics, they allow you to spot trends, identify problems, and make better business decisions faster – much more effectively than traditional spreadsheets. 1. What is BI reporting? BI reporting is about transforming raw, distributed operational data into clear insights that support fact-based decisions. It’s a structured process that involves pulling data from multiple sources, modeling it, and presenting it in the form of reports and analytics dashboards that are available to different teams in the organization. At TTMS, we look at Business Intelligence reporting not just as a technical task, but as a comprehensive analytical capability of an organization. It involves integrating data from multiple systems, building a semantic data model, ensuring proper management and security, and then sharing reports across workspaces, applications, and embedded analytics. The goal remains the same: to help organizations monitor performance, identify trends, and respond quickly to changes using up-to-date information instead of static spreadsheets. BI reports can take various forms: from management dashboards, through operational reports, to detailed analyses supporting specific areas of the business. They help teams at every level of the organization better understand what’s going on, why it happened, and what actions are worth taking next. 2. BI Reporting vs. Traditional Reporting: How Are You Different Traditional reporting usually focuses on the analysis of historical data. The data is exported from the system, organized in a spreadsheet, and then made available as a static file showing the situation at a specific point in time. By the time the team takes action on it, the information may already be out of date. BI reporting works differently. Instead of relying on isolated data sets, a BI system integrates information from multiple sources into one consistent, regularly refreshed model. Users can access up-to-date reports, apply filters, drill down into detailed data, and analyze information on their own without waiting for a new IT statement. This shift from passively receiving reports to actively exploring data is changing the way organizations work with information. The data becomes not only a summary of what has already happened, but a real support in making faster and more accurate decisions. 3. BI Reporting vs Business Intelligence: Where the Line Lies BI reporting and business intelligence are often used interchangeably, but they don’t mean exactly the same thing. BI reporting is primarily descriptive and diagnostic. It helps answer the questions: “what happened?” and “why did this happen?”, presenting historical and current data in a readable, structured form. Business analytics goes one step further. It also includes predictive and prescriptive analysis, which helps predict future events and indicate possible actions. BI reporting can show that the number of departing customers increased in the last quarter. Predictive analytics will help determine which customers may leave in the next month, and prescriptive analytics will tell you what actions to take to prevent this. Both approaches complement each other. A well-designed BI infrastructure creates a foundation on which to build more advanced analytics and make decisions based not only on what has already happened, but also on what may happen in the future. 4. Basic elements of a BI reporting system A modern BI reporting system is much more than a set of charts and tables. It is a layered architecture of interconnected components, each of which is responsible for a different stage of working with data – from its download, through organizing and securing, to presenting it in the form of clear reports. Such a system consists of, among others, data sources, integration processes, data model, security layer, visualization tools and report distribution mechanisms. Only when they are combined can you provide reliable and actionable information to the right people at the right time. In practice, the problem begins when sales, finance, and operations count the same KPI in three different ways. A good BI environment should sort out this chaos. This allows sales, finance, operations, and marketing teams to work on the same definitions, metrics, and reports, rather than creating their own versions of the truth in separate spreadsheets. It is also worth checking right away whether the solution will not stop at the first 50 users or when connecting another source system. A BI reporting system should grow with the organization: support new data sources, new users, new business areas, and increasingly advanced analytics needs. 4.1. BI Reports BI reports are structured statements that analysts, managers, and executives use to monitor performance and make business decisions. Unlike simply exporting raw data, a BI report is designed with specific audiences, their needs, and goals in mind. It can include calculated metrics, comparisons, filters, data slices, and visuals that help you quickly understand the most important information. This means that you don’t have to analyze big data on your own or build your own reports from scratch. A BI report can be a simple, one-page summary of key KPIs or an extensive, multi-page analytical report with the ability to drill down into detail. Its scope and level of complexity should always result from the real needs of the recipients and the decisions that the report is intended to support. 4.2 Dashboards The main point of contact for users with the BI system are dashboards. They provide a quick overview of key performance indicators by consolidating key metrics into a single, interactive view. A well-designed dashboard doesn’t try to show everything at once. Instead, it presents the right information at the right level of detail, with a clear visual hierarchy. This allows users to quickly spot problems, deviations from the goal, trends, and potential business opportunities. Modern dashboards are increasingly tailored to specific roles in the organization. A CEO may need a synthetic view of strategic KPIs, while a regional sales manager will use a more operational view of performance, sales funnel, or meeting goals in a given region. Both people can work on the same data model, but receive information presented in a way that suits their tasks and responsibilities. 4.3 Data visualization Data visualizations translate numbers into forms, colors, and layouts that the human brain processes faster than lines of text or complex tables. Charts, maps, scatter diagrams, and heat maps help you see the structure of your data: trends, anomalies, dependencies, and outliers that might go unnoticed in the table. Well-designed visualizations are one of the key elements of an effective BI platform. They are not only used to present data aesthetically, but above all to understand it. Thanks to interactivity, users can filter information, analyze details and discover dependencies on their own, instead of just passively reading ready-made statements. 4.4. OLAP and Ad Hoc Queries OLAP, or Online Analytical Processing, enables multidimensional analysis of data in different cross-sections at the same time. In practice, this means that you can analyze, for example, revenue by region, product category, sales channel and period within one consistent model. Ad hoc queries complement this functionality by allowing business users to ask new questions without having to wait for the next report to be prepared by the IT department. Thanks to this, data analysis becomes more flexible and better suited to the current needs of the business. When self-service data exploration is based on an ordered semantic model, your organization gains the best of both worlds: central control over metric definitions and the freedom for different teams to analyze data. This allows you to maintain reporting consistency while speeding up decision-making. 5. Types of Business Intelligence Reports Not all BI reports have the same function. Organizations use a practical division of reports according to their recipients, time horizon and the type of questions they are supposed to answer. Operational reports support the daily work of teams. They are based on data that is refreshed frequently or almost in real time. They can help the warehouse manager monitor inventory levels and the call center leader track the wait time of customers in the queue. Strategic reports are designed with management and a long-term decision-making perspective in mind. They typically span quarters or years, focusing on revenue trends, segment profitability, business objectives, and market changes. Analytical reports are more exploratory in nature. They help you understand the causes of phenomena, test hypotheses, and analyze relationships, for example, through cohort analysis, sales funnel analysis, or root cause analysis. A separate category is self-service BI, which is tools and environments that allow business users to create queries, reports, and visualizations on their own without the constant involvement of the IT department. This direction is becoming increasingly important as organizations expect faster access to information and greater independence for teams to work with data. Self-service BI works best when it’s based on an ordered semantic model and certified datasets. This allows companies to reduce the bottleneck on the part of analysts while maintaining consistency in definitions, data quality, and reporting reliability. 6. Examples of the use of Business Intelligence in different departments of the organization BI reporting is not a tool for one department. Each feature makes data-driven decisions, and real-world implementations show what is truly achievable. For example, a mid-sized healthcare provider in the U.S. implemented a centralized reporting solution based on Power BI, which replaced the operational reporting previously conducted in spreadsheets. The preparation time for monthly reports has been reduced from about 5 days to less than half a day, or about 90%. On the other hand, management queries that had previously been answered for several days could be handled on the same day. Similar effects can be achieved in the manufacturing sector. One manufacturing company has rebuilt its reporting in Power BI, by introducing automatic data refresh and standardized reporting models. As a result, the reporting time at the end of the month was reduced by 60-70% and the costs of overtime related to manual data preparation and merging were significantly reduced. A professional services company that integrated Power BI with CRM, PSA, and financial systems reduced the time it takes to prepare weekly reports on resource utilization and pipeline by 30-40%. Access to near-current data on billing hours also allowed for better monitoring of the level of consultant utilization and faster response to deviations. This translated not only into time savings, but also into a real impact on revenues. In practice, the greatest value of BI reporting is not the mere reduction of manual work. More importantly, however, the organization can make more accurate decisions faster based on current, reliable data. On the infrastructure side, retail and e-commerce organisations benefiting from Snowflake and Power BI achieve a 20-25% cost reduction for analytical computing by separating BI workloads into a dedicated virtual warehouse with auto-suspend functionality. This approach has also improved the responsiveness of dashboards during peak hours, as BI queries have stopped competing for resources with data retrieval and processing processes. The effect was twofold: lower infrastructure costs and a more stable user experience using reports and analytics dashboards. TTMS cooperated with customers who faced similar issues related to data fragmentation: multiple disconnected source systems, inconsistent metric definitions across departments, and reporting cycles counted in days rather than hours. The repeatable pattern is clear here: a well-managed Power BI semantic model, properly integrated into the customer’s data environment, solves the problem of metric consistency first, and only then saves time. In one such project, consolidating reporting under a single managed model eliminated conflicting margin definitions that previously led to recurring disputes between finance and commercial teams. Sales and marketing teams use BI dashboards to connect spend to pipeline performance and revenue. This replaces distributed reporting in spreadsheets with one consistent view that updates automatically. In each case, the basic mechanism remains similar: manual, fragmented reporting is replaced by a connected and managed BI layer. This not only saves time, but also improves the quality of decisions made based on data. 7. Key Benefits of BI Reporting The business case for investing in BI reporting is confirmed by independent market research. Study The Total Economic Impact™ of Microsoft Power BI conducted by Forrester Consulting showed a 366% return on investment (ROI), a 2.5% increase in operating revenue, and 125 hours of savings per year for each BI user. At the same time, the workload of analytical teams decreased by 42%. In practice, most organizations see the benefits of BI in three places: faster decisions, less manual work, and greater trust in data. The first is better decision-making. When leaders have access to up-to-date, reliable, and structured data, they can assess the situation faster, identify risks, and choose actions based on facts rather than intuition. The second important benefit is greater operational efficiency. Automated data flows reduce the time previously spent manually retrieving, combining, and formatting information. This allows teams to focus on analysis and recommendations instead of preparing subsequent versions of spreadsheets. BI reporting also supports organizational cohesion. Common dashboards, standardized metrics, and a single data model keep different departments working on the same version of the truth. This reduces data accuracy disputes and allows you to focus on making business decisions. Finally, BI strengthens strategic planning. Access to trend data, segmentation, and scenario analysis helps executives spot opportunities and threats earlier. That’s why organizations are increasingly treating BI reporting not only as an analytical tool, but also as a way to standardize decision-making processes, improve management, and reduce costly disparities between departments. 8. The biggest challenges of BI reporting and the causes of project failures The path to effective BI reporting is associated with real obstacles. Therefore, it is worth talking directly about why BI initiatives fail, instead of limiting ourselves to a general list of potential challenges. Research on the failure of BI projects in enterprises consistently points to two layers of problems. The first includes strategic errors: unclear business goals, poor support from the board of directors, or the lack of an owner responsible for defining key metrics. The second concerns the implementation of the project itself: low data quality, uncontrolled expansion of the scope of work and insufficient training of users. According to available analyses, 57% of BI deployments exceed budget or schedule due to lack of control over the scope of the project, and 55% of users do not trust BI tools due to insufficient training. Problems related to data management are particularly harmful. Gartner warned that by 2027, 80% of data governance initiatives will fail, and the cause will most often be a lack of responsibility on the part of the business, not the technology itself. When no one is responsible for clearly defining terms such as “revenue”, “margin” or “active customer”, each team begins to understand them differently. As a result, trust in the BI platform decreases, regardless of how well the data model is designed. This is one of the most common barriers that TTMS observes in organizations investing in BI tools but not achieving the expected adoption. Another recurring pattern of failure is starting a project with the choice of a tool rather than deciding which reporting you want to support. Organizations that create dashboards before defining business questions, decisions, and expected outcomes often end up with reports that look impressive but don’t change the way teams operate. BI built around available data, and not around important decisions, becomes a reporting exercise, not a real decision support system. It is the prioritization of results rather than effects that is one of the most frequently cited causes of failure in practitioners’ research and analytical literature. TDWI Survey they also point to the complexity of data integration as a major technical hurdle. Organizations that underestimate the difficulty of connecting legacy systems, SaaS applications, and distributed databases often encounter months of delays in BI projects. The source of these delays are integration works that have never been properly planned. Competence gaps further reinforce this problem. TDWI’s benchmark research indicates that the chronic shortage of BI specialists, data engineers, and analytical translators remains a permanent constraint for organizations looking to develop or modernize their BI capabilities. The solutions are structural. Establishing clear responsibility for metrics before choosing a tool, including data governance in the first sprint instead of treating it as a second-phase task, and matching BI investments to the actual level of maturity of the organization significantly increase the chances of successful implementation. 9. How to Build an Effective BI Reporting Strategy A BI reporting strategy that delivers long-term business value requires more than choosing the right tool and loading data. In projects that develop over several years, BI usually ceases to be an “implementation”. It becomes a product that is developed similarly to a business application – with a backlog, owner and subsequent iterations. This approach requires clearly defined business goals, appropriate data management policies, and continuous improvement of reports and analytics models. It is also crucial to define responsibilities for metrics, data quality, and the development of the BI environment. This allows reporting to evolve with the changing needs of the organization, rather than quickly losing relevance. The most effective BI strategies assume continuous iteration from the beginning. Reports are regularly evaluated for their relevance, and new business needs are gradually incorporated into data models and dashboards. Thanks to this, the reports do not end up as nice dashboards that no one looks into. They become a tool for making specific decisions. 9.1. Define goals and success metrics before you start working with data The first and most important step is to determine what success looks like before an organization opens up any BI tool. It is worth pointing out three to five decisions or processes with the greatest impact on the business that need improvement. This can be pricing policy, customer churn reduction, delivery planning, sales pipeline management, or financial closing process. For each of these areas, you need to determine how BI reporting can realistically improve outcomes. It’s best to put it as a value hypothesis, based on measurable KPIs. This allows an investment in BI to be evaluated with the same accuracy as any other business initiative. TDWI’s research shows that many organizations don’t have a clearly defined data and analytics strategy at the enterprise-wide level. This leads to ad hoc BI projects, inconsistent tools, and duplication of the same reporting activities across different teams. Starting with clearly defined goals helps avoid this fragmentation. 9.2. Data environment audit and organization maturity assessment Before designing any BI solution, it’s a good idea to reliably assess the current state of your data environment. Such an audit should include data quality, completeness of integration, maturity of management rules, organizational structure and team competencies. In organizations with a lower level of maturity, the priority should be the basic foundations: data integration, creating a single version of the truth, and implementing key KPI dashboards. Only on this basis can more advanced reporting and analytical capabilities be safely developed. In organizations with higher maturity, the scope of activities may include advanced analytics, self-service BI, and reporting embedded in business applications. Trying to skip earlier stages often leads to costly errors, low adoption, and a lack of trust in data. 9.3. Choose a BI tool that fits your organization’s needs Tool market Business Intelligence it is mature and very competitive today. Among the most frequently chosen platforms for large organizations, Microsoft Power BI, Tableau, Qlik and Cognos are regularly mentioned. Each of these solutions offers slightly different capabilities in terms of self-service analytics, data management, integration into the corporate ecosystem or the use of AI-based features. TTMS supports customers in building modern analytical environments, using Microsoft Power BI as part of a partnership with Microsoft and the Snowflake platform as a data storage and processing layer. This approach allows you to create a consistent environment covering the entire process – from the collection of raw data, through its integration and modeling, to interactive reporting and business analysis. The choice of the right BI tool should primarily result from the needs of the organization. It is worth evaluating the ease of use for target users, the ability to integrate with existing systems, the level of security and data access management, the scalability of the solution, and the availability of AI-supported features. Data governance mechanisms and consistency in metric definitions are also becoming increasingly important. In modern BI environments, they are no longer additional features, but one of the key criteria for choosing a platform. It is these data that determine whether an organization will be able to build trust in data and use it effectively in the decision-making process. 9.4. Design reports with your audience, not just your data in mind A technically correct report that no one uses is still a failure. That’s why BI reports should be designed around the specific decisions they’re meant to support, rather than just around the data available in the organization. Executives need a synthetic view of trends and key KPIs. Operations teams expect quick access to up-to-date information about the current situation. Analysts, on the other hand, need the ability to drill down, filter data, and explore on their own. Efficiency is also an element of a good reporting project. Users expect dashboards to respond quickly, and response times will be counted in single seconds rather than long waits for a view to load. If a report is slow, its adoption decreases, even if it contains valuable data. 9.5 Manage, monitor, and continuously optimize your BI environment BI management is an ongoing practice, not a one-time task performed at the beginning of a project. It includes defining and enforcing common metrics, managing role-based access, tracking data lineage, auditing report usage, and deprecating content that has become outdated or duplicates existing solutions. One of the most effective structures supporting the long-term quality of reporting is the BI Center of Excellence, which is a small, cross-functional team responsible for standards, good practices, user support and management of the BI environment. Data on the use of reports should feed the BI development backlog. This allows the organization to prioritize critical improvements, remove repetitive reports, and respond faster to changing business needs. 10. BI Reporting Best Practices for 2026 The most important BI reporting practices for 2026 reflect a broader shift in the approach to analytics. Organizations are moving away from passive dashboards created mainly by IT departments in favor of analytical environments supported by AI, self-service and real business decision-making needs. Five practices are particularly important. The first is to treat BI as a managed self-service product. This means building a central analytics platform with a product owner, backlog, and roadmap, while providing business users with the ability to create analytics on their own based on certified and managed datasets. The second practice is to standardize the semantic model and the reusable metrics layer. When terms such as “revenue,” “customer churn,” and “active customer” are defined once and used consistently across the organization, the company reduces data fragmentation and strengthens trust in reporting. The third practice is to embed AI-powered analytics into key workflows. Natural language queries, automatic anomaly detection or analysis of the main factors influencing results are no longer an experiment, and are becoming an expected element of modern BI implementations. As the TTMS points out in its analysis on the AI in business, 2026 will be a period of greater responsibility for investments in artificial intelligence. Experiments conducted between 2023 and 2025 must translate into measurable business results, stable management and greater cost discipline. The same direction will also affect the development of BI environments. The fourth practice is to design BI around decisions and actions, not the dashboards themselves. Reporting should be as close to day-to-day operational processes as possible to shorten the gap between gaining insight and taking action. The fifth practice is user-centered design. Performance, availability, responsiveness, and convenience of cross-device reporting should be considered as basic requirements, not add-ons. Even the best-designed visuals won’t increase adoption if the reports load too slowly or are difficult to use on a daily basis. 11. How TTMS can help with BI reporting For organizations that are in the early stages of BI implementation, TTMS starts with the foundations: data integration, structured semantic model, and KPI reporting. The goal is to create a single version of the truth on which the effectiveness of all subsequent analytical activities depends. For organizations ready to scale, TTMS expands the BI environment with self-service layers, role-aligned dashboards, embedded analytics, and Snowflake-based data warehouses. This approach allows you to separate BI workloads, improve reporting efficiency, and better control infrastructure costs. At every stage, TTMS combines technical competence with experience in change management. This helps to reduce the gap between a well-designed BI system and the solution that users actually use in their daily work. Talk to a TTMS BI professional about your current data environment and check where to start. What is BI reporting and how is it different from regular reporting? BI reporting is the process of collecting, organizing, modeling, and presenting data in the form of interactive reports and analytics dashboards. Its goal is to support business decisions based on up-to-date, consistent and reliable information. Unlike traditional reporting, which often relies on static statements and manually prepared sheets, BI reporting integrates data from multiple sources into one regularly refreshed model. This allows users not only to read the results, but also to filter the data, analyze details, and search for answers to subsequent questions on their own. What is Business Intelligence reporting used for? Business Intelligence reporting is used to monitor performance, track KPIs, identify trends, and support business planning. It helps organizations better understand what is happening in sales, finance, marketing, operations, customer service, or other areas of business. In practice, BI reporting can support both day-to-day operational decisions and long-term strategic planning. It all depends on how the data model is designed, what reports will be made available to users, and what decisions you want to make with them. What do BI reports mean for business users? For business users, BI reports mean access to up-to-date, trusted data in a form tailored to their role and daily decisions. They don’t need to know SQL, data architecture, or the technical details of source systems to use valuable insights. A well-designed BI report allows managers, specialists, and team leaders to independently analyze results, check for deviations, filter data, and react faster to changes. In many cases, it gives business users analytical capabilities that previously required the support of a dedicated analyst. How to implement BI reporting in a company? Successful BI reporting implementation starts with defining business goals and success metrics. Next, it’s a good idea to audit your existing data, choose the right platform, build a structured semantic model, and design reports with specific audiences in mind. Equally important are the processes of management, security, monitoring of data quality and continuous optimization. TTMS supports organizations at every stage of the process—from Power BI deployment and Snowflake, to data integration and report design, to training, user adoption, and managed services. What are the most commonly used BI reporting tools? Some of the most commonly used BI reporting tools include Microsoft Power BI, Tableau, Qlik, Cognos, and data platforms such as Snowflake, which support storing, processing, and sharing data for analytics. The choice of tool should depend on the needs of the organization, the existing infrastructure, security and management requirements, the number of users, and the level of complexity of reporting. The platform alone is not enough – data quality, a consistent semantic model, the right metrics and real adoption on the part of business users are also crucial.
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GPT-5.6 from OpenAI – What’s New? Pricing, Features, and Business Applications
For now, we can only talk about GPT-5.6 in Europe with a mix of professional curiosity and a slight sense of envy. OpenAI has initially made GPT-5.6 available only to a small group of selected partners working with the U.S. administration to evaluate the model’s safety, including potential cybersecurity risks. That’s why we prepared this article as a structured analysis based on official OpenAI materials, technical documentation, early expert evaluations, and publicly available market information. In this article, you’ll learn: What has changed in GPT-5.6 compared to GPT-5.5 and earlier OpenAI models? How do Sol, Terra, and Luna differ, and when should you use each model? How does GPT-5.6 compare with Claude, Gemini, DeepSeek, Grok, and other leading AI models? Which business areas are likely to benefit the most from GPT-5.6? OpenAI’s official statement reads: “We do not believe this government access process should become the long-term standard. It prevents our best tools from reaching the users, developers, businesses, cybersecurity defenders, and global partners who need them.” OpenAI says that broader availability is expected in the coming weeks. We look forward to updating this introduction with our own hands-on experience as soon as GPT-5.6 becomes available more widely. 1. GPT-5.6 – The Biggest Changes Compared to Previous Models 1.1 A New GPT-5.6 Architecture – Three Models Instead of One Universal Model The biggest change is architectural rather than incremental. OpenAI is moving away from the idea of a single flagship model for every task and introducing a family of models with distinct capability levels. In the new naming scheme, the version number represents the generation, while Sol, Terra, and Luna identify individual models that can evolve independently. If OpenAI continues down this path, future releases may no longer follow a simple GPT-5.5 → GPT-5.6 → GPT-5.7 progression, but instead develop as parallel model families. First, an important clarification: Sol, Terra, and Luna are not “modes” in the strict sense. They are three separate models within the GPT-5.6 family. The publicly announced operating modes currently include max reasoning effort and ultra, both available for Sol. Before we discuss them, let’s first look at how the three GPT-5.6 models differ and how OpenAI positions each of them. Model Positioning Best Use Cases Official API Pricing What We Know for Certain GPT-5.6 Sol Flagship model Most demanding tasks: advanced analysis, software development, AI agents, cybersecurity, and complex projects USD 5 input / USD 30 output per 1M tokens Supports max reasoning effort and ultra; the most capable model in the family GPT-5.6 Terra Balanced model Everyday business work, document analysis, automation, and the best quality-to-cost ratio USD 2.50 / USD 15 According to OpenAI, delivers GPT-5.5-level performance at roughly half the API cost GPT-5.6 Luna Fastest and most affordable model High-volume workloads, large-scale automation, frontline assistants, and cost-sensitive tasks USD 1 / USD 6 The fastest and most cost-efficient model in the GPT-5.6 family OpenAI describes ultra as a mode that uses sub-agents to speed up complex tasks. In practice, this means GPT-5.6 performs much better when a task requires multiple steps rather than a single answer. It can analyse large software projects, use external tools, conduct in-depth research, help identify software bugs, organise technical analysis, and prepare structured action plans. For organisations, this means higher efficiency in complex business processes, but also a greater need for monitoring, logging, and access control. 1.2 Stronger Reasoning and AI Agents – What Are max Reasoning Effort and ultra? The second major change is how the model approaches difficult tasks. For Sol, OpenAI introduces a new max reasoning effort level, allowing the model to spend more time analysing a problem before generating an answer. It also introduces ultra, a mode designed for the most complex tasks. In this mode, the model can break work into smaller stages and analyse different parts of a problem in parallel, reaching a solution more efficiently. This is more than a simple interface update. It reflects OpenAI’s shift from treating AI as a system that answers questions to one that helps complete entire tasks. 1.3 Better Programming, Cybersecurity and Scientific Research The third major improvement focuses on software development and tool usage. GPT-5.6 Sol is positioned as a model built for complex programming tasks, especially those that involve planning work, analysing repositories, debugging, using terminal environments, and completing multiple steps rather than simply generating code snippets. OpenAI highlights its strong performance on Terminal-Bench 2.1, a benchmark measuring how well AI models handle realistic software engineering tasks, as well as GPT-5.6’s availability through the API and Codex. For development teams, this represents an important shift. Rather than serving only as a coding assistant, GPT-5.6 increasingly supports the entire software development lifecycle—from analysing problems and refactoring code to generating tests and assisting with CI/CD workflows. The greatest benefits are likely to be seen by teams working on large software projects where AI can help manage complexity. Cybersecurity and scientific research are another area where GPT-5.6 has improved. According to OpenAI’s safety documentation, Sol and Terra can help identify vulnerabilities in IT systems and analyse how they could potentially be exploited. At the same time, internal testing showed that the models were not able to carry out complete attacks against well-protected systems on their own, highlighting both their growing capabilities and their current limitations. OpenAI and independent evaluators also report strong performance in biology and cybersecurity benchmarks, showing that GPT-5.6 is evolving beyond software development into a tool for highly technical and specialised domains. 1.4 Better Analysis of Documents, Images and Complex Data Another major improvement is GPT-5.6’s ability to work with different types of information. Rather than being viewed simply as a text model, GPT-5.6 is increasingly becoming part of a broader system for working with documents, images, research materials and business data. In practice, this means it is better suited to tasks that require combining multiple sources of information, such as reports, presentations, screenshots, technical documentation, meeting notes and visual materials. Instead of simply summarising individual files, the model can compare information, identify relationships and help build meaningful conclusions from different data formats. This is also where the difference between a standalone language model and a complete business solution becomes most apparent. Analysing enterprise documents requires more than just generating answers—it also involves access control, trusted sources, reporting workflows and compliance with company data policies. At TTMS, this is exactly the kind of functionality we build into solutions such as AI4Content. 1.5 GPT-5.6 Is More Autonomous, but Also Requires More Oversight OpenAI makes it clear that greater autonomy must be matched by stronger human oversight. According to the company’s safety documentation, GPT-5.6 Sol is more persistent than its predecessor when trying to complete a user’s objective and may occasionally take actions that go beyond the user’s original intent, although such cases remain relatively rare. Independent experts have reached similar conclusions. METR (Model Evaluation & Threat Research), an independent organisation specialising in evaluating advanced AI systems, found that GPT-5.6 Sol was more determined to complete tasks in certain tests, even if that meant attempting to bypass the rules of the testing environment. Meanwhile, Apollo Research, which studies AI safety, found no evidence that GPT-5.6 is more likely than previous models to take undesirable autonomous actions. In practice, this means GPT-5.6 can be more effective in long-running, agentic tasks, but it should operate within a well-designed environment that includes activity logging, access controls, human review and appropriate governance. 1.6 GPT-5.6 Features OpenAI’s Most Advanced Safety Architecture Yet OpenAI presents GPT-5.6 not only as a more capable model, but also as one designed for safer enterprise deployment. The model is intended to recognise risky prompts more effectively, reduce opportunities for misuse and operate within environments that provide stronger control over access, monitoring and usage policies. In practice, this means multiple layers of protection. Some safeguards are built directly into the model, others operate while responses are being generated, and others monitor suspicious usage patterns. Imagine a user repeatedly asking similar questions in slightly different ways to bypass the model’s safeguards and obtain instructions they should not receive. If the system detects a high risk of misuse, it can refuse the request, apply additional safeguards or route the interaction through stricter security controls. OpenAI also applies different access levels and extensive automated safety testing designed to determine whether GPT-5.6 can be manipulated into breaking its own safety rules—for example through jailbreak attempts. According to the company, these automated evaluations consumed more than 700,000 A100-equivalent GPU hours. This does not mean GPT-5.6 is immune to mistakes or misuse, but it does show that security has become a dedicated product layer rather than simply another part of model training. 1.7 GPT-5.6: Greater Flexibility and Lower AI Deployment Costs From a business perspective, one of the biggest changes is that organisations no longer need to rely on the most powerful—and most expensive—model for every task. Sol can be reserved for expert analysis, AI agents and technically demanding projects, while many day-to-day processes can run on the more affordable Terra or Luna models. This changes the economics of AI adoption. Organisations can now match the cost of a model to the value of the task, using different models for strategic analysis, high-volume customer interactions, document automation or internal business support. 2. How to Choose the Right GPT-5.6 Model and Mode for Your Task Using GPT-5.6 follows a simple process. First, you choose one of the three models: Luna, Terra or Sol. If you select Sol, you can also choose between two additional operating modes: max reasoning and ultra. Deep Research works independently of the selected model and is designed for comprehensive investigations across multiple sources, helping organise, analyse and synthesise information into coherent conclusions. Task Luna Terra Sol Max reasoning Ultra Deep Research Why This Choice? Fast responses and chatbots ✅ – – Lowest cost and very fast responses. Document classification ✅ ✅ – – Usually does not require advanced reasoning. Marketing content creation ✅ – – A good balance between quality, speed and cost. Legal contract and document analysis ✅ ✅ Complex documents benefit from deeper reasoning. Financial analysis and reporting ✅ ✅ Accuracy, consistency and stronger reasoning are essential. Programming and code review ✅ ✅ Additional reasoning time improves coding quality. Refactoring large software projects ✅ ✅ Ultra performs better in complex, multi-stage development tasks. Complex agentic workflows ✅ ✅ Ultra uses sub-agents to handle sophisticated workflows. Preparing reports from multiple sources ✅ ✅ Deep Research searches, compares and analyses multiple sources automatically. Expert articles and market analysis ✅ ✅ ✅ Combines in-depth research with advanced reasoning for the highest-quality results. Combining in-depth research with strong reasoning quality produces the best results. In practice, GPT-5.6 should not be treated as one model for every task, but as a set of configurations that can be matched to the difficulty of the task, the expected quality of the output, and the depth of research required. 3. What Will GPT-5.6 Pricing Look Like? The API pricing for the GPT-5.6 family is structured as follows: Sol – USD 5 / USD 30 per 1M input/output tokens, Terra – USD 2.50 / USD 15, Luna – USD 1 / USD 6. Sol remains at the same pricing level as GPT-5.5, so there is no price jump for the flagship model class. What is interesting is that OpenAI is clearly creating more affordable entry points: Terra is positioned as offering performance competitive with GPT-5.5 at roughly half the cost, while Luna is clearly focused on the best balance between quality and price. 4. The Evolution of OpenAI Models GPT-5.6 is best understood in a broader context. It is not just another model release with better benchmark results. It shows a shift in how OpenAI designs AI systems: from one universal model to a family of models with different costs, capabilities and use cases. Generation Release Parameters / Architecture, if Disclosed Context Length Multimodality Key Improvement Typical Business Use Cases GPT-1 2018 12-layer decoder-only Transformer, 768 hidden size, 12 attention heads 512 tokens No Generative pre-training as a universal transfer learning foundation Classification, basic NLP, research experiments GPT-2 2019 Up to 1.5B parameters; four variants from 117M to 1.542B 1,024 tokens No Major improvement in text generation and zero-shot transfer Content generation, summaries, experimental copywriting GPT-3 2020 175B parameters Not fully specified in the launch materials No Few-shot learning at production scale Chatbots, text automation, AI prototypes GPT-3.5 2022 Model from the GPT-3.5 series, fine-tuned for dialogue Later GPT-3.5 Turbo API versions supported 16k by default No Commercialisation of high-quality conversational AI through ChatGPT Support, FAQs, internal assistants, first enterprise deployments GPT-4 2023 Architecture and size not disclosed; large-scale multimodal model Not fully specified in the technical launch report Yes, image and text input Major leap in reasoning, exam performance, instruction following and safety Document analysis, expert knowledge work, advisory tasks, high-stakes deployments GPT-4o 2024 Frontier model optimised for practical multimodality Not explicitly stated on the cited launch page Yes, text, image, voice and broader product-level multimodality Omni model: faster, cheaper and more natural multimodal interaction Voice assistants, image analysis, customer service, multimodal copilots GPT-5 2025 Unified system with routing between fast and deeper reasoning paths 400k, with up to 128k output in API documentation Text and image input, text output Automatic routing, higher usefulness, fewer hallucinations and better tool use AI agents, software development, knowledge work, expert analysis GPT-5.5 2026 Frontier model for complex work; later matched by Sol-level pricing in GPT-5.6 1M Strongly oriented around documents and tools in ChatGPT and API Better persistence in long-running tasks, software work, research and data analysis Research, document analysis, modelling, customer operations, finance GPT-5.6 2026 No full public parameter specification; Sol/Terra/Luna model family Not publicly disclosed in a separate preview model card Recent OpenAI models support text and image input, but GPT-5.6 preview does not yet have a full public specification card Capability tiers, max reasoning, ultra mode, sub-agents and a stronger deployment safety layer Agentic software workflows, cybersecurity, enterprise document work, high-volume automation with better cost control The shortest way to summarise this evolution is this: from GPT-1 to GPT-3, OpenAI mainly scaled the model itself; from GPT-3.5 to GPT-4, it refined the human-model interface; and from GPT-5 onwards, it has been building a broader AI work system with routing, tools, longer task horizons, cost control and stronger safety layers. GPT-5.6 shows this direction clearly: OpenAI is moving from standalone chatbots towards systems that support work, automation and decision-making. 5. GPT-5.6 in Business: Where Will Companies Feel the Biggest Change? 5.1 GPT-5.6 in Marketing – Faster Content Operations and Better Data Analysis In marketing, the biggest change is about scale and cost efficiency in working with content and data. Sol can be used for research, strategy, more difficult analyses and multi-variant campaigns, while Terra and Luna are better suited to high-volume tasks: paraphrasing, content tagging, creative drafts, summaries, extracting insights from research and automating everyday content operations. In similar scenarios, AI4Localisation can be a strong fit. It is a TTMS solution supporting translation and localisation of business content. With AI, organisations can prepare multilingual materials faster while maintaining consistent terminology and communication style. 5.2 GPT-5.6 for Developers – Code Review, Refactoring and AI Agents The change is especially visible in software development. GPT-5.6 Sol is expected to perform better in long, multi-step tasks such as repository analysis, bug detection, refactoring, test generation and support for work in environments such as the API or Codex. This means AI can help not only with writing individual code snippets, but also with organising larger development tasks. This does not mean engineering oversight can be removed. The more a model can do independently, the more important code review, testing, permission limits and clear rules become. Teams need to decide what AI can execute automatically and what still requires human approval. 5.3 GPT-5.6 in Customer Service – Ticket Automation and Consultant Support In customer service, Terra and Luna may be especially useful as faster and more affordable GPT-5.6 variants. OpenAI positions Terra as a model for everyday business tasks, while Luna is the fastest and cheapest option in the family. This fits well with first-line support work: organising tickets, assigning priority, preparing response drafts, extracting key information from customer requests and suggesting next steps to consultants. 5.4 GPT-5.6 in HR and Recruitment – CV Analysis, Onboarding and Recruiter Support In HR, the greatest value of GPT-5.6 may come from combining better information analysis with more flexible usage costs. In practice, this means support with summarising CVs, comparing candidates, organising recruitment notes, preparing shortlists and creating onboarding plans. Terra may often be more cost-effective than Sol here, because many recruitment tasks are performed at scale but do not require the most advanced level of reasoning. In this area, AI4Hire fits naturally as a TTMS tool for CV analysis and matching skills to projects. It automates profile assessment, generates recommendations and helps teams find people who best match a specific requirement faster. 5.5 GPT-5.6 in Compliance – Document Analysis and Regulatory Support In compliance, accuracy, consistency and alignment with procedures matter most. GPT-5.6 may be useful here because OpenAI highlights several safety layers: response monitoring during generation, detection of suspicious usage patterns and different levels of model access. This does not mean GPT-5.6 can make regulatory decisions on its own. It can, however, support policy analysis, document review, preparation of evidence materials, checking whether outputs follow internal procedures and internal audits. AI4Legal uses similar capabilities in the legal sector. It is a TTMS solution supporting law firms in document analysis, contract preparation, work with case files and transcript processing. In practice, it shows that the biggest value of models such as GPT-5.6 comes not from giving users access to the model itself, but from integrating AI into a specific business process. Another example of AI in compliance is AML Track, a TTMS solution supporting AML processes such as customer verification, sanctions list screening, report preparation and audit trail maintenance. It shows that in compliance, AI does not need to replace expert judgement. It can organise data, automate repetitive work and support alignment with regulatory requirements. 5.6 GPT-5.6 in Finance – Report Analysis, Due Diligence and Controlling Support In finance and controlling, the real value of GPT-5.6 is likely to appear where teams need to combine documents, calculations, multi-step analysis and repeatability. GPT-5.5 was already positioned as a model that performs well in data analysis, information retrieval and work with large document sets. With GPT-5.6, organisations can more easily match the cost of AI usage to a specific task while gaining more advanced agentic capabilities. The biggest impact will therefore be felt not by simple financial chatbots, but by teams working with large volumes of documents and data: due diligence, report analysis, KYC processes, extracting key metrics and preparing materials for decision-makers. For now, these are conclusions based on the capabilities described by OpenAI and early tests, not yet on widely documented GPT-5.6 finance deployments. 5.7 GPT-5.6 in E-learning – Faster Training Creation and Personalised Learning In e-learning, GPT-5.6 may offer very practical benefits: faster breakdown of large knowledge sets into modules, creation of assessment questions, transformation of documents into training formats, personalisation of learning paths and the development of internal tutors. If this cost-and-capability model split continues, Terra and Luna may be used for high-volume content production and updates, while Sol can support the design of more advanced, expert-level or highly contextual materials. This is also the direction behind AI4E-learning, a TTMS tool that helps turn company materials, documents and presentations into ready-to-edit e-learning courses that can be exported to LMS platforms. 5.8 GPT-5.6 in Software Testing – QA Support and Test Automation GPT-5.6 may also be especially useful for QA teams. The model can help generate test cases, analyse regression issues, interpret logs, recreate error paths and prepare drafts of automated tests. What also matters is that companies can choose the model variant based on the task: Sol for more complex troubleshooting, Luna for large volumes of simpler, routine testing tasks. QATANA follows this direction as well. It is a TTMS solution for AI-supported software test management, helping QA teams generate test cases, analyse requirements, organise the testing process and improve control over application quality. 6. Is GPT-5.6 the Best LLM Today? A Comparison with Competitors Area Is GPT-5.6 the Best Here? Main Competitor Programming ✅ Yes Claude Opus AI Agents ✅ Yes Claude Documents ✅ Yes Claude Multimodality ⚠️ Tie Gemini Price ❌ No DeepSeek On-premise ❌ No Mistral / Llama Google Workspace ❌ No Gemini 6.1 Programming – GPT-5.6 Sol or Claude Opus? Both models are currently among the strongest options for software development. Claude Opus has long been valued for its ability to work with large code repositories and analyse existing projects. GPT-5.6 Sol, however, appears to go a step further thanks to its agentic capabilities, Max reasoning and Ultra modes, and strong results in benchmarks such as Terminal-Bench 2.1. If a task requires not only writing code, but also planning, using tools and completing several stages of work, GPT-5.6 Sol is likely to have the advantage. 6.2 AI Agents – Where OpenAI Has a Clear Advantage This is currently one of GPT-5.6’s strongest areas. OpenAI is developing the model not only as a classic chatbot, but as a platform for AI agents that can plan actions, use tools and carry out complex tasks. Claude is also developing agentic capabilities, but it does not currently offer a direct equivalent of Ultra, which uses sub-agents to solve complex problems in parallel. 6.3 Document Analysis – GPT-5.6 or Claude? Claude has long been considered one of the best models for working with long documents and complex text. GPT-5.6 Sol appears to be very close in terms of document analysis quality, while its stronger reasoning may help it draw conclusions from multiple sources at once. In practice, both models are likely to perform at a very high level, although GPT-5.6 offers broader options for using document analysis inside agentic business processes. 6.4 Multimodality – Gemini Still Sets the Direction If the main task is to analyse text, images, video and audio together, Gemini remains a very strong option. This is mainly because it was designed from the beginning as a natively multimodal model and is deeply integrated with Google’s ecosystem. GPT-5.6 also performs well in multimodal tasks, but in this area it is difficult to name a clear winner. 6.5 Price – DeepSeek Remains Hard to Beat When it comes to API costs, DeepSeek still clearly undercuts most major competitors. For organisations handling millions of requests per month, the price difference can translate into substantial savings. The trade-off is lower transparency around safety and a weaker tool ecosystem compared with OpenAI. 6.6 Local Deployments – Where Mistral and Llama Have the Advantage Not every organisation can use models that run only in the cloud. Companies in finance, public administration or defence often need full control over infrastructure and data. In such cases, models that can be run on private servers, without sending data to an external cloud, have an advantage. Examples include Mistral Large 3 and Llama 4. 6.7 Google Workspace – Gemini’s Natural Environment Organisations that use Gmail, Google Docs, Google Drive or Google Meet every day will often gain the most from Gemini. The model was designed for close integration with Google’s services, which allows it to use data from that ecosystem and support everyday user workflows. There is no single AI model today that clearly wins in every category. GPT-5.6 Sol appears to be one of the most versatile options for business use, but the best model still depends on the use case, budget, security requirements and the environment in which it will be used. 7. What Does GPT-5.6 Mean for Companies? GPT-5.6 does not look like a routine model update. More important than better answer quality is the fact that OpenAI gives companies more choice: Sol for difficult tasks, Terra for everyday work and Luna for processes where scale and cost matter most. For businesses, this means one thing: access to GPT-5.6 alone will not be enough. The real value will come from placing the model inside a specific process, connecting it with organisational knowledge, securing the data and clearly defining where AI supports people and where people still make the final decision. Full GPT-5.6 availability in Europe may still take some time, but the direction is already clear. The companies that benefit most will not simply be those that adopt the newest model first, but those that match AI to real tasks, costs, data and security rules. If you are considering how to introduce AI into your organisation, explore our AI Solutions or contact our team to discuss which approach fits your business processes best. Is GPT-5.6 available in Europe? Not yet for general public use. While ChatGPT and the OpenAI API are available across most European countries, GPT-5.6 has so far been released through a limited preview programme for a small group of trusted partners. This rollout is not specific to Europe – it affects nearly all markets outside the preview programme. OpenAI has confirmed that broader availability will be introduced gradually. When will GPT-5.6 become available in Europe? OpenAI has not announced a specific launch date for Europe. The company has stated that wider access is expected in the coming weeks, with availability expanding progressively across ChatGPT, the API and other OpenAI products. As with previous major releases, the rollout is likely to happen in stages rather than all at once. Are Sol, Terra and Luna GPT operating modes? No. Sol, Terra and Luna are three separate models within the GPT-5.6 family, not operating modes. The actual operating modes currently described by OpenAI are max reasoning effort and Ultra, both available for GPT-5.6 Sol. Each model is designed for different performance, cost and business scenarios. What is GPT-5.6 Sol? GPT-5.6 Sol is the flagship model in the GPT-5.6 family. It is designed for the most demanding tasks, including advanced reasoning, software development, AI agents, cybersecurity and complex enterprise workflows. Sol also supports the max reasoning effort and Ultra modes, making it the most capable model in the family. What is GPT-5.6 Terra? GPT-5.6 Terra is the balanced model in the GPT-5.6 lineup. OpenAI positions it as the best choice for everyday business work, document analysis and automation tasks where organisations need strong performance without paying for the most advanced model. According to OpenAI, Terra delivers performance comparable to GPT-5.5 at roughly half the API cost. What is GPT-5.6 Luna? GPT-5.6 Luna is the fastest and most affordable model in the family. It is intended for high-volume workloads such as chatbots, customer support, document classification and large-scale business automation. Luna is designed for situations where response speed and cost efficiency matter more than maximum reasoning capability. What does max reasoning effort mean in GPT-5.6? Max reasoning effort is an optional operating mode available for GPT-5.6 Sol. Instead of generating an answer as quickly as possible, the model spends more time analysing the problem before responding. This often improves performance in complex reasoning, programming, research and analytical tasks where accuracy is more important than speed. What is Ultra mode in GPT-5.6? Ultra is the most advanced operating mode available for GPT-5.6 Sol. OpenAI describes it as a mode that uses sub-agents to tackle complex problems by breaking them into smaller tasks and processing them in parallel. It is designed for long, multi-step workflows rather than simple question answering. How much does GPT-5.6 cost through the API? According to OpenAI’s published API pricing: GPT-5.6 Sol: USD 5 input / USD 30 output per one million tokens GPT-5.6 Terra: USD 2.50 input / USD 15 output GPT-5.6 Luna: USD 1 input / USD 6 output These pricing tiers allow organisations to choose the model that best matches both the complexity of the task and the available budget. Will GPT-5.6 be available through the API? Yes. OpenAI has confirmed that GPT-5.6 is being rolled out through the API as part of the preview programme and will become more broadly available as the rollout expands. The company also plans to make the models available across ChatGPT, Codex and other OpenAI services. Is GPT-5.6 safer than previous OpenAI models? OpenAI describes GPT-5.6 as its most security-focused model family to date. It introduces multiple layers of protection, including safeguards built into the model, real-time safety monitoring, usage pattern detection and different access levels. Independent researchers have not found evidence that GPT-5.6 is more likely than previous models to engage in undesirable autonomous behaviour, although its greater capabilities also make proper governance and human oversight more important. Is GPT-5.6 better suited for business than GPT-5.5? For many organisations, yes. GPT-5.6 introduces three specialised models instead of relying on a single universal model, allowing businesses to balance performance and cost more effectively. Companies can reserve Sol for highly complex work while using Terra or Luna for everyday automation, making enterprise AI deployments more flexible and cost-efficient than before. How can I get access to GPT-5.6? At the moment, access is limited to organisations participating in OpenAI’s preview programme. For everyone else, the best option is to wait for the wider rollout that OpenAI has announced for ChatGPT, the API and its other products. Availability is expected to expand gradually rather than becoming available worldwide on a single release date.
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Instructional Design: A Guide to Effective E-Learning Training
Imagine you need to train not one hundred, not one thousand, but hundreds of thousands of people. Each of them must develop the same skills, follow the same procedures, and make the right decisions under time pressure. Sounds like a challenge faced by modern corporations? In reality, this problem appeared more than 80 years ago. That was when people started asking a question that is still relevant today: why do some training programs genuinely change the way people work, while others end with a completed test, but the knowledge from the course is never really mastered? The answer is instructional design – an approach that helps organizations design training as a structured learning process, not just a set of slides, videos, and quizzes. In this guide to instructional design, we explain where this approach came from, what is instructional design in practice, and how it supports effective learning in education, corporate training, and modern e-learning. We also show why instructional design online learning matters so much today, especially when organizations need scalable, engaging, and measurable training experiences. 1. Where did instructional design come from and why was it created to solve a real problem? It is 1942. The United States enters World War II. The army needs a huge number of pilots, mechanics, radar operators, navigators, and technical specialists. The traditional training model – an instructor explains, participants listen, and everyone learns at their own pace – is no longer enough. The scale is too large, and the stakes are too high. What is needed is an approach that makes it possible to teach effectively, consistently, and in a way that can be measured. This is the context in which the foundations of instructional design began to emerge. One of the key figures in this process was Robert Gagné, a psychologist who worked on training programs for military aviation. While analysing how pilots learned, he reached a conclusion that may seem obvious today but was revolutionary at the time: not all knowledge is the same type of knowledge. We learn facts in one way, procedures in another, and decision-making in complex situations in yet another way.703,30-465,82 This insight became one of the foundations of modern instructional design in education and training. It influenced the way courses are created to this day, including e-learning instructional design, where the goal is not only to deliver content, but to help learners understand, practise, remember, and apply knowledge in real situations. What is instructional design? In the simplest terms, it is the process of designing effective learning. Its goal is not just to create an attractive presentation, course, or set of training materials. The real goal is to design a learning experience that helps participants gain specific knowledge, develop a skill, or change the way they behave in practice. So when people ask “instructional design – what is it?”, the answer is not limited to content creation. Instructional design is about planning the entire learning path: from understanding the learner’s needs, through defining learning objectives, to choosing the right methods, exercises, and ways to verify knowledge. In practice, the instructional design process includes: analysing the needs of learners, defining clear learning objectives, selecting appropriate educational methods, designing exercises and knowledge checks, evaluating whether the training has achieved the expected results. This is also where the importance of instructional design becomes clear. It helps answer not only the question “what should we teach?”, but also “how should we teach it so that learners can actually use this knowledge later?”. That is why instructional design is so important in education, online learning, and corporate training. A well-designed course does not simply deliver information. It guides learners step by step toward a specific outcome. 2. Why is instructional design important? Many organizations invest significant budgets in training programs that do not deliver the expected results. Participants complete the course, pass the test, and yet they still do not change their behaviour, apply new knowledge in practice, or remember the information for long. This is where the importance of instructional design becomes clear. Instructional design helps reduce this risk by using proven learning theories and a structured approach to training design. Instead of treating a course as a collection of materials, it focuses on the learning outcome: what the participant should know, understand, or be able to do after the training. This is why instructional design in training and development plays such an important role. It helps organizations create learning programs that are more engaging, more effective, and better connected to business goals. Instructional design is used not only in education and higher education, but also in employee onboarding, compliance training, skills development programs, technical courses, sales training, and instructional design for corporate training. In each of these contexts, the goal is the same: to make learning more purposeful, measurable, and easier to apply in real work or study situations. 3. Content creation vs. designing the learning process – what is the difference? This is one of the most common misunderstandings in the world of training. Content creation focuses on preparing educational materials. It may include writing text, creating presentations, recording videos, preparing quizzes, or designing visuals. Instructional design starts much earlier. Its role is to define what the learner should be able to do after completing the training and what kind of learning activities will help them get there. In other words, content is one part of a training course. Instructional design is the plan for the whole learning journey. A good instructional designer does not start by creating slides. First, they define the problem the training is supposed to solve, identify the expected outcomes, analyse the target audience, and only then choose the right content and teaching methods. That is why two courses can include almost the same information and still produce completely different results. Very often, the difference is not in the content itself, but in how the learning path has been designed. 3.1 What should you remember? Content creation Designing the learning process Starting point Starts with materials: text, presentation, video, quiz, or graphic. Starts with the question: what problem should the training solve and what should the learner be able to do? Main task Preparing educational content in a clear and engaging format. Designing the whole learning path: from objectives, through activities, to measuring outcomes. Role of content Content is the main output. Content is one of the tools that helps the learner reach a defined outcome. Key question “What do we want to communicate?” “What change in knowledge, skill, or behaviour do we want to achieve?” Order of work Materials are created first, and a quiz or exercise is often added later. Objectives, learners, and expected outcomes are defined first. Content and methods come next. Measure of success The course looks good, feels complete, and includes the required information. The learner can apply the knowledge in practice and reach the expected training outcome. Main risk The training may look polished, but remain superficial and ineffective. The process requires more analysis, but it increases the chance of a real change in behaviour. Main takeaway Providing information alone does not guarantee learning. The effectiveness of training depends on how the entire learning path has been designed. “In recent years, the focus has shifted from content production to designing real change. The length or volume of a course matters less than whether learners can apply new knowledge and skills in their work. The most common mistake organizations make is starting with materials instead of asking: what problem should this training solve? The result is often a polished course that looks good, but does not really work.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 4. Evidence-based learning – what actually works according to research? One of the biggest mistakes in training design is relying only on intuition. Many solutions that seem logical or attractive do not necessarily lead to better learning outcomes. Long presentations overloaded with information, multi-hour courses without breaks, or passive video watching may feel like intensive learning. In practice, they rarely support long-term retention or practical use of knowledge. Research on how people learn shows that effective training is not about delivering as much information as possible. What matters more is how learners work with knowledge, how often they need to recall it, and whether they have a chance to use it in realistic situations. This is where evidence-based learning connects with instructional design best practices. Good training is not built around what looks impressive on a screen. It is built around mechanisms that help people remember, understand, and act. 4.1 Retrieval practice – we learn when we recall information One of the best-documented learning mechanisms is retrieval practice, which means actively recalling information from memory. It may feel counterintuitive, but we do not learn most effectively by reading the same material repeatedly. We learn more effectively when we try to retrieve knowledge on our own. That is why well-designed training often uses: knowledge-check quizzes, open-ended questions, exercises that require a decision, scenarios and case studies. Each attempt to recall information strengthens memory and increases the chance that the learner will be able to use that knowledge later. 4.2 Spaced repetition – learning spread over time Another mechanism strongly supported by research is spaced repetition, which means returning to content at planned intervals. Learners remember more when they revisit material several times over time, rather than trying to absorb everything in one long session. This is one reason why shorter training modules delivered over several days or weeks can work better than a single, long training session. 4.3 Feedback – learning is faster when people understand their mistakes Learner activity alone is not enough. Feedback also matters. Useful feedback: shows what was done correctly, explains mistakes, helps learners understand the consequences of their decisions, points them toward the right course of action. That is why a quiz that only shows a percentage score has limited value. An exercise that explains why an answer was right or wrong gives the learner much more to work with. 4.4 Active participation instead of passive content consumption Research consistently shows that people learn more effectively when they are actively involved in the learning process. Watching a video or reading a text can be a good introduction to a topic. On its own, however, it rarely leads to lasting behavioural change. That is why modern training increasingly uses: decision-making scenarios, simulations, practical tasks, gamification, exercises based on real business problems. The learner is not just a recipient of content. They become an active participant in the learning process. The conclusions from research are surprisingly consistent. Effective training does not have to be the longest or the most complex. What matters more are mechanisms that support memory and practical application: recalling information, revisiting knowledge over time, receiving meaningful feedback, and working actively with real tasks. These are some of the most important best practices in instructional design and the foundation of modern, evidence-based e-learning. “Regardless of the industry, the same research-based learning mechanisms tend to work best: active recall through quizzes and decision-making exercises instead of passive reading, repetition spread over time, and specific feedback that explains “why”, not just “how many points”. It is also important to place learning in situations that are close to the learner’s real work. The industry changes the content, examples, and context, but the principles of effective learning remain the same.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 5. Learning science – what does it teach us about how people learn? Modern instructional design is strongly connected with learning science: the field that studies how people acquire, process, and retain knowledge. Research shows that the brain does not work like a hard drive where information can simply be “uploaded”. Exposure to content does not automatically mean that learning has happened. For knowledge to move into long-term memory, learners need to actively process it, connect it with what they already know, and use it in practice. This idea is reflected in andragogy, which highlights the role of adult learners’ experience, and in Bloom’s taxonomy, which shows that real learning goes far beyond memorising facts. For an instructional designer, the message is clear: effective training is not about giving learners as much information as possible. It is about creating the right conditions for them to build, practise, and retain knowledge. From our experience in corporate training projects, many organizations still associate training effectiveness mainly with quiz results. During course design, there is often an expectation to add as many test questions as possible, because they are seen as the main way to verify knowledge. In practice, a quiz usually checks whether a learner can recall information right after completing the course. An employee may achieve a very high score and still be unable to apply that knowledge a few days later in a real work situation. That is why modern instructional design puts more emphasis on case studies, decision-making tasks, simulations, and scenarios based on real challenges inside the organization. These activities help learners practise the behaviours and decisions that later translate into everyday work. Another common misconception is the belief that every organizational problem is caused by a lack of training. During training needs analysis, we regularly see situations where the real cause lies somewhere else: unclear procedures, weak onboarding, missing tools, limited support from managers, or not enough time to adopt new skills. Effective training projects should therefore start with a diagnosis of the business problem. Only when we understand what is actually limiting employee performance can we decide whether the right solution is training, process change, better communication, or managerial support. Not every business problem is a training problem. Researcher / theory Approximate date What does the theory say about learning? B.F. Skinner – behaviourism 1950s Learning is a change in behaviour. Knowledge should be reinforced through practice, repetition, and feedback. Benjamin Bloom – taxonomy of educational objectives 1956 Learning has different levels, from remembering and understanding to analysing, evaluating, and creating. Passing on information does not automatically mean developing competence. Robert Gagné – conditions of learning 1960s-1970s Different types of knowledge and skills require different teaching methods. The learning process should be designed intentionally. Malcolm Knowles – andragogy 1970s Adults learn differently from children. They need to understand the purpose of learning, use their own experience, and see the practical value of new knowledge. Cognitive load theory – John Sweller 1980s Working memory has limited capacity. Overloading learners with information makes learning and retention more difficult. Spaced repetition Research since the late 19th century, developed further in modern learning science Knowledge is retained more effectively when repetition is spread over time instead of concentrated in one intensive learning session. Retrieval practice 1990s-present Actively recalling knowledge strengthens memory more effectively than repeatedly reading the same material. Learning science / active learning 21st century Learners achieve better results when they solve problems, make decisions, and use knowledge in practice instead of only consuming content. 6. Cognitive psychology in training – how to design courses around the way the human brain works Effective instructional design takes into account not only business goals and learner needs, but also the way the human brain processes information. Cognitive psychology plays an important role here, especially cognitive load theory. This theory shows that working memory has limited capacity. In simple terms, learners cannot process too much information at the same time and still learn effectively. In practice, too many messages, overloaded slides, complicated language, or a lack of clear structure can make learning harder, even when the content itself is valuable. That is why modern training increasingly focuses on clarity, simplicity, and gradually building knowledge instead of trying to cover everything at once. 6.1 How can you reduce cognitive load? To reduce cognitive load, it helps to: divide the material into shorter modules, present only the most important information, use clear and simple language, build a logical content structure, increase the level of difficulty step by step. Designing training in line with cognitive psychology does not mean making the course easier. It means helping learners focus their attention on learning instead of forcing them to fight through too much information. “In our work, we sometimes support organizations that have already tried to implement e-learning with another provider, but did not achieve the expected results. During the analysis of materials and conversations with stakeholders, it often becomes clear that the problem is not the technology or the platform itself. The real issue is cognitive overload. We usually see two recurring mistakes. The first is focusing on memorisation instead of understanding. This is especially common in regulatory training, where course authors try to make learners remember procedure numbers, document names, or detailed regulatory provisions. From the perspective of everyday work, however, it is often much more important for employees to know when to use a given procedure, where to find the necessary information, and how to act correctly in a specific situation. Memorising content alone does not guarantee the right behaviour. The second common problem is adding too much information “just in case”. During reviews, subject matter experts often want to include every exception, special case, and additional explanation. This usually comes from a good place: they want to avoid leaving out something important. As a result, a course that was supposed to take 20 minutes grows to 40 or 50 minutes, without becoming proportionally more effective. During audits, we use a simple but very useful question: “After completing this screen, does the learner know what they should do differently in their work?” If the answer is not clear, or if one screen tries to communicate several different messages at once, we are most likely dealing with cognitive overload. This is one of the main reasons why training programs fail to deliver results, even when the source materials are accurate and complete.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 7. Scenario-based learning – why do people learn more effectively through experience? Scenario-based learning is based on realistic situations and decisions. The learner does not only read or watch the material. Instead, they face a specific problem, choose an action, and see the consequences of that decision. This is why scenarios and case studies often work better than traditional slides. They place knowledge in a practical context and help learners practise behaviours they can later use at work. 8. How to use scenarios in e-learning? An example from TTMS practice One of the most effective ways to use scenario-based learning is to combine it with elements of gamification. Instead of reading procedures or clicking through another set of slides, the learner enters a realistic work environment and makes decisions similar to those they may face in their everyday job. This is exactly the approach we used when creating a health and safety training course for one of TTMS’s clients. The learner took on the role of a character and followed them through a full working day. The scenario began before the character even entered the facility. During the commute, the learner had to remind them to fasten their seat belt and follow safe driving rules. The action then moved to a production plant, where the learner encountered further realistic situations and hazards. While completing daily tasks, the character faced problems that required decisions in line with safety procedures. Each choice had consequences. If the learner selected the wrong action, the training immediately explained the mistake, described the possible impact, and allowed them to try again. As a result, participants did not simply read about procedures. They repeatedly practised the right responses in a safe environment. This type of learning helps reinforce desired behaviours much more effectively than passive reading of instructions. We used a similar approach in information security training. In one of the games, the user moved through an office environment and had to identify potential risks, such as documents left on a desk, printouts thrown into a bin, or an unlocked computer screen. The learner’s task was to find all irregularities and choose the correct way to respond. Both projects show that a well-designed scenario allows learners to learn by doing, making decisions, and learning from mistakes. And this is often the way people learn best. “In practice, we see that learners remember situations in which they had to make a decision and see its consequences much better than information they only read on screen. Even after some time, they often remember a specific scenario or a mistake they made, even if they no longer remember the exact wording of the procedure. This is why scenarios work especially well in health and safety, information security, and compliance training – wherever the key issue is not only what an employee knows, but how they behave in a real situation.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 9. Performance support systems – does an employee really need to remember everything? For many years, training was expected to give employees all the knowledge they needed to do their jobs. In practice, this expectation no longer holds up. The number of procedures, regulations, tools, and internal rules keeps growing. Expecting employees to remember everything is simply unrealistic. This is why modern instructional design increasingly looks beyond the course itself and includes performance support systems. These are tools and resources that give employees access to the knowledge they need at the exact moment they need it. This kind of support can take different forms, including: checklists, knowledge bases, contextual instructions displayed during work, chatbots, AI assistants that support decision-making. This changes the way organizations think about employee development. Not every problem can or should be solved with another training course. Sometimes, a better solution is to give employees quick access to the right information while they are doing the task. That is why the line between training and workplace support is becoming less clear. More and more often, the goal is not to make employees memorise everything. The goal is to create an environment where they can easily find the knowledge they need and use it in practice. “The most common situation we see in training projects is treating e-learning as the final stage of employee development. In reality, training is usually only the introduction to a topic. This is especially clear when a company implements new software. Participants may complete the course and pass the test without any problem, but once they return to work, they regularly face new situations that cannot be fully practised during training. That is why more organizations combine e-learning with knowledge bases, instructions, and AI assistants. Training teaches the basics and explains the process, while workplace support helps employees find the right answer at the exact moment they need it. From our experience, this combination supports competence development much more effectively than trying to put all knowledge into one e-learning course.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 10. AI in instructional design – what does artificial intelligence change? Artificial intelligence is changing the way training is created faster than any technology before. Tasks that only a few years ago required many hours of work from an instructional designer can now be completed in minutes. Modern AI tools can support, among other things: generating course structures, creating quizzes and knowledge-check questions, building training scenarios, translating content into multiple languages, preparing narration and multimedia materials, analysing existing documents and turning them into training courses. For organizations, this is a major shift. AI can significantly reduce the time needed to prepare learning materials and help teams respond faster to changing business needs. At the same time, AI should be treated as a tool that supports the instructional design process, not as a full replacement for it. In theory, artificial intelligence can support the definition of business goals, data analysis, and the identification of skills gaps. More and more organizations are building dedicated solutions that use data from BI, LMS, HR, or ERP systems to support training-related decisions. However, the effectiveness of these tools still depends on the quality of the data and the expertise of the people who design them. The same applies to understanding the organizational context. A properly configured AI system can analyse processes, documentation, procedures, and company history much better than public models. But for this to work, someone first needs to identify that context, organize it, and turn it into a knowledge structure that AI can use. The biggest limitation is still expert experience. AI is very good at analysing theories, patterns, and existing knowledge. It is much harder for it to replace an expert who has spent years observing employee behaviour, running projects, making mistakes, and learning how a specific organization really works. That kind of experience often determines which solutions will work in practice and which will only look correct in theory. The future of instructional design will probably not be about replacing people with AI. It will be about combining the speed and scale of artificial intelligence with the knowledge of experts who can translate business goals into effective learning experiences. “Generative AI has taken over a large part of the “production” work. Draft scenarios, quizzes, and first versions of training content can now be created in minutes. As a result, the role of the instructional designer is moving more towards design and curation: defining objectives, understanding the organizational context, choosing the right methods, and critically reviewing what AI generates. Less time is spent on producing materials from scratch. More attention can go into making sure that the training teaches something useful and leads to a real change at work.” Mikołaj Korzeniowski, E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 11. Evaluating the learning path – how can you tell whether training works? One of the most common mistakes is judging training effectiveness only by the course completion rate. The fact that a learner has completed a course does not necessarily mean they have gained knowledge, changed their behaviour, or become better prepared to perform a task. This is why modern instructional design increasingly uses learning analytics: the analysis of data related to the learning process. In practice, it is worth looking not only at course completion, but also at: quiz and test results, learner activity, time spent in individual modules, the most common mistakes, repeated visits to training materials. This data helps organizations understand which parts of the training work well and which ones need improvement. It also gives learning teams a more realistic picture of how people actually move through the course, where they struggle, and where they may need additional support. Learning analytics makes it possible to look beyond the question of whether a course was completed. It helps answer a more useful question: did the training help learners understand the topic and use the knowledge in practice? The topic of learning analytics is broad, so we discuss it in more detail in a separate article. The same applies to xAPI, which can provide deeper insight into learning activity across different environments and tools. 12. What does modern instructional design mean in the age of AI? Modern instructional design combines knowledge about how people learn with business goal analysis, learning experience design, technology, and data. The history of instructional design shows that effective training was created as a response to a very practical problem: how to teach people to perform tasks in a way that is consistent, measurable, and useful in real situations. Today, the challenges are different, but the core question remains similar: how do you design training that does not end with course completion, but affects what employees know, how they make decisions, and how they behave at work? In the age of AI, this question becomes even more important. Artificial intelligence can speed up content creation, generate a course structure, prepare a quiz, suggest a scenario, translate materials, or support data analysis. But it does not replace the design process itself. Clear objectives are still needed. So is a good understanding of the audience, the organizational context, expert review, and a thoughtful way of measuring results. The best training programs are not created by a single tool or technology. They are created when an organization combines learning science, practical expert experience, a well-designed process, and modern technology. Only this combination makes it possible to create e-learning that not only looks professional but helps people work better in their everyday roles. 13. How does TTMS help organizations create effective e-learning training? At TTMS, we look at e-learning as more than a single course. Our goal is to help organizations build a complete learning ecosystem that supports employees during training and later, in their everyday work. We support organizations at every stage of the process: from training needs analysis, through instructional design, content development, and multimedia production, to implementation, improvement, and long-term maintenance of learning solutions. Our team brings together subject matter experts, instructional designers, graphic designers, developers, and LMS specialists. This allows us to design training from end to end, not only as content, but as a full learning experience. We also use our own AI4E-learning application, which helps organizations turn existing materials into e-learning courses much faster. This makes it easier to scale knowledge across teams while maintaining control over content quality and the training process. Our support does not end with the course itself. We help organizations build knowledge bases, implement SharePoint-based solutions, integrate LMS platforms, and create workplace support systems that allow employees to find the information they need quickly. We also develop dedicated AI solutions and knowledge assistants that can answer users’ questions based on company documentation, procedures, and instructions. As a result, organizations can build an environment where training is the beginning of competence development, not the end of it. FAQ What is instructional design? Instructional design is the process of designing effective learning experiences. It is not limited to preparing a presentation, course, or quiz. Its purpose is to plan the full learning path that helps a learner achieve a specific outcome, such as gaining knowledge, developing a skill, changing behaviour, or performing a task better at work. Instructional design – what is it in practice? In practice, instructional design starts with a simple but important question: what problem should this training solve? Only after that does the designer choose the right content, exercises, scenarios, quizzes, and ways to measure results. This approach helps avoid courses that look complete but do not lead to real learning or behaviour change. What is instructional design in education? Instructional design in education helps teachers, universities, and training teams build courses around clear learning objectives and learner needs. It can be used in schools, higher education, online programs, and corporate learning. The main goal is not just to organize content, but to make learning easier to understand, remember, and apply. How does instructional design support online learning? Instructional design in online learning is especially important because learners often go through the course without direct support from a trainer. The course needs to guide them clearly through the material, give them opportunities to practise, and provide useful feedback. Good online learning design usually includes short modules, logical structure, active tasks, quizzes, decision-making scenarios, and clear progress indicators. Why is e-learning instructional design important? E-learning instructional design matters because a digital course can easily become a passive content library instead of a real learning experience. A well-designed e-learning course helps learners stay focused, understand the purpose of each module, practise new knowledge, and check whether they are ready to use it in practice. This is particularly important in corporate training, compliance, onboarding, and technical training, where the goal is not only course completion, but better performance at work.
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Why Semantic Layers Matter for Enterprise AI
Many companies are already using AI in one way or another. Employees ask AI assistants for help, business teams test copilots, and technology leaders look at agentic AI as a way to automate more complex processes. But after the first wave of experiments, one thing is becoming clear: a powerful language model is not enough to create real business value. The problem is usually not the model, but the context around it. AI can only give useful answers when it understands what company data actually means. That means knowing how metrics are defined, which sources can be trusted, how different systems relate to each other, and what rules apply to specific business processes. This is why more organizations are now investing in semantic layers, data governance, and architectures that make enterprise data understandable not only for people, but also for AI systems. 1. Why Enterprise AI Often Produces Inconsistent Answers Modern large language models are remarkably capable. They can summarize information, generate reports, answer questions, and assist with decision-making. However, they do not inherently understand how a specific organization operates. Consider a seemingly simple question: “What is our current customer profitability?” To answer accurately, an AI system may need to access data from ERP systems, CRM platforms, financial applications, customer support tools, and analytics environments. Even if all required data is available, another challenge emerges. Different departments may use different definitions for the same metric. Finance may calculate profitability differently than sales. Operations may classify customers differently than marketing. Regional teams may follow different reporting standards than global headquarters. When AI interacts with fragmented or inconsistent information, it can produce answers that appear correct while actually reflecting conflicting business assumptions. This is one of the primary reasons many organizations struggle to scale AI beyond isolated use cases. 2. The Missing Piece: Business Context For years, organizations focused on collecting and storing data. Data lakes, warehouses, analytics platforms, and reporting tools became central elements of enterprise architecture. AI introduces a new requirement. Systems must not only access data – they must understand it. A customer record, product identifier, invoice number, or performance metric may seem straightforward from a technical perspective. However, every organization has its own definitions, relationships, policies, and business rules that shape how information should be interpreted. Without this context, AI is forced to infer meaning from technical structures alone. With context, AI can generate answers that align with how the business actually operates. This distinction becomes even more important as organizations move from AI assistants toward AI agents capable of making recommendations, triggering workflows, and supporting operational decisions. 3. What Is a Semantic Layer? A semantic layer provides a business-friendly interpretation of enterprise data. Instead of exposing raw tables, schemas, and technical metadata, it creates a structured representation of business concepts that both humans and AI systems can understand. A semantic layer typically defines: Business metrics and KPIs Relationships between datasets Common terminology Calculation logic Data ownership Business rules and policies For example, when an executive asks about revenue, customer churn, inventory availability, or working capital, the semantic layer ensures that everyone – including AI systems – uses the same definitions. This creates a single source of business truth that can be reused across reporting, analytics, applications, and AI initiatives. 4. Why Semantic Layers Matter for AI The value of a semantic layer increases dramatically in AI-driven environments. Traditional dashboards require users to interpret data manually. AI systems, on the other hand, must interpret information autonomously. Without a semantic layer, AI models may: Misinterpret business terminology Combine incompatible datasets Apply inconsistent KPI definitions Generate conflicting recommendations Reduce trust among business users With a semantic layer, AI systems gain access to the organizational context required to produce more accurate and consistent outputs. This is increasingly important for natural language interfaces, AI copilots, knowledge assistants, and agentic AI architectures. The quality of AI responses becomes directly tied to the quality of the semantic framework supporting them. 5. Why Governance Is Becoming a Strategic Requirement As AI becomes embedded in business processes, governance is evolving from a compliance topic into a strategic business capability. Organizations need confidence that AI systems are operating within defined boundaries and using trusted information. The growing importance of governance in the field of artificial intelligence is also reflected in emerging international standards. An increasing number of organizations are adopting the requirements of ISO/IEC 42001 – the world’s first standard defining the principles for establishing and maintaining an Artificial Intelligence Management System (AIMS). TTMS is among the pioneers of this approach, having achieved ISO/IEC 42001 certification as one of the first companies in Europe and the first organization in Poland. The certification confirms that our processes for designing, implementing, and managing AI solutions are aligned with internationally recognized standards for security, transparency, accountability, and risk management. Strong governance helps answer critical questions: Which datasets are approved for AI use? Who owns specific business definitions? How should sensitive information be protected? Which users can access which data? How can AI-generated outputs be monitored and audited? Without governance, AI may generate answers that are technically correct but operationally risky. With governance, organizations can scale AI adoption while maintaining trust, security, compliance, and accountability. This explains why governance is becoming a central component of modern enterprise AI platforms rather than an afterthought. 6. The Rise of Agentic AI Changes Everything The next phase of enterprise AI extends beyond answering questions. Organizations are increasingly exploring agentic AI systems that can execute tasks, coordinate workflows, analyze data, and support operational decisions. Unlike traditional AI assistants, these systems are expected to interact with business processes directly. That creates a much higher standard for data quality and contextual understanding. An AI agent responsible for inventory optimization, customer service, financial planning, or procurement cannot rely on ambiguous definitions or inconsistent information. It requires a governed environment where business concepts are clearly defined and consistently applied. This is precisely why semantic layers and governance frameworks are becoming foundational components of agentic architectures. 7. Why Leading Technology Vendors Are Investing in Semantic Architectures Across the enterprise software market, a common pattern is emerging. Technology providers are investing heavily in metadata management, business context layers, semantic models, governance capabilities, and AI-ready data architectures. The reason is straightforward. Organizations no longer need AI that can simply generate text. They need AI that understands their business. Enterprise value is created when AI can accurately interpret operational realities, financial metrics, customer relationships, regulatory requirements, and organizational objectives. The companies enabling this contextual understanding will play a critical role in the next generation of enterprise technology. 8. Building an AI-Ready Data Foundation For organizations evaluating their AI strategy, the focus should extend beyond model selection. Questions such as “Which LLM should we use?” remain important, but they are increasingly secondary to more fundamental considerations. Technology leaders should also ask: Do we have consistent KPI definitions? Can AI access trusted and governed data? Do business users agree on terminology? Is ownership of critical data clearly defined? Can our AI systems explain how conclusions were reached? The organizations that answer these questions successfully are more likely to achieve measurable business outcomes from AI investments. 9. Conclusion Enterprise AI is entering a new phase. The conversation is gradually shifting away from model performance and toward business understanding. Semantic layers, governance frameworks, and contextual data architectures are becoming critical enablers of trustworthy AI. They help transform disconnected data into business knowledge and ensure that AI systems operate with the context required to support meaningful decisions. As organizations move toward increasingly autonomous AI capabilities, competitive advantage will not come solely from access to advanced models. It will come from the ability to connect those models with trusted data, shared business definitions, and a clear understanding of how the organization operates. In the era of enterprise AI, context is becoming as important as intelligence itself. Organizations looking to move beyond AI experimentation should focus not only on selecting the right models, but also on building the data foundations that allow AI to deliver measurable business value. This requires a combination of data strategy, governance, system integration, and AI expertise. At TTMS, we help organizations design and implement AI solutions that connect advanced models with real business processes, enterprise data, and operational goals. Explore our AI solutions for business to learn how we can support your AI transformation journey. What are some signs that an organization needs a semantic layer? One of the clearest indicators is when different departments report different values for the same KPI. If finance, sales, and operations each have their own version of revenue, profitability, or customer metrics, AI systems will struggle to provide consistent answers. Other warning signs include long discussions about which data source is correct, difficulties scaling analytics initiatives, or a lack of trust in AI-generated insights. A semantic layer helps create a common business language that can be used across teams, applications, and AI solutions. Can small and mid-sized companies benefit from semantic layers, or are they only for large enterprises? While semantic layers are often associated with large organizations, smaller businesses can benefit as well. As companies adopt more systems and generate more data, maintaining consistency becomes increasingly difficult. Establishing common definitions and governance practices early can prevent future complexity and make AI initiatives easier to scale. For growing organizations, a semantic layer can serve as a foundation that supports expansion without creating data silos. How do semantic layers support regulatory compliance? Many industries operate under strict regulations regarding data access, privacy, reporting, and auditability. A semantic layer can help by ensuring that business metrics and definitions are applied consistently across the organization. When combined with governance controls, it becomes easier to track how information is used, who has access to it, and how AI-generated outputs are produced. This level of transparency can simplify compliance efforts and reduce operational risk. What is the relationship between semantic layers and knowledge management? Organizations often store valuable business knowledge in documents, spreadsheets, presentations, emails, and the experience of individual employees. A semantic layer helps connect structured business definitions with this broader organizational knowledge. As a result, AI systems can provide more relevant answers and recommendations by combining enterprise data with the business context behind it. This makes knowledge more accessible and less dependent on specific individuals. Will future AI systems automatically create semantic layers on their own? AI will likely play an increasingly important role in identifying relationships between datasets, suggesting business definitions, and helping build semantic models. However, organizations will still need human expertise to validate those definitions and ensure they reflect real business objectives. Business context, governance policies, and strategic priorities cannot be fully automated. Rather than replacing semantic layers, future AI systems will likely make them easier and faster to develop and maintain.
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