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ChatGPT 5.6 in Practice: Initial Compliments and Disappointments
OpenAI rolled out GPT-5.6 in stages. It first appeared in limited test access for selected partners. Access to ChatGPT 5.6 reached Europe, including Poland, gradually, so only recently have teams been able to test the model in everyday work. Expectations are high. In the second half of 2026, businesses expect language models to handle multi-step tasks and work with extensive context. Ease of use matters too. GPT’s interface has undergone a major redesign. Has it improved the user experience and the quality of responses? This article explores that question, as well as: which business processes ChatGPT 5.6 can support by improving productivity and the quality of working materials, how to plan an AI pilot in your organisation, measure results and maintain quality control, which limitations of ChatGPT 5.6 to consider before a wider rollout, how to establish a shared standard for prompts and output validation across the team, what early users think about working with ChatGPT 5.6. If you are looking for a full overview of the changes, pricing, models and capabilities of GPT-5.6, see our article GPT-5.6 from OpenAI: what has changed, pricing, capabilities and business applications. ChatGPT 5.6: our first impressions and early industry feedback Early expert reviews focus primarily on context handling. Reviewers note that when working with substantial material that goes through multiple rounds of edits, ChatGPT 5.6 is better at keeping the task on track. Most of us have experienced earlier OpenAI models losing their “bearing”. On top of that, the model itself encouraged endless revisions, which could pull the material away from the original intent of the prompt. GPT 5.5 had an irritating habit of suggesting more and more variations. Almost every response ended with a clickbait-style suggestion along the lines of: “If you want, I can help you add two elements that will create a wow effect and give the text around 50% more SEO power.” As a result, instead of closing the topic, we were drawn into the model’s endless doubts: could the material really not be improved further? GPT 5.6 is no less capable than the older model, but it finally respects what matters most: the intent behind the prompt and our time. Kajetan Terlecki SEO Specialist, TTMS Another recurring observation concerns the quality of the first draft—the material GPT produces after the first prompt. Reviewers emphasise that the model’s draft is usually well structured and much closer to a final version than it was with GPT 5.5. It is not a perfect ten yet, but a solid eight. In other words, a final version may be within reach after a relatively short time. With earlier GPT models, the “brainstorming” phase took much longer. The third—and most immediately noticeable—area is the way we use the tool, which we can simply call the “interface”. It is admittedly quite complex. Beyond writing a prompt, users must make a series of decisions: which workspace should I choose: Chat or Work? which model best fits my request: Luna, Terra or the most advanced Sol? Or is the older GPT 5.5 enough? does the task require Deep Research? how much effort should the model put into the task: low, medium, high, very high, max or ultra? should I use Turbo mode and generate a response 50% faster at the cost of higher token use? If we add the almost endless range of available plugins, writing the prompt turns out to be only half the work required to get a useful result. I would welcome an automatic mechanism that reads the prompt and selects the right settings on its own. One that uses a sufficiently capable GPT model without wasting tokens when they are not needed. How do you navigate all this? We have outlined a suggested configuration here, including which modes to use for different types of tasks. Where does GPT 5.6 outperform the previous version? 1. GPT 5.6 is better at preserving document layout and formatting The previous version of GPT had something of a goldfish memory. You could also compare it to a short blanket: pull it over one part, and another is left exposed. When we asked the model to update data in a document it had generated, it produced a factually correct response, but one that no longer followed the original format. It might use a different heading hierarchy, rearrange the information or omit elements that are essential for the company. GPT 5.6 is much better at preserving the structure of reference material. OpenAI illustrated the difference in materials introducing GPT-5.6. The company placed three slides side by side: the reference file, the GPT-5.5 output and the GPT-5.6 output. The task was to update figures in a presentation while retaining the original template. In the comparison, GPT-5.5 omitted some template elements, while GPT-5.6 preserved the slide structure more faithfully: layout, typography, spacing, colours and recurring template elements. OpenAI states that GPT-5.6 can also interpret rules saved in the slide template, including the Slide Master. In practice, this matters when a presentation needs to retain not only its colours and fonts, but also defined layouts, spacing and mandatory components. 2. GPT-5.6 moves beyond the chat window GPT-5.6 shows its greatest potential when it works not only with a single instruction, but also with files and tools made available by the user. It can then move quickly through a task: from gathering the materials to preparing a first draft. The new GPT model can identify related files in a project folder, flag places that need updating and prepare working versions of documents. There is a catch: the process still needs human oversight. Someone must check whether GPT found all the relevant files, understood the context correctly and left unchanged the elements that were meant to remain unchanged. Still, instead of manually digging through documents, the team starts with a list prepared by the model. 3. From an idea to a version you can show the team Experts testing GPT 5.6 point out that the first version of a simple application, dashboard or website is now more often suitable for showing to a team and collecting specific feedback. It is somewhat like an MVP: good enough to test an idea, present it to the team and gather initial comments. A product owner can see the whole process, a designer can assess the layout and usability, and a developer can spot technical constraints sooner. This does not mean that GPT-5.6 creates a finished product. The initial prototype still needs to be assessed for security, quality and architecture. The difference is concrete, however: the team can evaluate an actual solution earlier, rather than debating assumptions alone. 4. GPT 5.6: “I don’t know” — is this the end of answers given for the sake of answering? We all know the old classified ad: “Encyclopaedia Britannica, 40 volumes for sale. I got married a week ago, so I no longer need it. My wife knows everything better.” The know-it-all syndrome is a nuisance not only in old marriage jokes, but also for people who work with language models every day. GPT often lacks the information needed to give a reliable answer. GPT-5.5, like earlier versions, would rather provide an incorrect—yet convincing-sounding—answer than admit it did not know. What about the new version? The change is visible at first glance, even though it is hard to capture in a benchmark and easy to appreciate in day-to-day work. Our first days of working with the two most advanced models, Terra and Sol, suggest that GPT 5.6 is more likely to say “I don’t know”, “I don’t have enough data” or “I could not find anything else on this topic”. People still need to add or verify information manually, but this reduces the risk of an embarrassing error in material prepared for a client, the board or a project team. Before you give GPT-5.6 an important task: what to watch out for in early testing 1. A working prototype is not yet a finished product GPT-5.6 can prepare a website, dashboard or simple application that can be launched and shown to the team. This is a major step forward, particularly when testing an idea. The tests also reveal the other side: elements can become misaligned, interactions do not always work as intended, and visual details still require refinement. The first version can be an excellent starting point, but it should not automatically be sent to clients or other external audiences. Before treating it as finished, we need testing, a security assessment and, in some cases, a developer’s review. 2. The new Work environment can still be frustrating Model quality is one thing. The way we use it in practice is another. One reviewer pointed out that, in Work, it was difficult to access generated files and open a preview of the finished result. Others criticised the number of settings—discussed earlier in this article—as well as the unclear distinction between Chat, Work and Codex. GPT-5.6 may complete a task correctly, while the working environment still makes it difficult to retrieve or review the result. It is worth testing the entire process, not only the quality of the response in the chat window. 3. GPT needs clear boundaries One reviewer tested how GPT-5.6 would handle a complex mathematical problem. The model produced correct parts of the solution, but surrounded them with definitions, digressions and comments that added little value. Only after the instruction was made more specific did it produce a useful result. The same applies in a business context. We should not leave the model too much room for interpretation. It is better to state the expected result directly: “Prepare a one-page summary. Include the decision, three arguments, risks, missing information and next steps.” GPT then has fewer opportunities to pad the topic with peripheral content. 4. GPT can still be wrong The fact that GPT-5.6 appears more likely to signal that it lacks data or a basis for drawing a conclusion does not mean it is free from hallucinations. Luna, Terra and Sol—with Sol seemingly the least prone to this—can still provide an incorrect date, number, source or conclusion without batting an eyelid. The rule to “check after AI” still applies and will likely remain relevant for many future GPT releases. 5. Start with one problem, not a large system Once GPT-5.6 has access to files, a browser and company tools, it is easy to imagine a system that instantly organises the inbox, analyses team communication, updates the CRM and writes responses to clients. This vision can quickly turn into a project larger than the problem it was meant to solve. One expert working with an extensive Codex environment recommends starting with a single, repeatable task. It might be preparing a meeting summary, gathering open project issues or updating an offer after data changes. Only once the team sees measurable results and understands the tool’s limitations is it worth adding further automations. How should you run your first ChatGPT 5.6 test in the company? A pilot should answer one straightforward question: does GPT-5.6 genuinely improve a selected stage of work, and does the benefit justify the time, cost and additional quality control? The first test should not begin with building an extensive automation system. It is better to choose one repeatable task that currently takes up the team’s time and has a clearly defined outcome. This might be a meeting summary, a brief or a status report. What matters is that the team knows which materials it provides to the model, what result it expects and who reviews the final document. Before starting the pilot, answer five questions: Choose one process: for example, preparing meeting summaries, sales briefs or materials for project decisions. Set a baseline: measure the time needed to prepare the material, the number of revisions, the number of people involved and the most common errors. Prepare a shared prompt: use the same input materials and clearly describe the outcome the team expects. Assign expert review: nominate a person who will verify the facts, assess quality and approve the result before it is used further. Assess the outcome: compare time, the number of iterations, completeness of the material and the usefulness of the result for the next stage of the process. Pilot element Question for the team Process Which stage of work do we want to shorten or organise? Outcome What should be produced: a brief, decision list, analysis, recommendation or communication draft? Data Which materials are needed, and can they be used in the selected AI environment? Quality control Who confirms the facts, completeness and alignment of the material with the process? Metric How will we compare working time, the number of revisions and the usefulness of the result? After a few attempts, it becomes easier to assess whether the model is genuinely helping. Compare the time needed to prepare the material, the number of revisions and the effort required to verify the result. Only then decide whether to extend the pilot to further tasks. Three processes worth starting with 1. Summaries after client meetings The model can organise notes, gather decisions, identify open questions and prepare a list of next steps. The team confirms the arrangements and assigns task owners. This helps them move from discussion to action more quickly. 2. A brief for a sales conversation Based on selected sales materials, previous arrangements and public information about the company, GPT-5.6 can prepare a brief, discovery questions and a list of topics that require clarification. The salesperson remains responsible for the client relationship and decisions regarding the offer. 3. A status report for the project team The model can organise information about progress, blockers, risks and planned actions. The project owner confirms that the information is up to date before the report is shared further. This reduces the time the team spends manually consolidating data from several sources. How do you embed AI in a business process? After the pilot, it becomes clear whether ChatGPT 5.6 genuinely shortens the preparation of materials, reduces the number of revisions and helps the team move more quickly to the next stage of work. It also reveals where the model needs a better brief, access to data or expert oversight. Proven use cases can then be extended to other processes. At this stage, it is worth addressing data security, integration with existing tools, output quality and a clear division of responsibilities. These factors determine whether AI becomes lasting support for the organisation. At TTMS, we help organisations identify processes where automation and AI create business value. We then design solutions tailored to their data, regulatory requirements and ways of working. We combine engineering experience with a responsible approach to AI governance, confirmed by ISO/IEC 42001 certification. Let’s discuss the processes AI could support in your organisation. FAQ How do you choose a process for your first ChatGPT 5.6 test? The best candidate is a repeatable process that requires gathering several pieces of information and producing a predictable result. Examples include meeting summaries, sales briefs, status reports and document analysis. The team should know the current turnaround time and typical issues, as these provide the baseline for assessing the test. Start with one process and expand the use of AI only after evaluating the outcome. How do you measure the business value of ChatGPT 5.6? During a pilot, measure the time needed to prepare the first version of the material, the number of revisions before approval, the completeness of the output and the expert time required for verification. It is also useful to track metrics related to the next stage of the process – for example, faster meeting preparation, a shorter time to close agreed actions or fewer missing details in a report. This data helps assess team productivity based on actual results and supports decisions about integrating AI into further processes. What data should you prepare for working with ChatGPT 5.6? The model produces better results when the team provides current, well-organised source materials. Before starting, identify which documents take priority, which data must remain unchanged and how unverified information should be marked. The organisation should also define which data can be shared in the chosen AI environment. For personal, financial and confidential data, access rules, retention and compliance are essential. How do you maintain human oversight of the model’s work? Human oversight should be part of the process from the start. The process owner defines the task scope, an expert verifies facts and alignment with requirements, and an authorised person approves external actions. This division of responsibilities is particularly important for client communication, publications, data changes in systems and materials with legal or financial implications. It allows the team to use automation while retaining responsibility for the outcome. Where can I find information about GPT-5.6 pricing, models and capabilities? We have covered the changes in GPT-5.6, pricing, the Sol, Terra and Luna models, and business applications in a separate article: GPT-5.6 from OpenAI: what has changed, pricing, capabilities and business applications. This article focuses on the practical use of ChatGPT 5.6 in team workflows, early user experiences and how to run an AI pilot in an organisation.
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5 Most Common Gaps Identified When Preparing for KSC 2.0
Preparing an organization for KSC 2.0 involves more than drafting security policies and incident response procedures. Only an assessment of how the organization actually operates can show whether documented rules are followed in practice, responsibilities have been clearly assigned and teams can respond effectively under time pressure. It is particularly important now that the Polish amendment to the Act on the National Cybersecurity System, implementing the NIS2 Directive, is already in force. The provisions took effect on 3 April 2026. Entities that met the criteria for classification as a key or important entity on that date and are not entered ex officio in the KSC Register should submit an application for entry by 3 October 2026. Organizations are therefore no longer preparing for a future regulation; they are implementing specific obligations concerning, among other things, risk management, incident handling, business continuity and supplier security. Based on gap analyses and compliance audits conducted by TTMS experts in 2026, we have observed that the issue is rarely a single isolated non-compliance. More often, organizations face several interconnected deficiencies that can make it harder to meet statutory requirements and delay incident response. This article presents the five gaps we identify most frequently, their practical consequences and the areas that should be verified first. 1. What Is a NIS2 Audit and Why Does Your Organization Need One? A NIS2 audit is an assessment process used to determine how effectively an organization meets the requirements of the Directive and the Polish Act on the National Cybersecurity System. In practice, TTMS auditors review IT systems, risk management procedures and incident response plans, and then compare the actual state of operations with the applicable legal obligations. The assessment of security measures is based primarily on Article 21 of the NIS2 Directive and Article 8 of the KSC Act, which requires the implementation of an information security management system. Organizations that verify compliance early gain time to implement improvements in a controlled manner instead of acting under the pressure of an inspection. 1.1 The NIS2 Directive in Brief The NIS2 Directive is an EU legislative act on the security of network and information systems that replaced the earlier NIS framework. It introduces significantly stricter requirements than its predecessor, particularly for organizations whose operations are important to the functioning of the state and the economy. Its purpose is to harmonize security standards across the European Union and materially strengthen resilience against cyberattacks. 1.2 Purpose and Scope of a NIS2 Compliance Audit The purpose of an audit is to assess the extent to which an organization meets the Directive’s requirements and to identify specific security gaps together with a remediation plan. The scope covers both technical matters, such as network configuration and access management, and organizational matters, including security policies, risk management procedures and business continuity plans. A well-executed audit produces an actionable implementation roadmap, not merely a list of deficiencies. 2. Who Is Subject to KSC 2.0 and When Is an Audit Required? The amendment covers key and important entities operating in the sectors listed in Annexes 1 and 2 to the Act, including energy, transport, healthcare, digital infrastructure, selected manufacturing industries and digital services. Whether an organization falls within the scope of the Act depends on its sector, type of activity, company size and specific statutory criteria. Some entities are covered regardless of their headcount or turnover. 2.1 Covered Sectors and Company Size The threshold of 50 employees or EUR 10 million in turnover should not be treated as a standalone test. In many sectors, medium-sized or large-enterprise status is the starting point, but the Act provides exceptions and separate qualification rules. The first step should therefore be to compare the organization’s actual activities with Article 5 and Annexes 1 and 2 to the KSC Act. 2.2 Key Entities and Important Entities: Differences in Requirements Key and important entities are generally subject to a similar set of obligations relating to risk management, incident handling and supply-chain security, subject to the exceptions provided for in the Act and sector-specific regulations. The primary differences concern the supervision and audit model. Under Article 15 of the KSC Act, a key entity must conduct a security audit at its own expense at least once every three years. The competent authority may order an external audit of a key entity at any time and of an important entity following a significant incident or another breach of the Act. 3. Is a NIS2 Audit Mandatory and When Should It Be Performed? Not every gap analysis offered on the market constitutes a statutory audit. The periodic audit obligation under Article 15 applies to key entities, while a voluntary gap analysis can help both key and important entities assess readiness, set priorities and gather evidence of compliance. A statutory audit must be conducted by an organization or by at least two auditors meeting the qualification requirements set out in Article 15(2), while complying with the independence requirement in Article 15(2a). 3.1 Key KSC 2.0 Deadlines in Poland Poland implemented the NIS2 Directive through the Act of 23 January 2026 amending the Act on the National Cybersecurity System and certain other acts (Journal of Laws of 2026, item 252). The Act was published on 2 March 2026, and its principal provisions entered into force on 3 April 2026. For entities that met the criteria for classification as a key or important entity on the effective date, self-registration in the KSC Register runs from 7 May to 3 October 2026, unless the entity is entered ex officio. Entities in this group should comply with the obligations in Chapter 3 no later than 3 April 2027. Key entities in this group must conduct their first statutory security audit by 3 April 2028. For entities brought within the scope of the Act at a later date or entered by administrative decision, the applicable deadline must be determined under the provision governing the relevant procedure. 3.2 How Often Should a Compliance Audit Be Repeated? Under Article 15 of the KSC Act, the statutory audit of a key entity must be conducted at least once every three years. Irrespective of that requirement, we recommend an annual internal compliance review and an additional assessment after any material change, such as an IT infrastructure upgrade, implementation of a new system, a significant incident or a change of a critical service provider. Security cannot be configured once and then forgotten. 4. Consequences of NIS2 Non-Compliance Failure to perform the obligations arising from the KSC Act implementing NIS2 may have serious consequences. These include supervisory measures, orders to remedy infringements and administrative fines. 4.1 Financial Penalties and Administrative Sanctions Entities that fail to perform their obligations under the KSC Act may be subject to supervisory measures and administrative sanctions. The Act provides for high maximum penalties and, where an infringement creates a particularly serious threat, a fine of up to PLN 100 million. Under Article 35 of the amending Act, the new penalties specified in that provision may first be imposed two years after the Act entered into force, generally from 3 April 2028. This does not postpone the deadlines for registration, implementation of obligations or incident reporting. 4.2 Management Liability and Reputational Risk Failure to perform statutory obligations may also result in a personal fine being imposed on the head of a key or important entity. Article 73a of the KSC Act provides for a fine of up to 300% of the person’s remuneration and, for certain public-sector entities, up to 100% of remuneration. The person regarded as the head of a particular entity depends on its legal form and governance structure. Irrespective of sanctions, an incident and disclosed negligence may also undermine the trust of customers and business partners. 5. What Does a NIS2 Audit Cover? This part of our work as auditors is particularly revealing because it shows precisely where organizations encounter the most common difficulties. Below, we describe the five areas in which we most frequently identify gaps during KSC 2.0 readiness projects, together with practical examples and the consequences of leaving them unresolved. 5.1 Unclear Accountability and Immature Risk Management The first thing we verify is who formally holds responsibility for cybersecurity within the organization. Our experience shows that unclear accountability is one of the most frequently identified issues. Roles across IT, security and management may be documented, yet in practice there is no unambiguous decision-making path for every type of significant incident. Valuable hours are then spent determining who is authorized to make a decision instead of responding to the incident. This issue is closely linked to immature risk management. Many organizations have a document entitled ‘Risk Management Policy’, but the assessment was performed only once and has not been updated since. Article 21 of the NIS2 Directive and Article 8 of the KSC Act require appropriate and proportionate technical, operational and organizational measures based on systematic risk management. If an organization cannot demonstrate a recurring process, it also lacks a reliable understanding of where it is genuinely most exposed. 5.2 Incomplete IT and OT Asset Inventory An incomplete or outdated inventory of IT and OT assets appears very frequently in our assessments. A typical example is a manufacturing company that declares full control over its infrastructure, yet during workshops no one can clearly state how many active servers it operates, which systems are outdated or which OT devices can access the corporate network. Without a reliable inventory, risk assessment becomes largely theoretical: an organization cannot assess the risk associated with an asset it does not know exists. During an incident, the team then loses time determining what has actually been compromised. 5.3 Untested Incident Response Procedures Our observations indicate that, in most organizations assessed, the incident response procedure existed only as documentation and had never been tested in practice. Article 23 of the NIS2 Directive and Article 11 of the KSC Act provide for multi-stage reporting: an early warning must be submitted without undue delay and no later than 24 hours after detecting a significant incident, followed by an incident notification no later than 72 hours after detection. The required reports must then be submitted, including a final report generally within one month of the incident notification. The procedure must therefore work at night, at weekends and when key personnel are unavailable. 5.4 Inadequate Business Continuity Plans An incident response procedure is not sufficient if the organization cannot maintain or restore critical services. In practice, we verify whether business continuity and disaster recovery plans cover critical dependencies, suppliers, backups, crisis communications and realistic recovery times. Article 21(2) of the NIS2 Directive and Article 8 of the KSC Act identify business continuity, backup management, disaster recovery and crisis management as elements of cybersecurity risk-management measures. A plan that has never been tested remains an assumption rather than evidence of resilience. 5.5 No Systematic Supplier Risk Assessment Supplier security management remains one of the greatest challenges. In the vast majority of organizations assessed by TTMS, there was no systematic evaluation of risks associated with service providers or partners that had access to the organization’s systems. Article 21(2)(d) of the NIS2 Directive and Article 8 of the KSC Act expressly cover supply-chain security. A typical example from our work is an external IT provider with remote access to company systems whose security controls have never been verified. An attack on such a partner can directly threaten the organization using its services. 5.6 Summary of the Five Most Common Gaps Area Observation from TTMS Projects 1. Accountability and risk management Frequently identified issue 2. IT and OT asset inventory Very frequent 3. Testing of incident response procedures Most organizations assessed 4. Business continuity Often requires additional testing and clarification 5. Supplier risk assessment The vast majority of organizations assessed High-risk gaps identified in a single audit Usually between one and several The data in the table consists of anonymized qualitative observations from gap analyses and audits conducted by TTMS in 2025–2026. It is not a representative market study. 7. How a NIS2 Audit Works: Step by Step Below, we explain how we conduct a NIS2 audit for a client, step by step, from the initial contact through to the completed remediation roadmap. Step 1: Determine Whether the Organization Is Subject to KSC 2.0 The first step is to establish whether the organization is subject to the KSC Act and whether it qualifies as a key or important entity. This determination defines the subsequent scope of the assessment and the obligations that must be considered. Step 2: Questionnaire and Baseline Data Collection We then conduct a detailed questionnaire and collect baseline information from the IT, security and management teams. This allows us to build an initial picture of the organization’s security posture before examining the documentation in detail. Step 3: Review of Documentation and Processes The next stage involves reviewing the documentation and existing processes, comparing what is written on paper with what actually happens within the organization. This is where the discrepancies described earlier most often become visible, such as an incident response procedure that exists but has never been tested. Step 4: Workshops and Team Interviews We conduct workshops and interviews with employees from different departments because documentation rarely tells the whole story. A conversation with a network administrator or the person responsible for supplier relationships often reveals more than a formal review of documents. Step 5: Findings and Recommendations Report At the end of the assessment, we prepare a detailed report presenting the findings and specific remediation recommendations in language that is understandable not only to IT, but also to the organization’s management. The head of the entity and the relevant governing bodies are responsible for approving and overseeing implementation of the measures to the extent required by the Act and the entity’s governance structure. Step 6: Remediation Roadmap The final report includes a prioritized remediation roadmap. In practice, we typically identify between one and several high-risk non-compliances during a single audit. The roadmap is therefore not about implementing every recommendation at the same time, but about sequencing activities to reduce the most significant business risks as quickly as possible. 8. How to Prepare Your Organization for a NIS2 Audit Preparing for a NIS2 audit requires involvement from every department, not only IT. It is worth collecting current security policy documentation, a list of systems and external suppliers, and appointing a person to act as the auditors’ primary point of contact. The better prepared the organization is at the outset, the faster and more efficiently the process can be completed, reducing both cost and pressure on the team. 9. NIS2 Audits and Other Security Audits: Key Differences A NIS2 and KSC 2.0 compliance assessment differs from other security reviews because it addresses specific regulatory obligations arising from the Act on the National Cybersecurity System. ISO/IEC 27001 certification is generally voluntary, while a GDPR compliance audit focuses on personal data protection obligations. These scopes may partially overlap, but none of them automatically replaces an assessment of compliance with KSC 2.0. 10. Benefits of Commissioning a NIS2 Audit from TTMS TTMS is a global IT company specializing in the implementation and maintenance of bespoke IT systems, business process automation and outsourcing services. With experience in systems integration, Salesforce, Microsoft and AEM implementations, as well as IT service management, our consultants understand not only regulatory requirements but also the real-world IT infrastructure architectures our clients operate. 10.1 Scope and Delivery of Our Service We provide a comprehensive NIS2 and KSC 2.0 readiness and gap assessment covering all the areas described above: from asset inventory, risk management and incident response procedures to supply-chain security. We follow a proven process, starting with an initial questionnaire, continuing through team workshops and concluding with an actionable roadmap. If the engagement includes a statutory audit under Article 15, the scope, auditor qualifications and independence requirements must be confirmed separately. 10.2 Support with Implementing Post-Audit Requirements The real value of an audit lies in implementing its recommendations, not merely producing a report. After completing projects, we observe that clarifying accountability, updating documentation and implementing remediation measures shorten incident response times, improve asset records and reduce the number of non-compliances found during subsequent reviews. Our support includes security process automation, integration of monitoring systems and development of procedures that work in teams’ day-to-day operations. 11. Contact a TTMS Expert and Prepare Your Organization for a NIS2 Audit 11.1 Make Sure Your Organization Is Ready for KSC 2.0 KSC 2.0 readiness is difficult to assess from documentation alone. The key is to verify whether responsibilities, processes and safeguards work in practice and whether the organization can demonstrate compliance during an audit or inspection. If you would like to discuss your organization’s situation, contact TTMS experts. We will help determine which areas require verification, what audit scope is appropriate and where preparations should begin. We will tailor the engagement to the entity’s status and its obligations under KSC 2.0. 12. Legal Basis and Sources Directive (EU) 2022/2555 of the European Parliament and of the Council (NIS2), in particular Articles 20, 21, 23, 32 and 33; the Act of 5 July 2018 on the National Cybersecurity System, as amended by the Act of 23 January 2026 (Journal of Laws of 2026, item 252), in particular Articles 5, 8, 11, 15, 73 and 73a and Annexes 1 and 2; Articles 33–35 of the amending Act; and communications from the Polish Ministry of Digital Affairs concerning the KSC Register and the S46 System. The legal status and implementation timeline were verified on 13 July 2026. 13. FAQ Is a Gap Analysis the Same as a Statutory KSC Audit? No. A gap analysis is a voluntary readiness assessment that helps identify deficiencies and prioritize actions. A statutory security audit under Article 15 of the KSC Act must meet the requirements relating to scope, auditor qualifications and independence. What Is a NIS2 Compliance Audit? A NIS2 compliance audit is a market term for an assessment process that verifies an organization’s readiness for the requirements of the NIS2 Directive and the KSC Act. It may cover IT systems, risk management and incident response. However, not every such review constitutes a statutory security audit under Article 15 of the KSC Act, which must meet the applicable requirements concerning scope, auditor qualifications and independence. What Does NIS2 Involve? NIS2 is an EU directive that introduces rigorous network and information systems security requirements for organizations in key and important sectors. Its purpose is to harmonize security standards across the European Union and strengthen resilience against cyberattacks. How Much Does a NIS2 Audit Cost? The cost of a NIS2 audit depends on the size of the organization, the number of systems and locations covered by the review, and the scope of support required to implement the recommendations. An accurate quotation can be provided after a short initial discussion in which we establish the actual scope of work.
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Best QA Practices in Software Testing – 2026 Guide
Quality assurance has moved well beyond end-of-cycle sign-offs. Today, the best QA practices in software testing are woven into the full development lifecycle, shaping how teams write requirements, review code, deploy releases, and measure outcomes. Yet despite widespread awareness of this shift, many organizations still struggle to close the gap between knowing what good QA looks like and actually executing it at scale.
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E-Learning Analytics in practice: How to interpret e-learning platform data?
It is easiest to measure what is visible right away: logins, clicks, time spent on the platform, completed modules, and quiz scores. That is why many organizations finish their training analysis right there. However, there is a difference between LMS activity and real learning. And completing a course does not always mean that the participant has acquired knowledge and will apply it in their job. So, how do we measure the real impact of training on an organization? We answer this question in this article using professional methods of learning analytics for e-learning. 1. Why does the interpretation of e-learning data make it difficult for organizations to assess training effectiveness? Assessing training effectiveness often looks simple only at the beginning. The platform shows reports, charts, quiz scores, and completion statuses. However, in our work with clients, we see that the problem begins when we need to answer a much harder question: did this training actually change anything in people’s work? It is easiest to measure activity. The LMS will show who completed the training, how much time they spent in the course, what their test score was, and whether they returned to the materials. This is necessary e-learning analytics data because it helps to see whether the participants went through the learning process at all and where they might have stopped. However, it does not yet tell us whether the employee used the new knowledge after closing the course. This is exactly where many organizations fall into a trap. Course completion starts to be treated as proof of effectiveness. Yet, a salesperson might pass a test on knowledge of a new product but still not introduce it into conversations with customers. A customer service employee may know a new procedure but, under time pressure, revert to old habits. It is even more difficult to show the impact of training on business. Here, LMS data analytics alone are no longer enough. You need to combine them with what is happening in the organization: sales results, customer satisfaction, the number of operational errors, onboarding time for new employees, or the level of compliance with procedures. Only such a combination of data allows you to check whether the training translated into real change. 2. The Kirkpatrick Model. How to use it in practice? In assessing the effectiveness of training, the Kirkpatrick Model is often used. Implementing the Kirkpatrick model is the foundation on which professional data analytics e-learning processes are based. It helps to structure thinking: from participant reaction, through learning, behavior change, all the way to business results. And it clearly shows why “course completed” alone is not enough if an organization wants to know whether e-learning really works. In practice, it is worth planning the measurement method even before the training begins. Thanks to this, the organization knows from the very start what data it will collect and what effects it wants to achieve. At the first level, participant reactions can be measured using short satisfaction surveys. At the second level, knowledge is checked – most often through tests, quizzes, or practical tasks completed after the course. The third level requires observing what happens after the training. Depending on the topic, this could involve conversations with supervisors, work quality analysis, evaluation of new skills, or comparing results before and after the training. This is precisely where organizations most often discover that a high test score does not always translate into a change in behavior. The fourth level focuses on business results. For onboarding, this could be the time to reach independence, the number of errors, or the completion of the onboarding path. In compliance training, the level of adherence to procedures, audit results, or the number of incidents are often analyzed. In sales, it might be the results of salespeople and the use of product knowledge in customer conversations, and in customer service, the level of customer satisfaction and the time to resolve inquiries. From our experience, organizations achieve the best results when they do not limit themselves to a single metric. Combining LMS data, behavioral observations, and business metrics provides a much more complete picture of training effectiveness than a test score or course completion rate alone, showcasing the true power of corporate e-learning analytics. Kirkpatrick Level What do we measure? Example metrics Key question 1. Reaction How participants received the training satisfaction survey, usefulness rating, participant feedback Was the training clear and useful to them? 2. Learning What the participants learned test score, quiz, practical task, certificate Did the participant actually acquire new knowledge or skills? 3. Behavior Whether participants apply knowledge at work supervisor observation, work quality, number of errors, feedback conversations Is the employee doing something differently after the training? 4. Results What is the impact on business sales, onboarding time, customer satisfaction, compliance with procedures, number of incidents Did the training bring a real benefit to the organization? One of the most common mistakes is treating the LMS report as a complete answer to the question of training effectiveness. If we look exclusively at course completion, test scores, or time spent on the platform, we only see participant activity and not a real change in their work. Without referencing the assumed training objectives to the employee’s performance before and after the training, it is difficult to assess whether the program actually improved skills. In practice, this means that LMS data should be contrasted with supervisor observation, the quality of tasks performed, the number of errors, employee independence, or other metrics linked to the training goal. The simplest way to enrich such an analysis is through post-training surveys delayed in time. A question asked a week, a month, or a quarter after the training often says more than a survey completed immediately after the course. Only then can you check whether the knowledge was used in practice, rather than just memorized for the sake of the test, using advanced e-learning data analytics. Norbert Kulski Head of BI & Automation Solutions | TTMS 3. What statistics do most e-learning platforms provide and what do they measure? Modern LMS platforms allow you to track dozens of different performance indicators. The problem is that not all of them say the same about the actual results of learning. It is worth knowing which data are truly valuable and how to interpret them correctly with the help of professional e-learning analytics. 3.1 Completion Rate The Completion Rate shows what percentage of participants completed the training. It is one of the most frequently monitored metrics because it is simple to measure and allows for a quick assessment of participant engagement. If a large portion of users do not finish the course, it may indicate problems with the training’s length, difficulty level, or attractiveness. At the same time, a high Completion Rate does not automatically mean that the training was effective. A participant may complete a course solely because it is required by the organization. The mere fact of clicking the “Complete” button says nothing about whether they acquired new competencies and are using them at work. 3.2 Time Spent Time Spent measures the time spent by a user in the course. At first glance, it may seem that the more time a participant dedicated to the training, the more they learned. In practice, this metric can be highly misleading. A long duration does not always mean active learning. A user might leave a browser tab open while performing other duties, take a coffee break, or get distracted from the training by other tasks. On the other hand, a very short time does not necessarily indicate a problem – an experienced employee may go through the material quickly because they already know some of the topics. Therefore, Time Spent is worth analyzing only in combination with other metrics. 3.3 Quiz Scores Quiz Scores show the results of tests and knowledge checks. This is one of the best ways to assess whether a participant has retained key information from the training. Test results also help identify areas that require additional explanation or improvement of materials. However, it is important to remember that a high test score does not always mean acquiring competence. A participant might memorize the correct answers to questions but have difficulty applying this knowledge in a real-world professional situation. Therefore, tests are best at verifying knowledge, not actual behavior change. 3.4 Login Frequency Login Frequency shows how often users return to the training platform. Regular logging in can indicate participant engagement and that the training serves as a source of knowledge used in their daily work. This is a particularly valuable metric for development programs, competence academies, or knowledge bases. However, the login frequency metric itself does not show what the user did after logging in. Frequent visits do not always mean active learning, just as less frequent logins do not have to mean a lack of interest. 3.5 Course Progress Course Progress allows you to track which stage of the training participants are currently at. This is one of the most practical metrics for instructional designers. Thanks to it, you can quickly spot the places where users most often drop out of learning or lose interest. If the majority of participants drop out in the same module, it is worth analyzing its length, difficulty level, way of presenting content, or quality of interactions. Such data often allow for the detection of problems that satisfaction surveys or test results do not show. From our experience, analyzing participant drop-out points is one of the fastest ways to improve the quality of existing courses and increase the effectiveness of the entire development program using LMS data analytics. Norbert Kulski Head of BI & Automation Solutions | TTMS Metric What does it show? What is worth remembering? Completion Rate Whether the participant completed the course Training completion does not yet mean that the participant has acquired competencies or changed their way of working. Time Spent How much time the user spent in the course A long time in the course can mean learning, but also an open tab, a break, or a lack of concentration. Quiz Scores What results the participant achieved in tests A high test score shows information retention, but not always the ability to apply it in practice. Login Frequency How often the user returns to the platform Frequent logins can indicate engagement, but they do not show the quality of learning. Course Progress What stage of the course the participants are at The most valuable are the places where users stop learning – that is often where the real problems of the course are visible. 4. E-Learning Analytics – what is the difference between learning analysis and activity reporting? Many organizations today use reports available in LMS platforms. Thanks to them, you can quickly check who completed the training, how much time they spent on the course, or what score they achieved in the test. The problem is that these are primarily descriptive data that show what happened. Proper analysis of LMS platform data must go beyond simple reports. The answer to this is learning analytics for e-learning, which goes a step further. Instead of focusing solely on numbers, it helps understand the reasons behind specific behaviors and discover patterns that can affect learning effectiveness. You could say that the LMS answers the question “what happened?”, while e-learning analytics helps answer the question “why did it happen?”. For example, a report alone might show that 30% of participants did not complete the training. E-learning analytics, however, allows you to check where users drop out most frequently, what content causes them difficulty, and whether the problem stems from the course design, the difficulty level of the material, or the way the knowledge is presented. In practice, training data analysis should lead to asking questions that help improve the learning process: Which modules cause the participants the most difficulty? Which test questions most frequently result in errors? At what stage do users most often interrupt the training? Which materials do they return to repeatedly? Which content is skipped or scrolled through the fastest? Which elements of the course best support knowledge retention? Effective reporting in e-learning requires going beyond standard LMS data. From our experience, the greatest value comes not from simply collecting data, but from using it for continuous training improvement. Even minor changes in places where users encounter difficulties can significantly improve the effectiveness of the entire development program. Norbert Kulski Head of BI & Automation Solutions | TTMS Reporting in LMS E-Learning Analytics Shows what happened Helps understand why it happened Who completed the training? Why did some participants not complete the training? What was the test score? Why do participants make errors in specific areas? How much time was spent in the course? Which elements of the course engage, and which cause dropouts? How often do users log in? Which materials are actually being used at work? Historical data Conclusions leading to training optimization Measuring activity Improving the learning process I see the greatest value in training data when it stops serving a purely reporting function and begins to support course development. By analyzing the points where participants most frequently stop learning, make mistakes, or return to specific materials, we can very quickly identify elements that need improvement. From an e-learning design perspective, this rarely means having to rebuild the entire course. More often, precise adjustments are enough: simplifying a selected module, adding a practical example, shortening a lesson that is too long, or changing the form of interaction. Such decisions are worth making based on actual data, rather than on intuition alone. The most effective organizations treat training as solutions that constantly evolve. Each subsequent edition of a course provides new information, allowing them to systematically increase its effectiveness and better respond to the needs of the participants. Mikołaj Korzeniowski E-learning Tech Lead at TTMS | Product Owner of AI4E-learning 5. Measuring completion rate is not enough. For years, researchers studying learning processes have pointed out that the ease of learning can be misleading. Robert Bjork, a professor of psychology and author of the concept of desirable difficulties, showed that conditions that make learning seem easy and fluid often lead to poorer long-term knowledge retention. A good example is mathematics exercises. If we solve only one type of task for an hour, by the end of the class we might feel that the material has been mastered. Both the teacher and the students see rapid progress. However, when the test takes place a few weeks later, the results are often disappointing. Research shows that better results are achieved by interleaving different types of tasks, even though participants make more mistakes during learning and feel it is more difficult. Paradoxically, this extra effort leads to more permanent retention and more effective use of knowledge in the future. This is an important lesson for e-learning creators too. A training course that is fast, easy, and hassle-free to complete will not always be the most effective. Sometimes, greater value is delivered by a course that requires active thinking, decision-making, problem-solving, or recalling previously acquired knowledge. Therefore, it is worth keeping in mind the three levels of training effectiveness (Kirkpatrick Model) in relation to the course completion rate: Completion does not mean understanding A participant can go through all modules and obtain a certificate without absorbing key information. Understanding does not mean application An employee can answer test questions correctly but fail to use the new knowledge during their daily work. Application does not yet mean a business result Even if employee behavior changes, the organization still needs to check whether this translated into better sales, higher service quality, fewer errors, or other expected results. This is exactly why the best organizations do not stop at completion rate analysis, but instead investigate the metrics of e-learning analytics much more thoroughly. They treat it as a starting point, not proof of training effectiveness. Real value only appears when participant activity data is combined with information on behavior change and business outcomes. Learning Analytics Myth Reality A high completion rate means effective training. The completion rate only shows participant activity. High test scores guarantee behavior change. Knowledge does not always translate into action. Behavior change automatically improves company performance. Business impact requires additional measurement and analysis. A single metric can evaluate training effectiveness. Effectiveness must be analyzed on multiple levels simultaneously. 6. SCORM limitations and xAPI (Experience API) capabilities in training process analysis For many years, SCORM was the standard in the e-learning world. It allowed organizations to check basic information about participant progress: who completed the training, what test score they achieved, or how much time they spent in the course. The problem is that modern learning is increasingly less likely to take place solely within an LMS. Employees watch instructional videos, use knowledge bases, participate in webinars, perform practical tasks, learn in mobile apps, and collaborate with other employees. Traditional SCORM was not designed to track such activities. In practice, SCORM primarily answers the question: “Did the participant complete the training?” On the other hand, more and more organizations want to know: How did the participant learn outside the LMS? Which materials did they return to? Which resources do they use in their daily work? What actions do they perform after completing the training? Is the knowledge still being used after several weeks or months? It is precisely for these needs that the xAPI (Experience API) standard, also known as Tin Can API, was developed. SCORM xAPI Tracks primarily activity within the LMS course Tracks activity across the entire learning ecosystem Course completion Every learning experience Test results User behaviors Time spent in the course Use of materials after training Limited to LMS Data from LMS, apps, webinars, simulations, and other sources Answers the question “what happened?” Helps analyze “how does the learning process occur?” 6.1 How does xAPI work? xAPI is based on a simple model of logging user experiences. Every action is recorded in the format of: “Someone did something.” For example: Anna completed the onboarding course. Tomasz watched the instructional video. Karolina solved the sales scenario. Michał downloaded the safety procedure. Ewa participated in the webinar. This information is sent to a special data repository called a Learning Record Store (LRS), which can collect data from many different sources, not just a single LMS. 6.2 What data can be collected thanks to xAPI? The greatest advantage of xAPI is the ability to track the entire learning path rather than just activities inside a course. For instance, an organization can analyze: watching training videos, using the knowledge base, downloading documents and procedures, participating in webinars, activity in mobile applications, performing simulations and scenarios, training game results, participation in classroom workshops, implementing onboarding tasks, using supporting materials after training is completed. This makes it possible not only to measure course completion but also to analyze actual learning-related behaviors. 6.3 Why does this matter for Learning Analytics? If the LMS primarily shows what happened in the course, xAPI allows you to observe the entire learning process. The organization can check which materials are most frequently used, which resources employees return to over time, and which activities actually support competency development. This is exactly why xAPI is often seen as one of the foundations of modern e-learning analytics. It allows you to move from simple course completion reporting to the analysis of participants’ actual educational experiences. xAPI Data Examples Activity Example Watching a video User watched 80% video Simulation User selected incorrect response Knowledge base User searched procedure Mobile app User completed microlearning 7. What metrics for e-learning analysis are truly worth monitoring? Metrics available on the LMS platform alone rarely allow you to assess the actual effectiveness of training. Information about course completion, the number of logins, user activity, or quiz scores is valuable, but only combining it with other business data allows you to understand whether the training delivered the expected results. 7.1 In the case of onboarding new employees, the most important metric is the time to reach independence A lot depends on the goal of the training and the organizational area it concerns. For example, if a course was prepared as part of onboarding new employees, the HR department will be interested in more than just whether the participant completed all modules. Much more important information will be the moment when the newly hired person reaches independence and no longer requires constant support from a supervisor or more experienced colleagues. It is this moment that shows when the employee begins to bring full value to the organization. 7.2 In sales, training effectiveness should be evaluated through the lens of business results The analysis of training data in sales looks completely different. Let’s assume that the sales department has completed training on a new product. All indicators available in the LMS look perfect: employees viewed all materials, actively used the knowledge base, completed the training, took part in simulations, and achieved high test scores. At this stage, we can only state that the participants went through the training process. This does not automatically mean, however, that the training was effective. Only combining LMS data analytics with sales results allows you to assess its real impact. Among other things, it is worth checking whether sales representatives offer the new product to customers more often, whether the number of closed deals has increased, whether sales value has improved, and whether employees can use product knowledge during sales calls. Such a comparison of data can lead to very different conclusions. If training activity was high but product sales did not increase, the problem may lie in the training itself, the way knowledge was transferred, or in the sales process. If, on the other hand, the best salespeople achieve high results both in training and in sales, the organization can identify practices that are worth spreading across the entire team. It is also possible to detect individuals who perform well in tests but have difficulty using knowledge in practice, which may point to the need for additional exercises or manager support. 7.3 In customer service, training data should be combined with service quality metrics Similar dependencies can be observed in customer service departments. In this case, training data is worth contrasting with metrics such as average handle time, the number of first-contact resolutions, or customer satisfaction levels. Only combining this information allows you to assess whether the training translated into improved service quality and team efficiency. Learning analytics, therefore, is not about analyzing single metrics in isolation from the context. Its goal is to combine training data with actual business results and find the answer to the most important question: did the training affect the way participants work and the results achieved by the organization? Training area Metrics worth combining with LMS data Onboarding time to reach independence, number of errors made by the new employee, onboarding path completion Compliance level of compliance with procedures, knowledge test results, number of incidents or violations after training Sales sales representatives’ results, number of offers or transactions for a given product, use of product knowledge in customer conversations Customer service customer satisfaction level, ticket resolution time, number of issues resolved at first contact From an analytical perspective, the most valuable metrics are those that can be directly linked to the training goal and the business outcome. The shorter the path between training and a measurable effect, the easier it is to assess the actual value of the development program. Norbert Kulski Head of BI & Automation Solutions | TTMS 8. Artificial intelligence and the future of e-learning analytics Traditional training reports primarily show what has already happened: who completed the course, what score they achieved, how much time they spent in a module, and where they stopped learning. This is important data, but organizations increasingly need something more. They want to know not only what happened, but also what might happen next. This is exactly where AI is beginning to change the way we think about e-learning analytics. Instead of analyzing solely the past, it can help predict risks and point out areas that require support. We describe this in more detail in our article “How to Measure E-learning Training Effectiveness with AI? Every CLO Should Know This”, in which we show why training data should be connected with business goals and competency development. In practice, AI can help answer questions that were previously difficult to capture in standard reports: which participants are likely to drop out of the training, which modules cause the most issues, at which points users make errors most frequently, which competencies require additional support, which groups of employees need a different learning path, whether the training can translate into specific business metrics. 8.1 What does AI bring to training analytics? The greatest value of AI is not that it generates another report. Its strength lies in detecting patterns that a human might not notice right away. If the system sees that participants from a specific department often stop in the same module, achieve lower scores on similar questions, and return to the materials less frequently, this could be a sign that the problem does not lie in engagement, but in the training design or a mismatch in the difficulty level. AI can also support the personalization of learning paths. A participant who struggles with a given topic can receive additional materials, shorter reviews, practical exercises, or an alternative module. On the other hand, someone who quickly mastered the basics does not have to go through all the content at the same pace as the rest of the group. This is particularly important in larger organizations, where a single training path rarely fits all employees. A new hire in onboarding needs different data and support than a sales representative learning about a new product, or an employee undergoing mandatory compliance training. From our experience, AI in e-learning analytics works best when it does not replace human decisions but helps make them faster and based on better data. The system can point out a risk, a pattern, or a competency gap. The ultimate interpretation should still belong to the L&D team, managers, and those responsible for employee development. Traditional Reporting vs. AI-supported E-Learning Analytics Area Traditional Reporting Learning Analytics with AI Data approach Shows what happened in the course Helps predict what might happen next Training completion Informs who completed the course Can indicate who is at risk of dropping out Course issues Shows scores and progress Helps detect modules that cause difficulties Competency gaps Often visible only after test results Can be identified earlier based on behavioral patterns Learning path The same for all participants Can be personalized to the level and needs of the user Human role Analyzes the report after the training is completed Interprets AI recommendations and makes development decisions TTMS Expert Commentary: Today, the concept of AI covers much more than generative artificial intelligence. In the context of learning analytics for e-learning, solutions in the area of data science and machine learning also play a huge role, as they can analyze large datasets and detect relationships that are difficult to notice during traditional report analysis. In practice, this means the ability to identify anomalies and predict problems before they affect the effectiveness of the training program. The system can indicate groups of participants at risk of not completing the course, detect modules that consistently cause difficulty, or identify behavioral patterns pointing to competency gaps. Thanks to this, the organization is not limited to analyzing the past, but can react faster and continuously improve both the training content and the entire process of employee development. Norbert Kulski Head of BI & Automation Solutions | TTMS 9. Summary – Analyzing training data with AI E-learning analytics is much more than analyzing reports from an LMS platform. The mere fact of completing a training course, a high test score, or frequent logins to the system are not yet proof of an effective learning process. The biggest challenge for organizations is moving from measuring activity to measuring the real impact of training on employee behavior and business results. This is precisely why models such as Kirkpatrick, more advanced data collection standards like xAPI, and solutions using artificial intelligence are gaining more importance. From our experience, the most valuable organizations do not only ask “was the training completed?”. They ask much more difficult questions: what did the participants learn, how do they apply this knowledge at work, and does the training contribute to achieving business goals? Data alone does not improve training quality. Value only appears when an organization can translate insights from e-learning data analytics into concrete actions: improving content, changing learning paths, providing better support to participants, and creating more effective development programs. In the coming years, the role of corporate e-learning analytics will likely continue to grow. Thanks to AI, organizations understand better and better not only what happened during training, but also what actions are worth taking to increase learning efficiency and develop employee competencies faster. Key Conclusion What does it mean in practice? Completion rate is not enough Course completion does not mean acquiring competence. Learning Analytics is not reporting The most important thing is to understand the reasons behind participant behaviors. Knowledge does not always translate into action A high test score does not guarantee behavioral change at work. Training data should be combined with business KPIs Only then can the real impact of the training be evaluated. AI helps predict, not just report It becomes possible to detect risks, competency gaps, and training needs earlier. 10. How TTMS helps organizations measure training effectiveness? At TTMS, we help organizations not only create e-learning courses but also better understand whether they actually work. We combine experience in instructional design, data analytics e-learning, and the deployment of AI-based solutions to support companies at every stage of the process: from material preparation, through course publishing, to outcomes analysis. Our solutions allow for the transformation of corporate knowledge, documentation, procedures, and expert materials into online courses, and then the analysis of participant progress, test results, and engagement data. Thanks to this, organizations can quickly notice which content works well, where participants face difficulties, and which areas require additional support. In practice, this means moving from the simple question “did the employee complete the training?” to much more important questions: did they understand the material, can they use the knowledge at work, and does the training support the business goals of the organization? By combining e-learning, LMS data analytics, and AI, we help companies design training programs that do not end with a certificate but realistically support competency development, onboarding, compliance, sales, and customer service. FAQ What is learning analytics for e-learning? Learning analytics for e-learning is the measurement, collection, analysis, and reporting of data about learners and their contexts. Unlike traditional LMS activity reporting—which only tells you what happened (e.g., who completed a course)—learning analytics focuses on understanding why it happened. It connects learning experiences with behavioral changes and business KPIs to continuously optimize the training process and improve organizational performance. What is the difference between LMS reporting and e-learning analytics (Learning Analytics)? LMS reporting lets you see “what happened” (e.g., who completed the course, what the test score was), while advanced e-learning data analytics helps you understand “why” it happened. Thanks to these analytics, you’ll not only find out that participants didn’t complete the training, but also where the problem occurred and how to optimize the content to increase its effectiveness. Is the SCORM standard sufficient for modern e-learning analytics? SCORM is sufficient for tracking basic activity within the LMS platform (completion, test scores). However, to measure the actual impact of training on the organization and track the learning process outside the system (e.g., webinars, working with a knowledge base, simulations), the xAPI standard is essential. It allows for the recording of every employee’s learning experience in one place. How can you link e-learning data to a company’s business results? Effective training process analytics requires aligning data from the LMS with your organization’s specific KPIs—such as sales results, customer service levels, or onboarding time. This transforms training from merely a “requirement” into a measurable tool that supports the company’s actual business goals. How does artificial intelligence support the analysis of training processes? AI is transforming analytics from reactive to predictive. Instead of analyzing only historical data, artificial intelligence can detect behavioral patterns that humans overlook. Among other things, it can identify groups of participants at risk of dropping out, pinpoint skill gaps before they arise, or personalize training paths by tailoring them to the level of difficulty an employee is facing.
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Practical AI Training Methods for Employees That Work
Organizations are spending significant money on AI tools, yet many of those investments stall at the experimentation stage. According to McKinsey’s 2025 workplace AI report, nearly 70% of large-scale transformations fail to achieve their intended goals, and that failure rate cuts directly through the training layer. The bottleneck is rarely the technology. It is the workforce’s ability to use it confidently and consistently in real work. This guide lays out a practical, step-by-step approach to AI employee training methods that actually translate into changed behavior on the job. It draws on current research, documented case studies, and TTMS’s experience designing and delivering AI-powered e-learning programs across industries. Whether you are an L&D manager building your first AI curriculum or an HR leader trying to scale something that already exists, the six steps that follow will help you move AI training from intention to measurable impact. The table below maps those six steps at a glance before the full detail follows. Step Goal Key Action What Success Looks Like 1. Assess AI Readiness Understand current skill gaps Role-by-role audit and workforce segmentation Workforce grouped by proficiency level with clear gaps identified 2. Define Objectives Tie learning to business outcomes Set measurable performance baselines Trackable KPIs linked to productivity, cost, or quality 3. Design Curricula Build role-specific learning tracks Separate content by function and AI exposure High completion rates paired with strong learner-reported relevance 4. Choose Methods Build real, applicable skills Hands-on, blended, AI-adaptive delivery Skill application visible in actual workflows 5. Launch and Sustain Drive adoption across the organization Phased rollout with active change management Engagement rates, reduced resistance, manager-reported uptake 6. Measure and Iterate Connect training to business performance Track leading and lagging indicators Productivity gains, error reduction, and ROI 1. Why Most AI Training Programs Fail to Change How People Work The failure pattern is consistent enough to study. Organizations procure an AI tool, assign a generic “AI 101” course to all staff, and then wait for productivity gains that never arrive. The root problem is not motivation but design. Most AI training methods for employees are built around content delivery, not behavior change. Training gets treated as a box to tick rather than a system to build. Courses go live without clear connections to the work employees actually do. There is no segmentation by role, no baseline measurement, and no mechanism to reinforce learning after the course window closes. Employees end up able to describe what AI is but unable to explain how to use it safely in their specific function. What distinguishes programs that work is a deliberate architecture: role-specific content, objectives tied to business outcomes, methods that build real skills, and a sustained engagement model that does not end on day one. 2. Step 1: Assess AI Readiness and Identify Skill Gaps Before You Build Anything The most common reason AI training programs miss the mark is that they begin with content rather than context. Before a single module is created, the organization needs to understand what its people already know, what they need to know, and how wide the gap is. Skipping this step leads to training that is either too basic for some employees and too advanced for others, or simply irrelevant to either group. 2.1 Conduct a Role-by-Role AI Skills Audit A skills audit should go deeper than a quick survey. The goal is to map current AI-related capabilities against the skills each role will require as AI tools become embedded in day-to-day workflows. This means looking at how employees currently interact with AI, whether they are using it at all, and where adoption is stalling by function. 2.2 Distinguish Between AI Literacy, Fluency, and Role-Specific Proficiency Not all AI skills are equivalent, and conflating them leads to curricula that feel misaligned to employees. AI literacy is the foundation: understanding what AI is, what it can and cannot do, and what the ethical and data considerations are. Fluency moves further, referring to the ability to actively create, adapt, and apply AI tools to generate original work or solve novel problems. Role-specific proficiency is the narrowest and most practical level: using AI competently within a defined job context, following the organization’s rules and with appropriate human oversight. Each level requires a different training response. Literacy covers what AI is and where the risks lie. Fluency addresses how to create and adapt with AI across different tasks. Role-specific proficiency gets into how to use AI safely and effectively within a particular job. A good audit clarifies where each employee currently sits across this spectrum. 2.3 Use Assessment Results to Segment Your Workforce Once assessment data is in hand, the next step is segmentation. Rather than assigning the same learning path to everyone, employees should be grouped by current proficiency level and AI exposure: those with minimal AI exposure, intermediate users who interact with AI tools occasionally, and advanced users who are ready to integrate AI into complex workflows. This segmentation is not permanent. It is a starting point that allows training resources to be allocated intelligently. It also reduces a common source of disengagement: advanced employees sitting through introductory content, or newer employees being overwhelmed by concepts they lack the context to apply. 3. Step 2: Define Learning Objectives Tied to Business Outcomes Getting this step right is what separates training programs that demonstrate value from those that generate reports about course completions. The objective of AI training is not more educated employees in the abstract. It is changed behavior that produces measurable business results. 3.1 Set Goals Around Productivity Gains, Not Just Course Completions Course completion rates are a leading metric, not a success metric. They tell you that content was consumed, not that anything changed. Effective AI training methods connect learning objectives directly to performance outcomes: shorter processing times in a specific department, reduced escalation rates in customer support, faster drafting cycles in content teams, more accurate forecasting in finance. Josh Bersin’s 2026 research on AI-enabled learning maturity frames this shift clearly. In his analysis of Level 4 AI-native learning organizations, the emphasis moves away from course catalogues toward “dynamically sharing information, enabling people to explore, question, and apply new ideas” in their work. Learning goals should be written with that same emphasis from the start. 3.2 Establish Measurable Baselines So Progress Is Trackable Before training launches, establish baselines for the indicators that matter. If the goal is to reduce the time a sales team spends on proposal drafting, measure the current average. If it is to cut support ticket resolution time, benchmark it. These baselines become the reference points against which the program’s impact will later be evaluated. This approach also keeps the conversation honest inside the organization. When training is anchored to a number, it becomes much easier to defend investment, identify what is working, and make the case for iteration when the first version falls short. 4. Step 3: Design Role-Specific AI Training Curricula One of the most consistent findings across recent enterprise AI research is that generic training programs underperform. MIT CISR research covering 152 enterprises identifies “creating AI-ready people, roles, and teams while redesigning work around AI capabilities” as a defining characteristic of organizations achieving above-industry-average financial performance. That does not happen with a single company-wide AI course. 4.1 Build Separate Learning Tracks by Role and AI Exposure Level Each track should be organized around the tasks that role actually performs, the AI tools most relevant to those tasks, and the risks and governance requirements that apply. A separate track for a finance analyst, a customer service agent, and a procurement manager is not a luxury. It is the design principle that makes training stick. Stanford Digital Economy Lab’s analysis of 51 enterprise AI deployments found that the same AI technology produced “weeks vs. years” differences in realized value depending on whether work was redesigned and training was tailored to specific roles. When Moderna built its AI Academy with differentiated tracks for scientists, clinicians, manufacturing, support functions, and executives, it achieved course completion rates 240% above industry benchmarks and a 400% year-over-year increase in enrollment. Role relevance drives engagement. TTMS’s work in building e-learning for healthcare illustrates this in a regulated context. In an industry where accuracy, compliance, and patient safety leave no room for generic content, TTMS designed role-specific e-learning tailored to the distinct knowledge requirements and compliance obligations of different clinical and administrative functions. Each professional group completed only content directly mapped to their responsibilities, reducing time-to-competency and eliminating the disengagement that comes from irrelevant material. 4.2 What Every Employee Needs: Core AI Literacy Before anyone receives role-specific content, there is a shared foundation that the entire organization needs to hold. This is not about making everyone a data scientist. It is about building the common language and judgment that allows AI to be used well at scale. 4.2.1 Understanding How AI Tools Work (Without the Technical Deep Dive) Employees do not need to understand neural network architecture. They do need to understand that AI systems generate outputs based on patterns in training data, that those outputs can be wrong or biased, and that the quality of their inputs significantly influences the quality of what they get back. A clear mental model of how AI tools work, at an accessible level, prevents both overreliance and unnecessary avoidance. 4.2.2 Responsible AI Use, Data Privacy, and Compliance Every employee using AI tools needs to understand the boundaries: what data can and cannot be entered into AI systems, what the organization’s approved tools and use cases are, and what the legal and regulatory implications of misuse look like. This is especially critical in healthcare, finance, and legal services, where the consequences of a compliance failure are significant. TTMS’s Safety First case study demonstrates the value of building compliance and responsible use directly into e-learning design. Rather than attaching compliance content as an afterthought, TTMS integrated safety requirements into the learning flow itself, so that correct behavior became the natural outcome of completing the training. Post-deployment assessments showed employees could accurately apply the relevant safety protocols in scenario-based tests, with compliance knowledge embedded rather than requiring separate recall. 4.2.3 Evaluating AI Output and Knowing When Not to Trust It AI outputs should be treated as drafts, not authoritative answers. Teaching employees to evaluate outputs critically, including how to ask for sources, recognize hallucinations, and verify claims before acting on them, is one of the highest-value skills in any AI training program. This is realism about how to use AI well. 4.3 What Power Users and Functional Teams Need: Applied AI Skills Once the foundation is in place, specific groups need training that goes beyond awareness into actual skill-building. This is where training methods for employees become most instructive, because generic descriptions only go so far. 4.3.1 Prompt Engineering and Workflow Integration Prompt engineering is worth demystifying. Structuring instructions to AI systems so they produce reliable, useful outputs is a learnable skill, not a technical specialty. For non-technical employees, this typically means learning to include clear task descriptions, relevant context, desired output format, and iterative refinement loops. More advanced techniques, such as breaking complex tasks into sub-steps, using examples to guide style, or asking the model to critique and improve its own output, can be layered in once the basics are solid. Beyond single prompts, employees benefit from learning how to connect AI tools into repeatable workflows. This might mean chaining AI steps in a process, using integration platforms to connect AI outputs to other business systems, or building standardized templates that apply across a team rather than being reinvented by each individual. 4.3.2 Role-Specific Use Cases: Sales, Marketing, HR, Finance, Operations The fastest way to make applied AI training relevant is to anchor it to use cases that employees recognize from their own work. Sales teams benefit from training that addresses how AI can support prospecting, proposal drafting, and objection handling. Marketing teams need content around brief writing, content iteration, and campaign analysis. HR teams benefit from exploring AI-assisted job description drafting, benefits query handling, and onboarding content generation. Finance and operations teams see the most immediate gains from AI-supported data analysis, report summarization, and exception flagging. 4.4 What Leaders Need: Strategic AI Oversight Leaders need a different kind of training from the rest of the organization. They are not the primary users of AI tools in most cases. Their role is to set direction, ask the right questions, allocate resources appropriately, and ensure that AI deployment aligns with business goals and risk tolerance. That requires understanding what AI can and cannot do at a strategic level, how to evaluate AI-related proposals, and how to support their teams through the adoption process. Leadership training should also cover governance: how to establish responsible use policies, how to communicate AI strategy clearly, and how to model the behavior they want to see. 5. Step 4: Choose Training Methods That Build Real Skills The design of the curriculum determines what is taught. The choice of training method determines whether it is learned. Passive content delivery, whether that is a recorded lecture or a slide deck, rarely changes behavior on its own. The methods that work are those that create practice, context, and feedback. 5.1 Hands-On, Project-Based Learning Over Passive Video Consumption The most effective way to learn a skill is to use it. AI training programs should be designed around realistic tasks that require employees to apply what they are learning, not just recall it. This might mean drafting a work document using a specific AI tool and then reviewing the output against a rubric. It might mean completing a workflow exercise that replicates an actual process in the employee’s team. The key is that the learning activity produces something, and that the employee gets feedback on what they produced. Project-based learning also tends to surface questions that generic content does not anticipate. When employees work through a realistic scenario, they encounter the specific points of confusion and judgment that define their role’s AI challenges. That experience is difficult to replicate in any other format. 5.2 Microlearning and Spaced Repetition for Long-Term Retention Microlearning refers to delivering training in short, focused modules that address a single concept or skill at a time, typically three to ten minutes in length. Spaced repetition means returning to the same material at increasing intervals to reinforce retention, rather than concentrating all learning into a single session. These two principles work together. A concept introduced in a five-minute module is better retained when revisited briefly two days later, then again a week later, with small practice exercises each time. This approach is especially effective for AI training, where employees are learning both conceptual frameworks and practical skills that need to become habitual rather than occasional. 5.3 Peer Learning, Internal AI Champions, and Cohort-Based Models No training program scales as effectively as a community of practice. Identifying employees who engage early and enthusiastically with AI tools and equipping them to support their peers creates a multiplier effect that formal training alone cannot produce. These internal AI champions become the first point of contact for questions, the source of role-specific tips and shortcuts, and the visible proof that AI adoption is possible in the specific context of that organization. Cohort-based learning, where groups of employees move through training together and share their experiences, also builds the social dimension of learning that individual self-paced courses miss. When employees learn alongside their peers, they develop shared vocabulary and shared confidence. 5.4 Blending Self-Paced Courses With Live Instruction Self-paced courses offer flexibility and scale. Live instruction, whether delivered in person or virtually, offers dialogue, depth, and the ability to address unexpected questions. The most effective AI training programs use both. Self-paced modules handle foundational content efficiently, while live sessions focus on discussion, scenario work, and the kind of judgment-based challenges that benefit from human facilitation. This hybrid structure also accommodates the reality of different learning preferences and schedule constraints within the same workforce. The self-paced component ensures a consistent baseline; the live component ensures depth. 5.5 AI-Powered Learning Tools That Personalize the Training Experience One of the more compelling developments in corporate learning is the emergence of AI tools that adapt training based on each learner’s progress, knowledge gaps, and engagement patterns. Rather than serving the same content to everyone, these platforms identify where a learner is struggling, adjust the difficulty level accordingly, and surface the most relevant material for that individual at that point in their learning journey. TTMS has developed the AI4E-learning authoring tool to help organizations build and deploy exactly this kind of adaptive e-learning. The tool enables L&D teams to convert existing organizational materials, whether documentation, procedures, or policy documents, into structured e-learning courses with AI-generated quizzes, summaries, and scenarios. Courses are SCORM-compliant, making them deployable across existing LMS platforms without requiring a full infrastructure rebuild. TTMS applied this capability when a company operating a helpdesk needed to rapidly onboard new employees and address knowledge gaps in their ticket-handling processes. In the helpdesk AI training case study, TTMS used AI to build training that adapted to each learner’s current proficiency and provided real-time feedback during exercises. Managers gained visibility into individual and team progress through integrated analytics, so they could intervene early where gaps appeared. New hires reached independent ticket-handling significantly faster, and knowledge check scores tracked upward across successive cohorts as the content was refined based on usage data. 6. Step 5: Launch and Sustain Engagement Across the Organization The quality of training content means very little if the organization does not engage with it. Launching an AI training program is not only an L&D exercise; it is a change management challenge. The decisions made in the launch phase, and the follow-through in the weeks that follow, determine whether training becomes embedded in the culture or quietly abandoned. 6.1 Secure Leadership Buy-In Before Rolling Out to the Workforce Leadership buy-in is not a formality. It is a precondition. When executives actively endorse and visibly participate in AI training, it signals to the broader workforce that this initiative is serious and connected to where the company is going. When they are absent, employees often interpret that as a sign that the training does not really matter. The most effective way to secure executive support is to connect training directly to the business priorities that leaders are already accountable for. Framing AI upskilling as a productivity initiative, a risk management measure, or a competitive positioning strategy, depending on the audience, is more persuasive than framing it as an HR program. Getting leaders trained first, so they can speak to AI with informed confidence rather than vague endorsement, further strengthens their ability to champion the initiative. TTMS’s work on the Hitachi Energy safety training program demonstrates how leadership alignment enables effective rollout at scale. For Hitachi Energy’s 10 Life-Saving Rules initiative, TTMS designed an e-learning program directly tied to measurable safety outcomes and built with visible organizational backing. The program’s phased deployment and consistent governance framework enabled a large, geographically distributed workforce to complete the curriculum within a defined rollout window, with completion tracking across business units giving leadership a clear view of adoption progress. 6.2 Use a Phased Rollout to Reduce Overwhelm and Build Momentum A full organization-wide launch on day one is rarely the right approach for AI training. Starting with a pilot group, typically a high-engagement team or a function where AI use cases are clearest, lets the program be tested, refined, and validated before wider deployment. Measurable results from the pilot create the evidence base for the broader rollout and reduce the skepticism that often greets large-scale training mandates. After the pilot, rolling out in waves by function, geography, or business unit lets the organization absorb and apply learning progressively. Each wave benefits from the lessons of the previous one, and the internal community of AI users grows with each phase. 6.3 Address AI Anxiety and Resistance Directly The data on AI anxiety is hard to dismiss. EY research finds that 60% of employees are anxious about AI adoption, 75% worry AI will make certain jobs obsolete, and 48% say they are more concerned than they were a year ago. Pew Research reports that 52% of U.S. workers worry about AI’s future impact on their work. Designing a training program that ignores this is not neutral; it is a design flaw. Addressing anxiety directly means being transparent about what AI will and will not change in each role. It means communicating clearly about the organization’s approach to job impact before rumors fill the silence. It also means building psychological safety into the training experience itself, so employees feel comfortable making mistakes and asking questions without judgment. Academic research from 2025 confirms that the negative impact of AI job anxiety on wellbeing and work engagement is significantly reduced by vocational training, emotional regulation support, and social connection at work. Training is a trust-building exercise, not just a skills intervention. 6.4 Reinforce Learning With On-the-Job Application Opportunities Training that ends at course completion does not change behavior. The link between learning and work needs to be made explicit, and it needs to be supported by the employee’s immediate environment. This means giving employees real tasks that require them to use their new AI skills. It means giving managers the context they need to coach rather than ignore. It means building AI tool use into workflows rather than leaving it as an optional extra. TTMS has consistently applied this principle in its e-learning programs. In the Safety First case study, safety training was designed to be directly integrated with operational responsibilities, with what employees learned immediately testable in their actual work environment. That integration between training and application is what converts knowledge into habit. 7. Step 6: Measure Impact and Iterate Continuously Without measurement, there is no way to know what is working, where to invest more, or how to make the case for continued resources. The key is understanding which metrics reveal what, and at what stage in the program’s life they become meaningful. 7.1 Leading Indicators: Engagement, Completion, Confidence Scores Leading indicators are signals of early health. Engagement rates, completion rates, and confidence scores tell you whether employees are showing up, finishing what they start, and feeling more capable. Low engagement signals a problem with relevance or accessibility. Flat confidence scores point to something off in the learning design. These are not proof of business impact, but they are early warning signals worth tracking from day one. Learning analytics from AI-powered platforms can provide these indicators in real time, allowing L&D teams to make adjustments while training is still in progress rather than waiting for end-of-program evaluations. TTMS’s AI-enhanced e-learning solutions are built with exactly this feedback loop in mind, tracking individual and group progress so that both employees and managers can see where capability is improving and where it is not. 7.2 Lagging Indicators: Productivity Gains, Error Reduction, Business Outcomes Lagging indicators take longer to emerge but are where the real evidence of training value lives. The metrics that matter to business leaders are things like productivity gains in trained functions, fewer errors or rework cycles, faster process completion, and cost savings tied to AI-assisted workflows. Josh Bersin’s research on AI-native learning organizations provides further context. Organizations at Level 4 of AI-enabled learning maturity are 10 times more likely to be innovation leaders and 6 times more likely to exceed their financial targets. These figures describe what is possible with mature, embedded AI learning systems, not what should be expected from an initial program deployment. 7.3 When to Expect ROI From Corporate AI Training Programs Realistic expectations matter. In most cases, measurable productivity gains from AI training begin to appear within the first three to six months of consistent application, but business-level financial outcomes often take longer, particularly if they depend on workflow redesign rather than individual skill change alone. Gartner’s guidance on measuring AI value is useful here. The recommendation is to evaluate AI programs across three dimensions: financial return (cost and productivity gains), employee return (engagement, retention, capability), and long-term return (adaptability and innovation readiness). Tracking all three prevents organizations from declaring failure prematurely when immediate cost savings have not materialized, while still holding the program accountable for demonstrable outcomes over time. 8. Common Mistakes to Avoid When Training Employees on AI Patterns in where AI training programs go wrong show up repeatedly across industries and organization sizes. Awareness of them makes it possible to design around them from the start. The most common mistake is building one course for everyone. Without role-specific content, employees cannot make the connection between what they are learning and how it applies to their actual work. Engagement drops, retention is poor, and behavior does not change. Close behind this is the absence of any measurement framework. If success is only defined as “employees completed the course,” the program will never be able to demonstrate or improve its real impact. Another recurring problem is treating AI training as a standalone event rather than a component of a larger change management effort. When training is deployed without addressing organizational culture, leadership behavior, or workflow redesign, it remains an isolated experience. The OpenAI 2025 enterprise report found that many enterprises still fail to connect AI tools to their core data and workflows, with AI sitting largely unused because the enablement work was never done. Training content alone cannot compensate for an environment that does not support application. Ignoring AI anxiety is a design failure with predictable consequences. JFF research found that only 36% of workers say they have the training and resources they need to use AI in their jobs, and that insufficient employer-provided training is directly linked to growing anxiety and resistance. Programs that skip the change management component often create the very resistance they were designed to overcome. 9. Building an AI-Ready Workforce Is an Ongoing Process, Not a One-Time Event AI literacy training delivered once is not a solution. It is a starting point. PwC’s 2025 Global AI Jobs Barometer reports that skills for AI-exposed jobs are changing 66% faster than in other roles. The tools employees learn this year will evolve significantly by next year, and the use cases that seem advanced today will become standard practice within a short time. A training program designed as a one-time event is already outdated before it finishes deployment. Sustainable AI training programs are built as living systems. They include a skills taxonomy that gets updated as AI capabilities and organizational needs change. They provide universal AI literacy as a continuous baseline, with deeper, role-specific pathways that are regularly refreshed. Learning is embedded in the flow of work, not confined to an annual course calendar. The program is governed by real data: skills gaps, learning analytics, AI usage patterns, and business outcomes that feed back into curriculum decisions on an ongoing basis. IBM’s own internal research estimates that 40% of the global workforce will need to reskill in the next three years due to AI and automation. IDC forecasts that more than 90% of organizations worldwide will face critical skills shortages by 2026, with AI and IT bottlenecks potentially costing the global economy up to $5.5 trillion. L&D and HR
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