How Managers Use the Alan Turing Institute AI Skills Framework

How can managers use the framework to identify a team's training gap?

Use the framework to name the capability expected for one real AI-related duty, then compare it with evidence of how that work is performed. A gap becomes a training need only when its cause is learnable knowledge, skill, or behaviour. First check whether staff have approved access, clear rules, usable inputs, enough time, and a workable process. It is a map, not a validated team score or job-loss measure. The deposited record for the AI Skills for Business Competency Framework describes role-aligned knowledge, skills, and behaviours for planning and training. The Innovate UK Business Connect resource explains its parts: personas describe how people interact with AI; duties name tasks; competencies and knowledge, skills, and behaviours specify capabilities; lifecycle phases place them in the work; proficiency levels indicate depth. These descriptions help managers state what a duty requires. They do not establish what a team can demonstrate. Keep the diagnosis narrow: one duty in one workflow. For example, when preparing client updates with AI assistance, ask whether staff verify claims against source records before approval. Compare that action with safe examples and ask what conditions shaped the work. The CDC’s “Assess Training Needs: Conducting Needs Analysis” guidance recommends finding a gap’s source and considering people and system causes; policy, technology, organization, or procedures may need attention. Map the capability, gather fair evidence, test competing causes, match the response, then review whether learning transfers to the duty. This can guide focused practice without a broad course or staff ranking.

Sources: AI Skills for Business Competency Framework; AI Skills for Business Competency Framework & Resources; Assess Training Needs: Conducting Needs Analysis

What the framework can—and cannot—tell you

The deposited release is Version 3.0.0, published on 9 March 2026. The Zenodo record describes the AI Skills for Business Competency Framework as a role-aligned account of the knowledge, skills, and behaviours needed for responsible and effective AI engagement. Its stated purposes include capability planning, curriculum design, governance, and targeted training. It is not thereby a validated team test or proof of improved performance. The record provides no pass mark, diagnostic score, or team-level measurement procedure. Treat it as a structured reference for defining expectations, then use evidence from the work itself to decide whether training is needed.

Version status matters because the publisher’s overview has not caught up with the deposited release. The Alan Turing Institute page describes four audiences: AI Citizens, AI Workers, AI Professionals, and AI Leaders. It still says Version 3 is open for national consultation and gives a closing date of 19 January 2026. That conflicts with the later Zenodo record, which identifies Version 3.0.0 as published on 9 March 2026. The overview remains useful for its audience descriptions, but not as the current release record. When documenting a local exercise, name the deposited version and date so colleagues can identify the material used.

The framework’s value is its structure. The Innovate UK Business Connect resource page explains that personas describe how people interact with AI in their roles; duties identify associated tasks; competencies describe capabilities needed for those duties; and knowledge, skills, and behaviours break competencies into building blocks. It also places capabilities in AI project lifecycle phases and uses proficiency levels to indicate expected complexity or maturity. It links broad audience categories to specific expectations. It does not tell the manager whether an employee meets that expectation. That requires a separate review of work evidence and conditions.

This distinction prevents the framework from becoming a ranking system. A manager can use it to clarify capabilities relevant to a duty, then define acceptable performance in that setting. A locally written standard is a management aid, not an official Turing assessment result. If someone lacked an approved tool, practice time, or clear instructions, their performance cannot fairly establish an individual skill shortfall. The framework can make an expectation visible; it cannot determine the cause of a gap.

Nor is the framework a measure of whether a job is exposed to AI or likely to disappear. Its stated subject is workforce capability for engaging with AI across roles and responsibilities. Capability expectations, tool use, organizational adoption, and displacement are separate questions. A mapping exercise addresses only the first: what knowledge, skill, or behaviour may matter for a duty. Other evidence is needed for workflow change, organizational adoption, or staffing outcomes. A capability gap is not a job-loss estimate.

Managers should keep similarly named resources separate. Skills England has its own AI Skills Framework and tools package, which government describes as complementary to the Turing framework. They have different structures and should not be presented as one instrument or interchangeable versions. When consulting more than one resource, record which informed each expectation rather than merging categories into an invented score.

The practical boundary is simple: use the Turing framework to focus the question, not declare the answer. Select a relevant persona and duty, identify the linked competency and proficiency expectation in the deposited version, and translate it into an observable local standard. Then gather appropriate evidence before deciding whether the difference reflects a learnable capability need. It supplies a common vocabulary for that first step. It does not certify a team, provide a validated benchmark, or determine the remedy.

Sources: AI Skills for Business Competency Framework; AI Skills for Business Competency Framework; AI Skills for Business Competency Framework & Resources; AI skills tools package

How do you translate a persona into a duty-specific target?

Start with the responsibility in the workflow, not a job title alone. Select the relevant persona, identify the duty the person performs, then use the associated competency and knowledge, skills, and behaviours to describe the required result and expected independence. A drafter, reviewer, and manager accountable for rules may share a workflow but need different targets. The Innovate UK Business Connect page, “AI Skills for Business Competency Framework & Resources,” describes linked components: personas show how people interact with AI in their roles; duties name related tasks; competencies describe capabilities needed for those tasks; knowledge, skills, and behaviours break competencies into building blocks; lifecycle phases place them in the wider AI process; and proficiency levels indicate complexity and maturity. Use this as a route from context to expectation, not a checklist to apply wholesale. It supplies a structure for planning; managers still decide which duty matters and what competent work looks like locally. Draw a boundary around one duty. Identify what starts it, what information or tool the worker may use, what output or decision must result, and who relies on it next. Then locate the duty in the workflow: is the worker preparing inputs, using a system, checking an output, communicating a decision, or overseeing use? Someone preparing source material may need to select relevant, authorized records. A reviewer may need to compare claims with source records and identify uncertainty. A manager responsible for governance may need to set rules, assign accountability, and provide an escalation route. These duties can concern the same tool yet call for different capability. Consider an example: a team prepares client updates with assistance from an AI tool. “Use AI responsibly” is too broad to assess or teach. The person assembling source material must select relevant records; the drafter must produce a clear update without treating generated text as verified fact; the reviewer must check material claims against records and escalate unresolved discrepancies; and the manager must set the approval boundary. Tone judgment may also matter, but name it separately if it is central to the task. It illustrates workflow decomposition, not prevalence or prescribed duties. Translate the chosen duty into a target people can interpret consistently. State the action, evidence or standard to use, and expected independence. For example: “Before release, verify material claims in the client update against the approved source record and escalate any unresolved mismatch.” Depending on the role, one worker may do this with a checklist or coaching, while another may handle routine cases independently and recognize when escalation is needed. Do not present a locally written target as official framework wording unless that wording has been checked in the relevant version. It is an operational statement informed by the framework, not an official assessment item. The Alan Turing Institute page, “AI Skills for Business Competency Framework,” distinguishes AI Citizens, AI Workers, AI Professionals, and AI Leaders by their relationship to AI. This helps resist role-title shortcuts: an operations employee affected by AI may be an AI Worker for a duty, while a colleague procuring or governing a system may need a different perspective. The page’s Version 3 status text still describes consultation-era material, so use it for audience descriptions rather than release status. The Innovate UK page explains component links; the Zenodo record establishes Version 3.0.0 in the evidence base. Neither implies that everyone in a persona needs identical depth. Translate the duty first, then set the level its responsibility calls for. The finished target can be a short statement of who performs which duty, what competent work looks like, what support or independence is expected, and where judgment or escalation belongs. “Verify material claims against the source record and escalate unresolved uncertainty” points to evidence that can later be examined. “Be AI literate” names no task, standard, or learning outcome. It gives the manager an expectation to compare with fair evidence.

Sources: AI Skills for Business Competency Framework & Resources; AI Skills for Business Competency Framework; AI Skills for Business Competency Framework

Which evidence shows a capability gap rather than unequal conditions?

A difference between expected capability and observed work is a training gap only after the manager checks the conditions in which the work happened. Compare performance on the same duty, with the standard stated in advance, and ask what explains any miss. A safe work sample shows what someone does; a conversation can clarify the information, permission, time, or support available. Neither a confidence poll nor one unusual mistake answers both questions. The aim is a provisional diagnosis tied to a duty, not a label or team score. Turn the target into observable criteria before reviewing examples. For checking an AI-assisted draft against source records, illustrative criteria might include identifying claims that need verification, locating the source, flagging a mismatch, and escalating uncertainty. Adapt these to the actual work; they are not official framework test items. Decide what counts as adequate evidence and what help is allowed. Removing reference material or an approved tool that the normal task permits changes the task. Do not ask staff to put confidential material into an unapproved system for a realistic test. Use low-risk examples, redaction, or a controlled demonstration that avoids exposing live data. Gather a small set of evidence rather than relying on one source. Representative completed work can show whether criteria are met; a structured demonstration helps when real work is sensitive or inconsistent. Ask workers to explain their choices and where they needed help, then compare that account with the work and manager observation. Explanation is context, not proof: confidence may be high while a check is missed, or low while work is careful. A manager’s impression also depends on which cases they see. Comparable examples and more than one viewpoint make the diagnosis easier to discuss, without turning a small sample into a permanent judgment. Before calling a shortfall a skill issue, test the conditions. Could the worker access the approved tool and source records? Were rules for sensitive inputs and human review clear? Was there time to practise, or did workload and handoffs make the method impractical? Were records complete enough to verify output, and was an escalation route available? If several people stumble at the same point, consider a shared problem with policy, access, workflow, or materials. If someone has not had a chance to practise, missing evidence does not establish that they cannot learn. The CDC’s “Assess Training Needs: Conducting Needs Analysis” says to identify a performance gap’s source before deciding whether training is needed. It lists policy, work environment, technology, organization, and procedures as possible solutions alongside changes in knowledge, skills, or behaviour. This is general guidance, not an AI-specific validation of the Turing framework, but it supports a cause-checking sequence. The England-focused Skills England report “Barriers to AI skills development” describes expert-reported ambiguity around “AI skills,” foundational digital-literacy barriers, fragmented provision, costs, and limited employer understanding. These are reported themes in England, not estimates for every organization or proof that one team faces them. Treat them as questions to investigate, not assumptions about workers. Record the duty and criterion, examples used, observations, worker explanation, conditions that helped or blocked performance, and unresolved questions. Separate causes. If a person cannot verify a claim because its source record is missing, repair the input or handoff. If policy blocks the available tool, resolve access and governance first. If access and instructions are clear but comparable examples repeatedly miss the same verification step, focused practice with feedback may address a learnable skill. If evidence is mixed, review another safe example or clarify the standard. “Research evidence, analysis and methodology: What works for AI upskilling in the UK,” another Skills England report, cautions that qualitative workshop findings are not statistically representative and describes limits in quantitative AI-skills evidence. External reports suggest causes to investigate; they cannot diagnose this team. A useful result is a revisable statement: “On these comparable examples, the verification criterion was missed; approved access was available, but the escalation rule was unclear.” This identifies performance and a condition to fix; it does not prove which intervention will work. Use the evidence to decide whether to clarify the rule, provide practice, change the workflow, or gather more information.

Sources: Assess Training Needs: Conducting Needs Analysis; Barriers to AI skills development; Research evidence, analysis and methodology: What works for AI upskilling in the UK

How should the manager choose between training and a workflow fix?

The right response depends on why the duty is falling short. Use the framework to keep the expected capability specific, then choose the smallest effective intervention. If people have approved access, clear rules, usable inputs, and time, but still cannot verify an AI-assisted output or explain when to escalate uncertainty, targeted practice may address a learnable skill gap. If they cannot access the approved tool, do not know which information may be entered, or receive unreliable source records, a course cannot remove those obstacles. Fix the conditions first, or pair that fix with practice where both are needed. The CDC’s “Assess Training Needs: Conducting Needs Analysis” guidance says to identify the gap’s source before deciding whether training is needed. It lists policy, technology, organization, and procedures as possible causes or solutions. This general guidance is not a Turing-framework trial, but it cautions against starting with a course catalogue. | Evidence from the duty | Likely first response | | --- | --- | | Staff have access and a clear checking standard, but do not compare material claims with source records | Coach verification using safe examples | | Approved-tool permission differs or remains unclear | Clarify access and the approved route before assessing tool use | | Staff are unsure whether sensitive information may be entered | Set the organization’s data-handling rule, then practise applying it | | Source records are incomplete or handoffs omit context | Repair the input or handoff process and assign responsibility | | People interpret the quality standard differently despite workable conditions | Make the standard observable, calibrate examples, then teach the relevant step | These are prompts, not automatic findings. One poor output may reflect an unusual case, missing context, or an unclear standard; it does not establish that a worker lacks competence. Equally, a process fix should not conceal a learning need when people have fair conditions but still cannot perform the expected step. Record the observed issue, working conditions, and reason for the response so the diagnosis can be revisited. Framework-led practice differs from a generic AI course in its starting point. The Turing framework connects a role or persona to duties and competencies, then to the knowledge, skills, and behaviours relevant in context. A manager can use that map to shape an exercise around actual work: checking a generated client update against its source record, marking unsupported claims, and deciding what needs human review. A broader course may help with a shared foundation need, but attendance alone does not show a workflow gap has closed. Skills England’s “Barriers to AI skills development,” updated 4 November 2025 and applying to England, reports expert-identified barriers such as inconsistent meanings of “AI skills,” foundational digital-literacy needs, fragmented provision, training costs, and limited employer clarity about workforce needs. These are expert-reported themes, not estimates of how often barriers affect each team. Check whether a course is understandable, accessible, affordable in time and money, and connected to a defined duty. The UK “Skills for AI” insight briefing describes PRIMES principles: training should be practical, reachable, integrated into work, modular, expandable, and sustainable. PRIMES is a separate training-design framework, not an evaluation of the Turing framework and not proof that a course improves performance. It supports a modest design choice: when practice is warranted, tie it to the task, give feedback, and make room to apply it at work. When policy or access is the obstacle, repair that first. If both conditions and capability are weak, sequence the operational fix and learning so staff are not asked to demonstrate something they are not permitted or equipped to do. The manager’s comparison is three-way: target practice for a learnable duty, choose a broader course for a shared foundation need, or change workflow conditions when they explain the shortfall. A combined response can fit, but name each part and review the duty under fair conditions rather than counting attendance.

Sources: Assess Training Needs: Conducting Needs Analysis; Barriers to AI skills development; Insight briefing based on research: What works for AI upskilling in the UK

Why should the first response fit the work and the evidence?

Choose the response by the work that needs to change. A short guide can clarify a shared rule, such as which information must not enter an unapproved tool. It cannot show whether staff can apply that rule while doing the task. Coached practice is a better fit when evidence points to a specific skill, such as checking a generated summary against source records or deciding when uncertainty requires escalation. A structured course makes more sense when several duties rely on a deeper foundation that staff do not yet share, such as understanding how to evaluate outputs, protect information, and recognize when a system is outside its useful limits. The format should match the depth needed: a handout can state a rule, but it cannot provide repeated practice and feedback on judgment. Workflow redesign is indicated when the difficulty sits in handoffs, review capacity, or unclear responsibility. For example, if a draft is checked by several people but no one owns verification against the source, another prompt-writing lesson leaves the key control unresolved. Make the check explicit in the workflow and assign who records and resolves discrepancies. Access or policy changes come first when people lack an approved tool or cannot tell what data they may use. Asking them to demonstrate a capability under those conditions confuses a blocked task with an individual learning gap. The Skills for AI insight briefing based on research, updated in July 2026 and applying to England, draws on national workshops, a UK-wide employer survey, and case studies. It describes training as more useful when practical, linked to real tasks, embedded in day-to-day work, accessible, and developed over time. Its reported survey findings also identify flexibility, protected time, cost, and staff capacity as participation issues. They support a design question for the manager: can the affected workers attend, practise on relevant tasks, and use the learning in their workflow? The same briefing presents PRIMES as separate guidance for training design: practical, reachable, integrated, modular, expandable, and sustainable. Short modules may fit shift patterns or limited release time; flexible formats can help workers with different schedules or access needs. Practical examples should reflect the role's responsibilities and build on staff's existing domain knowledge. Where basic digital foundations are missing, allow time to address them before expecting a new tool routine. Research evidence, analysis and methodology: What works for AI upskilling in the UK explains that its evidence combines 23 workshops involving close to 150 organisations, 10 case studies, and a survey with 536 responses. The workshops were purposively sampled and provide contextual accounts, while the survey was not fully representative of the UK economy and did not support robust sector-level analysis. The report also cautions that workshop findings are not statistically representative and case studies are illustrative. England's Barriers to AI skills development reports expert-identified problems including inconsistent definitions of AI skills, foundational digital-literacy needs, fragmented provision, costs, and limited employer understanding of workforce needs. It does show why a course catalogue or a confidence poll may miss the cause. A manager can ask workers what prevented success, check whether the needed tools and rules were available, and compare those answers with observed task performance. Then select the smallest adequate response: clarify a rule, repair a handoff, provide safe access, coach one skill, or commission broader learning only where multiple mapped duties reveal a shared need. If the evidence remains mixed, make the next step a short, supported practice period that can clarify the cause instead of making a permanent training decision. That keeps the intervention tied to an observable duty while allowing the diagnosis to change if conditions or evidence change.

Sources: Insight briefing based on research: What works for AI upskilling in the UK; Research evidence, analysis and methodology: What works for AI upskilling in the UK; Barriers to AI skills development

How can a manager check whether the intervention closed the gap?

To check whether an intervention closed a team’s AI-related capability gap, revisit the same duty after people have had a realistic chance to apply what they learned. Use the original criteria on comparable work, and check two outcomes: whether participants learned the skill and whether they used it in the workflow. Attendance and satisfaction describe participation and reaction, not either outcome. If performance remains uneven, investigate again before ordering another course. Before learning begins, define acceptable performance for the duty in observable terms. Record representative examples or use a safe demonstration. Note available tools, sources, guidance, time, and permissions. The baseline need not become a score; it makes comparison concrete and helps distinguish capability from conditions. Provide practice and feedback tied to the duty. Use a demonstration, supervised exercise, or de-identified examples suited to the work’s risk. Do not use sensitive material in an unapproved system to make practice realistic. If access is restricted, use a safe alternative and record that limit. Allow an interval in which people can apply the learning. The CDC’s “Evaluate Training: Measuring Effectiveness” says timing should reflect the evaluation question, resources, training scope, and learners’ opportunity to apply the material. It calls delayed follow-up the best way to assess transfer because learners have returned to work. Choose timing based on how often the duty occurs and whether conditions remain comparable; for rare tasks, a safe simulation may be more useful than an arbitrary wait. At follow-up, apply the original criterion to comparable examples and ask what helped or prevented use. “Evaluate Training: Measuring Effectiveness” recommends checking learning and transfer where possible: a learning check shows whether someone can explain or demonstrate the skill; workplace review shows whether it was applied. These observations differ. Record what evidence shows and what it cannot show. A few examples can guide a decision, but are not a validated team score. Keep the task and review conditions as similar as practicable, and note deviations rather than silently treating unlike cases as equivalent. If several people encounter the same barrier, consider whether the remedy belongs in the process rather than in individual instruction. Interpret change cautiously. Stronger performance in comparable demonstrations and work samples is encouraging evidence of learning and transfer, but does not prove training alone caused it: tools, workload, task difficulty, staffing, guidance, or source quality may also have changed. No visible improvement does not prove training was useless; the task may be infrequent, practice mismatched, or access and time inadequate. The CDC’s “Assess Training Needs: Conducting Needs Analysis” recommends identifying specific knowledge, skill, or behavior gaps while examining people and system causes. It names policy, technology, organization, and procedures as possible remedies. Reopen the cause analysis before deciding what to change. Keep the follow-up note brief: name the duty and criterion; describe examples and conditions reviewed; separate learning from workplace application; record barriers; and state the next response. If the skill is developing and conditions are adequate, add focused practice or coaching. If the skill is demonstrated but workflow blocks use, address access, guidance, process, or accountability. When causes interact, pair modest practice with a process change and review again. The decision is not whether a course received a favorable reaction. It is whether the defined duty can now meet the agreed standard under actual working conditions. Keep the diagnosis provisional and let staff explain constraints; do not rank people on a small development sample. If a shortfall remains, examine what changed in the evidence and environment before repeating training.

Sources: Evaluate Training: Measuring Effectiveness; Assess Training Needs: Conducting Needs Analysis

What is the next practical step for a manager?

Choose one consequential workflow and one duty, then define acceptable performance in observable terms. For example: when preparing a client update, check each material AI-drafted claim against the approved source record and flag unresolved points before approval. This is a local standard, not a score supplied by the framework. The Version 3.0.0 record describes role-aligned capability planning; it does not establish a validated team test.

Collect a few safe, comparable examples or use a controlled demonstration. Before calling errors a training gap, check approved tool access, clear rules, usable source material, time, and a workable handoff. The CDC’s “Assessing Training Needs: Conducting a Needs Analysis” guidance recommends examining system conditions alongside knowledge, skill, and behaviour. If conditions were missing, address them first; if staff had a fair chance but need verification practice, provide focused instruction and feedback.

Set a follow-up after workers can apply the practice, then review comparable work against the same standard. The CDC’s “Evaluate Training: Measuring Effectiveness” guidance distinguishes learning from workplace transfer; satisfaction alone does not show effectiveness. If results remain weak, revisit the diagnosis before adding training. Train a demonstrated, learnable shortfall; fix barriers training cannot remove. An individual task checker describes personal change-pressure signals, not a team’s training needs.

Sources: Assess Training Needs: Conducting Needs Analysis; Evaluate Training: Measuring Effectiveness; AI Skills for Business Competency Framework

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