How Do We Assess Technical AI Competency?

Every financial services firm deploying AI into technical functions faces the same underlying question: how do we know staff actually understand the systems they are working with?

Completion of a training course tells you someone attended. It does not tell you whether they can identify a flawed model output, challenge an unreliable data pipeline, or recognise the limits of a tool they are relying on.

As AI moves deeper into model development, data engineering, quantitative analysis and trade processing, firms need a more defensible answer than self-declared confidence or a training certificate.

That answer is structured, evidence-based competency assessment — and it is a distinct discipline from AI awareness training.

Why assessment must go beyond training completion

Training tells you what someone was exposed to. Assessment tells you what they can actually do.

For roles that sit close to AI systems — model developers, data engineers, quantitative analysts, model risk reviewers, and technically-adjacent operations staff — the gap between the two matters enormously.

A member of staff may have completed every module in an AI literacy programme and still be unable to identify why a reconciliation model is producing unreliable outputs under certain market conditions. Conversely, someone with less formal training may have built strong practical judgement through hands-on exposure.

Regulatory expectations around AI governance are increasing, and supervisors increasingly expect firms to demonstrate — not merely assert — that staff working with AI systems understand what they are doing. Self-reported confidence and course completion records do not provide that evidence. Structured assessment does.

What technical AI competency actually means

Technical AI competency is different from general AI literacy.

General AI literacy is broad organisational awareness: understanding what AI is, where it is used, and what risks it carries at a conceptual level. It is appropriate for most of the workforce.

Technical AI competency is role-specific and task-based. It asks whether a particular person, in a particular role, can perform the tasks that role requires safely and effectively — for example, validating a model's assumptions, recognising data drift, or knowing when to escalate an AI-flagged exception rather than override it.

Defining technical AI competency therefore starts with the job, not the technology. What does this role actually need to do with, or around, AI systems? What could go wrong if they get it wrong? Those questions determine what should be assessed.

Traditional assessment methods still form the foundation

Financial services firms already have a long history of assessing technical competency in other domains — credit risk modelling, market risk, trading mandates. The same methods apply to AI-related skills.

Common approaches include:

• Practical exercises. Candidates work through a realistic task, such as identifying why a model output is unreliable, using real or representative data.
• Case-based evaluation. Candidates analyse a scenario and justify their reasoning, revealing whether they understand underlying concepts or are simply following a checklist.
• Structured interviews. A trained assessor asks probing questions about model limitations, failure modes and escalation triggers, going beyond what a written test can capture.
• Peer review. Experienced colleagues assess the quality of a candidate's actual work output over time, which is often the most reliable indicator of sustained competency.
• Certification. External or internal certification can validate a baseline, but should be treated as one input among several, not a standalone proof of capability.

Each method has strengths and weaknesses. Practical exercises are strong on realism but time-consuming to design well. Structured interviews are flexible but depend heavily on the assessor's own expertise. Combining methods produces a more reliable picture than relying on any single one.

Where AI helps

AI-enabled tools can genuinely support this process, without replacing the judgement at its core.

AI can generate varied practical scenarios at scale, so that candidates are not simply memorising a fixed set of test cases. It can help standardise scoring criteria across large numbers of assessments, making outcomes more consistent between assessors and business units. It can also flag inconsistent or anomalous responses for human review, drawing attention to cases that warrant closer examination.

This matters particularly for firms that need to assess large or distributed technical populations, where manually designing fresh scenarios for every candidate would be impractical.

What AI should not do is make the final determination of competency. Deciding whether a given performance meets the bar for a given role — particularly where that decision grants authority to override AI-flagged exceptions or sign off on model outputs — is a judgement call that carries governance weight. That judgement should remain with experienced human assessors, informed by AI-supported tools rather than delegated to them.

Operational considerations for running this well

A few practical points determine whether a technical AI competency assessment programme is credible or cosmetic.

Assessment criteria must be tied to actual job tasks. Generic questions about AI concepts test general literacy, not the specific competency a role requires. A model risk reviewer and a data engineer need different assessments, even if both work near the same AI system.

Results should feed directly into governance decisions. If assessment outcomes do not influence who is allowed to deploy, review or approve AI-related work, the exercise becomes a paper exercise rather than a control. Firms that link assessment to sign-off authority and oversight responsibilities get considerably more value from the process.

Finally, assessment programmes need refreshing as AI tools and associated risks evolve. A competency framework built around last year's tools may not capture the risks introduced by newly deployed systems. Reassessment frequency should track the pace of change in the underlying technology and tasks, rather than following a fixed annual cycle regardless of relevance.

Example

A London-based clearing member's technology team introduces an AI-assisted reconciliation tool for trades cleared through the LCH.

Before granting staff authority to override or approve AI-flagged discrepancies, the firm runs a structured technical competency assessment. This combines a practical reconciliation case study, a structured interview on the model's limitations, and an AI-supported scenario generator that varies the test cases presented to each candidate.

The technology and data team lead designs the assessment around real reconciliation tasks, while a model risk and compliance reviewer signs off on the criteria and reviews borderline results.

Only staff who demonstrate practical competency — not just course completion — are granted override authority on AI-flagged reconciliation exceptions, giving the firm a defensible, evidence-based basis for its governance controls.

FAQs

• What is the difference between AI literacy and technical AI competency?

  AI literacy is broad organisational awareness of what AI is and where it is used. Technical AI competency is role-specific and task-based: it measures whether a particular person can perform the AI-related tasks their role requires, such as validating model outputs or recognising data drift. Literacy is assessed at an organisational level; competency must be assessed against real job tasks.

• Can AI tools be used to assess AI competency without introducing bias or unfairness?

  AI tools can help generate varied scenarios and flag inconsistent responses, which supports consistency across large numbers of assessments. However, human oversight is essential to review assessment criteria, check for bias in how scenarios or scoring are generated, and make the final competency determination.

• How often should technical AI competency be reassessed?

  There is no universally correct fixed schedule. Reassessment frequency should be tied to how quickly the underlying AI tools, associated tasks and risks change for a given role, rather than an arbitrary annual or biennial cycle applied regardless of relevance.