What is the Financial Services Skills Compact?

Every financial services firm is currently answering the same question, whether or not it has been asked directly yet: can you show that your people know how to use AI responsibly?

Clients, regulators and boards are all starting to expect a demonstrable answer, rather than an assumption that AI competence will simply develop on its own.

The Financial Services Skills Compact has emerged as the industry's response to that expectation. It sets out a shared commitment that firms will actively build workforce AI literacy, rather than leaving it to individual initiative or informal on-the-job learning.

For many organisations, this is the first time workforce AI capability has been framed as something to be planned, measured and reported on — rather than something that happens quietly in the background.

This article explains what the Compact is, why it exists now, and what it practically means for firms beginning to respond to it.

Operational Context

AI adoption across financial services has moved faster than most workforce planning cycles.

Tools that assist with trade analysis, client communication, underwriting support, reconciliation and reporting are now embedded in day-to-day operations across trading floors, back offices and client-facing teams alike.

Regulators have taken note. Supervisory attention increasingly extends beyond the models and systems themselves to the people using them — asking whether staff understand the limitations, risks and appropriate use of AI tools in their specific roles.

At the same time, clients and counterparties are beginning to ask due diligence questions about workforce AI competence, not just system controls.

The Financial Services Skills Compact responds directly to this shift. It formalises an expectation that firms will treat workforce AI literacy as a deliberate, organisation-wide commitment, rather than something that develops informally as staff pick up tools on their own.

This matters because informal upskilling is uneven by nature. Some teams experiment readily; others avoid new tools altogether. Without a coordinated approach, firms end up with pockets of strong AI practice sitting alongside significant blind spots — precisely the inconsistency that regulators and clients are now probing for.

Traditional Approaches

Financial services firms have long-established mechanisms for workforce skills development, and these remain relevant. Mandatory compliance training, role-specific certification and periodic refresher modules are well understood by HR, risk and compliance functions.

This traditional model works well for stable, well-defined obligations — anti-money laundering training, conduct risk modules, or product-specific certification, for example. The content changes infrequently, the audience is well defined, and completion can be tracked against a fixed curriculum.

AI literacy does not fit this model comfortably.

The technology, its capabilities and its risks are changing faster than annual or biennial training cycles can reasonably track. A module written twelve months ago may already understate what AI tools can do — or fail to address a new category of risk that has since emerged.

Traditional approaches are also often role-siloed, treating AI literacy as a technology or data team concern rather than something relevant to operations, underwriting, trading, risk and customer-facing staff alike.

The result is that many firms have compliance training records that look complete, while workforce AI understanding in practice remains inconsistent and reactive.

Where AI Helps

AI-assisted learning tools can genuinely help firms respond to the Compact at the scale and pace it requires — but they support the programme rather than define it.

Adaptive learning platforms can adjust content to a learner's existing knowledge, role and demonstrated gaps, rather than delivering identical modules to every employee regardless of relevance. This is particularly useful given how differently AI literacy needs look across a trading desk, a claims team and a customer service function.

AI-assisted assessment can also help identify literacy gaps more efficiently — analysing where staff struggle with concepts, scenarios or terminology, and surfacing this to L&D and risk teams rather than relying solely on self-reported confidence or generic quizzes.

This does not remove the need for structured governance. Decisions about what constitutes an acceptable literacy benchmark, how gaps are prioritised, and how the programme is reported to the board remain matters of human judgement. AI can reduce the manual effort involved in delivering and monitoring training at scale, but it does not substitute for organisational ownership of the outcome.

Operational Considerations

Responding to the Skills Compact credibly requires several things to be in place.

First, cross-functional ownership. AI literacy touches HR and L&D, risk and compliance, technology, and business unit leadership. A programme owned solely by one function tends to miss significant parts of the workforce or fail to reflect operational reality.

Second, measurable benchmarks. Firms need a working definition of what "AI literate" means for different roles — a trader's needs differ from a settlement analyst's, which differ again from a customer service adviser's. Without benchmarks, progress cannot be demonstrated to a board or a client.

Third, board-level visibility. Given the regulatory and reputational stakes, senior leadership are increasingly expected to be able to describe workforce AI capability with the same confidence they would describe financial or operational risk exposure.

Firms that treat the Compact as a compliance checkbox, rather than a genuine capability-building exercise, are likely to find the gap between their paperwork and their actual workforce readiness becomes visible at the worst possible moment — during a regulatory review or a client due diligence process.

Example

A mid-sized London-based commodity trading firm receives a client due diligence questionnaire asking how it ensures staff are equipped to use AI tools responsibly in trade execution and settlement decisions.

The Head of Operations, responsible for commodity trade settlement, realises the firm has no formal answer beyond informal assurances that "the team knows what they're doing."

Working with the Head of HR/L&D, they establish a cross-functional working group to map current training against Financial Services Skills Compact expectations. The review identifies clear gaps in AI literacy among settlement and reconciliation staff, particularly around understanding the limitations of AI-assisted matching tools already in use.

The firm sets a board-reported plan to close these gaps within two quarters, with progress tracked against defined literacy benchmarks rather than simple training attendance.

FAQs

• Is the Financial Services Skills Compact a legal requirement?

  The Compact is not a standalone piece of legislation. It functions as a sector-wide commitment and workforce expectation that is closely tied to the direction of regulatory attention on AI governance. Given the current regulatory environment, firms are well advised to treat alignment with it as effectively non-optional, even though it is not enforced in the same way as a specific statutory obligation.

• Which employees does the Skills Compact apply to?

  It is intended to apply broadly across the workforce, not just to technology or data teams. Operations, underwriting, trading, risk, compliance and customer-facing staff are all expected to have a level of AI literacy appropriate to their role.

• How is compliance with the Skills Compact measured?

  Measurement typically involves demonstrable workforce literacy benchmarks, evidence of training completion, and board-level reporting on progress. Detailed approaches to assessment are covered in related articles within this knowledge base.