ANS has expanded delivery of its new AI leadership apprenticeship unit from two planned 2026 cohorts to six after receiving stronger-than-expected demand from employers across technology, housing, government, infrastructure, fintech, and financial services.
The Manchester-based technology company launched its Level 5 AI Leadership Strategy unit in July, initially expecting to support around 30 learners during the year. Its current pipeline has increased to 71 potential learners, prompting ANS Academy to triple the number of planned cohorts.
The early demand provides a narrow but useful indication of how organisational AI requirements are changing. Companies that began by developing technical capability are increasingly looking for managers who can decide where AI should be deployed, assess the commercial case, understand risk, and oversee implementation across business functions.
Participants currently span a wide range of roles, including people directors, operations managers, heads of internal communications and engagement, pre-sales leaders, research and development directors, customer-success teams, compliance specialists, and information-security leaders.
That breadth is notable because responsibility for AI has often been concentrated in technology or data departments. As systems become embedded in customer processes, workforce decisions, financial controls, compliance, and operational workflows, decisions about their use increasingly sit with managers who may not have conventional technology backgrounds.
Toria Walters, Chief People Officer at ANS, said: “What we’re seeing is that AI leadership is no longer something that sits solely with technology teams. People leaders, operations teams, compliance, customer functions and commercial teams all have a role to play in understanding where AI can create value and how it can be adopted responsibly.
“That is exactly what this programme is designed to support, giving people the confidence and structure to move beyond simply understanding AI and start making better decisions about where and how it should be used.”
The eight-week programme is aimed at senior managers, existing leaders, and people moving into positions where they will influence AI strategy, investment, risk, or organisational change.
Learners are required to develop a workplace AI opportunity into a business case, assessing potential benefits, costs, feasibility, measurable outcomes, governance, security, auditability, stakeholder engagement, and workforce impact.
That structure reflects a wider change in the AI investment cycle. Experimentation can often begin with relatively limited budgets and small groups of enthusiastic users. Scaling a system into normal operations is more demanding because organisations need to decide who owns it, what information it can access, how outputs are checked, and what happens when something goes wrong.
Commercial evaluation also becomes more important as budgets rise. Leadership teams need to distinguish between applications that offer measurable productivity or service improvements and those that demonstrate technical capability without producing sufficient operating value.
The responsibility cannot sit entirely with technical teams. A model may perform as designed while still being unsuitable for a particular commercial process, customer interaction, or regulatory environment. Managers responsible for those areas need enough understanding to challenge assumptions and identify where implementation creates unintended consequences.
Richard Thompson, chief executive of ANS, said: “The conversation is moving from ‘what can AI do?’ to ‘where should we use it, what value will it create, and how do we do it safely?’.
“Those are distinctly leadership-level questions, so organisations looking to become Frontier Firms need people across the business who can evaluate opportunities, challenge assumptions, understand risk and connect AI investment to meaningful business outcomes.”
The scale of the current ANS pipeline should not be mistaken for a national measure of demand. Seventy-one potential learners represent the experience of one training provider rather than a broad survey of UK employers, and expressions of interest do not necessarily translate into completed enrolments.
Its composition is nevertheless revealing. Interest is coming from functions that increasingly encounter AI as part of normal management rather than specialist technology deployment.
That creates a different skills requirement from training engineers or data scientists. Managers do not necessarily need to build models themselves, but they need to understand enough about data, automation, security, governance, cost, and organisational change to make credible decisions about where systems should be introduced.
The apprenticeship format also connects training with an active workplace project rather than treating AI capability as a purely academic exercise. That can make it easier to link learning with existing investment decisions, although the longer-term value of the programme will ultimately depend on whether participants convert those projects into measurable operational outcomes.
ANS has already been building a wider proposition around AI readiness and responsible deployment, with its leadership team publicly discussing the management requirements associated with organisations adopting AI at greater scale. Its official material identifies Walters as Chief People Officer and Richard Thompson as chief executive.
The increase from two cohorts to six remains early evidence rather than a settled market trend, but it points towards the next stage of the skills challenge. As access to AI tools becomes easier, organisations increasingly need managers who can determine which systems should move beyond experimentation, what controls they require, and whether the investment produces enough value to justify deployment.
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