By Greg Mills, Senior Consultant, Finance Transformation, Stanton House

Over the past year, one theme has appeared in an increasing number of the Finance Transformation conversations I have with CFOs, Finance Directors and Transformation Leaders: the growing expectation that AI should be part of almost every hire.

Whether organisations are implementing a new ERP platform, redesigning finance processes or building out transformation teams, AI is increasingly finding its way into role specifications. In many cases, it sits alongside requirements for programme leadership, stakeholder management, process improvement, reporting and systems expertise.

On the surface, that makes perfect sense. Finance leaders are under pressure to improve productivity, increase forecasting accuracy, automate manual processes and deliver better insight to the business. At the same time, AI has moved from a future consideration to a board-level priority for many organisations.

However, there is a question I believe more organisations need to ask before adding AI to a job description: What business outcome are we actually trying to achieve?

Because in many of the conversations I have, the answer is less clear than the requirement itself.

Why AI Is Appearing in Every Job Description

The growing prominence of AI is understandable.

Modern finance functions are expected to provide better analysis, support faster decision-making and operate with greater efficiency, often without a corresponding increase in resources. Technology vendors are also embedding AI capabilities into finance systems, reporting platforms and automation tools, creating new possibilities for finance teams.

Against this backdrop, many organisations feel they should be hiring AI capability.

The challenge is that "AI experience" has become one of the least precise phrases in Finance Transformation hiring.

When clients tell me they need someone with AI experience, my next question is usually simple:

"What do you need that person to do?"

The answers vary significantly.

Some organisations want somebody who can identify automation opportunities.

Others want help improving forecasting and reporting.

Some are looking for stronger governance around AI usage.

Others want leaders who can drive adoption across the finance function or work alongside technology teams implementing AI-enabled solutions.

These are all very different requirements!

Each requirement points towards a different profile. Someone who can govern AI is not necessarily the same person who can implement AI tools. Someone who can improve forecasting capability may not be the right person to lead behavioural change across a finance function.

Yet they are often grouped under a single heading in a job description.

Our own analysis of Finance Transformation client conversations, over the last six months, backs this up. AI came up in more than a third of discussions we’ve had with clients, but what hiring managers actually meant by "AI experience" was often much less clearly defined. We also found that over 60% of role intakes combined multiple disciplines, often bringing together systems, transformation, reporting, AI, change and leadership within a single brief.

That creates a risk that AI becomes a requirement because it sounds strategically important, rather than because it is linked to a clearly defined business need.

The Risk of Hiring for Technology Instead of Outcomes

One of the most valuable conversations we have with clients often happens before recruitment begins.

It starts by stepping away from job titles, technology requirements and capability wish lists and focusing on a more fundamental question: What business problem are you trying to solve?

That question frequently changes the discussion.

We've seen organisations request AI capability when the real challenge was poor reporting quality and a lack of confidence in financial data.

We've seen transformation programmes where AI was described as a priority, only to discover that the biggest barrier to progress was manual finance processes and inconsistent ways of working.

We've seen hiring managers ask for specialists with AI expertise when the immediate need was actually programme leadership, stakeholder alignment or stronger governance.

These examples illustrate an important point. Technology rarely solves a business problem on its own.

Successful transformation typically depends on a combination of people, processes, governance and data. Technology enables the change, but it is usually not the capability gap that needs addressing first.

Again, our analysis of client conversations over the last six months reinforces this. In just under half of cases, hiring managers were not yet completely clear on the capability they needed, with defining the role often proving a greater challenge than finding the right candidate.

Interestingly, wider market research points in a similar direction. McKinsey's 2025 State of AI survey found that while AI adoption is now widespread, most organisations remain in the early stages of scaling AI and achieving enterprise-wide value. The research highlighted workflow redesign and business transformation as critical factors in capturing meaningful impact.  

In other words, outcomes still matter more than technology labels.

Why Data Quality & Process Maturity Still Matter

One of the biggest misconceptions surrounding AI in Finance Transformation is that new technology can compensate for weak foundations.

In reality, the opposite is often true.

If finance data is inconsistent, reporting structures are fragmented or processes remain heavily manual, introducing AI does not remove those challenges. In many cases, it exposes them more clearly.

This is one of the reasons why conversations that begin with AI frequently move towards discussions about data quality, governance and process maturity. Before organisations can realistically determine where AI will add value, they often need to address the underlying processes and data it will rely on.

Wider research supports the importance of getting those foundations right. Microsoft's 2026 Work Trend Index highlights that successful AI adoption depends on organisations changing how work is structured and performed, rather than simply introducing new technology.

For finance leaders, this means the work already being done to improve data quality, strengthen governance and redesign processes is not separate from the AI agenda. It is what creates the foundations for it.

How AI Should Fit Within a Wider Finance Transformation Programme

The organisations that seem to gain the greatest value from AI tend to approach it differently.

Rather than treating AI as a standalone objective, they position it within a broader transformation strategy.

They begin by defining the business challenge.

They identify the outcome they want to achieve.

They determine which capability is missing.

Only then do they evaluate how technology, including AI, can help.

That approach aligns closely with what we see across successful Finance Transformation programmes.

The strongest programmes are outcome-led.

They focus on improving reporting, strengthening controls, increasing productivity, redesigning processes and enhancing decision-making. Technology becomes an enabler of those outcomes rather than the objective itself.

The Questions Every Finance Leader Should Ask Before Adding AI to a Job Description

Before making AI a requirement, I would encourage leaders to answer five questions:

1. What business outcome are we trying to improve?
2. What capability is genuinely missing today?
3. Is AI central to the role, or simply part of a wider transformation roadmap?
4. How will success be measured six to twelve months after the person joins?
5. Are we asking one individual to solve several different problems?

That final question is often the most important.

Many job descriptions now combine ERP implementation, process improvement, reporting, automation, AI, change management and leadership. Individually, each requirement may be valid. Combined, they can create unrealistic expectations and unnecessarily narrow the talent pool.

Final Thoughts

AI will continue to influence the future of Finance Transformation.

However, organisations do not create value by hiring people because they have AI on their CV.

They create value by solving business problems, delivering measurable outcomes and building the capabilities that support long-term success.

The most successful transformation programmes begin with clarity around the challenge, the outcome and the capability required to deliver it. Only then can organisations determine whether AI expertise is genuinely critical to the role.

The most productive transformation conversations focus on outcomes first and technology second.

Technology enables transformation, capability delivers it.

Continue The Conversation

Before writing AI into your next job description or launching a Finance Transformation search, consider working through Stanton House's Finance Transformation Capability Workbook. The framework helps finance leaders identify the business challenge they are trying to solve, define the capability required, assess whether AI is genuinely central to the role and determine the most effective route to securing that capability.

By starting with outcomes rather than technology, organisations can reduce hiring risk, improve role definition and build the capability needed to deliver meaningful, measurable Finance Transformation.

For a copy of our Finance Transformation Capability Workbook, please reach out, I'd be happy to send it across.

Sources:

• Microsoft Work Trend Index 2026 Annual Report - Microsoft 2026 Work Trend Index Annual Report
• McKinsey, The State of AI 2025 - The State of AI: Global Survey 2025 | McKinsey