Artificial intelligence is changing how finance teams operate. Tasks that once required hours of manual work, from reconciliations and reporting to data analysis, can increasingly be completed with the help of technology.
For businesses, the benefits are clear: improved efficiency, faster insights and more time for strategic decision-making.
But there is another question worth asking: if we automate the work that traditionally develops junior finance professionals, how will we develop the experienced professionals and managers we will need tomorrow?
This is where the risk of a future missing middle emerges. As businesses rethink their workforce structures, the challenge is no longer simply finding the right talent.
It is ensuring that the next generation of finance professionals has the opportunity to gain the experience, develop the judgement and build the commercial understanding required to progress.
The changing path to finance leadership
Traditionally, finance professionals developed their expertise through experience. Junior employees learned by preparing reports, investigating discrepancies, supporting audits, reconciling accounts and working through month-end processes.
These tasks were not always exciting, but they provided a foundation for understanding how a business operates, recognising patterns, identifying risks and making informed decisions.
Over time, that experience helped professionals progress into senior accounting positions, financial management and leadership roles.
AI is changing this pathway. When technology can complete routine tasks more quickly, businesses may need fewer people performing them. Junior roles may become more specialised, while expectations around digital capabilities continue to evolve.
However, the concern is not necessarily that finance professionals will become redundant. It is that the opportunities to gain practical experience could become more limited if businesses focus on automating tasks without redesigning how people learn.
A professional who can generate a financial report using AI is not automatically equipped to question its assumptions, identify an error or explain its implications to a leadership team.
Producing an answer and understanding what that answer means are two different capabilities.
Efficiency today, experience tomorrow
Research highlights why this deserves attention.
Deloitte’s 2025 Global Human Capital Trends research found that 66% of surveyed managers and executives felt recent hires were not fully prepared for their roles, with a lack of experience being the most commonly cited shortcoming.
At the same time, research from Stanford’s Digital Economy Lab has identified declining employment among younger workers in certain AI-exposed occupations in the United States. The findings suggest that reduced hiring may be one factor, although they do not establish AI as the sole cause.
These findings point to a challenge for employers. Businesses already face difficulties finding professionals with the right combination of technical expertise, practical experience and commercial judgement. If entry-level opportunities become harder to access, developing that combination could become even more challenging.
The consequences may not be immediately visible. A business can reduce costs and improve productivity in the short term, only to discover several years later that it has fewer experienced professionals ready to step into senior positions.
This is not an inevitable outcome of AI adoption. It depends on how businesses choose to redesign their roles, develop their people and plan for future leadership needs.
The risk of a missing middle
The potential impact goes beyond entry-level recruitment.
The missing middle may not necessarily be a shortage of candidates, but a shortage of professionals with the practical experience, commercial judgement and leadership capabilities required to step into more senior roles.
- A weaker succession pipeline
If fewer junior professionals enter finance teams, or if their development is deprioritised, businesses may have fewer internal candidates ready to progress into senior roles. This can increase reliance on external recruitment, particularly when specialist knowledge and leadership experience are required.
- Technical ability without commercial judgement
AI can help employees produce analysis, but professionals still need to understand the context behind the numbers. They must be able to question results, recognise unusual patterns, assess risk and translate financial information into practical business decisions.
These capabilities develop through experience, feedback and exposure to increasingly complex situations.
- Greater pressure on experienced professionals
As routine tasks become automated, experienced finance professionals may be expected to manage more complex work, oversee AI-generated outputs and maintain accountability for decisions.
If they are also responsible for training junior colleagues without sufficient time or support, the pressure can increase. Removing layers of work does not remove the need for guidance, oversight and development.
- Increased competition for experienced talent
When businesses do not develop enough talent internally, they often look to the external market. If many employers follow the same approach, competition for experienced finance professionals could intensify.
The result may be a disconnect between businesses seeking ready-made talent and professionals who have not been given sufficient opportunities to develop the experience those businesses require.
Rethinking how finance professionals develop
The answer is not to preserve manual tasks simply because they have always been part of a finance role. Nor is it to assume that every junior position must remain unchanged.
Instead, businesses need to consider how they will replace the learning opportunities that disappear when tasks are automated.
This starts with identifying which responsibilities build essential skills and ensuring employees can continue gaining that experiences in new ways.
For example, a junior finance professional might use AI to prepare an initial variance analysis, but still be responsible for investigating unexpected movements, checking the underlying data and presenting their findings to a manager.
Similarly, automation may reduce the time spent preparing reports, creating more opportunities for junior employees to participate in discussions about budgets, forecasts and business performance.
When used effectively, AI can also accelerate learning by giving junior professionals more opportunities to interpret information, test their understanding and engage with complex financial scenarios under experienced supervision.
Structured mentoring, practical case studies, supervised decision-making and regular feedback can also help professionals develop skills that technology alone cannot replace.
The objective is to move beyond measuring productivity solely by the volume of work completed. Businesses must also consider whether their people are developing the capabilities required for greater responsibility.
Hiring for the finance function of tomorrow
These changes also have implications for recruitment.
As finance roles evolve, employees need to look beyond qualifications, years of experience and systems knowledge to assess whether candidates can apply their technical expertise, exercise sound judgement and contribute to commercial decision-making.
Employers should also consider whether their job descriptions reflect realistic expectations. Combining accounting expertise, advanced systems knowledge, data analysis, AI proficiency and leadership capability into a single junior position may create unnecessary barriers to entry.
A more effective approach is to distinguish between the capabilities required from day one and those that can be developed through training and experience.
For candidates, this creates an opportunity to demonstrate value beyond technical proficiency. Being able to explain how an insight was developed, how an error was identified or how financial information influenced a decision can help distinguish a capable professional from someone who can simply produce an output.
Ultimately, the strongest finance teams will need a combination of technology, expertise and people who know how to apply both.
Building the next generation of finance leaders
AI presents an opportunity for finance functions to become more strategic, efficient and commercially valuable. But achieving these benefits requires more than implementing new tools.
Businesses must also think carefully about how their workforce will evolve, how junior employees will gain practical experience and how future leaders will develop the judgement required to guide teams and make decisions.
The future missing middle is not a prediction that finance managers will disappear. It is a question of whether businesses are investing enough in the people who will become those managers.
If companies want experienced finance leaders tomorrow, they need to create the opportunities for those professionals to develop today.
At Talexus, we believe that effective workforce planning means looking beyond immediate hiring needs to consider the skills, experience and development required for long-term success.
As finance continues to evolve, the question for employers is clear: are we simply filling the roles we need today, or are we also developing the talent that will lead our finance teams tomorrow?