🏠︎ | Past Sessions | Making AI a Daily Habit – Taking Our Teams With Us
- Event: Finance Forum 25
- Date: 7 October 2025
- Speaker: Oliver Deacon, Executive Coach, Oliver Deacon Consulting
- Estimated read time: 7-8 minutes
Quick summary
This session focused on how finance leaders can turn AI from an occasional experiment into a daily working habit across their teams.
The core message was that AI adoption fails when it feels risky, abstract, or disconnected from real work. Instead of delegating finance tasks to AI, which is often unrealistic and unsafe today, leaders should focus on using AI to improve how work is done.
By reframing AI as a way to solve “how” problems rather than “what” problems, finance teams can unlock meaningful productivity gains without increasing risk. The shift is less about tools, and more about behaviours, habits, and leadership expectations.
Why AI matters now, even if jobs are not disappearing
Oliver Deacon opened with a stark but practical framing. Many finance roles as they exist today will not exist in the same form in the coming years. The issue is not job loss, but job change.
The work finance teams do, and the skills required to do it well, are already shifting. In most organisations this change is likely to play out over the next three to five years, not decades.
What makes this risky is not AI itself, but low awareness. Only a small proportion of finance professionals are actively adapting how they work today. That gap creates a growing divide between teams that experiment early and those that wait.
The aim of this session was to close that gap with practical steps finance leaders can take now.
Habit beats hype, usefulness drives adoption
A recurring theme was that AI only sticks when it is useful. Finance teams do not adopt tools because they are impressive, they adopt them because they make work easier.
Excel is a good comparison. It became a daily habit because it solved real problems quickly. AI needs to meet the same standard.
Highly visible AI features, such as video generation or advanced automation demos, may be impressive, but they are rarely relevant to day to day finance work. They do not create habit.
The real test for AI adoption is simple. Does it help someone do their existing job better tomorrow morning?
The risk versus impact gap in finance AI
Oliver introduced a simple way to think about AI use cases using two dimensions, risk and impact.
At the low risk, low impact end are simple uses such as replacing Google searches or asking AI for generic guidance. Many people already do this, but it rarely changes how they work.
At the high impact, high risk end is delegating finance work directly to AI, such as forecasting or reporting using live company data. Today, this approach is often unreliable and carries serious confidentiality risks.
Most finance teams sit stuck between these two extremes. The challenge is how to move forward without waiting for perfect technology or exposing sensitive data.
Why delegating work to AI is not realistic yet
In theory, getting AI to build forecasts or run core finance processes sounds transformational. In practice, very few teams can make this work consistently today.
The technology is still immature, and for most organisations it is not safe to input confidential financial data into public AI models. Even well resourced finance teams struggle to get reliable results.
This creates frustration. People try once, fail, and conclude that AI is not useful.
The result is stalled adoption rather than progress.
The power of “how” problems
The most important concept in the session was the distinction between “what” problems and “how” problems.
- “What” problems ask AI to do the work.
- “How” problems use AI to help people do the work better.
- “How” problems are where finance teams can make real progress today.
Most finance processes are not unique or proprietary. Month end close, journals, reconciliations, reporting and forecasting all follow familiar patterns across organisations.
AI does not need company data to help improve how these processes are designed and executed. It can suggest better approaches, alternative structures, or ways to reduce manual effort.
This keeps risk low while increasing impact.
Tools matter, but only if they are useful
Not all AI tools are equally helpful for finance work.
Built in workplace tools are often limited in capability and can feel slower than doing the task manually. General purpose AI tools tend to offer more useful responses, especially for problem solving and technical reasoning.
Different models also have different strengths. Some are better at structured, technical questions such as accounting or tax. Others are better at research or summarising information.
The key is not choosing the perfect tool, but choosing one that reliably produces useful answers for the types of “how” problems finance teams face.
Building AI capability is a journey, not a jump
Oliver emphasised that AI capability develops in stages.
Basic use cases such as drafting, editing, or researching put someone in the AI novice category. These activities can save time, but the gains are limited.
The bigger shift comes when teams start using AI to solve complex problems, redesign processes, and think through decisions. This is where finance professionals move into expert territory and unlock larger productivity gains.
Waiting until AI can do everything automatically is risky. Teams that delay building skills will struggle to adapt when more advanced tools arrive.
What finance roles are moving towards
Looking ahead, Oliver suggested that most finance roles will increasingly centre on two areas.
The first is business partnering, interpreting information, influencing decisions, and working cross functionally.
The second is managing AI, shaping how tools are used, validating outputs, and embedding them safely into ways of working.
Routine processing will continue to reduce, but the uniquely human aspects of finance, judgement, context, communication, and trust, become more important, not less.
What good looks like in practice
For finance leaders, making AI a daily habit is not about forcing usage targets or mandating tools. It is about shaping behaviour.
That means encouraging teams to use AI to question how they work today, not to replace it outright. It means rewarding curiosity and experimentation, not perfection.
Most importantly, it means positioning AI as a support to professional judgement, not a substitute for it.
Practical actions finance leaders can take now
Questions to ask your team
- Which parts of your role feel repetitive or inefficient today
- Where do manual steps exist simply because they always have
- What processes frustrate you most at month end
- Where could better structure or sequencing reduce effort
- What decisions would benefit from clearer analysis or framing
- Teams using AI to think, not just to write
- Fewer manual workarounds in core processes
- Improved confidence in challenging how work is done
- Faster onboarding and knowledge sharing
- More time spent on insight and discussion
- Expecting AI to automate everything immediately
- Allowing experimentation without clear boundaries
- Treating AI as a technology project rather than a behaviour change
- Waiting for perfect tools before starting
- Assuming adoption will happen without leadership example
What good looks like
A finance team that uses AI daily is not one that hands work over to machines. It is one that consistently asks better questions about how work is done, and uses AI as a thinking partner to improve quality, speed, and confidence.
Conclusion, AI habit is a leadership choice
The session closed with a clear message. AI will not suddenly transform finance on its own. The real shift comes when leaders deliberately build habits that make AI useful, safe, and relevant to everyday work.
By focusing on “how” problems, finance leaders can help their teams adapt steadily, build confidence, and stay in control of their professional impact as the role continues to evolve.
Speaker
Executive Coach, Oliver Deacon Consulting, Finance leadership coach helping CFOs and finance teams futureproof their careers, improve performance, and adapt to the evolving role of finance.