There is a lot of noise around AI right now, and most of it is not particularly useful.

Every week seems to bring a new platform, a new promise, or a new prediction about how businesses will operate in the future. Vendors claim their software will transform entire organizations overnight. Social media is full of people declaring that entire professions are about to disappear.

I don't believe most business owners should pay much attention to that.

What I do believe is that something meaningful is happening beneath the hype. For the first time in a long time, small and mid-sized businesses can operate with a level of leverage that previously required significantly larger teams. As a CFO, that is the part that interests me — not the technology itself, but what it allows disciplined operators to accomplish with the people and resources they already have.

The finance function is one of the first places this shift becomes visible, because finance has historically carried a tremendous amount of work that must happen but does not directly create value.

Finance has always been buried in assembly work

Walk into most finance departments and you will find talented people spending much of their time gathering information rather than acting on it.

Reports need to be assembled. Data needs to be reconciled. Forecasts need to be updated. Presentations need to be built. Information needs to be pulled from multiple systems that often do not communicate well with one another.

None of this work is optional. It is necessary for the business to function. But much of it sits between leadership and the decisions they actually need to make.

Historically, the answer to growth has been straightforward: add people. As complexity increased, companies hired additional analysts, accountants, coordinators and administrators to absorb the workload. There is nothing wrong with that approach. Growing businesses will always need talented people.

What AI changes is the sequence. It creates opportunities to gain leverage before adding headcount rather than after. For an owner trying to balance growth, profitability and cash flow, that distinction matters.

What a modern finance function actually does

The strongest finance teams I have worked with spend far less time producing reports than they do helping leaders make decisions. They focus on questions like these:

  • Where should we invest our next dollar?
  • Which products, customers or channels generate the highest returns?
  • How much inventory should we carry?
  • What happens if growth slows?
  • Where is cash being consumed?
  • Which initiatives deserve additional resources?

Those questions shape the future of a business. And many finance teams become trapped spending so much time assembling information that they have little time left to analyze it.

That is where AI has the potential to create real leverage. Not by replacing financial expertise, but by reducing the time required to gather, organize and interpret information.

Five places I see real leverage being created

1. Turning existing information into actionable answers

Most businesses are not suffering from a lack of information. They are suffering from a lack of accessibility.

Over the years I have watched organizations accumulate thousands of spreadsheets, reports, contracts, emails, operating procedures, meeting notes and strategic plans. The information exists, but finding it requires knowing exactly where to look and who to ask.

AI is becoming genuinely effective at searching across those sources and surfacing answers quickly. The value is not the search. The value is the time between a question being asked and a decision being made.

2. Compressing the analysis cycle

One of the more interesting shifts I have experienced personally is how much faster analysis moves when the first draft of the work is accelerated.

In a business operating across multiple sales channels, departments and product categories, financial analysis usually requires gathering data from numerous sources before any conclusion can begin to form. Today many of those preparation steps move much faster. Scenario analysis, trend reviews, variance explanations, forecast assumptions and operational reviews can go from days to hours. The expertise is still required — someone still has to understand the business and interpret the result. But less time is spent gathering and more is spent evaluating.

An example. In a manufacturing business, leadership had become convinced that a particular sales channel was a money loser. The P&L showed it running at a loss period after period, and the instinct was to scale it back. Testing that assumption properly meant pulling cost allocations apart across every channel — a multi-day exercise that rarely gets prioritized when a team is already stretched thin. Done quickly, the finding reframed the entire conversation: the channel's reported loss was almost entirely a function of how overhead was being allocated to it. Its real contribution to the business was positive. Decisions were being made about that channel based on a number that did not reflect reality.

The analysis itself was nothing exotic. What changed was that it finally got done, because the cost of doing it had dropped from days to hours.

3. Reducing administrative communication work

Most executives underestimate how much of their week is consumed by communication. Board updates, management reports, meeting summaries, policy documents, planning materials and operational updates all take time and attention.

In most cases AI is not replacing the thinking. It is creating a starting point. Rather than beginning with a blank page, a leader begins with a draft and spends the energy on refining the message, challenging the assumptions and improving the quality of the discussion.

4. Building internal tools faster

Plenty of useful tools never got built because they were not important enough to justify a development project. A pricing calculator. An inventory planning dashboard. A project approval workflow. A KPI tracker.

These ideas lived permanently inside spreadsheets because the cost of building something better exceeded the perceived benefit. That equation is changing. Moving from idea to working prototype is fast enough now that businesses can test things that would otherwise have stayed on a wish list indefinitely.

5. Creating organizational memory

One of the least discussed problems in a growing business is knowledge concentration. Critical information lives inside the heads of a handful of people. Processes are hard to scale. Decisions get revisited repeatedly. New hires spend months learning things the organization already knows.

This may turn out to be the most valuable long-term application of all — not because it generates new knowledge, but because it helps a company use the knowledge it already has.

Don't focus on the tools

One lesson from working with these technologies is that most owners are asking the wrong question. The question is not which AI tool we should use. The better question is where we are experiencing friction, and what can remove it.

Some tools are excellent for strategy and communication. Others are better at document analysis, reporting, workflow automation or building applications. The specific platforms will keep changing — new ones will appear, existing ones will improve, and today's leaders may be tomorrow's followers.

The underlying principle is more durable. Technology creates value when it removes friction from an important decision. Automating the wrong process only lets you do something unnecessary more efficiently.

What this means for owners

I do not believe AI replaces experienced operators. If anything it increases their value.

Businesses still require leadership, judgment, financial discipline, operational understanding and strategic thinking. None of that goes away. What changes is the leverage available to the people doing it.

The companies that benefit most will not be the ones using the most tools. They will be the ones combining strong leadership, clean process, quality data and thoughtful implementation.

Where a fractional CFO fits

The job has always been to help build systems that scale — forecasting processes, reporting structures, performance metrics, financial controls, decision frameworks.

Increasingly it also means helping an owner understand where technology creates leverage and where it does not. Not every process should be automated. Not every tool is worth adopting. Sometimes the answer is better process discipline rather than new software.

When the right systems go in thoughtfully, they improve visibility, reduce administrative burden and help a leadership team make better decisions. Which is what finance has always been about. Not producing reports. Not implementing software. Not chasing trends. Helping a business make better decisions.

The tools are changing. The objective is not.