Twelve questions worth asking before your yearly AI plan is signed
Eight weeks of conversations about planning season, and the same problem kept arriving wearing different faces. A board chair who cannot see what half a million in AI spend bought. An owner using AI daily who cannot explain how the pieces fit. A marketing team sitting on an attrition list nobody has printed. An analyst who keeps being told it is too late to bring data into the decision.
The first and third of those usually work in the same building.
What follows is a list rather than a framework: the questions that, asked in the right room before the plan is signed, would have surfaced most of what went wrong.
notepad with pen
On the money
What in this plan will our competitors also have by June, and are we funding it as if that were true?
Which single line item would we defend hardest if the board cut ten percent, and does the plan reflect that ranking?
If the AI budget produced one artifact this year that a director could hold up, what would it be?
On the data
What do we know about our members that nobody outside this building could know, and what are we doing with it?
Which of our current initiatives would still work if we lost access to every third-party data source tomorrow?
Where is the data we already paid for, and who used it last?
Many doors, one unlocked
On the people
Who owns the step between an insight arriving and somebody acting on it, by name?
If that person left, what stops?
What are we asking a team to do next year that nobody on it has done before, and what have we budgeted for that gap?
On accountability
When a model produces a bad outcome for a member, who signs the response?
If the answer is not settled, what exactly are we planning to deploy?
The captain of the ship
The last one
This is the question I would put in front of any leadership team in the next three weeks, and it is uncomfortable on purpose.
What decision did we make differently this year because of the data?
Not what built or bought, but what decision changed. If the room cannot name one, the return you are looking for was never going to show up in the reporting, because the gap sits in what you did with the data rather than in what you have.
Consider the project that aimed to redesign the member onboarding flow last quarter. We had metrics showing drop-off at the third step, and the team believed a new UI would fix this. The data showed something else, though: most members were leaving because the necessary document upload was too complex. The screen's layout was not the core issue.
Hence, the decision to spend three months on UI changes was reconsidered. The team instead focused on simplifying the document process itself, perhaps even automating some parts. This shows data prompting a different strategic direction, preventing effort on a solution that would not address the actual problem.
Teams often address engagement issues with visual changes. The data showed us the real friction was in the process, not the interface. An expensive UI redesign would not have fixed this. Calibrating our response to the data saves time and money, and it delivers actual progress.
That gap is cheap to fix relative to what has already been spent, and it closes faster than anything else on the plan. But it only closes if somebody names it out loud while the budget is still open.
Budgets close at the end of this month. After that, this conversation waits a year.
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Elias Kruger, MBA, is the Managing Principal of Long-Range AI Consulting LLC, where he provides advanced analytics strategy and AI-powered business transformations tailored for midmarket sectors, including community banks, credit unions, and fintechs. His career spans over 22 years of continuous reinvention across finance, data science, and enterprise AI leadership, notably serving as a Vice President at Wells Fargo where he co-led an internal analytics consulting program of over 60 analysts. As a diagnostic-first practitioner, Elias designs customized human-empowering AI-enabled solutions ranging from multi-agent orchestration, RAG-powered workflows to predictive modeling that drives operational efficiency and valuation increases. He is a frequent speaker at major industry conferences like Finnovate.