Why Hype Fails
AI is the business buzzword of the moment. Headlines promise transformation, vendors promise miracles, and leadership teams are curious to know “what finance is doing with AI.”
But here’s the danger: when finance professionals oversell or underplay AI, credibility is lost. Leadership either grows suspicious (“too good to be true”) or disappointed (“this isn’t worth the time”). The real skill today is learning to talk about AI with measured clarity: not as a gimmick, not as a magic wand, but as a tool that helps finance deliver faster, more accurate, more actionable insights.
A Wake-Up Call from MIT
A recent MIT study found that 95 percent of enterprise AI projects fail to deliver measurable business impact. Out of hundreds of pilots reviewed, only 5 percent translated into P&L outcomes.
Why do so many attempts fall short?
Projects are launched without a clear business problem
Tools are bolted on instead of integrated into workflows
Teams chase headlines instead of fixing pain points
Expectations are inflated beyond what the tools can deliver
This is a warning to finance professionals: AI adoption is fragile. Success depends not just on the tech, but on how we frame it, test it, and explain it to decision-makers.
How to Frame AI in Leadership Material
When you’re asked to present on finance’s AI efforts, structure matters.
Start with the problem, not the tool
Wrong: “We are piloting Microsoft Copilot in finance.”
Right: “We are testing automated variance explanations to shorten month close by two days.”
Focus on outcomes, not features
List what changed in business terms:Hours saved
Errors reduced
Faster reforecasts
Margin or cash visibility improved
Use plain language
Swap “generative AI” for “AI that drafts reports.”
Swap “anomaly detection” for “spotting unusual spend before it becomes a problem.”
Flag risks and limits
This builds trust. For example: “Pilot has improved narration speed, but outputs still need human validation for statutory reporting.”
The Role of Finance Professionals
AI is not just for the CFO to explain. Everyone in finance has a part to play in shaping the narrative.
Analysts: show how AI changes daily work. Example: “Variance explanations now draft automatically, freeing us to focus on analysis.”
Controllers: emphasize compliance and accuracy. Example: “AI reduced duplicate vendor entries by 40%, improving audit readiness.”
FP&A teams: connect AI insights to future action. Example: “Collections predictions now inform working capital reforecasts.”
Each role adds credibility by tying AI back to its impact.
Tips for Clear, Non-Hype Communication
Lead with numbers, not acronyms
Avoid words like “transformational” unless you can prove it
Anchor every AI update to the decision it enables
Keep vendor names in the background; keep business outcomes in the foreground
Apply the “AI word test”: if you remove the word AI, does the sentence still make sense?
Final Thought
The MIT study is a reminder: most AI initiatives stumble not because the tech is weak, but because the story is weak. Finance professionals are the bridge between what the tools can do and what the business needs to hear.
Communicate AI pilots the way you would communicate any finance result: focused on impact, measured in outcomes, and honest about limitations. That’s how you cut through hype and build trust.