ShareA few weeks ago we asked the question on our LinkedIn page: What’s blocking your AI adoption?
Amongst the poll results, one stood out the most, so let’s talk about the elephant in the room: Cost and ROI uncertainty.
For many teams, it’s not that they don’t believe in AI. They’ve seen the demos, tried ChatGPT or even use it on a daily basis, or maybe even piloted a tool or two. Each industry has its own AI-powered tools, such as writing texts, reviewing codes or do complicated calculations. The real blocker is this nagging question:
“If we invest in AI… will it actually pay off?”
And that’s a completely valid concern.
Destroy the illusion of AI sounding expensive
Most AI conversations start with big promises:
· “Automate your workflows!”
· “10x your productivity!”
· “Unlock new insights!”
All great in theory. But when it comes to budget discussions, leadership wants numbers, not slogans:
· How much will it cost per month?
· How many hours will it actually save?
· When will we see a return?
Because many AI projects start as “experiments,” they’re not framed with clear success metrics. That makes AI feel like a nice-to-have innovation project, not a strategic investment.
Hidden costs = hesitation
Teams aren’t just afraid of license fees. They worry about the hidden costs:
· Time needed to implement and integrate tools.
· Training employees to use them effectively.
· The risk of choosing the wrong solution and starting over in 6 months.
Without clarity, AI becomes a perceived cost center instead of a value generator.
The ROI problem: you can’t improve what you don’t measure
A lot of AI usage today is ad hoc: someone in marketing uses ChatGPT, someone in sales drafts emails with AI, someone in operations experiments with automation.
Useful? Yes. Measurable? Rarely.
To reduce ROI uncertainty, teams need to move from random acts of AI to intentional AI use cases:
· “We want to reduce time spent on X by 30%.”
· “We want to cut manual reporting effort from 10 hours/week to 3 hours/week.”
· “We want to increase tender/lead qualification accuracy by Y%.”
Once you define that, suddenly AI ROI is no longer abstract. You can compare before vs. after.
How to unblock yourself
If cost and ROI uncertainty are holding you back, try this approach:
1. Start small and specific Pick one painful, repetitive process (e.g. tender discovery, reporting, email drafting). Don’t “do AI everywhere”, do it somewhere meaningful.
2. Put numbers on the pain How many hours per week are spent on this task? What’s the approximate hourly cost? That’s your baseline.
3. Run a time-boxed pilot Test an AI tool for 4–8 weeks with a small group. Measure time saved, quality improvements, or error reduction.
4. Translate results into money If your team saves 10 hours/week, what does that equal in salary cost or freed-up capacity? That’s your business case.
5. Decide with data, not fear At that point, AI investment is no longer a leap of faith – it’s a decision backed by numbers.
Key takeaways
The biggest blocker to AI adoption often isn’t the technology – it’s uncertainty. Once you define a clear use case, measure the impact, and translate it into ROI, the conversation shifts from:
“Can we afford AI?” to “Can we afford not to use it?”