The Number That Should Worry You
McKinsey's latest data says 92% of executives expect to increase AI spending in the next three years. 87% believe generative AI will drive revenue growth.
Those are confident numbers. But confidence and results are different things.
From the work we do with SMBs, the more honest number is this: most businesses cannot tell you what their AI spend has actually returned. They know what they paid. They do not know what they got.
That is not an AI problem. That is a measurement problem — and it is fixable.
Why Pilots Die Without Producing Anything
The pattern I see repeatedly. A business owner sees a demo, gets genuinely excited, and starts a trial. The tool is real, so is the enthusiasm, but only a few staff use it inconsistently for four to six weeks.
Then one of three things happens:
- The subscription renews because nobody owns the decision to cancel
- The tool gets used by one enthusiastic person but never spreads to the team
- The team uses it, but nobody tracked what changed — so when the CFO asks "what did we get from this?", the answer is a shrug
None of these outcomes is the tool's fault. The failure is that the pilot had no exit criteria. There was never a defined answer to: what does success look like at 90 days, and who is measuring it?
The Three-Question ROI Test
Before any AI tool goes into a team, I run three practical questions:
Question 1: What task does this replace or accelerate — and how long does that task take today?
Get specific. Not "it helps with content" but "my ops manager spends 3 hours every Monday producing the weekly report. This tool cuts it to 45 minutes." You now have a baseline: 2.25 hours saved per week, 9 hours per month.
Question 2: What is that time worth in ringgit, pesos, or SGD?
Take the salary cost of the person doing the task. Divide by working hours per month. Multiply by the hours saved. If your ops manager costs SGD 5,000 per month across 160 working hours, their time is worth SGD 31 per hour. Nine hours saved is SGD 279 per month in recovered capacity.
Now compare that to the tool's monthly cost. If it's SGD 30 per user, you're at roughly 9x return — just on one task, for one person. That is a fundable use case.
Question 3: Where does the recovered time actually go?
This is the question most frameworks skip — and it is the most important one. Time saved is only valuable if it is redirected. If your ops manager uses the 9 recovered hours to do higher-value analysis that was previously skipped, that is a real gain. If those hours disappear into longer lunch breaks and slower email replies, the ROI calculation was fiction.
Define in advance where the time goes. Build it into the role expectation.
A Calculation Any SMB Can Run
Here is a simple model. Use it on a napkin before your next renewal decision.
Monthly Hours Saved = (Old Task Time − New Task Time) × Frequency
Time Value = (Monthly Salary ÷ 160) × Monthly Hours Saved
Net Monthly ROI = Time Value − Tool Cost
Payback Ratio = Time Value ÷ Tool Cost
Run this for every AI subscription your team is currently using. Most business owners are surprised to find they have three to five tools running simultaneously, with clear ROI on one or two and no data on the rest.
Revenue Cases Are Harder — But More Valuable
The McKinsey report is right that revenue growth is where the bigger opportunity sits. But revenue ROI is harder to attribute cleanly, which is why most businesses default to cost-saving calculations.
A practical approach for revenue-side cases we recommend.
Pick one customer-facing workflow — proposals, follow-up sequences, onboarding materials, client reporting — and run a simple A/B comparison. Use the AI-assisted version with half your pipeline for 60 days. Compare close rate, response time, and client feedback against the non-AI version.
This is not a perfect controlled experiment. But it gives you directional data. If AI-assisted proposals close 8 percentage points better across 25 deals, that is a number you can take to your CFO. It is also a number that justifies expanding the tool to the full team rather than leaving it in pilot purgatory.
The Governance Step Nobody Does
Spending more on AI without governance is how you get a sprawling, unmeasured tool stack that nobody owns.
One simple fix: assign an AI owner per tool. Not IT. A business-side person who uses the tool daily, tracks the metric it is supposed to move, and reviews it quarterly. One person, one tool, one number they are accountable for.
This does not require a committee or a policy document. It requires a name next to each subscription in your finance system and a quarterly 30-minute review in the calendar.
For a 30-person firm running six AI tools, that is six people each spending 30 minutes a quarter. Two hours of total review time to manage what might be SGD 15,000 in annual subscriptions. That is not overhead. That is just basic financial hygiene applied to a new category of spend.
What to Do This Week
Pull your current AI tool subscriptions — all of them, including what individual staff are expensing. For each one, answer the three questions above.
You will find at least one tool you should cancel immediately. You will find at least one tool that is delivering strong ROI and deserves broader rollout. And you will likely find one or two tools sitting in the middle — used inconsistently, with no owner and no metric.
Those middle tools are your decision for Q3. But you cannot make that decision without running the numbers first.
Ninety-two percent of executives plan to spend more. The ones who win are the ones who measure what they already have.
