The KPIs I Give to Leaders Who Ask: ‘How Do We Know If AI’s Working?’
A CEO leaned across the boardroom table and asked me
“Morris, how do we actually know if our AI is working?”
Not performing.
Not saving money.
Working — as in making us smarter, better, more human
It’s the right question
And one I get asked more often than you’d think
Because while most leaders are tracking ROI, speed, and automation output, what they’re really trying to work out is
Are we becoming more effective, or just more efficient?
That’s where my measurement tools come in
They’re not just KPIs
They’re signals
Signals that show you whether your organisation is evolving with AI or just installing it
These are the same tools I use in keynotes, workshops, offsites and closed-door strategy sessions across sectors
They offer clarity where standard dashboards do not
And they help leaders deal with PTFA — Past Trauma Future Anxiety — the invisible tension that grows when change moves faster than understanding
At the centre is HUMAND – Human + Machine + AI
This list shows you how to measure whether that blend is actually working
Why You Need KPIs Beyond Dollars and Outputs
Money is easy to count
But it’s a lagging indicator
It shows you what happened
Not what’s happening
If you only measure what’s easy, you’ll miss what’s essential
Things like
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Trust erosion between human and machine
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Mindless automation adoption
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Decision fatigue hiding under compliance
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Innovation drop-offs in highly optimised teams
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People behaving like systems while systems get more human
These are not tech problems
They’re early warning signs
The organisations that thrive next aren’t the ones with the best AI
They’re the ones reading these signals first and responding fast
The Real-World KPIs I Use to Measure Human + Machine + AI Collaboration
Final Thought: What You Measure Shapes What You Become
We’re not just redesigning dashboards
We’re redesigning meaning
If your metrics only track speed, scale or ROI, you’ll miss the signals that matter most
But if you start measuring curiosity, context, stretch and override
You’ll start seeing your future before it arrives
That’s the kind of clarity I build with leadership teams and strategy groups every day
Because strategy doesn’t live in spreadsheets
It lives in the dance between human and machine and AI
So. What are you measuring?
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Frequently Asked Questions
Q: What are the most common mistakes in how organisations measure AI performance?
The most common mistake is measuring AI capability rather than AI impact — tracking how accurate the model is, how many queries it handles, or how fast it responds, rather than whether the decisions, products, or experiences it contributes to are actually better. Capability metrics are important for engineering teams; they are the wrong primary metric for executive oversight. The second common mistake is measuring only the benefits while ignoring the costs — the hidden costs of AI deployment include oversight labour, error correction, technical debt, and the organisational capability that atrophies when AI substitutes for human judgment.
Q: What KPIs actually tell you whether AI is working?
Decision quality improvement — measured by comparing outcomes in decisions made with AI assistance versus comparable decisions made without it, over sufficient time to capture tail-risk outcomes as well as typical ones. Human capability development — whether the people working alongside AI are developing judgment, or whether they are becoming dependent and less capable over time. Trust calibration — whether the people using the AI system have appropriately calibrated confidence in it (not over-trusting in domains where it fails, not under-using in domains where it performs well). And exception rate — how frequently the AI requires human intervention, which is the most direct measure of where the system is operating outside its reliable range.
Q: How does the HUMAND framework inform AI performance measurement?
HUMAND provides the prior question: what is this AI system supposed to be doing that humans, machines, or combinations could not do as well? The answer to that question defines the right measurement framework. If the AI is supposed to be freeing human time for higher-judgment work, the KPI is the quality of the higher-judgment work — not the volume of tasks the AI processed. If it is supposed to be improving decision accuracy in a specific domain, the KPI is decision outcome quality in that domain — not the model’s internal accuracy metrics.
Q: Can Morris Misel run a workshop on AI governance, performance measurement, and the HUMAND framework for our executive team?
Yes. AI governance and performance measurement are core workshop topics for executive teams, boards, and technology functions. Book at morrismisel.com.
