Almost 4 years into the generative AI era, and we’re still treating productivity as the main metric. Specifically, time saved. And you know what? Time saved isn’t the right metric for judging AI because that time is quickly filled with rework, verification, and fixing bad output.
Time saved is a productivity metric, yes. Impact is a business metric. The two aren’t necessarily correlated. One of the biggest mistakes companies make when evaluating AI is confusing activity with outcomes.
An AI tool that helps an employee complete a task in 10 minutes instead of 30 looks impressive. But what happens when you account for the time spent checking the output, correcting errors, and redoing work? And even if the 20-minute saving is real, what does the organization gain?
That’s the more important question.
Three levels of AI value
I’d distinguish between three levels:
- Efficiency — Are we doing things faster? Time saved, task throughput, processing cost. Useful measures, but incomplete.
- Augmentation — Are employees becoming more capable? Better decisions, higher-quality work, greater autonomy, more complex problems solved.
- Impact — Are we producing better outcomes? More satisfied customers, increased retention, higher revenue, better service, stronger competitive differentiation.
The first level is what most organizations can easily measure. The second is where AI begins to transform how people work. The third is where AI creates meaningful business value.
And here’s something important: augmentation isn’t necessarily about doing the same work faster. It’s about doing work you couldn’t do before.
Consider a customer service representative. Without AI, they handle 30 cases a day. With AI, they handle 45. That’s efficiency. But what if they keep handling 30 cases while resolving more complex issues, anticipating customer needs, delivering more personalized service, and improving first-contact resolution?
That’s augmentation producing customer impact. The second scenario could be far more valuable than the first.
Employees becoming faster doesn’t necessarily mean employees are becoming better. If AI quietly substitutes for their judgment, capabilities could actually deteriorate. Organizations should measure whether their people are becoming more capable, not simply more dependent on AI.
For example, when deploying an AI voice agent, the success metric shouldn’t simply be that it handled 80% of incoming calls. It should be whether the organization resolved more customer issues successfully, improved the customer experience, and freed its human agents to handle higher-value interactions.
An automated interaction isn’t necessarily successful.
For me, AI should expand capacity, not just free up capacity. Because it’s not about asking, “How much time can AI save our employees?” It’s about “What can our employees and customers accomplish now that wasn’t possible before?”
The last question is a fundamentally different starting point for AI transformation. It changes what you build, how you deploy it, and what you measure.
Does time saved matter? Yes. But it’s not the north star metric. So, AI’s goal isn’t to give people more time. It’s to enhance their capabilities so they can use their time better to drive business outcomes.
Bottom line: Don’t measure AI by how much work it eliminates. Measure it by how much value it creates.
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