How to Know When Your AI Strategy Is Actually Working

By Mike Caprio

You’ve launched an AI pilot. Maybe two. You’ve built a chatbot, experimented with generative copy, maybe plugged a model into a few dashboards. But here’s the hard part — is any of it actually working?

The uncomfortable truth is that most organizations have no real framework for answering that question. Metrics are either too vague (“AI innovation scorecard”) or too superficial (“How many teams are using ChatGPT?”).

Let’s fix that.

The Real Goal Isn’t AI — It’s Leverage

AI should be a means to create leverage — more output for the same or fewer inputs. That could mean faster decisions, higher conversion, fewer errors, or deeper insights.

The best AI strategies don’t just automate tasks. They improve outcomes. But those outcomes need to be measurable and aligned to actual business goals.

Before we get into KPIs, start here:

“If this AI system disappeared tomorrow, what would break?”

If the answer is “not much,” it’s probably a side vibe hustle, not a true business initiative.

Three Real Signals of AI Success

Let’s move past vanity metrics. Here are three real indicators your AI strategy is landing:

1. Decision Quality Is Improving

Are humans making better decisions because of AI input? Not just faster — better.

Look for:

• Higher win rates

• Fewer escalations

• More accurate forecasting

2. Friction Is Shrinking

Is AI reducing operational drag? That could be in sales enablement, data prep, onboarding — anywhere time or effort used to get lost.

• Shorter cycles

• Fewer handoffs

• Increased self-service

3. Uptake Without Mandates

Are teams choosing to use the tools — or are they being forced to? Organic adoption is a sign that the AI solution actually helps. If people are bypassing it, that’s your clearest red flag.

Watch for False Positives

Just because something uses AI doesn’t mean it’s strategic. 

A common trap: teams build a slick GenAI prototype that demo well but isn’t tied to any recurring workflow.

Another: automating a process that shouldn’t exist in the first place.

Ask yourself: Would we do this without AI? If not, why are we doing it with AI?

Build a Feedback Loop

You can’t evaluate AI impact once a year. You need a rhythm:

• Monthly: Track usage, error rates, and feedback

• Quarterly: Assess business outcomes tied to AI use

• Annually: Reevaluate the roadmap based on what’s driving real value

Also, keep a graveyard — a list of things you tried that didn’t work. Share it. Celebrate the lessons. It builds cultural confidence and institutional memory.

TL;DR

You’ll know your AI strategy is working when:

• People rely on it daily without being told to

• It helps you move faster and smarter

• Business results are improving — and you can prove it

AI isn’t about building cool tools. It’s about building a better business.