Implementing an AI tool is just the first step. The novel part of AI is how quickly it can learn and adapt, especially as your team figures out how to interact with it. But that learning doesn’t happen automatically. It needs to be guided.

It’s been a while since our last story, so let’s start with one.

We had spent months and months implementing a new ERP system. The goal? Get our inventory stock levels to properly integrate with our financials. Pretty standard stuff. We brought in consultants, ran endless training sessions, handed out shiny new tablets for the warehouse teams. Nothing wildly complex, but we had a lot of locations with multiple warehouses. Accountants had spent six months mapping data, reconciling accounts and loading everything into the new system.

Then came go-live day. We flipped the switch… and things went quiet.

Given the nature of our business, a lull was expected. We weren’t moving inventory at a crazy pace, so we patted ourselves on the back and shifted focus to other projects, like budgeting. We figured the system was up and running and the numbers looked solid due to the automated reconciliations. So, we left it alone.

Fast forward a few months to our next major distribution cycle and red flags started flying. It turned out, staff on the ground had just trusted the system to work without review. They had used an “auto-reconcile” function to bypass any actual stock counts and moved on. It worked… until we got to audit prep and found our inventory was off by nearly 20% with ins and outs that looked insane. And across product lines? It was chaos.

The core issue? We had exhausted ourselves getting to go-live and then let the follow-through slip. The inventory teams never saw a balance sheet so they had no trigger to fix things.

So yes, everything we’ve covered so far in terms of consolidating data, prioritizing problems, centralizing teams set the stage. But real, lasting payoff comes after go-live, when you are making smarter, faster decisions.

Let’s get to work.

1. Define Success Metrics Up-Front (and Stick to Them)

Ideally, this happens before you even think about implementation. But as the AI starts running, you must quantify the impact you expect and track it. It sounds obvious, but this step often gets overlooked. Done right, it builds credibility, reveals what’s working (or not), and sharpens future rollouts.

• Translate outcomes into hard KPIs. Focus on business results. Not just activity metrics or technical stats. Some examples:

  • Days to Close Books. Direct measure of Finance efficiency.

  • Retention rates. You should take a cohort of customers that you are piloting AI with, in say CS, to measure the retention.

  • Employee->ARR. You should see an uptick in scale as employees are able to deal with more.

  • Forecast Accuracy % (Sales, Demand, Financial). Tells you if predictive models are improving.

  • Cost per Invoice Processed. Shows real operational savings.

• Gather baselines. Know exactly where things stood before the AI rollout. Use hard numbers, not estimates or gut feel.

• Make AI KPIs part of MBRs/QBRs. These aren’t just side metrics. Bake them into your regular business reviews. Have an agent itself pull, format, and present these metrics and make sure they are correct.

• Track time saved, carefully. This one’s trickier but still essential. Consider short surveys, time studies, or calendar audits to measure how your team’s workload shifts. Are they spending less time cleaning up spreadsheets and more time on strategic analysis?

By setting these standards clearly, based on tangible outcomes, you'll ensure everyone is laser-focused on delivering successful results, not just "implementing AI."

Tip: Create data quality scorecards that assign a grade (A, B, C) or a percentage score to the key datasets feeding your AI. Review and update these monthly. If garbage goes in, garbage comes out, no matter how smart the AI.

2. Effective Training

AI skepticism is real and understandable. Training should be about removing the mystery, building confidence, and showing teams how to work with AI. Poor knowledge sharing is a guaranteed path to low adoption and wasted investment.

• It’s a mindset shift, not just a skillset. Frame AI as a partner. Focus on how it takes work nobody likes off their plates so they can spend more time on strategy, analysis, and creative problem-solving.

• Make knowledge sharing role-specific, not one-size-fits-all. Your teams don’t need a tutorial on prompt engineering. They need to understand strategic implications and how to ask the right business questions of AI. Analysts need to grasp inputs, assumptions, and outputs. Front-line users need to see exactly how AI fits into their daily workflow.

• Build champions and make learning ongoing. Identify early adopters and empower them to be internal super-users. Peer learning spreads faster than formal sessions.

• Use AI to answer ‘how.’ The biggest benefit of a chat-based interface is that it’s dead simple. People should ask questions at their own pace and dig down where they need to. 

For more on this, check out how to build a finance team in the world of AI agents.

Tip: Encourage the use of AI in management reviews, but no need for formal training programs. You need to change the mindset, which is tough to do in formal training systems.

3. Analyze AI Use: Are They Using It, and Is It Working?

While outcomes are king, usage is the leading indicator. Measure its use. Of course, high usage doesn’t automatically equal high value, but when you see usage climbing alongside improved business outcomes, it's a strong story. What to track:

• Accuracy. Track how often the AI implementation is right. You can do this by sampling questions against reports that you already trust or asking everyone to do this for the first month and giving a like/dislike response to the answers they get.

• User adoption count: Track how many intended users have actively started using the AI system (e.g., "15 out of 20 accountants ran at least one AI-driven forecasting report in the first month").

• Frequency of use: Measure the number of AI sessions, queries processed, models run, or reports generated per user/team per week/month.

• Feature utilization: If your AI tool has multiple features (e.g., forecasting, anomaly detection, natural language querying, report generation), track which ones are being used most and by whom.

• AI Usage vs. legacy: This is crucial. If possible, compare the usage of the AI tool against the old method it was meant to replace. A proxy might be to monitor the "number of manually created forecast spreadsheets submitted" going down as AI forecasting usage goes up.

If you track this at an individual employee level, you can start to correlate AI usage with performance on specific tasks. This allows you to not only prove the AI is working but also to identify power users who can champion the tool and help others get up to speed.

Tip: Get rid of dead reports. Things that were created but aren’t being used. It's probably for a reason and creates a tremendous amount of headache.

4. Review and update

Once your AI setup is live, it needs ongoing attention. Regular reviews ensure it becomes even more valuable as time goes on.

• Monthly operational huddles. Focus on usage and functionality. Are people using the tool? What’s broken or needs tuning? Review adoption, usage trends, data quality, and early KPI wins. Keep it tactical, fast, and focused on immediate improvements.

• Quarterly strategic reviews. Step back and assess business impact. Review outcome-based KPIs against your original goals. Is AI delivering material ROI? What new opportunities has it unlocked? Adjust your roadmap and resource plan accordingly.

• Know when to walk away. Set clear kill criteria upfront. Low adoption, no measurable impact, or rising costs with no return? Time to sunset. Don’t let zombie projects drain momentum.

This is nothing different from any other project. Assess how the rollout is doing, plan the next steps.

Tip: Like a broken record… but let the AI draft its own quarterly performance report. Use it to guide the discussion and demonstrate its capabilities.

5. Keep up to date

AI will continue to evolve quickly. Stay current to maintain your competitive edge and take advantage of the significant roll-outs that are happening, at this point, about once a quarter.

• Tap into peer networks and communities. Encourage your team to join industry groups, webinars, and AI communities. Hearing what’s working (or failing) for others accelerates your own progress.

• Make learning part of the culture. Teams using it need to treat learning and adaptation as part of the job. Bake continuous improvement into the way you work.

• Assign ownership to track what’s next. Make sure your centralized team is periodically looking for new use cases once you have your data consolidated.

• Get the most from vendors. Your vendors are building these tools. Push for roadmaps and new feature access. Hold them accountable to help you stay ahead.

• Balance innovation with ROI. Stay current but don’t chase every shiny AI upgrade. Vet new initiatives like any investment: strategic fit, value delivered, and cost to implement. Innovation is only useful if it moves the needle.

This technology is moving faster than previous ‘tool’ upgrades which usually came yearly. Now, you should expect improvements at least each quarter.

Tip: Your problems should send you searching for technology that will fix it, not for technologies searching for a solution.

In conclusion

Getting AI live is just the start. The real value comes from disciplined follow-through. You need to define success with hard KPIs, track usage closely, and regularly review what’s working and what’s not.

Train your team to partner with AI, not just use it, and stay current as the technology evolves. The edge goes to companies that treat AI as a capability to grow.