Reviewing Your AI Tools at 3, 6, and 12 Months

A Framework for Sustaining and Scaling AI

Six months after a claims team goes live with an AI tool, someone asks a simple question in a leadership meeting: is it still doing what we bought it to do? 

Hreviewing-your-ai-tools-at-3-6-and-12-monthsalf the room assumes yes, because the tool is still being used. But nobody has actually checked whether it’s being used well, whether the original problem is still the right one to solve, or whether the team has found better ways to use it since launch.

This is where AI value in claims can quietly slip through the cracks. 

A rollout gets planned in detail: a business case, a target claim type, a training session before go-live. The months after launch rarely get the same rigor, often with adoption left to happen naturally, rather than managed as deliberately as the initial rollout plan.

The Writer’s 2025 AI Survey reports, companies with a formal AI strategy report 80% success in adoption and implementation, compared to just 37% for those without one.

Teams shouldn't have to wait until renewal to learn whether the AI is delivering. That's why it's worth treating the first year as a series of checkpoints:

• Three months: Is the tool being used as intended, or are people working around it?

• Six months: What have we learned about the workflow, and are there better ways to use or configure the tool?

• Twelve months: Did the investment actually improve outcomes, and where else should we expand it?

Three months: Reality check

At this stage, you might still be working through implementation or customizing the solution to your workflow, but there should be some initial usage data to compare the designed workflow against what you actually have.

Start with the original use case

If the tool was meant to help adjusters review medical records faster, are they using it for that or glancing at the AI summary and starting over manually? 

A high login count doesn’t necessarily mean the tool is embedded in the workflow; you want to know what people are doing in the tool, not just whether they’re opening it.

Check the value against the use case

Once you know the tool is being used as intended, ask whether it's producing the outcome you expected. If the original use was based on reducing review time, compare actual review time with the previous baseline. If it was about improving consistency, look at the relevant quality measures.

Don't assume the value has to look exactly like the original business case.

A team might implement AI to speed up record review and discover the bigger win is adjusters catching gaps earlier. Or they might find it shines on complex files or specific case types, even if the original plan was to use it across the board.

Pay attention to workarounds 

This is also a good time to look for friction. Are adjusters skipping steps, exporting data elsewhere, or ignoring certain outputs?

Those patterns aren’t problems to stamp out, they’re clues. If adjusters consistently work around the same part of the workflow, find out why. Maybe the feature isn't useful, maybe it needs to be configured differently, or maybe part of the process needs to change.

This checkpoint is about getting the value you intended, while staying alert to the value you didn't anticipate.

Six months: What have we learned?

By now the team has habits, adjusters have hit edge cases, and you should have more than the initial usage data to review. The question becomes: what have we learned that we didn’t know when the AI went live?

Start with the workflow

Look at which features get used constantly, which get ignored entirely, and which claim types have benefited the most. 

A team that rolled out AI to review medical records across every injury claim might find that time savings are concentrated on files with dense record sets, while more straightforward files see the most benefit with just a summary overview.

That’s not necessarily a sign that the original implementation was wrong, it just means you've learned enough to adapt.

Learn from users and capture discoveries

Your adjusters are a source of intelligence. People using AI every day will often find use cases that weren’t part of the original plan, maybe changing how they handle other tasks in the workflow because the AI made a different approach possible.

Be careful to separate user preferences from workflow gaps that are causing friction. 

AI moves quickly. Between the new workflow and the discoveries users are making, it’s worth checking on the tool itself - it’s likely that in the months since your team was onboarded, new capabilities have emerged that could automate another step in the process.

This checkpoint isn't about forcing the workflow back to its original design - it's about improving it based on what you've learned.

Twelve months: What should we scale, expand, or change?

By twelve months, the value should be clear from what you’ve tracked throughout the year. The conversation then shifts to what comes next. Do you want to expand the tool into other areas of the workflow, bring it into other departments, or apply it to new case types?

Revisit the original use case

Did the tool deliver on review time, productivity, or quality, and how consistently?

Then look at what you’ve discovered along the way - where else has the team found value, and are there other areas where expansion would make sense? The mistake to avoid is treating one successful use case as an automatic reason to expand.

When an AI tool works - say on bodily injury claims - you need to ask, “What made this work?” Maybe it was the volume of documents, complexity of the files, or the repetitive nature of review where AI added value. Those conditions help identify where the tool is likely to work next.

Scale what works

A successful AI initiative should make the next implementation smarter. 

You could replicate the same use case everywhere, but understanding what made it work, why it worked, and then using those lessons to identify where else the tool is likely to deliver value can help you roll it out bigger and better in the next phase. 

Ask what you’d include if you were implementing AI for the first time today - this tends to surface capabilities that were never turned on, never utilized in the first phase, or opportunities that weren’t realistic for the first use case.

Finally, make sure ownership has evolved with the technology. A year in, AI should be part of the standard business operations, not a project. Someone should be accountable for whether or not the AI is still delivering value, where it should expand, and what should be stopped.

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What happens in the months after an AI initiative kicks off determines whether the investment holds up and adds meaningful value. Build the checkpoints as you’re building the rollout plan, and next time someone asks if the AI is working, you’ll have a strong answer instead of an assumption.