Report Generation - A Smart Way To Start With AI

Many AI adoption efforts stall - not because companies lack tools, but because they start in the wrong place.

They launch pilots, experiment with chat interfaces, or push AI initiatives that sound promising but don’t integrate into actual workflows. The result? AI remains a side project, disconnected from the real work people do every day.

Report generation is a better way in. Not just because reports are structured and repetitive, but because the people who create them already know what good looks like. When AI is applied here in the right way, it not only makes reporting more efficient - it also helps domain experts integrate AI into their workflows in a way that feels natural and useful.

Why Report Generation?

A huge amount of knowledge work boils down to turning unstructured information into structured reports. Finance teams compile earnings summaries. Legal teams generate compliance filings. Analysts build performance dashboards. These reports follow predictable formats, but assembling them is slow and manual.

AI can help at most every stage — retrieving inputs, structuring data, summarizing key points, and formatting the final output - but the biggest impact comes when domain experts can actively shape how these AI-generated reports are created. They understand what information is essential, how different inputs affect the final output, and how reports need to be structured for clarity. When they’re involved in defining correctness and refining AI-generated drafts, AI stops being an abstract technology project and becomes something useful in daily work.

AI That Works with People, Not Around Them

Many AI projects fail because they are treated as purely technical initiatives — built by engineers and handed off to business teams with little involvement. That rarely works. The best AI deployments happen when the people who understand the work drive the implementation.

Report generation naturally encourages this kind of collaboration. The people writing reports already know where the data comes from, what needs to be included, and how reports are structured. They don’t need to be technical to refine AI-generated drafts — they just need a way to provide feedback.

In the right setup, domain experts can compare AI-generated reports to past versions, shaping how the AI retrieves and structures information. Over time, they iterate on the outputs, adjusting how different sections are written and what details are emphasized. This process turns AI from something imposed on them into something they can actively refine and improve.

Instead of AI being a static tool, it becomes part of an evolving workflow — one that domain experts help shape over time.

How AI Becomes More Useful Over Time

Over time, small interactions with AI-generated reports start to compound. Teams begin spotting patterns — where the AI gets things right, where it falls short, and what adjustments make the biggest difference.

Sometimes, the AI output is structured correctly but lacks the right level of depth. Other times, it pulls in too much information or emphasizes the wrong details. As teams refine AI-generated reports, they start tweaking inputs, adjusting retrieval strategies, and improving formatting. Engineers don’t have to guess what needs to be improved — domain experts tell them directly.

And something else happens. When teams have to define correctness quantitatively — establishing rules for what makes a report “good” or “complete” — it forces a deeper conversation about why the report exists in the first place. What decisions are being made based on this report? Who is using it, and how? Are people actually relying on it, or has it become an unnecessary artifact of an old process?

This often leads to unexpected discoveries. Some reports turn out to be redundant or outdated. Others need to be structured differently to better support decision-making. In some cases, the most valuable outcome of automating a report isn’t efficiency — it’s clarity on what the report should be and whether it’s needed at all. AI-driven report generation becomes a Trojan horse for product discovery, helping teams rethink the job to be done.

A Forcing Function for Pragmatic AI Adoption

One of the biggest challenges in AI adoption is defining what “correct” looks like. Many AI initiatives fail because they lack a clear evaluation framework — outputs feel useful, but no one knows exactly how to measure success. Report generation avoids this problem because teams already have a gold standard to compare against.

Companies producing reports have past versions that serve as objective benchmarks. These documents define what good outputs look like, allowing teams to evaluate AI-generated reports in a structured way. Instead of relying on vague intuition, domain experts can point to specific correctness criteria — ensuring that AI-generated reports are measured against real-world standards, not just subjective impressions.

This process forces teams to develop discipline around evaluation. Engineers and domain experts work together to refine what correctness means, breaking it down into structured elements — format, completeness, accuracy of key details. Over time, this approach strengthens the organization’s ability to apply AI in measurable ways, ensuring that AI systems don’t just generate outputs but generate useful, high-quality outputs that align with real business needs.

Why This Approach Works

Companies that start with report generation don’t just improve efficiency. They build the fundamental capabilities needed to scale AI across the business. Since domain experts are engaged from the beginning, AI adoption feels organic rather than imposed. Instead of a tool being handed to them, they help shape it—turning AI from an abstract initiative into a practical solution that fits their work.

That shift is critical. AI adoption spreads not because it’s mandated, but because it actually makes work easier. People see firsthand that AI is at the baseline automating tasks, and also removing tedious work / giving them more time for high-value thinking. When that happens, AI stops being something employees have to learn and starts being something they want to use.

The Best First Step in AI Adoption

Most AI projects fail because they start with the tool instead of the problem. Teams over-index on model selection, vector databases, or LLM architectures before understanding what problem they’re solving.

Report generation flips that approach. It forces organizations to start with the workflow, not the technology. It ensures AI adoption is measurable, structured, and valuable from day one. But most importantly, it brings the right people into the AI conversation — the domain experts who actually shape how work gets done.

AI adoption works best when it starts with real work, not abstract experiments. Start with reports, let domain experts lead, and build from there.