The AI Era Shift: Build Data Products, Not Service Products

Disclaimer: The ideas, arguments, and analysis in this article are my own, based on my observations and experience. I used AI tools to help refine the writing and structure (editing support), but the underlying thoughts and viewpoints are original.

Most AI conversations still start with the same instinct: “Let’s mine our historical data.” “Let’s unlock value from legacy documents.” “Let’s run an LLM across what we already have.”

That’s not wrong. It’s just not enough.

"Because the biggest advantage of AI doesn’t come from reading the past better. It comes from building an infrastructure where new data is born with meaning, so AI can act with precision, not guesswork."

If we only focus on historical mining, we keep hitting the same ceiling: the richest context was never captured, the people who knew “why” are no longer in the loop, versions are unclear, ownership is fuzzy, exceptions aren’t documented, and the system of record is ambiguous.

So AI ends up doing what humans did for years: interpreting messy information, repeatedly.

To take full advantage of AI, we need to shift the focus: Don’t just mine historical data. Build a data infrastructure that captures context in-time, at generation. That’s how you reduce noise, cost, and risk, and how AI becomes a multiplier instead of a reviewer.

1. The Context You Can’t Rebuild Later

There’s a reason historical enrichment is hard. Not because the tools aren’t good enough, but because the missing ingredient is situational context—and it disappears fast.

When data is generated, a lot of meaning is present in the moment:

• Why the decision was made
• What alternatives were considered
• What was “normal” vs. an exception
• Which fields mattered
• What the user actually intended
• Which version was used
• What was assumed but never written

A month later, that context is gone. A year later, you’re left with a record that looks complete but isn’t. That’s why “enriching later” becomes expensive: you’re not enriching, you’re reconstructing. And reconstruction is always probabilistic.

2. What “In-Time / Local Enrichment” Actually Means

“In-time / local” enrichment is not about adding more metadata fields. It’s about capturing high-quality context at the source, inside the workflow that produced the data.

Local = the team/system closest to the truth. In-time = when the context is still alive.

So instead of a central team trying to enrich everything after ingestion, enrichment happens: when a ticket is closed, when a claim is approved, when a contract is drafted, when a customer call ends, or when a transaction is produced. This is the only time you can reliably capture “why” and “what changed”. Later, you can add labels and tags, but you can’t reliably recreate intent.

3. A Use Case That Shows the Difference: Incident Management

"Historical enrichment is archaeology. In-time enrichment is design."

4. From Service Products to Data-Rich Products

Once you see this, you realize the bigger point: **AI enablement isn’t primarily a model problem. It’s a product and infrastructure problem.**

Most software products today behave like service products—they complete workflows, store records, generate files, and move tasks along. But they don’t produce high-quality, AI-ready data as a first-class output.

A data-rich product treats data as a product outcome, not a byproduct. It is designed to natively produce: linked entities, stable definitions, consistent classifications, traceable provenance, version clarity, trust markers, and machine-usable structures. And when such products exist, AI does wonders—not because the model is smarter, but because the input is finally meaningful.

5. What Product Managers Must Change in the AI Era

This is where “product thinking” needs an upgrade. In the pre-AI era, PMs focused on UX flows, features shipped, adoption metrics, and time-to-complete. In the AI era, a PM must think like a **data product builder**: your product is training the future system. Every interaction produces future intelligence.

So PMs must ask new design questions:

• What context is present at creation time that will disappear later?
• What minimal fields capture that context without adding user friction?
• What should be mandatory vs. optional?
• What controlled vocabularies do we need to avoid semantic drift?
• How will we link records to core business entities consistently?
• What trust signals (reviews, confidence thresholds, authoritative sources) should be captured?

This is not “extra documentation”—this is how you design a product that is AI-ready by default, moving from "AI bolt-ons" to "AI-native outcomes."

Conclusion: Build for Future Data, Not Just Past Data

Yes, you should mine historical data. But it will always be limited by what context you failed to capture. If you want to take full advantage of AI, the real shift is forward-looking: build the infrastructure to capture meaning as data is generated, enrich locally in-time, design products to output structured context, and treat enrichment as part of the product contract.

Because the richest context is not in the past. It exists in the moment, and disappears fast. So the best AI strategy isn’t just better retrieval—it’s better future data.