AI rarely fails in the model. It fails in the minutes between a real-world event and your AI seeing it. One upstream schema tweak can stall an entire chain. That’s an architectural miss that slows revenue work while teams chase breakages.
We overbuild for hypotheticals and underbuild for reality. One schema change, a compliance tweak, or a traffic spike, and pipelines grind. Meanwhile, the AI features that should lift conversion and NPS sit in the backlog.
The cost is real: marketing waits on stale data, sales lack real-time scoring, and product changes queue behind pipeline work. The remedy is a standard event-to-AI path that keeps data fresh, enforces contracts, and leaves an audit trail.
In this edition, I will show how to measure decision latency, run a simple operating model, and fail closed when inputs break.
AI impact hinges on decision latency
Many AI initiatives fail to improve core KPIs such as conversion rate, revenue per session, fraud catch rate, and time-to-resolution because events take too long to reach the feature that should respond.
That is decision latency. The time from an event in a source system to the moment the updated fact is visible in your product or API.
When decision latency is measured in hours rather than seconds, the chance to influence a customer, prevent a loss, or fix an order has already passed.
Infrastructure tuning helps, but business value tracks event-to-action time. Treat it as a product metric and instrument it the same way you track conversion or retention.
Real-time capability is now built into mainstream cloud services, which makes strict freshness targets achievable for mid-market teams.
What you can do
- Measure decision latency on two or three money journeys. Report p95 in seconds.
- Replace nightly rebuilds with streaming, so updates flow continuously to the feature.
- Set a freshness SLO with a named owner and a weekly review.
- Define “done” for each journey: Event timestamp captured, index or feature updated, visible in the UI or API.
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One shippable use case beats a platform
Launch value faster by committing to one outcome, a minimal event-to-AI path, and a task-tuned small model you can actually operate.
Smaller, task-tuned models are often the fastest path to impact: they’re cheaper to run, easier to govern, and specialize quickly for narrow jobs. Microsoft’s Phi-3 family shows how compact models can match or beat much larger baselines on standard benchmarks while fitting enterprise constraints; that’s why many teams now start small and scale only where needed.
Mid-market teams that anchor on a single use case and a short change→feature path see measurable wins—like Kinectify’s 96% faster decisioning once they built a lean Azure backbone for their AML workflows.
What you can do
- Select one use case with a single metric. Example: “Reduce p95 decision latency for order-cancellation refunds to <60s.”
- Wire only the essentials. Event capture → stream route → clean table → feature or index. No extra services until the first result lands.
- Start with a compact, task-tuned model. Fine-tune or prompt-tune a small model for your task; keep costs and governance simple.
- Measure time to first live impact. Track when the feature changes a user-visible decision (or API response) and iterate there, not in the toolchain.
Scale comes from a standard operating model
Big stacks centralize knowledge and slow delivery. A shared event-to-AI playbook lets any product team move changes from systems of record to user features in seconds and handle failures safely.
Assign clear owners for each money topic. A topic owner is accountable for the KPI and the latency target. A producer owner upholds schema, SLAs, and the data contract. A small platform steward maintains the template, naming rules, and the dashboard.
When inputs are off spec, the system pauses the feature, quarantines the records, alerts the topic owner, and replays only from the source. Planned schema changes are announced before release.
What you can do
- Hold a short weekly review of four numbers — freshness SLO attainment, p95 decision latency, incident rate tied to data quality, and features unblocked.
- To start, provide a ready-to-deploy template and onboard two journeys. Add new topics only after the first ones are stable.
Fail-closed policy keeps AI safe at speed
Fresh data is not enough. AI features also need a fail-closed policy so they pause rather than guess when inputs violate a contract.
A contract is a promise a producer makes regarding a monetary topic, such as Orders, Inventory, or Customers.
When a check fails, the feature does not update, the record is quarantined, and the owner is alerted. A replay from the source fixes the state. The cost of a short pause is lower than the cost of a confident wrong action.
Make the behavior visible. Serve the last known good answer with a brief notice, or display a ‘temporarily unavailable’ state. Provide product owners with a clear alert indicating the failing rule and a one-click link to replay the issue once it has been corrected. Audit trails provide evidence of what happened and when.
What you can do
- Define the three non-negotiable checks per topic, for example, non-negative totals, valid status values, and sane timestamps.
- Add pause and replay as first-class behavior in the feature, including last-good display and an owner alert.
- Track the contract compliance rate and mean time to data fix, alongside freshness and decision latency, during the weekly review.
The point of a minimal viable AI data platform is focus. When teams stop chasing exhaustive models and oversized data programs, they can ship one feature, measure one loop, and show one board-level KPI moving. That repeatable loop compounds faster than a perfect architecture ever will.
P.S.: We provide a data platform modernization assessment to help your teams stand up that foundation quickly, within a scope that you can run.
