Practitioner's Corner
Lessons from the field—what we see building at scale
Practitioner's Corner
Lessons from the field—what we see building at scale
The Six-Hour Price Check
An analyst opens their laptop at 9 AM. The task: check competitor pricing across five major players in three product categories. Document changes. Flag anything significant. Seems straightforward. By 3 PM, they're still at it. This is "just check the website" in practice. Six hours navigating to pricing pages, documenting numbers in spreadsheets, cross-referencing yesterday's data. Repeat tomorrow. And the day after.
The Six-Hour Price Check
An analyst opens their laptop at 9 AM. The task: check competitor pricing across five major players in three product categories. Document changes. Flag anything significant. Seems straightforward. By 3 PM, they're still at it. This is "just check the website" in practice. Six hours navigating to pricing pages, documenting numbers in spreadsheets, cross-referencing yesterday's data. Repeat tomorrow. And the day after.
The Oncologist Who Builds AI for Two-Minute Cancer Decisions
Two minutes. That's how long most UK tumor boards have to discuss each patient's cancer treatment plan. In that window, clinicians synthesize radiology reports, pathology results, biomarker tests, and treatment guidelines to make decisions that directly affect survival. When information gaps appear—missing test results, incomplete staging—the case gets postponed. This happens in 7% of cases.
Dr. Andrew Soltan saw this from inside. He's a practicing oncologist at Oxford University Hospitals who decided to build AI agents for his own workflow. When someone who actually makes these two-minute decisions designs the system meant to support them, the constraints look different. And what "production-ready" means becomes something you can measure in survival outcomes.
The Oncologist Who Builds AI for Two-Minute Cancer Decisions
Two minutes. That's how long most UK tumor boards have to discuss each patient's cancer treatment plan. In that window, clinicians synthesize radiology reports, pathology results, biomarker tests, and treatment guidelines to make decisions that directly affect survival. When information gaps appear—missing test results, incomplete staging—the case gets postponed. This happens in 7% of cases.
Dr. Andrew Soltan saw this from inside. He's a practicing oncologist at Oxford University Hospitals who decided to build AI agents for his own workflow. When someone who actually makes these two-minute decisions designs the system meant to support them, the constraints look different. And what "production-ready" means becomes something you can measure in survival outcomes.
The Number That Matters
Data professionals spend 40% of their time on quality tasks, with freshness violations causing nearly a third of all data downtime. Engineers end up running constant fire drills instead of building new pipelines.
The freshness problem shifts wildly by context. Fraud detection algorithms need sub-second latency. Marketing dashboards can wait a week. The same extraction system serves both, each with its own decay curve and operational burden.
What gets logged as "successful extraction" often hides the real work: validating timestamps, flagging stale records, managing refresh schedules across dozens of sources that age at different rates.
Practitioner Resources
