AI in the real world
If your team is still rekeying documents, second-guessing reports, or manually holding a pilot together, the model is not the main issue. The workflow, inputs, and trust conditions are.
Where enterprise AI starts costing more than it delivers
The pilot looked promising. The license is live. But reporting still gets questioned, document-heavy work still needs rekeying, and the workflow still depends on manual intervention to stay standing.
It's Monday morning. Three dashboards show three different numbers. The meeting starts in 20 minutes, and no one can explain which report is right. Managers spend 4+ hours a week rebuilding decks no one fully trusts.
Your team rekeyed data from 47 PDFs into a spreadsheet on Tuesday. By Thursday, three errors surfaced in the downstream report. Nobody knows which PDF caused them. Manual processing felt safe — the 12.1% error rate was invisible.
The demo was impressive. Then came exceptions, handoffs, approvals, and three-team coordination. Now it runs on invisible manual glue, and no one wants to admit the license fees are running whether the value is there or not.
Why buyers trust us
This work touches reporting, clinical data, and operating processes where mistakes are expensive. Here is why organizations trust Marquee with it.
One of 4 people worldwide with dual MVP in Data Platform & AI Platform.
24 years at Microsoft, Starbucks, Wachovia (Wells Fargo), and Inmar.
Seven technology books. MBA from Wake Forest.
Where enterprise AI breaks
Most enterprise AI underdelivers in the same four places. Which one sounds like yours?
PDFs, medical records, invoices, and contracts still drive core processes. When information stays trapped, teams read, rekey, correct, and reconcile by hand — hiding cost and multiplying downstream error.
Document Intelligence →When numbers change depending on the report, owner, or meeting, the organization slows down. Time gets spent debating definitions and rebuilding confidence instead of making decisions.
Reporting Trust →Without a disciplined screen, budget gets spread across attractive ideas that never create durable value.
AI Priority Audit →The pilot worked in a controlled environment. Then exceptions, handoffs, and invisible manual glue showed up.
Production Readiness →What has to become true
The best starting point is the decision, bottleneck, or document-heavy process where better information would change an actual business outcome.
If the underlying information is trapped, inconsistent, or incomplete, the AI layer scales confusion faster.
If leaders still need to double-check the result before acting, the system hasn't earned the right to change decisions.
Handoffs, exceptions, ownership, and visibility determine whether the process becomes part of the business or just another fragile workaround.
Real workflows. Measured results.
"We thought our process was working. Marquee showed us what it was actually costing — and built something that leadership could trust without second-guessing."
— Director of Clinical Informatics, Global Healthcare Organization
Copilot reality
At $30/user/month, a 500-seat organization spends $180,000 a year on Copilot. Every month without measurable ROI is another $15K your CFO will question.
We find the one workflow where Copilot can produce measurable, repeatable value — then build the foundation that makes it work.
See the Copilot reality page →Honest answers
You're weighing alternatives. You should. Here's what we've seen happen with each — and why they tend to leave the root problem in place.
Training teaches people to use the tool. It doesn't repair unclear use cases, weak data, or broken workflows. If the foundation isn't ready, training accelerates frustration.
Software doesn't fix broken use cases, poor information quality, or outputs nobody trusts. If it did, your current tools would already be working.
Your internal team knows the business deeply. What they may lack is cross-industry pattern recognition — seeing the same failure modes across dozens of organizations and knowing exactly where to intervene first.
The license fees are running whether the value is there or not. And foundation problems — bad data, unclear use cases, untrusted reporting — compound with time. They don't resolve on their own.
Buying questions
This work sits between strategy, workflow design, data reality, and implementation. The right questions are about fit, scope, and what changes.
We'll tell you where it is breaking, whether it is fixable, and what the smartest first step is.
Request a workflow review →You'll receive a written assessment with a clear recommendation within 2 business days.