Insights
What we know about making AI work.
Why Most AI Pilots Never Make It to Production
Every firm had a ChatGPT pilot running somewhere. Most of them would never ship. The problem was not the technology.
The Difference Between AI Automation and AI Integration
These two terms are being used interchangeably. They describe different things. The confusion is causing organizations to build the wrong systems.
What RAG Actually Solves
Retrieval-augmented generation had become the dominant architectural pattern for enterprise AI. Most of the discourse around it was either too technical or too vague.
Why Your Operations Team Should Own Your AI Initiative
The default assumption is that AI is a technology project, so it belongs to the technology team. This assumption is responsible for a large share of stalled initiatives.
How to Measure AI ROI Without the Noise
AI ROI discussions tend to go one of two ways. Either the numbers are inflated to justify the investment, or they are so hedged they are useless for decision-making.
The Hidden Cost of Doing Nothing
There is a version of prudence that looks like caution but is actually delay. The cost of doing nothing is not zero. It is accruing.
What 2024 Taught Us About Enterprise AI
Twelve months of enterprise AI deployments produced a clearer picture of what works and what does not. Some of the lessons were expected. Several were not.
Agentic AI in Operations: What Is Real Right Now
Agentic AI had become one of the most discussed topics in enterprise technology. It had also become one of the most overhyped. Here is the honest picture.
The Middle Manager and AI: The Conversation No One Is Having
The executive conversation about AI is happening. The frontline conversation is happening. The one about middle managers is not, and it should be.
Document Intelligence in the Enterprise: Beyond Basic OCR
For years, document intelligence meant optical character recognition. The current generation is a different category entirely, and the enterprise use cases are finally clear.
Why AI Projects Take Too Long
The average enterprise AI project takes substantially longer to deliver than the initial estimate. This is consistent across industries. It is not primarily a technology problem.
What Fabrication Shops Lose Every Week to Manual Quoting
Most fabrication shops run their estimating the same way they did fifteen years ago. A skilled estimator, a spreadsheet, and a stack of supplier price sheets. The margin erosion is quiet and consistent.
The Enterprise AI Stack: What Actually Matters
Two years of enterprise AI deployment have produced clarity about what technology decisions actually matter and which ones absorb attention without affecting outcomes.
Two Years In: What Enterprise AI Adoption Actually Looks Like
Two years of serious enterprise AI deployment have produced a picture that is both more encouraging and more complicated than the projections suggested.
AI for Healthcare Administration: Where the ROI Is Clear
Healthcare administration runs on documentation, approvals, and follow-up. Most of it is still handled manually by staff who could be doing something more valuable. The case for AI here is not theoretical.
Freight Exception Management Is Eating Your Team's Day
Every logistics operation has exceptions. Late pickups, damaged freight, missing proof of delivery, carrier disputes. The volume is predictable. The handling is not.
Construction's Document Problem and What AI Actually Does About It
A mid-size commercial construction project generates thousands of documents. RFIs, submittals, change orders, daily reports, safety records, subcontractor correspondence. Most of it is managed through email threads and shared drives.
The Shift Handoff Problem in Manufacturing
Every manufacturing operation has three or four shift handoffs every day. Each one is a point where information is lost, context is dropped, and problems that started on one shift become crises on the next.