Strategic perspectives on enterprise AI, technology architecture, and the decisions that shape how organizations adopt and operate intelligent systems.
AI has collapsed the cost of producing content while the cost of verifying it has not moved. The next enterprise data moat is adjudicated data: authority, provenance, correction history, and outcome evidence. A 90 day trust audit checklist is provided.
ReadFoundation models are commoditized; the model is no longer the hard part. Enterprise AI stalls on integration — wiring models into systems of record efficiently, without a runaway token bill. Most pilot failures trace back to architecture, not model capability.
ReadGoogle's Open Knowledge Format points to a real problem. Enterprise knowledge is scattered across documents, wikis, catalogs, and systems of record, so every AI project rebuilds context its own way. The format matters less than the direction: cleaner, more portable knowledge that isn't locked to one vendor.
Read on LinkedInThe next AI planning constraint isn't compute or cost. It's power, and the politics of who pays for it. State moratoria, federal moratorium proposals, ratepayer pledges, and midterm politics are turning AI capacity into a public resource allocation problem most 2028 roadmaps haven't priced in.
The AI stack is changing faster than enterprise planning cycles. Build targeted capabilities that deliver value now, then standardize based on what actually works.
With enterprise AI, the output works or it doesn't. Most organizations haven't built the evaluation environments needed to hold AI to that standard.
Inference costs dropped 99% in a year, but enterprise AI still isn't accelerating. The bottleneck is the cost of wiring AI into production systems.
Model selection shifted from vendor procurement to portfolio management. Different tasks need different cost, latency, accuracy, and compliance tradeoffs.
Enterprise AI often fails because organizations commit to a model that isn't good enough for the task and compensate with process instead of correcting the decision.
Model choice still matters, but the harder problem is turning capable models into dependable systems — reliable tool use, structured outputs, and operational control over time.
AI-assisted knowledge work shows 10-30% productivity gains — real but uneven. Organizational friction absorbs much of the speed increase before it reaches outcomes.
The autonomous-agent vision driving today's vendor pitch isn't coming from enterprises — it's coming from vendors. What's actually working inside enterprises is AI-assisted engineering: integrations, legacy cleanup, test generation, accelerated debugging. Deliberate modernization, not hands-off cross-system automation, is where the real progress is.
Foundation models are commoditizing fast. The real competitive edge is integration — how efficiently you wire AI into workflows, manage token costs, and architect for production.
AI projects fail structurally, not culturally. Coordination overhead, disconnected compliance teams, and too many stakeholders stall initiatives before the model ever becomes the issue.
AI-native labs aren't built by buying more AI. They're built on data discipline — governed, contextualized lab data products, an edge-aware foundation, and a layered model stack that fits regulated science.