As Yogi Berra said, “Predictions—especially about the future—are a tricky business.”
So forget predictions. The only question that matters in 2026 is how enterprises spend AI money.
One bucket is efficiency.
LLMs are a new component —useful for drafting, search, summarization, and conversational interfaces.
Paired with mature event-driven architectures, productized data, and reliable ML for speech, vision, and anomaly detection, this stack should already be cutting cost and cycle time.
If it isn’t, buying more tools won’t fix it, your problems are elsewhere.
The other bucket is offense—and this is where advantage is created.
Moats come from domain-specific intelligence, aggressive exploitation of unstructured “dark data” (documents, tickets, contracts, SOPs, logs), and tightly retrieval-grounded systems competitors cannot copy or buy.
This is where asymmetric bets belong: reasoning systems, agentic workflows, edge + IoT, and selectively hardened consumer breakthroughs.
BI’s decades-long promise of faster time-to-insight is finally becoming real—not through prettier dashboards, but through semantic layers and natural-language access that actually explain decisions across structured and unstructured data.
“If your AI spend can be copied by a competitor with a couple of programmers, it isn’t a moat.”
If your investment doesn’t leverage proprietary data, complex regulatory workflows, or deeply entrenched vertical expertise, you aren’t building a moat—you’re building a bridge for your competitors to follow you across.
Happy 2026.
Let your Enterprise AI/ML go forth—and actually prosper.