Insights
Frameworks, analysis, and field notes from a team that has built at Carousell, Goldman Sachs, and Bain.
Agentic AI is moving from research demos to production deployments. Here is how to separate signal from noise.
Companies are hiring AI engineers when they should be hiring AI-literate product managers and strategists first.
CES revealed that the real AI bottleneck is shifting from software to silicon, and most enterprises are unprepared for the implications.
Enterprise AI budgets have shipped. The companies that spent 2025 experimenting now need professionals to make it all work.
A year-end synthesis of what separated successful enterprise AI strategies from expensive failures in 2025.
Cutting through Q4 noise: the market shifts that matter for enterprise AI strategy heading into 2026.
Most AI vendor renewals are rubber-stamped. A rigorous evaluation before you sign saves money and prevents lock-in.
The consensus AI predictions for 2026 are boring. Here are the ones that will make people uncomfortable, and probably be right.
Forget the AI landscape maps with 500 logos. Here is the lean stack that production AI teams are actually using heading into 2026.
Intellectual honesty demands reviewing our own predictions. Here is where we were wrong, what we learned, and why it matters.
Most AI maturity assessments tell enterprises what they want to hear. A useful assessment requires measuring what actually hurts.
The GCC's AI market matured significantly in 2025. Sovereign investments bear fruit, but enterprise adoption tells a nuanced story.
AI agents are the next wave of enterprise AI, and the build-versus-buy calculus is fundamentally different from simple LLM applications.
Every 2026 AI budget we have reviewed makes the same three errors. The fix requires rethinking how you categorize AI spending.
Most companies have an AI feature list masquerading as an AI product strategy. A real strategy starts somewhere else entirely.
US AI companies entering Europe treat it as one market, ignore regulatory nuance, and underestimate local competitors. All fixable.
Retrieval-augmented generation was supposed to solve enterprise AI's accuracy problem. The reality is more nuanced than the hype.
The annual technology planning cycle assumes a level of predictability that AI has completely destroyed.
After working with dozens of enterprises on AI, five patterns separate the companies that ship from those that stall.
Billions are pouring into AI data centers and compute. History says overbuilding precedes the real value creation phase.
The AI model wars make great headlines. For enterprise buyers, benchmarks matter far less than deployment reliability and integration.
Retailers have spent two years building AI into commerce stacks. Black Friday 2025 will reveal what actually works under pressure.
Most data science teams are cost centers producing dashboards. Restructuring them around revenue outcomes changes everything.
Companies are publishing AI governance frameworks that look impressive on paper and change nothing in practice.
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