Controversial opinion: Most "AI strategy" consulting is a waste of money. The gap between PowerPoint and production is enormous. The most valuable advice comes from people who have actually built and deployed AI systems at scale, not those who've just read about it.

The biggest trap with AI implementation: thinking talent is your bottleneck. Most companies have plenty of capable engineers and data scientists. What they lack is: • Clear business objectives • Clean, unified data • Leadership willing to rethink core processes Fix these

When a company says "our AI needs to be perfect before we deploy it," what they're really saying is "we're not ready for AI." AI is never perfect, even from Day 1. The right approach: 1. Deploy something good enough to be useful 2. Establish human review processes 3. Monitor

AI implementation rule I live by: Meaningful progress requires both a concrete target and space for serendipity. Structure your AI efforts to optimize for both: • 70% focused on known problems with clear ROI • 30% focused on exploration and discovery The biggest

AI adoption is following a predictable path in every industry: Phase 1: "It will never work for us. Our industry is different." Phase 2: "Maybe for some tasks, but humans will always be essential." Phase 3: "Wait, this actually works better than we expected." Phase 4: "How did

An overlooked benefit of AI: It forces companies to confront their technical debt. You can't plug advanced AI into legacy systems built on shaky foundations. This painful but necessary infrastructure upgrade is creating massive downstream value far beyond the AI implementation

The most underrated competitive advantage in AI implementation: Speed of learning cycles. Companies that can go from idea → prototype → feedback → iteration in days rather than months will inevitably outpace those stuck in waterfall processes. In AI, your rate of learning

The smartest companies aren't treating AI as a cost center—they're treating it as a profit center. They're not just asking "How can we use AI to cut expenses?" They're asking "How can we use AI to create entirely new revenue streams?" Different question, vastly different

A pattern I'm seeing in AI implementation: The hardest barriers aren't technological, they're organizational. Specifically: • Budget ownership across departments • Data access across silos • Process changes requiring multi-team coordination • Success metrics that cross

Your AI strategy is only as good as your talent strategy. Building advanced AI systems is hard. Retaining the people who can build them is harder. Companies winning with AI know it's not just about tech—it's about creating an environment where the best AI talent wants to work.

Don't confuse AI buzz with AI impact. Companies making the biggest waves on Twitter are rarely the ones driving the most business value. The real AI transformation is happening quietly in industries most people find boring: manufacturing, logistics, insurance. No keynotes. No

The most effective AI leaders understand that implementation is as much about psychology as technology. People don't resist AI because they don't understand it. They resist because they fear it will devalue their expertise. Your AI rollout strategy must address both the

My prediction: By 2027, every industry will have its "AI native" disruptor. These companies won't view AI as a bolt-on feature or efficiency tool. Their entire business model, organizational structure, and culture will be built from the ground up around AI capabilities. And

I'm on the waitlist for Perplexity's new agentic browser, Comet: perplexity.ai/comet