AI adoption works best when it's grounded in reality. That’s why Ops, Legal, and IT need a seat at the table from day one. They’re not blockers, they’re the ones who make AI scalable, safe, and genuinely useful.

The era of passive AI is ending. Agentic AI is arriving: systems that don’t just respond but act breaking down goals, choosing tools, adapting on the fly, and escalating only when needed. These aren’t just smarter assistants; they’re autonomous operators reshaping workflows, team structures, and decision-making. For organisations, the shift is clear: stop scripting tasks, start delegating outcomes. The future of work isn’t about writing better prompts, it’s about building AI that gets the job done.

As AI adoption grows, the question facing businesses isn’t just Should we use AI?, it’s Which model fits our use case best? While GPT-4 set the bar, other models like Claude, Gemini, Llama, and Mistral now offer specialised strengths, from long-context processing to open-source flexibility. Stop asking which model is best in general. Start asking which model is best for your workflow.

Most enterprise AI initiatives never make it past the prototype stage, not because the models don’t work, but because the organisation isn’t ready to scale them. Lack of ownership, unclear success metrics, disconnected teams, and fragile infrastructure are what really stop AI from moving into production.

To succeed, companies need to stop treating AI pilots as experiments and start treating them as the first version of a live system. That means building for real users, defining business outcomes, and investing in the processes and platforms that make AI sustainable, not just impressive.