Enterprise AI strategy: How to choose and build a winning template

There's a quiet philosophical scandal hiding inside Cohere's pragmatic guide to enterprise AI strategy. Strip away the procurement checklists and vendor evaluations, and you'll find an exercise in applied epistemology: choosing an AI strategy template is choosing a theory about what machine intelligence is and how it relates to human organization. As Carl Sagan might have said, we're not just selecting software—we're selecting a cosmology. Every template encodes assumptions about whether AI is a tool, a teammate, a mirror, or something stranger, and those assumptions quietly shape what an enterprise believes is even possible. The article's central insight is that templates work because they simplify, and simplification is itself an act of power. In physics, a model of the atom is a bet about which details matter and which can be discarded; similarly, an enterprise AI strategy decides whether the organization sees AI as a utility, a copilot, or a transformation of its very substrate. That choice determines what the company notices in its own data, its own workflows, its own people. The template doesn't just describe reality—it constructs it, the way a measurement apparatus in quantum mechanics doesn't passively record a state but participates in bringing one into being. This is where the weirdness creeps in. These templates are being applied to systems that are themselves pattern-detectors of staggering complexity. A large language model doesn't just sit inside your strategy like a toaster in a kitchen; it actively models the organization that is modeling it. There's a recursive loop here that should make any strategist pause: your AI is forming hypotheses about you, just as you form hypotheses about it. The template, then, is less a plan and more an entry point into an entanglement—a strange attractor in the organizational phase space, bending the trajectory of every decision that follows. Perhaps that's why Cohere's practical advice matters more than it seems. By insisting that enterprises evaluate AI capabilities with rigor, run controlled pilots, and iterate based on evidence, the article is essentially prescribing the scientific method as an organizational survival strategy. In a world where the object of study is a moving, learning target, the only stable ground is the commitment to keep updating your map. Read the full guide at https://cohere.com/blog/ai-strategy, and you'll find a blueprint not just for corporate efficiency, but for a civilization learning to live alongside an intelligence that watches us back.