Synthèse
McKinsey keeps publishing that software development tops enterprise AI use cases, with companies reporting 30–50% productivity gains in pilots. The pilot numbers are real. They rarely translate to top- or bottom-line impact in production. Our own company data tells the same story: Between Q1 2025 and Q1 2026, our total AI tool usage grew by 328% (over 4x). Over that same period, PR throughput grew by just 49%. That gap — adoption way up, outcomes inching along — is the context gap. Plug a generi
McKinsey keeps publishing that software development tops enterprise AI use cases, with companies reporting 30–50% productivity gains in pilots. The pilot numbers are real. They rarely translate to top- or bottom-line impact in production. Our own company data tells the same story: Between Q1 2025 and Q1 2026, our total AI tool usage grew by 328% (over 4x). Over that same period, PR throughput grew by just 49%.
That gap — adoption way up, outcomes inching along — is the context gap. Plug a generic agent into raw, uninterpreted data, and it will act inefficiently at best, harmfully at worst. An agent optimizing sales without your customer segmentation or product hierarchy will confidently recommend the wrong thing. Anthropic framed the shift directly: building with language models is becoming “less about finding the right words and phrases for your prompts, and more about answering the broader question of what context configuration is most likely to generate our model’s desired behavior. ” That second question — what context configuration — is the entire game.
Most organizations are still answering the first one.
Source originale
mckinsey.com
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