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Shoppers of corporate AI are increasingly looking past flashy demos and into the engine room of work: frontier enterprises are embedding Codex-style agents deep in engineering, claims and finance processes, and the results, faster builds, fewer defects, and thousands of saved hours, matter to any firm thinking seriously about AI.<\/strong><\/p>\n Essential Takeaways<\/p>\n The headline stat is simple and striking: frontier enterprises are producing far more AI-generated output per person than their peers, and that increase is accelerating. That change has a sound sensory feel, you\u2019ll notice codebases getting tidier, reports drafted faster, and routine calls handled by quiet, efficient systems. According to OpenAI\u2019s B2B Signals, token usage is the proxy here, and it points to AI doing the heavy lifting rather than just answering quick questions. For businesses, that means focusing on what AI completes for you, not how many seats you\u2019ve bought.<\/p>\n The shift is from assistance to delegation. Agentic workflows let models operate across files, systems and longer horizons, which makes tools like Codex far more than a clever autocomplete. Firms sending many more Codex messages per worker are effectively scripting multi-step processes for AI agents to execute. That\u2019s how tasks that once needed handoffs between teams now stay in a single, faster loop. If you\u2019re thinking of adopting this approach, start by mapping repeatable processes you\u2019d happily hand off to a careful, rule-aware assistant.<\/p>\n Concrete examples help: Cisco treats Codex as part of its engineering stack and saw 20% shorter build times and massive time savings month to month. Likewise, Travelers Insurance automated first notice of loss calls and policy queries with an AI Claims Assistant handling six figures of calls annually. These aren\u2019t pilot projects, they\u2019re production workflows that free humans for higher-value work. Still, the faster you deploy, the more essential governance and security become.<\/p>\n Writing and comms remain widespread uses, but the interesting gains come from specialisation. Security teams use AI for procedural guidance, dev teams use it for code generation and debugging, and finance leans on AI for calculations and scenario analysis. That functional tailoring reduces friction and increases adoption because people get tools that solve their daily headaches. Practical tip: pick one function, define clear success metrics and scale horizontally once you\u2019ve proven ROI.<\/p>\n Frontier firms don\u2019t win by accident. They measure depth of use, build governance frameworks for production workflows, and invest in user training. That combination keeps systems reliable and helps teams trust delegated agents. According to OpenAI\u2019s guidance, moving beyond general chat into fully delegated agents requires clear rules about when AI acts, how outputs are validated, and who owns the decision-making. Start small with guardrails, then iterate, your next step is often cultural, not technical.<\/p>\n It’s a small change in thinking, from buying AI to designing work around it, that can make every workflow smarter.<\/p>\n\n
Why \u201cdepth\u201d of AI use is the new competitive metric<\/h2>\n
Agentic workflows: delegating work, not just asking questions<\/h2>\n
Case studies that prove the point, and raise the stakes<\/h2>\n
Where specialised AI use is paying off across functions<\/h2>\n
Building the scaffolding: governance, measurement and enablement<\/h2>\n
Source Reference Map<\/h3>\n