3 Key Lessons From OpenAI Report on Enterprise AI Rollout

The following is derived from our analysis of OpenAI's research on seven frontier companies.

Key Lesson 1: Start with Evals for Organizational Confidence

An “eval” is simply a rigorous, structured process for measuring how AI models actually perform against benchmarks in a given use case.

With Morgan Stanley needing to ensure value in a highly competitive and personalized business, they invested significantly in Evals prior to any AI roll out.

Key Lesson: testing model performance against business benchmarks created organizational confidence to deploy sophisticated AI capabilities at scale.

Key Lesson 2: Tailor Your Models for Cost

Indeed's use of GPT for explaining why candidates should apply to a job is the example cited. They used GPT-4o mini to create personalized explanations for why specific opportunities fit candidate backgrounds.

But the key to roll out was the fine-tuning GPT-4o mini so the model was production ready across all users for accuracy and cost.

Why? Fine-tuning makes models smaller, preserves tokens (i.e. cost) and increases model specialization.

The results: 60% fewer tokens used.

Key Lesson 3: Roll Out AI Cross Enterprise for All Teams

Traditional Approach: "Let IT handle AI implementation"

Strategic Reframe: "Create Organizational Capabilities That Scale AI Strategy"

BBVA's approach demonstrates the strategic value of bridging strategy and execution teams. By deploying ChatGPT Enterprise globally and enabling business experts to create custom applications, they generated over 2,900 custom solutions in five months, thus reducing project timelines from weeks to hours.

The strategic insight: prototype roadmaps are accelerated with blanket rollouts of AI cross enterprise as teams are empowered to get work done with it.

Inkeep’s Take: Why Architecture Determines Strategic Advantage

Traditional enterprise AI deployments follow predictable patterns: tactical use cases, linear workflows, and point solutions that deliver efficiency gains but create no sustainable competitive moat. Market leaders think differently as they build AI systems as strategic platforms with sophisticated relationships that make AI systems self-learning and self-improving over time.

Beyond Automation: The Strategic Framework for AI Excellence

The most successful enterprises don't deploy AI, they strategically architect AI ecosystems even at the most basic and internal use cases. This is important because it future-proofs AI roll out strategies as AI use cases & capabilities expand from single Q&A to agentic workflows.

This wasn't discussed at all in the OpenAI report.

Consider the strategic difference here: A traditional AI implementation automates a customer service workflow through simple Q&A. A future-proof system creates an ecosystem where customer insights automatically flow to product teams, compliance monitoring happens in real-time, and strategic intelligence identifies market opportunities, all through dynamic coordination AI Agents that adapt to learnings and business needs over time.

This architectural sophistication becomes a competitive moat because it positions enterprises to roll out AI Agents. And it helps enterprises build organizational capabilities, trust infrastructure, and strategic thinking that most enterprises lack today. It's a capability gap that compounds over time.

The Strategic Imperative

As AI capabilities continue evolving rapidly, the organizations that establish sophisticated foundations, grounded in architecting self-learning and self-improving systems, will be positioned to leverage emerging technologies effectively.

If you’re thinking about this for your enterprise, then reach out to us at Inkeep. We work with leading companies to help bring Agentic AI capabilities to their teams.