Scaling AI Use Cases with OpenAI: An Enterprise Journey

Unlocking the Power of Open AI: From Building Use Cases to Scaling Enterprise-Wide Adoption

  • * OpenAI operates with two core engineering teams: Research team and Applied team.
  • * Research team invents foundational models, while Applied team builds products from these models.
  • * Go-to-market team helps deploy OpenAI's products in end-user hands and gathers feedback for improvement.
  • * OpenAI's customer journey typically happens in three phases: building an AI-enabled workforce, automating AI operations, and infusing AI into end products.
  • * Enabling the workforce typically starts with chatGPT, while automating operations internally can be done partially with chatGPT or using the API for more complex use cases.
  • * Infusing AI into end products is primarily API use cases.
  • * Enterprises craft their strategy in three steps: setting a top-down strategy based on broader business objectives, identifying one or two high-impact use cases and executing them, and building divis
  • * OpenAI partners with enterprises to determine their AI strategy, identify high-impact use cases, build divisional capability, and deploy use cases.
  • * The partnership involves dedicated teams from both sides, early access to models and features, internal experts from research, engineering, and product teams, and joint roadmap sessions.
  • * OpenAI has helped enterprises like Morgan Stanley improve their core metrics through the use case journey by introducing new methods and techniques.
  • * A common use case is building agents in the "agentic workflows" space, with 2025 being the year of Agents.
  • * OpenAI identifies patterns and anti-patterns prevalent in agent development and shares four insights based on their experience.
  • * The first insight is to start simple, optimize when needed, and abstract only when it makes the system better.
  • * It's essential to understand data, failure points, and constraints before introducing abstraction.
  • * Developing agents in a scalable way requires understanding the task, data, and constraints rather than just choosing the right framework or abstraction.
  • * Starting simple is recommended, as it helps identify real bottlenecks, hallucinations, low adoption, high latency, and poor retrieval performance.
  • * Complexity should be incrementally added based on intense failure cases and constraints.
  • * A network of agents can work in concert to resolve complex requests or perform a series of interrelated tasks.
  • * Handoffs allow one agent to transfer control of an active conversation to another agent while preserving the conversation history and context.
  • * Guardrails are essential for safety, security, and reliability within an application, ensuring that systems maintain integrity and prevent misuse.
  • * Keeping model instructions simple, focused on the target task, and using guardrails for edge cases allows for maximum interoperability, predictable accuracy and performance gains, and reliable syste

Source: AI Engineer via YouTube

❓ What do you think? What role do you believe AI will play in shaping the future of work, and how can we prepare ourselves for this transformation? Feel free to share your thoughts in the comments!