ChatGPT Enterprise from pilot to operating model

At the time, generative AI interest was rising across the company, but leadership needed more than enthusiasm. The bar was not novelty. The bar was a safe, measurable, credible implementation that could demonstrate business value without creating governance risk.

The contact center was the right proving ground. It had clear workflow friction, measurable business outcomes, and high enough operational visibility that results would matter. Agents were spending time on pre-call preparation, drafting, and context switching across systems. If AI could improve that environment in a controlled way, it could become the foundation for a broader operating model.

This work started inside a high-leverage operational environment rather than as a centralized innovation exercise. The contact center had visible friction in day-to-day work: agents needed faster access to context, better support for on-brand communication, and less time lost to repetitive preparation and switching between tools. At the same time, any implementation had to preserve human judgment and respect governance boundaries.

Leadership also needed more than anecdotal success stories. For enterprise AI to become credible, it needed to show measurable improvement in both revenue and efficiency, not just excitement from early adopters. That requirement shaped the pilot from the beginning.

• The real risk was not missing the AI wave. It was adopting it badly.
• Without a controlled pilot, AI usage could have fragmented into isolated power users, inconsistent prompting behavior, weak governance practices, and no clear business case.
• Meanwhile, agents still needed help with workflow friction that directly affected speed, quality, and commercial outcomes. Leadership needed evidence that AI could improve performance in a meaningful way while remaining safe and manageable to scale.

1. 1

   Identified the right workflow opportunities

   I partnered with supervisors and frontline agents to identify the highest-friction jobs to be done, then translated those into purpose-built GPT-supported workflows. The goal was not general AI usage. It was targeted support in moments where time, clarity, and consistency mattered most.

2. 2

   Built trust through governance design

   I worked across Legal, Security, Engineering, enterprise systems, and Operations to define acceptable data handling, review steps, and usage boundaries. This mattered because enterprise AI adoption only becomes real when the guardrails are practical enough for teams to use and credible enough for leadership to support.

3. 3

   Structured the pilot around measurable proof

   The pilot was designed with a cohort and matched control group, using metrics centered on Revenue Per Call, Items Per Transaction, Average Order Value, and supporting efficiency signals. That measurement model turned the initiative from an AI experiment into a business case.

4. 4

   Designed the enablement system, not just the workflows

   I created training assets, reusable prompting patterns, and the early grassroots version of ChatGPT Champions so the organization could scale from one team to many. This piece is strategically important: the value did not come only from model access, but from making the workflows repeatable, the adoption pattern teachable, and the community around it strong enough to spread good practice.

5. 5

   Socialized results in executive language

   I translated the pilot into a leadership-ready narrative: concrete business value, repeatable rollout mechanics, and a credible path to broader investment.

Selected artifacts

Workflow discovery exhibit from the early stage of the pilot.

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Frontline ideation that shaped early GPT workflows

Sanitized excerpt from a structured ideation session with contact center stakeholders. The team started with free brainstorming, then built on the strongest concepts through collaborative expansion and lightweight voting. The session helped surface several of the early workflow opportunities that shaped the rollout, including Gear Companion, a GPT I created to recommend complementary products customers often needed alongside a primary purchase, such as accessories for a digital keyboard. Gear Companion went on to become one of the most-used GPT workflows after launch.

The most important outcome was not just the pilot result. It was the operating model that came out of it.

The rollout became scalable because usage patterns and governance were designed together. What emerged was a reusable system for enterprise AI adoption: workflow discovery, governance, measured pilots, enablement, feedback loops, and clear boundaries for use.

What scaled beyond the pilot was not just usage, but the system around it. Alongside workflows and governance, I helped build the human adoption layer through a grassroots ChatGPT Champions model that surfaced strong use cases, spread practical best practices, and created momentum across teams. Over time, that effort gained broader cross-functional structure, partnership with OpenAI GTMs, and C-suite sponsorship, helping turn early momentum into a broader enterprise capability.

The operating model and champions layer below show how the rollout scaled through governance, enablement, community, and executive backing rather than broad access alone.

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Enterprise AI operating model

Sanitized view of the operating model that helped ChatGPT Enterprise move from pilot to scale. The rollout combined use-case discovery, governance, measured pilots, enablement, and feedback loops so adoption could grow without losing trust. This system made it possible to move from an initial proof point in the contact center to broader enterprise usage.

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Grassroots champions model that scaled into enterprise support

Sanitized view of the adoption model that helped ChatGPT Enterprise scale beyond the initial pilot. I started the ChatGPT Champions program grassroots to share workflows, surface strong use cases, and build trust across teams. As adoption grew, the effort gained cross-functional structure, partnership with OpenAI GTMs, and C-suite sponsorship, helping turn early momentum into a broader enterprise capability.

This case reinforced a pattern I believe in strongly: enterprise AI does not scale because access is available. It scales because one operating environment becomes measurably better first.

Product leaders have an outsized role in that transition. The job is not just picking tools. It is designing the conditions for trust: workflow fit, measurable outcomes, governance that teams can actually work within, and an adoption model that can spread without falling apart.

That is what turned this from a pilot into an operating model.