A global entertainment conglomerate was moving fast on GenAI, but the effort was scattered. Teams across the enterprise were chasing their own use cases with no shared way to intake, evaluate, or ship them, and promising ideas stalled before production. The company launched a centralized engine to intake, evaluate, build, and deploy GenAI products at enterprise scale. Tristellium designed the operating model behind it, standing up the intake and prioritization framework, the staffing model, and the execution discipline that turned a collection of independent experiments into a coordinated, measurable program delivering across 12 business units.
Designed a centralized intake and prioritization framework, scoring GenAI use cases against feasibility, technology and data readiness, and business impact
Diagnosed why the framework was stalling under use-case volume through structured interviews and firsthand time in Scrum and engineering forums
Designed a future-state staffing model splitting BU-aligned and solution-aligned Program Manager roles to separate intake from delivery
Implemented standardized milestone tracking for POCs, MVPs, and production deployments against consistent definitions
Built executive reporting that gave leaders a single view of which AI products were on track, which needed support, and which should be deprioritized
Delivered a repeatable delivery engine built to scale across the enterprise, not a set of one-off deployments
The company had a healthy pipeline of GenAI ideas and business units eager to build. What it did not have was a way to turn that energy into deployed products. Use cases were sourced ad hoc, prioritized reactively, and often stalled before production, leaving ROI unclear and deployment slow.
Scaling GenAI across an enterprise comes down to two questions:
They had partial answers to both. It needed a program that made those answers systematic and repeatable across the enterprise.
Two problems sat under the program:
Tristellium partnered with the EVP of Enterprise AI and the Enterprise AI Program Lead to diagnose the root causes — reactive prioritization, unclear development ownership, and limited visibility into progress and risk. That diagnosis set the core design principle for the framework: industrialize the GenAI lifecycle, from intake to deployment. To find the real bottlenecks, the team ran structured interviews with program leadership and on-the-ground Program Managers and sat in on Scrum and engineering forums to watch the delivery workflow firsthand.
The team built a centralized mechanism for business units to submit use cases, paired with clear evaluation criteria tied to feasibility, technology and data readiness, and business impact. Leadership could finally compare opportunities on a level playing field and direct resources to the projects that mattered most.
The future-state design split Program Management into two complementary roles: BU-aligned PMs owning intake, stakeholder management, valuation accuracy, and executive reporting; and solution-aligned PMs embedded with product and engineering squads to manage development pipelines, surface risks, and translate technical progress into business terms. The split relieved workload strain and closed the information silos between business and delivery.
Standardized milestone tracking for POCs, MVPs, and production deployments put every team on consistent definitions. That structure unlocked executive trust: for the first time, leaders could see which AI products were on track, which needed support, and which should be deprioritized.
By the end of the engagement, their approach had evolved from a collection of independent AI experiments into a coordinated, measurable program deploying real solutions across the enterprise.
A scalable delivery model for GenAI use cases operating across 12 business units
Faster pipeline velocity through reduced bottlenecks and tighter handoffs between business, program, and delivery teams
A clean separation of intake and delivery responsibilities across the PM organization
Improved leadership visibility into pipeline progress and risk, enabling faster decisions on where to invest, support, or stop
Increased PM bandwidth and reduced risk of overextension across the program
They fail on organizational design — fragmented efforts, duplicated work, and no shared path from idea to production. A centralized engine puts structure around intake, evaluation, risk, and deployment, and that is what lets AI scale past isolated experiments.
That is the work Tristellium does on these programs. Diagnose the real constraint. Design the intake, the roles, and the tracking around how the enterprise actually delivers. Build for both the now and the next stage, because the model will have to evolve as the work matures.
It's not a faster way to run AI pilots. It's the way they turn into products.
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