Gerson Lehrman Group
Gerson Lehrman Group
Gerson Lehrman Group

AI Operations & Product Enablement

AI Operations & Product Enablement

Building an AI-First Design Organization
Building an AI-First Design Organization
Close-up of black road bike with orange frame detail in low light.

ai-strategy

tool-development-(r-d)

workflow-automation

product-enablement

adoption-governance

process-architecture

Logo
Logo
Logo
Senior Manager, Product + Experience Ops
Senior Manager, Product + Experience Ops
Senior Manager, Product + Experience Ops

Project Type

AI R&D function, Product Experience Org.

Released

Released

Technology

Claude AI Suite Figma Suite MCPs Agentic Workflows VS Code/iTerm Notion

Timeframe

Timeframe

9 months

9 months

Turning AI Into an Operating Advantage

Custom AI tools, workflows, and systems built around real work, backed by the operating model to scale them. The goal was to compound the speed, consistency, and decision quality of designers, PMs, and engineers across the organization.

Brief

Find the Leverage, Then Build It

Move beyond scattered experimentation to a deliberate AI strategy. Identify the workflows where AI can materially change the economics of the work, then build the prompts, tools, automations, and systems needed to capture that value across teams.

Find the Leverage, Then Build It

Move beyond scattered experimentation to a deliberate AI strategy. Identify the workflows where AI can materially change the economics of the work, then build the prompts, tools, automations, and systems needed to capture that value across teams.

Challenge

Bridging the Gap Between AI Hype and AI Value

Teams were experimenting with off-the-shelf AI tools in isolation, without a clear view of where they created meaningful value or how to operationalize them. Closing that gap meant building both the tools for real work and the systems, standards, and workflows to scale them.

Bridging the Gap Between AI Hype and AI Value

Teams were experimenting with off-the-shelf AI tools in isolation, without a clear view of where they created meaningful value or how to operationalize them. Closing that gap meant building both the tools for real work and the systems, standards, and workflows to scale them.

Impact

From Experiment to Enterprise Capability

AI moved from isolated experimentation into daily execution, supported by a repeatable operating model spanning evaluation, governance, adoption, enablement, and optimization. The result was an AI capability designed to compound over time, improving how teams create, decide, collaborate, and deliver.

1

AI Operating Model

//001

1

AI Operating Model

//001

6+

AI Platforms Integrated

//002

6+

AI Platforms Integrated

//002

10+

Operational Workflows

//003

10+

Operational Workflows

//003

more projects

more projects