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Learn how making work visible, value stream management, and flow metrics can affect change in your organization.
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Multiple award-winning CTO, researcher, and bestselling author Gene Kim hosts enterprise technology and business leaders.
In the first part of this two-part episode of The Idealcast, Gene Kim speaks with Dr. Ron Westrum, Emeritus Professor of Sociology at Eastern Michigan University.
In the first episode of Season 2 of The Idealcast, Gene Kim speaks with Admiral John Richardson, who served as Chief of Naval Operations for four years.
Exploring the impact of GenAI in our organizations & creating business impact through technology leadership.
DevOps best practices, case studies, organizational change, ways of working, and the latest thinking affecting business and technology leadership.
The debate over in-office versus remote work misses a fundamental truth: high-performing teams succeed based on how they’re organized, not where they sit.
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The values and philosophies that frame the processes, procedures, and practices of DevOps.
This post presents the four key metrics to measure software delivery performance.
April 21, 2025
Moving from isolated pilots to enterprise-wide GenAI implementation requires thoughtful strategies that balance innovation with business value. This article explores proven approaches for scaling adoption across large organizations, drawing on the experiences of industry leaders who have successfully navigated this journey.
Before diving into technology implementation, successful organizations establish a clear vision for how GenAI will transform their business. Joe Beutler from OpenAI describes a framework that helps enterprises think about AI impact across different levels:
This progression mirrors how many organizations adopt GenAI, starting with internal productivity tools before moving to more transformative applications. Fernando Cornago describes adidas’ journey: “We need our technology to scale both regionally and for special moments…We are consumer-facing, so we need to be fast, stay fast…and we need our ecosystem to be smarter.”
A strategic vision ensures that GenAI initiatives align with business objectives rather than pursuing technology for its own sake.
With seemingly endless possibilities for GenAI applications, organizations often struggle with prioritization. Brian Scott from Adobe emphasizes the importance of a structured approach: “The most important one is really creating a single funnel, a single entry point, meaning that we have the single form that everyone goes to submit their use cases.”
Based on the experiences of leading companies, three categories of use cases consistently deliver significant value:
Applications that help employees work more efficiently are often the easiest starting point. Fernando Cornago describes adidas’ approach: “In the first wave, we put copilot to 500 engineers.” The results were impressive: 82% used it daily, 91% found it useful, and two-thirds showed measurable productivity improvements.
Google has seen similar benefits in software development. Paige Bailey notes that “26% of all of the code at Google is generated by machine learning,” while also accelerating code review, documentation, and testing.
Productivity applications typically:
The next category focuses on enhancing core business processes. Joe Beutler describes how Klarna, a fintech organization, transformed their customer service: “They had actually already reached 50% automation on their customer service tickets, and they worked with our teams to automate two-thirds of the remaining tickets.” This pushed their overall automation rate to 85%.
Operational applications often:
The most transformative applications embed GenAI directly into products and customer experiences. Fernando Cornago shares how adidas implemented AI-powered personalization: “We just released a month ago our new personalized [experience] and it’s converting three to 5% better…And our consumers are engaging four times more with it.”
Customer-facing applications typically:
Rather than attempting enterprise-wide deployment immediately, successful organizations follow a phased approach. Fernando Cornago outlines adidas’ strategy:
Adidas began with a targeted implementation: “This is an exercise that we did Q4 Q1 last year… In the first wave we put copilot to 500 engineers.” This allowed them to:
The adidas team carefully tracked results: “82% of the 500, they are using it every day already… 91% find it useful. And two-thirds of them, they really increase their quantitative metrics.”
Fernando even conducted a deeper analysis of time usage to understand impact: “We run an analysis and we brought seven teams to track their time for one month…We wanted to focus on how much time they spend on pure IDE, right? So is coding testing is this really 25% of the time only?”
Clear metrics help build momentum and secure support for broader implementation.
As adoption grows, successful organizations develop platforms and patterns that enable faster implementation. John Rauser from Cisco explains: “We’re taking the approach of using platforms to achieve some of the goals…making sure that we don’t experience that sprawl.”
Anand Raghavan describes Cisco’s platform approach: “We have five different apps we are building in parallel on a common platform. We are thinking about common infrastructure across all of Cisco, not just security.”
As GenAI initiatives expand, organizations need robust technical infrastructure to support them. Leading companies focus on three key areas:
Rather than having each team build their own infrastructure, successful organizations create shared services. The “Enterprise GenAI Delivery Patterns” paper describes several critical components:
These services accelerate implementation while ensuring consistency and governance.
To deliver value, GenAI systems must connect with existing enterprise applications and data sources. Fernando Cornago describes adidas’ approach to architecture: “We catalog every reusable component that we have… into package business capabilities, which is basically a set of microservices over some data layer that communicates with the wall with some APIs and even streams.”
Effective integration strategies include:
AI systems improve through feedback, making monitoring essential. Anand Raghavan emphasizes: “Feedback management cannot emphasize the importance of that as customers give you thumbs up and thumbs down, just like you do in chat GPT, you want to make sure that your data analysts and your machine learning team are in a tight loop of improving the models.”
Effective monitoring includes:
Even with careful planning, organizations face several common challenges when scaling GenAI:
Patrick Debois highlights the financial implications: “This is not anymore unless you run the model yourself. This is GPU pricing, but they’re charging by the token. And token think is pieces of text you’re sending not only the input you’re going to be charged, it’s also the number of output.”
Successful cost management strategies include:
GenAI requires new skills across the organization. Fernando Cornago describes adidas’ focus on cultural transformation: “We foresee that more than 50% are going to need to change technology or products or focus in the last two years.”
Leading organizations address this through:
The rapid pace of GenAI development creates challenges in managing technical debt. John Willis warns: “There’s a shadow AI coming, and it’s going to be way worse if unattended. I’ve seen this movie, I’ve been doing it, I’m old. I’ve seen this many, many times.”
Strategies to address this include:
To sustain momentum, organizations need clear metrics that demonstrate business impact. Fernando Cornago shares adidas’ approach: “So how did it go? Thanks to this and thanks to this community really. I mean we can say very proudly that we are linear. So with half of our engineers working in our ecosystem, we also decreased 25 million per year, our running cost and we open it in 20 new markets for the last two years.”
Effective measurement approaches include:
Several organizations demonstrate different aspects of successful implementation:
Google’s Developer Experience
Paige Bailey describes how Google has implemented AI throughout their software development life cycle: “We’re writing code faster. That’s accelerating that one small aspect of the developer workflow, about 6% of where they spend their time. But we’re also accelerating code review…All you have to do is say yes, accept this machine learning edit, and you’re off to the races.”
Their comprehensive approach includes:
Moderna’s Enterprise-Wide Deployment
Joe Beutler explains how Moderna rolled out ChatGPT across their knowledge workers after rigorous testing. The results were impressive: “Their team has actually built over 700 custom GPT in just the first two months after they launched… They’ve also created their own GPT store, which is really interesting. So they wanted to create their own directory so that they could help employees with discoverability of solutions.”
Adobe’s Governance-Led Approach
Brian Scott describes how Adobe balances innovation with governance through their structured approach to use case evaluation. Their framework ensures all stakeholders understand “all the data that’s going in and all the data that’s going out,” enabling faster, more consistent implementation.
Successful enterprise-wide GenAI implementation requires a comprehensive approach that addresses strategic alignment, use case prioritization, technical infrastructure, and organizational change. As Fernando Cornago concludes, “Our DevOps transformation paid off… GenAI tools are already a commodity for the team. So you cannot remove this from them.”
The organizations achieving the greatest impact are those that treat GenAI not as an isolated technology initiative but as a fundamental transformation of how work gets done. By following the strategies outlined in this article, enterprises can accelerate adoption while managing risks and delivering sustainable business value.
In our final article, we’ll explore emerging trends and future directions for enterprise GenAI.
Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.
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