Organizations have entered an era where AI adoption is key to creating internal transformation. But how do these teams approach the adoption and integration process? According to our most recent Stack Overflow Knows survey of over 1,000 technologists, 69% of experienced developers trust open-source AI for strategic and creative work. Yet only 1% of leaders believe that their gen AI deployment has reached “maturity,” according to a McKinsey study. Where is this mismatch coming from?

In the latest episode of Leaders of Code, CTO of Cloudflare Dane Knecht talks with Stack Overflow’s CPTO, Jody Bailey, about what it looks like for companies to adopt AI for better productivity and innovation and what it means for humans to work alongside AI while still taking ownership of their work. As a company striving to be AI-first, Dane says, “You really have to engineer your way out of the problem… [this has] historically been the culture at Cloudflare. And using AI is just another way of engineering your way out of the problem.”

This article explores how organizations can strategically integrate AI and how real-world companies are putting these strategies to action.

Why organizations are adopting AI

Knowledge moves humanity, and companies, forward. For employees at all levels, from individual contributors to leaders, the ability to access information quickly allows for better efficiency and productivity. For instance, OverflowAI within Stack Overflow for Teams gives users the ability to quickly search existing knowledge from experts on their teams. A recent Stack Overflow Knows survey found that many developers prefer to learn through collective knowledge, turning to collaborative methods in order to master skills. Including AI agents helps employees quickly access this information, especially when answers are already readily available through company knowledge management. This means reduced search time and friction in the process.

Meanwhile, widely available AI chatbots like Gemini access data from wide reaches of the internet to quickly deliver information and sources to users. When they need more details or context, teams are able to dig deeper without the needle-in-the-haystack experience of combing through a heap of information for trusted, validated answers.

Many leaders are also using AI for strategy and innovation. AI tools help leaders think outside the box, allowing them to be more innovative and creative with their strategies and decision-making. Predictive analytics software allows companies to get a fuller picture of how their work is contributing to business success, while giving them a look into the future based on trends and past insights.

At every level, AI usage can mean better productivity, a deepening of skills and knowledge, and more innovative strategies for businesses.

How real teams are leveraging AI

When teams no longer have to work through repetitive tasks, it allows them more space for creativity and collaboration. That’s why many organizations are using AI automation to reduce routine toil. Whether this is using virtual transcription, streamlining data entry so there is no need for manual inputting, or helping to compose emails, AI gives employees the ability to automate these tasks seamlessly within their workflow. Maryam Ashoori, Head of Product for watsonx.ai at IBM, shared on the Stack Overflow Podcast episode that a recent IBM survey of a thousand developers saved an average of one to two hours a day by integrating AI into their workflows. From an organizational perspective, those time savings add up quickly.

For Cloudflare, adopting AI has gone hand-in-hand with their innovative business culture and structure. The company aims to become an AI-first organization in service of their mission to build a better internet. To do this, Cloudflare CTO Dane Knecht and his team started by giving AI tools to their most senior developers who were already high-performers. 

“The way we've rolled out the pilot so far is only the most senior developers on some of our hardest code bases were given the tools first. We really want to understand what their productivity would look like,” Dane shared on an episode of the Leaders of Code podcast. For Dane, an AI coding tool “doesn't take a mediocre developer and make them great.” Instead, he says, “it makes the best developers just really even better.” By adopting AI from the top down, Cloudflare is able to better measure the productivity results of their tools by looking at how those tools have positive effects on their highest-performing team members. They can then structure their AI-first strategy around tools that have proven impact.

On another episode of Leaders of Code, GitHub’s Global Field CTO Lee Faus shared that GitHub’s AI integration is allowing developers more time for strategic work, documentation, and collaboration with other teams like product and marketing.

AI has allowed both GitHub’s developer and non-technical teams not only to work more efficiently in their particular verticals, but also to function cross-departmentally, breaking down silos created from technical skill gaps. Developers are now able to spend less time working through technical debt and more time strategizing and thinking creatively, while non-technical teams can foster innovation and agility without having to wait for technical teams to implement changes on their behalf.

At Abnormal AI, Head of Machine Learning Dan Shiebler recognizes that adopting AI is not without its challenges. “But the results,” he says on an episode of the Leaders of Code podcast, “are truly transformative in every area where they touch.” AI has had compounding effects on productivity for Dan’s teams, allowing for easier automation and more seamless performance of products. For Abnormal AI, being able to really understand the obstacles teams may face when adopting AI into their workflows is key to getting fully realized productivity benefits.

Whether it’s adjusting AI tools to better fit the way teams are currently working, or upskilling employees so they can use these tools to maximize efficiency, Dan warns that “things don't necessarily work out of the box. It takes a little bit of investment. It takes a little bit of key decisions in order to shape our organization, shape our artifacts, shape the tools that we use in order to match it optimally for our environments.” Even with these challenges in mind, Dan’s teams are using the compounding effects of AI agents in their work, and other teams can use AI agents to automate work in customer support, marketing operations, and of course DevOps, as expanded on in our recent piece on agentic AI in organizations.

While these real-world examples are good inspiration points for leaders to integrate AI into their organizations, how each individual team uses AI in their work is entirely dependent on their own business goals and workflows.

How to integrate AI onto your teams

Whether you’re just beginning AI adoption or are leveraging it in your everyday work, it’s important to remember that AI is only a tool to help real people do their work better. For business leaders, AI integration can make the lives of your teams easier, as long as you start with these core values in your work:

• Connect AI use to business goals and give teams measurable outcomes for what success with AI looks like;
• Give teams concrete starting points for leveraging AI in their work, and give them the support and time they need in order to integrate these tools into the work they're already doing;
• Get your data ready and organized so that AI agents are better able to access and share existing knowledge, and make predictions based on past trends and data points;
• Set standards and visibility so that there is no guessing what is and isn’t allowed with AI usage, and make sure there is a set governance to protect employee privacy and keep data secure;
• And make it clear that AI is a tool for human workers, and not a replacement.

Ultimately, where AI can take your organization is dependent on the humans inside of it. Past just buy-in from leadership, the deep understanding of every employee on why AI integration is necessary will help to build an internal culture of experimentation with and curiosity for AI tools. Only then can companies expect to see better efficiency, productivity, and innovation with AI.