Darryl Wright: Why AI Adoption Will Fail Just Like Agile Did—Unless We Change

Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.

 

"People are looking to AI to solve their problems, and they're doing it in the same way that they previously looked to Agile to solve their problems for them. The problem with that is, of course, that Agile doesn't solve problems for you. What it does is it shines a light on where your problems are." - Darryl Wright

 

The world has gone AI crazy, and Darryl sees history repeating itself in troubling ways. Organizations are rushing to adopt AI with the same magical thinking they once applied to Agile—believing that simply implementing the tool will solve their fundamental problems. But just as Agile reveals problems rather than solving them, AI will do the same. Worse, AI threatens to accelerate existing problems: if you have too many things moving at once, AI won't fix that, it will amplify the chaos. If you automate a bad process, you've simply locked in badness at higher speed. 

As Darryl points out, when organizations don't understand that AI requires them to still do the hard work of problem-solving, they're setting themselves up for disillusionment, and in five or twenty years, we'll hear "AI is dead" just like we now hear "Agile is dead." The challenge for Scrum Masters and Agile coaches is profound: how do you help people with something they don't know they need? The answer lies in returning to first principles. Before adopting any tool—whether Agile or AI—organizations must clearly define the problem they're trying to solve. 

As Einstein reportedly said, "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." Value stream mapping becomes essential, allowing teams to visualize where humans and AI agents should operate, with clear handovers and explicit policies. The cognitive load on software teams will increase dramatically as AI generates more code, more options, and more complexity. Without clear thinking about problems and deliberate design of systems, AI adoption will follow the same disappointing trajectory as many Agile adoptions—lots of activity, little improvement, and eventually, blame directed at the tool rather than the system.

 

Self-reflection Question: Are you adopting AI to solve a clearly defined problem, or because everyone else is doing it? If you automated your current process with AI, would you be locking in excellence or just accelerating dysfunction?

 

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About Darryl Wright

 

Darryl is an Agile Coach and Instructor dedicated to helping organisations and leaders be both successful and humane. He has over two decades in IT delivery and business leadership, he champions Agile ways of working to create thriving workplaces where people are happy, productive, and deliver products customers truly love.