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MD MEETS · 5 min · Nov 29, 2025

AI gains require redesigning work, not just adding tools

Satya Nadella · CEO, Microsoft

Satya Nadella explains why AI pilots fail and describes the changes companies need to make.

The episode’s best minutes

Full episode
  1. 16:32AI gains require redesigning work, not just adding toolsPro pick 5 min
  2. 20:56AI investment must produce broad productivity and GDP growthPro pick 5 min
  3. 25:43Microsoft and OpenAI compete while sharing key infrastructurePro pick 8 min
  4. 5 more moments in the full episode
“So if you really think about composing that mindset, tool set, skill set, and data set”
Satya Nadellaat 20:08 · MD MEETS
20:08

Takeaways Pro pick

  1. Use AI to rethink processes, not just automate existing ones.
  2. Equip teams with tools and skills for the new processes.
  3. Normalize data across systems so AI can use it.

About this segment

Satya Nadella explains why AI pilots fail and describes the changes companies need to make. The approach combines process redesign, tools, skills and accessible data.

Satya Nadella

Satya Nadella is CEO of Microsoft.

Led Microsoft's cloud infrastructure and services transformation, and has been CEO since February 2014.

From “Microsoft CEO Satya Nadella: AI Boom, Energy Battle & His Complicated Alliance with Sam Altman”, Nov 29, 2025. Watch the full episode

Answers “Is AI paying off for companies?” See all 43 answers

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 16:45

    Everybody's talking about productivity, efficiency gains. If it is about artificial intelligence, very few people talk about quality and new tasks and so on. But let's stick to the to the productivity and efficiency. I saw a Gartner survey that was really interesting. They said that 74% of the CFOs in big corporations believe in the significant efficiency gains and cost advantages through AI, but only 5% have already seen any efficiency gain or cost effect because of AI. How do you explain that gap? So do we miss the capabilities to deal with AI. By the way, this Gartner survey was on another level, also very pessimistic. They said by 27, 40% of AI tools and projects that have been used by corporations will have failed. Where does this new kind of skepticism come from? And how do you see the gap

  2. 17:45

    between CFOs, optimism and real cost advantages? Yeah, no, I think that's a great question. And in fact, this is probably one of the fundamental things that'll cause, I'll call it firm level differences, right? I mean, when I just speak for myself at Microsoft, I mean, this is making massive productivity changes, whether it's in our customer service, whether it's in our supply chain operations, you talked about finance or in finance forecasting, there isn't, I mean, every day, in fact, it's no longer even the top down, it's the bottom up revolution that in some sense is happening, which is taking out drudgery, improving the quality of the outcomes, improving the flow of work, right? Somebody, like, just to give you one little example, we are building, I mean, we have become a very capital intensive industry, as you may have noticed. And so we're

  3. 18:33

    building all these data centers. We have 500 plus fiber operators around the world. And fiber, at the end of the day, is a physical asset. Things happen to it, right? They get cut or what have you, and someone has to repair it. And it's a very manual interaction with even the fiber operators. So the person who is like basically managing all of that internally built a full agentic system to deal with all of that, right?

  4. 18:59

    Because that's the only way, because if you think about it, we grew as much DC capacity in the last 18 months as in the first 15 years of Azure. I mean, it's pretty crazy in the sense of the scale. So the only way we can manage that type of scale is by using some of these tools. But to your point, though, Matthias, which is, I think, very, very important to your Gartner report or what have you, I think that there are four or five things that companies have to learn.

  5. 19:28

    One is a mindset. The mindset is not about sort of applying AI just to what you have today, but it's the change management that has to be imagined with AI. So that first thing is you kind of have to, you know, in the 90s, we used to talk about business process re-engineering. Guess what? It's time to think about business process re-engineering mindset. The second thing you do need is a new tool set, right? So you kind of have, you know, that's the type of stuff that we can provide or what have you, but you got to equip yourself.

  6. 20:00

    with a new set of tools that then can be applied to the new process. Then you need the skills to apply the tool, right? And that to me is the combination. And by the way, then you need data sets. The data sets can't be captive to your existing systems. You have to normalize the data across multiple systems. So if you really think about composing that mindset, tool set, skill set, and data set, and then go and apply, oh, I have a new evaluation, right?

  7. 20:32

    That's kind of how you do these AI systems. So there's a lot of learning, right? So if you go and say, oh, I'm going to approach it like any other IT project from even last year, it's going to fail by definition. And so that's where I think a lot of the hard work that's got to be done at the economy level is the re-plumbing of the IT skillset. And that's kind of hard because that's change management. It requires enlightened leadership. It requires people to get re-skilled and then scope the projects appropriately, pick the projects appropriately.