Microsoft’s CEO Says Everyone Is Wrong

Microsoft CEO just explained why 95% of companies are missing the mark on AI and why that is an enormous opportunity.

Everybody is talking about productivity, efficiency gains. If it is about

efficiency gains. If it is about artificial intelligence, very few people talk about quality and uh new tasks and so on. But let's stick to the to the

so on. But let's stick to the to the productivity and efficiency. I saw a Gartner survey that was really interesting. uh they said that 74% of

interesting. uh 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 uh effect

because of AI. How do you explain that gap? So do we miss the the capabilities

gap? So do we miss the the capabilities to deal with AI? By the way, the Gartner survey was on another level also very uh

pessimistic. They said by 27, 40% of AI

pessimistic. They said by 27, 40% of AI tools and projects that have been used by corporations will have failed. Uh how

how do you where does this new kind of skepticism come from and how do you see the gap 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 uh revolution that in some sense is happening which is taking out drudgery improving the quality of the outcomes,

improving the flow of work. Uh right,

somebody like like just to give you one little example, we are building I mean know we've become a very capital intensive industry as you would have may have noticed and so we're building all

these data centers. We have 500 plus fiber operators around the world. Uh and

fiber at the end of the day is a physical asset. Things happen to it,

physical asset. Things happen to it, right? They get cut or what have you and

right? They get cut or what have you and someone has to repair it. uh and it's a very manual interaction with even the fiber operators. So the person who is

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 uh because that's the only way because if

you think about it we grew as much DC capacity uh in the last 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 Matias 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. One is a mindset. The mindset is not about sort

mindset. The mindset is not about sort of applying AI uh just to what you have today, but it's the change management that can has to be imagined with AI. So

that first thing is you kind of have to you know in the '9s 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 with a new set of

tools that then can be applied to the new process. Then you need the skills to

new process. Then you need the skills to apply the tool. Um, 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 system. So you have to normalize the

system. So 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 then and then go and apply oh I have a new evaluation, right? That's kind of how you do these AI systems. So there's a lot of learning, right? So if if you

go and say I'm going to approach it like any other IT project from even last year, it's going to fail uh by definition. And so that's where I think

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 replplumbing of the IT skill set. Uh and

that's kind of hard because that's change management. It requires

change management. It requires enlightened leadership. It requires

enlightened leadership. It requires people to get reskilled and then scope the projects appropriately, pick the projects appropriately. We need to stop

projects appropriately. We need to stop thinking of AI as just a smart chatbot that writes emails and start seeing it as an autonomous worker, an agent that

manages tasks humans physically can't handle anymore. Nadella shares a wild

handle anymore. Nadella shares a wild stat. Microsoft built as much data

stat. Microsoft built as much data center capacity in the last 18 months as they did in the first 15 years combined.

To manage the physical optic fiber cables for all that growth, they didn't just hire more people. They built an agent system. This system actively

agent system. This system actively manages the repairs and maintenance of the physical network without constant human handholding. This is the

human handholding. This is the difference between a chatbot and an agent and it changes everything about how we work. A chatbot is like a library asset. It sits at your desk and waits

asset. It sits at your desk and waits for you to ask a specific question. It

is reactive. An agent is like a junior employee who walks around the office looking for things to fix. If a fiber cable breaks, the agent notices the problem, figures out the solution,

creates a work order, and calls the repair crew, all without a human boss telling him or her what to do. The

reason this matters is speed and scale.

When a company grows as fast as Microsoft is right now, there literally aren't enough humans to check every wire and switch. If they tried to do this the

and switch. If they tried to do this the old way where a human has to approve every step, the whole system would crash under its own weight. Humans get tired, we miss details, and we are slow. Agents

don't sleep and they can watch a million things at once. This means the future job market isn't about doing the busy work yourself. It's about becoming a

work yourself. It's about becoming a manager of those digital workers. You

won't be paid to type data into a spreadsheet. You'll be paid to supervise

spreadsheet. You'll be paid to supervise the agent that types the data. You

become the architect of the workflow deciding what the agent should focus on.

If you are just using tools, you are behind. You need to be managing

behind. You need to be managing outcomes. But even if you have this

outcomes. But even if you have this amazing technology, Nadella admits that most companies will fail because they're trying to put a jet engine on a horse and car. But an expensive AI technology

and car. But an expensive AI technology is useless if your company's data is messy and your teams don't know how to share information. Nadella lists four

share information. Nadella lists four things you must fix before AI works.

Mindset, tools, skill set, and data set.

He specifically mentions that data can't be captive. This backs up the Gartner

be captive. This backs up the Gartner stat predicting that 40% of AI projects will fail by 2027. They fail not because the AI is broken, but because the

business uses bad data or refuses to change old habits. Imagine you hire a super smart robot chef to cook a five-star dinner, but your kitchen is a disaster zone. The ingredients are

disaster zone. The ingredients are hidden in random boxes. The fridge is locked and the recipes are written in a secret code that only one person understands. That is part of what

understands. That is part of what Nadella is calling a data silo. Most

companies have their information trapped in different computer programs that don't speak the same language. The sales

team has one list and the shipping team has a totally different list. If the AI can't read all those files at the same time, it can't do its job. It will give you wrong answers because it only sees

half the picture. And this is why Nadella talks about replplumbing the business. It's not fun and it's not the

business. It's not fun and it's not the flashy part of AI. It's the boring hard work of organizing years of digital files and forcing different departments to share their secrets. This is also a

people problem, not just a computer problem. Employees often hoard

problem. Employees often hoard information because they think it makes them valuable. Microsoft CEO is saying

them valuable. Microsoft CEO is saying that for AI to work, that culture has to die. You have to break down the walls

die. You have to break down the walls between teams. If you skip this hard step and just buy the AI software, you don't get a faster business, you just get a faster mess. Success in 2025 comes

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