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The AI Value Gap: Why 82% of Companies are Failing to Gain from AI (Digital Reset Episode 486)

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8% of companies have adopted AI. Only 6% are seeing "significant" value. That leaves a staggering 82% value gap most businesses face.

If your team feels like they are running faster just to stay in place, you aren’t suffering from a tech problem — you’re paying an "AI Tax."

In this episode, Tim Peter breaks down the data from McKinsey, Section, and Workday to reveal why the C-Suite thinks AI is a miracle while the front line sees it as a burden. More importantly, he talks about what you can do to close the gap for your business.

Key Insights for Strategic Leaders to Close the Gap

  • Acknowledge the Productivity Paradox: 76% of Execs claim AI saves them 4–8 hours a week, while 40% of workers say it saves them nothing. This disconnect is where strategy goes to die.
  • Avoid the "Efficiency Trap": Chasing "more with less" often leads to "more noise for less impact." We look at the Klarna case study and why cutting costs too fast can erode the very brand equity you’ve spent years building.
  • Move from Tasks to Objectives: A mandate to "use AI for 20% of tasks" is a vanity metric. Real leaders set business goals (occupancy, conversion, satisfaction) and let AI earn its keep as a tool to reach them.
  • Protect Your Crown Jewels: AI is a commodity; your first-party data is not. The 6% who win are those who feed their specific, proprietary data into the models to create a "moat" that Big Tech can’t cross.

The AI Value Gap: Why 82% of Companies are Failing to Gain from AI (Digital Reset Episode 486) — Headlines and Show Notes

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Transcript:

Welcome back to the show. 88% of companies use AI. 88% of companies use AI. Only 6% see significant value from AI. 6%. That is a massive 82% gap between those who use AI and those that actually get value from its use.

82%, for those of you who don’t love math, is a lot of percent. It’s hard to have a whole lot more percent than that.

That gap was first reported by McKinsey towards the end of last year. And in the three months since the report dropped, I’ve had conversations with C-suite and digital leaders at companies ranging from the Fortune 100 to individual hotel owner/operators that usually tell me something strikingly similar. Lots of companies are "playing with AI" or "testing AI" in some portion of their business or other. They’re mostly looking to drive efficiencies, to do more with less.

Despite these efforts, they’re mostly not seeing the value. They’re falling into that 82% value gap too.

What we’re going to talk about today is what lives in that gap and how you can get to the other side of it. This is Digital Reset with Tim Peter. I’m your host, Tim Peter. Let’s dive in.

Okay, so there’s an 82% AI value gap. That is 100% real. There’s also a big gap between the people who see real gains from AI and those that don’t.

The Wall Street Journal reported a few weeks back on a research study from a group called Section that found 76% of C-suite execs thought AI was saving them “at least four to eight hours each week.” Essentially one in five claimed that they saved more than 12 hours per week using AI.

By contrast, 40% of non-management workers said that AI didn’t save them any time at all. Another 27% said that it saved them less than two hours per week. Only 14% said that it saved them greater than four hours a week.

So that’s 76% of execs versus 14% of line employees saving more than four hours. That’s a 62% productivity gap. As the article notes,

“A new report from the business software company Workday goes so far as to call frustrations with the technology an AI tax on productivity. Though 85% of the roughly 1,600 employees it surveyed reported saving one to seven hours a week by using AI, much of the time was offset by having to correct errors and rework AI-generated content.”

Think about those numbers I’ve just talked about. Those are huge differences. An 82% value gap. A 62% productivity gap. An AI tax that employees are paying. What in the world is happening here?

First, let’s unpack McKinsey’s 82% value gap. Note that AI does produce value every day. And admittedly, a small number, 6%, see “significant” value. There’s also a larger share who see some value, just not at “significant” levels.

Similarly, think about the AI tax on productivity that 62% of workers see here.

It’s not a question of does AI work. It’s far more about how and where the folks that get the value put AI to work. I am seeing too many companies, you know, “play around with AI” or “test AI” without clear plans, clear objectives, and a clear strategy for what they really want AI to do. The McKinsey data says it. I see it every single day.

I know of companies whose execs delivered mandates to their team, things like, “Everyone will use AI for X percent of their tasks,” and similar such messages.

That’s not a strategy. It’s not even a goal, really. It’s more of a hope of what could be. What it lacks is a strategic underpinning.

You might remember Klarna famously announced a couple of years ago that, “AI did the work of 700 human agents and does it much faster.” As their CEO said in December 2024, "I am already of the opinion that AI can do all of the jobs that we as humans do."

Less than five months later, they reversed course and started hiring again in serious numbers. And the reason is simple. They moved too far, too fast, and with too little thought about what they really needed.

I didn’t say that, by the way. The company CEO, Sebastian Semetkowski, said it himself. MLQ.ai had a write-up where they wrote, "We went too far, he said, noting that the focus on efficiency and cost ultimately reduced the quality of the company’s offerings and eroded trust with customers."

Let’s start with the fact that companies that get the most out of AI, by contrast, have a clear strategy in place for what they expect of their AI efforts. They’ve got well-defined objectives they want to reach.

And those objectives aren’t, “We want to use AI 50% of the time.” Their objectives are specific. They’re relevant. They’re time bound.

Consider this hotel client of mine. They want to drive more occupancy, put more heads in beds. They want to increase the revenue they achieve from their guests. They want to increase guest satisfaction and retention. They want to lower their costs for achieving these. And they want to accomplish this within a particular time frame.

I want you to notice something about those objectives. Did you notice how none said, “by using AI?”

You know why?

Because it doesn’t matter if they achieve those goals with AI or without it. It matters if they get more customers, keep those customers happy, encourage those customers to provide more positive ratings and write more positive reviews, and reduce their operational costs while they do that. That’s how their hotel succeeds. That’s how their business succeeds.

Of course, they then looked at AI for ways to meet those objectives. And they were super willing to rethink internal processes where it made sense to do so. But the conversation started with strategic objectives and a strategy for how to get there that included AI, not “use AI and hope for the best!”

They were also realistic about where AI might not work — usually because they didn’t have the right data or the right skills available — so that they didn’t waste time and resources along the way.

Companies achieving value from their AI initiatives target growth and innovation, not just efficiency. They put in place well-defined success metrics that allow them to evaluate if they’re moving in the right direction or, you know, not. They create these metrics because they’re focused on business results, not just technology, not just AI, not just whatever the new shiny thing is.

You’ve heard me talk many times before about how data is the crown jewels. Well, your metrics are one type of data. They keep you on course. They tell you when you’re succeeding, and they tell you when you’re not.

Another type of data that matters is the data that your AI tools need to learn from. Companies succeeding right now make their data available to the tools that they’re working with. That data is a massive strategic asset. It’s what makes AI useful. They’re using AI to better understand their data and improve their products and services. And when they don’t have the data, they know that that’s not a place where AI is going to help them.

Now another thing you’ve heard me say is that content is king. That’s still true. The funny thing about AI is we can use it to create content and that’s good. We also can use AI to help our content be better and that’s better.

But weirdly, it’s not just about creating high quality content, though that’s important. It’s that you need to be careful when you use AI to create large volumes of content.

I’ve long said that content isn’t expensive, but content that doesn’t convert is. Well, that needs a little bit of modification in the era of AI in which we live, because you can use AI to crank out a ton of content.

What we’re finding though is that quality now matters even more than quantity, much, much more. Because it turns out that low quality content has a massive, massive cost to your business. Google hates low quality content. They think it’s spam. And I expect most of the other AI extra engines are working their way to that too.

Lower ranking traditional search engines is a big deal — something I talked about during last week’s show when I was referring to “first do no harm” in your search engine optimization efforts. If you have low quality content, guess what happens? You don’t show up there and you hurt your overall appearance.

In a very related way, you’ll also get less visibility in AI answer engines because they will not see your website as a trusted source.

Now, does that mean that every piece of content needs to be created by human beings? No, no, definitely not. What it does mean is that someone, a person who can exercise judgment, must review your content to make sure it works well for its intended audience, to make sure it actually speaks in your voice and is quality. You can compare your content with Google’s Search Quality Rater Guidelines; I’ll link to those in the show notes.

Those aren’t just what Google tells its quality raters. It’s also how those quality raters train Google’s various AI tools to recognize high quality content. We internally have actually built a series of instructions around using Google’s Search Quality Rater guidelines in our prompts when assessing client websites and content. Drop me a line if you want to learn more about that.

But that’s how it all works together. It’s the right specific relevant time-bound goals to measure against. It’s content that answers your customers questions and shows up in both search and AI answer engines. It’s AI initiatives that are focused on improving customer experience and greater revenue. And it’s customer data, your company’s crown jewels, that power those insights and experience.

So there are three questions I’d like you for you to think about this week that I think are really important:

  • What is it that you want AI to do for your business? Is it drive efficiency or drive effectiveness? Keep in mind that focusing on effectiveness usually has better long-term payback. That’s what Klarna found out.
  • Then, second, do you have the right first-party data infrastructure to make AI useful? If you don’t, that’s actually where you need to start.
  • Third, and finally, are you planning to use AI to reduce your dependency on big tech, or are you just looking to save money? Again, one of those has a much, much better future ahead of it.

As I mentioned in an episode a couple of weeks ago, Google saw its best quarter and year ever by applying AI to its ads product. And that’s cool, you would expect that from a Big Tech giant.

Individual independent hotels can see the same results, though. Businesses of any size can. Size does not matter. Strategy does. A clear goal does. Content, customer experience, data, and execution do, too.

AI is not a magic wand. It’s a mirror. It reflects the quality of your strategy and the depth of your data. If you’re just using it to crank out more content or check a box on mandated tasks by the C-suite, all you’re really doing is paying an AI tax on your brand’s future.

The 6% of companies who win, the 6% who have closed that gap don’t have an AI strategy. They have a business strategy that AI makes possible. Focus on effectiveness, protect your data, and don’t let the pursuit of efficiency kill your customer’s trust. Because ultimately, your customer’s trust is what will make your strategy pay off in the long run. And that’s where we always want to be.

If this episode helped you bridge that value gap for your business, if it helped you think about how you can bridge that value gap for your business, please do me a favor. Send it to one of your colleagues who’s currently staring at their 2026 budget and wondering how to make the numbers work.

You can find the links to the McKinsey and Workday reports and everything else we’ve talked about in the show notes, as well as a full archive of all of our past episodes at timpeter.com/podcasts.

And if you’re ready to move beyond big tech, my book, Digital Reset, is your roadmap to reclaiming your demand.

Thank you so much for listening. I genuinely appreciate you. Until next time, please be well, be safe, and be excellent to each other. I’ll see you soon.

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