{"id":16996,"date":"2025-10-21T10:20:48","date_gmt":"2025-10-21T10:20:48","guid":{"rendered":"https:\/\/topat10.com\/?p=16996"},"modified":"2025-10-21T10:20:48","modified_gmt":"2025-10-21T10:20:48","slug":"experts-say-the-high-failure-rate-in-ai-adoption-isnt-a-bug-but-a-feature-has-anybody-ever-started-to-ride-a-bike-on-the-first-try","status":"publish","type":"post","link":"https:\/\/topat10.com\/?p=16996","title":{"rendered":"Experts say the high failure rate in AI adoption isn\u2019t a bug, but a feature: \u2018Has anybody ever started to ride a bike on the first try?\u2019"},"content":{"rendered":"\n

Despite mounting skepticism about artificial intelligence in the enterprise, three leaders from Microsoft<\/a>, Bloomberg Beta, and an AI startup gathered at Fortune<\/em>\u2018s Most Powerful Women conference last week with a unified message: high failure rates are not a bug in AI adoption\u2014they\u2019re a feature of learning how transformative technology actually works.<\/p>\n

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The panel discussion, titled \u201cWorking it out: How AI is transforming the office<\/a>,\u201d tackled head-on a widely circulated MIT study suggesting that approximately 95% of enterprise AI pilots fail to pay off<\/a>. The statistic has fueled doubts about whether AI can deliver on its promises, but the three panelists\u2014Amy Coleman<\/a>, executive vice president and chief people officer at Microsoft; Karin Klein<\/a>, founding partner at Bloomberg Beta; and Jessica Wu<\/a>, co-founder and CEO of Sola<\/a>\u2014pushed back forcefully on the narrative that failure signals fundamental problems with the technology.\u200b<\/p>\n

\u201cWe\u2019re in the early innings,\u201d Klein said. \u201cOf course, there\u2019s going to be a ton of experiments that don\u2019t work. But, like, has anybody ever started to ride a bike on the first try? No. We get up, we dust ourselves off, we keep experimenting, and somehow we figure it out. And it\u2019s the same thing with AI.\u201d<\/p>\n

Klein went further, encouraging the audience to become what she called \u201cvibe coders<\/a>,\u201d or people who use accessible AI tools to build applications without traditional programming backgrounds. Coleman echoed Klein\u2019s perspective, saying \u201cthis is all about experimentation.\u201d <\/p>\n

\u201cWe\u2019re on that jagged frontier, which is we\u2019re going to have some wins, and then we\u2019re going to see that trough, and then we\u2019re going to have some more wins,\u201d she added.\u200b<\/p>\n

The Microsoft executive, who shared that her own CEO challenged the senior leadership team to vibe code, emphasized that creating the right organizational culture matters more than the technology itself. \u201cI think the study is really important because it actually reflects how many people feel right now, which is, is it really something that\u2019s going to help me at work? Will it give me more joy and take away the toil?\u201d Coleman said.<\/p>\n

Wu provided important context in an attempt to reframe the MIT findings. \u201cI think the actual study says that only 5% of the AI tools people are testing are making it into production. What\u2019s really interesting is if you actually take a step back and look at what percent of studies of IT tools being brought in actually made it into production before AI, it actually wasn\u2019t particularly high either,\u201d she said, noting success rates for large enterprise technology deployments historically hovered around 10% or lower.\u200b<\/p>\n

Wu\u2019s company, Sola, builds what she described as \u201cagentic process automation\u201d tools that help enterprises automate manual back-office work. She emphasized that the sheer volume of AI experimentation happening now makes lower success rates inevitable. \u201cMy guess is, there\u2019s a lot more tools happening right, there\u2019s a lot more tools to test, there\u2019s a lot more things being brought in,\u201d she said. \u201cAt the same time, AI is very new. It\u2019s going to hallucinate. You\u2019re going to have to work with experimentation in ways that previous [generations] wouldn\u2019t have.\u201d\u200b<\/p>\n

The conversation moved beyond defending failure rates to discussing what successful AI implementation actually requires. Coleman stressed the importance of building \u201cAI fluency\u201d across workforces and recommended a collaborative approach where technical experts work alongside business users. \u201cHow do we pair somebody that\u2019s really good at either tech or continuous improvement, or some of these other sort of breakthrough ways to look at processes, and sit side-by-side and not make something for you, but do something with you so they could learn how to actually put AI into your workflow,\u201d she said.<\/p>\n

Coleman also pushed back against the notion that enthusiasm for AI diminishes the value of human work. \u201cThe more we talk about AI, the more people think that we don\u2019t trust humans,\u201d she said. \u201cIt\u2019s really important that we\u2019re talking about the criticality of humans in all these workflows. So, it\u2019s about talking about what time I get freed up to do what I can uniquely do as a human.\u201d<\/p>\n

Wu outlined what she sees in successful customer deployments: a combination of top-down leadership support and bottom-up engagement from employees who understand daily workflows. \u201cLeadership really enabling employees to test and build things safely obviously, but giving people the flexibility to experiment, try new tools, encourage them to use and build AI and help them build fluency,\u201d she said. \u201cYour companies are full of people that live and breathe the business and they\u2019ve been around for decades, sometimes even centuries. And so for AI to be deployed really effectively, you need the tool to work really alongside the people who are doing the work every single day.\u201d<\/p>\n

Klein emphasized that experimentation doesn\u2019t require enterprise-scale deployments. \u201cWe also see startups working side by side, bringing engineers and business leaders together,\u201d she said. \u201cEven if we\u2019re in a regulated industry, we can be trying this in our personal lives and you know using on the weekend for nonsensitive information and just starting to see some of how this technology works because that\u2019s really where you\u2019re going to get the gains, and advancements, and big ideas.\u201d<\/p>\n

When an audience member asked what organizational conditions must be true for AI transformation to succeed, Coleman\u2019s answer revealed the cultural shift she believes is necessary. \u201cYou have to be okay with failure. You have to be okay with messy,\u201d she said. \u201cWe\u2019re talking about the entry point of this transformation. You have to be okay with experimentation, and you have to be okay with that jagged up and down.\u201d<\/p>\n

She added that companies need to embrace what she called \u201ca learning organization\u201d where \u201cmanagers need to stop assessing tasks and start teaching learning.\u201d The key conditions, she said, include \u201cvulnerability and courage\u201d as organizations navigate technology that moves faster than previous transformations.<\/p>\n

The discussion underscored a central tension facing enterprises: the risk of moving too slowly on AI adoption may ultimately exceed the risk of experimentation itself.<\/p>\n

You can watch the full discussion from Fortune<\/em>\u2018s Most Powerful Women event below:<\/p>\n