{"id":10657,"date":"2026-05-27T13:49:18","date_gmt":"2026-05-27T11:49:18","guid":{"rendered":"https:\/\/mio.one\/blog\/your-company-has-ai-your-team-doesnt\/"},"modified":"2026-07-09T10:18:15","modified_gmt":"2026-07-09T08:18:15","slug":"your-company-has-ai-your-team-doesnt","status":"publish","type":"post","link":"https:\/\/mio.one\/en\/blog\/your-company-has-ai-your-team-doesnt\/","title":{"rendered":"Your company has AI. Your team doesn’t."},"content":{"rendered":"
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Jaime Mazuecos | Business Strategy | AI Transformation & CX <\/em> <\/figcaption><\/figure>\n<\/div>\n

Estimated reading time: 3 minutes<\/strong><\/p>\n\n

You have the most powerful ChatGPT and Claude Cowork licenses installed; you have a demo that impressed the executive committee; but the reality is that your team is still working pretty much the same way it did two years ago. <\/p>\n\n

You’re not alone. <\/p>\n\n

According to McKinsey<\/a>, 90% of the best use cases for agent-based AI never make it past the pilot phase. And 80% of these projects fail to deliver the promised value, according to Gartner. <\/p>\n\n

However, global investment in AI continues to skyrocket. Something doesn’t seem to add up, and the question is: why? <\/p>\n\n

The problem isn’t technology, but what we think technology does.<\/strong> <\/h2>\n\n

Most organizations make the same mistake when adopting AI: they think the technology is the solution. They buy the model, hire the vendor, launch the pilot… and hope that something will change. <\/p>\n\n

And no, it doesn’t change. Or at least not enough. <\/p>\n\n

BCG has been advocating the 10-20-70<\/strong> principle for years: 10%<\/strong> of the effort in a transformation should be devoted to algorithms, 20%<\/strong> to technology and data, and 70%<\/strong> to people and processes. <\/p>\n\n

The problem is that most companies do it the wrong way around, and the element that is underestimated, put off, or simply ignored\u2014redesigning how the organization works\u2014is where success or failure really hinges. <\/p>\n\n

There’s a name for what happens when companies invest in technology to avoid the fear of falling behind, rather than to achieve measurable results: “Change Management Theater<\/strong>.” Transformation theater. <\/p>\n\n

A lot of activity, but little real change. <\/p>\n\n

What Sets Climbers Apart from Non-Climbers<\/strong><\/h2>\n\n

It’s neither magic nor a budget; AI projects that generate real, sustainable results have one thing in common:\u202f <\/p>\n\n

1. First the problem, then the tool. <\/strong> <\/p>\n\n

An AI platform is adopted not because it will solve a specific problem, but because the competition already has it, because it was featured at a conference, or because the vendor gave a demo that impressed the right people. <\/p>\n\n

No one asked which process was going to be improved, how much that process costs today, or which actual business KPIs would be used to measure the change. <\/p>\n\n

2. The challenge lies in the operational model, not the agent.<\/strong> <\/p>\n\n

An autonomous agent reasons, decides, and acts in a chain of events. Integrate it into an organization with silos and manual approvals, and you\u2019ll get friction, not transformation. It\u2019s essential to redesign who does what and who is accountable when AI makes a mistake. <\/p>\n\n

3. Contextual data, rather than LLMs.<\/strong> <\/p>\n\n

If there isn\u2019t a well-organized data<\/a> model, the CRM has duplicates, or the systems in the stack don\u2019t communicate with each other\u2026 The model learns from chaos and generates more chaos. Reviewing data quality should be the first step in any implementation; otherwise, it will be the cause of the first failure. <\/p>\n\n

4. Adoption should be a priority<\/strong> <\/p>\n\n

A team that doesn’t understand why and how a tool is being implemented won’t use it effectively. Adoption doesn’t happen on its own; it must be proactively designed. <\/p>\n\n

The conversation has changed\u2014what about your company?<\/strong> <\/h2>\n\n

Over the past 2 or 3 years, we’ve asked ourselves\u2014and heard others ask\u2014the same question: Should we use AI?<\/em> That conversation is now over. <\/p>\n\n

Now other kinds of questions arise: What are we using AI for<\/a>, and what operational model will we use to support it?<\/em> <\/p>\n\n

The major market players have already understood this: OpenAI\u2019s<\/strong> move to create a dedicated unit for enterprise deployment, or Anthropic\u2019s<\/strong> commitment to integrating AI into everyday tools with a focus on compliance and governance, are no coincidence; they confirm something that those of us who have been working in business transformation for some time already know: that true competitive advantage lies in the ability to genuinely integrate AI into a company\u2019s operations, connecting processes, data, culture, and people<\/strong>. <\/p>\n\n

Companies don’t need more technology salespeople; what they need are true integrators\u2014what we call ” build-with<\/em>,” not ” slide-ware<\/em>.” <\/p>\n\n

At MIO One, we understand that AI is a driver of growth, not an end in itself, which is why we don\u2019t start by deciding which LLM to use, but rather by identifying which business KPIs we want to improve and what operational model we need to support that. <\/p>\n\n

Because technology without that foundation doesn’t bring about any change\u2014it just costs more. <\/p>\n","protected":false},"excerpt":{"rendered":"

Estimated reading time: 3 minutes You have the most powerful ChatGPT and Claude Cowork licenses installed; you have a demo that impressed the executive committee; but the reality is that your team is still working pretty much the same way it did two years ago. You’re not alone. According to McKinsey, 90% of the best […]<\/p>\n","protected":false},"author":4,"featured_media":10659,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[240,303,231,234],"tags":[310,224,309],"class_list":["post-10657","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-customer-experience-2","category-customer-intelligence-en","category-marketing-and-advertising-strategy","tag-ai-agents","tag-artificial-intelligence","tag-cx"],"acf":[],"yoast_head":"\nWhy 80% of AI Projects Fail and How to Avoid It<\/title>\n<meta name=\"description\" content=\"80% of AI projects fail to deliver value. 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