Amidst all the hype surrounding AI, there’s a key topic missing from discussion: the AI transformation gap.
By now, organizations of every size are familiar with AI's promise to revolutionize business efficiency, productivity, and revenue. More than three-quarters of respondents in the latest McKinsey Global Survey on AI say that their organizations use AI in at least one business function.
Yet few projects are actually delivering on AI’s promise—just 5%, to be exact, according to a recent MIT study, State of AI in Business 2025. 95% of generative AI pilots are failing, and these are the low-hanging fruit of AI implementation.
Agentic AI, defined as AI applications that perform tasks autonomously, can be far more transformative than generative AI. But it’s also more complex. So much so that Gartner® predicts that “by 2027, over 40% of agentic AI projects will be canceled due to escalating costs, unclear business value or inadequate risk controls.” (Gartner, “Emerging Tech: Avoid Agentic AI Failure: Build Success Using Right Use Cases,” May 15, 2025).
Should organizations throw in the towel and accept that AI transformation is too complex to implement effectively?
Of course not. Data from McKinsey & Company suggests that companies with leading digital and AI capabilities outperform laggards by two to six times on total shareholder returns.
That ROI is too good to pass up. But in order to see those returns—to close the gap between AI’s promise and AI’s realized value—organizations need to understand where AI initiatives are breaking down.
This is where I bring good news: AI transformation may be complex, but there are simple steps you can take today to get your business ready for agentic AI.
What is AI transformation?
AI transformation is a comprehensive and strategic process in which an organization integrates artificial intelligence (AI) into all aspects of its operations, products, and services. Most companies plan to use AI to automate tasks, accelerate decision-making, improve customer experience, and create products or services.
AI transformation goes beyond simply adopting a few AI tools; it represents a fundamental shift in business strategy, operational processes, and organizational culture. And this is precisely why it’s so difficult. To adapt and shift aspects of the business, you need to understand them properly—but most companies don’t.
Let me elaborate.
What are the greatest challenges in AI transformation?
The greatest challenges in AI transformation occur in integrating AI into the day-to-day workflows of the people who need to use it.
To understand this challenge better, we can turn to an analogy from logistics: the last mile problem.
The last mile problem refers to the disproportionate difficulties and cost in the final leg of a delivery—that is, getting the product into the user’s hands. For example, in consumer goods, it doesn’t matter how efficient the warehouse distribution or packaging process is if the customer never receives their package (or receives it late or damaged).
In AI transformation, the last mile is the crucial but often overlooked step of embedding AI into real-world workflows. Many organizations are building powerful AI models or training algorithms on massive data sets, but they struggle to connect these models to the humans using them. It simply doesn’t matter how powerful AI is if it’s not integrated effectively into business operations.
Which leads me to my next point: You can’t integrate AI effectively into your business operations if those operations are not clearly documented.
AI agents are intended to interact with various business systems and processes, and they require significant context to work effectively. Forrester states, “AI agents need step-by-step instructions on how to execute tasks. For most businesses today, this know-how lives in fragmented workflows, undocumented data, and unofficial processes.” (Forrester blog, “Autonomy Is The Future, But AI Agents Still Deliver Value Today,” July 2025).
Lucid’s most recent survey confirms the lack of business documentation:
- 49% of knowledge workers say that their organization's current operational workflows are somewhat or hardly well-documented.
- 60% say that half or more of their team’s workflows rely on informal or person-dependent knowledge.
- 80% rely on tribal or institutional knowledge to complete work.
Bottom line: AI adoption, especially agentic AI, will never reach its full potential until current enterprise processes, workflows, architectures, data flows, and collaboration practices are understood and documented.
How to take AI transformation through the last mile
We know that the biggest barrier to agentic AI adoption is caused by a lack of documentation, which means that the first step in taking AI the last mile is seemingly simple: Create documentation.
I find it helpful to think of the last mile of AI transformation in three stages: readiness, strategy, and execution. Each stage requires different documentation to effectively integrate AI in workflows.
