At this point, almost every organization has embraced AI in some form. But, as every tech leader knows, there’s a wide gulf between, say, giving your team Copilot access and redesigning a product, service, or an entire business line around AI.
The former is transactional, while the latter is truly transformational. The gap between the two is where most organizations sit today.
Case in point: A recent survey from Decidr found that 40% of U.S. businesses are getting most of their AI value from general AI assistants like chatbots. While chatbot-style AI deployments can lead to productivity gains and even help automate some tasks, they can’t transform a business line, reimagine a product or service, or fundamentally change how a company does business.
Clearly, there’s a gap between where most organizations are today and where they want to be tomorrow. But bridging that gap will require strategy, investment, buy-in, and, in many cases, some expert help.
Don’t get us wrong: Chatbots can deliver real value for businesses. They can enable organizations to offer always-on customer service, empower workers to discover information faster, help non-coders build simple apps, and more. In each case, chatbots are extremely helpful. In no case, however, are chatbots transforming a business.
On the other hand, IBM defines AI transformation as:
“a strategic initiative whereby a business adopts and integrates artificial intelligence (AI) into its operations, products and services to drive innovation, efficiency and growth. AI transformation optimizes organizational workflows by using a range of AI models and other technologies to create a continuously evolving and agile business.”
This is a lofty vision indeed, and reaching it is something few organizations have achieved. In fact, Deloitte's 2026 State of AI in the Enterprise report found that organizations broadly fit into one of three categories when it comes to AI transformation:
What, exactly, sets that top tier apart?
While AI chatbots are a tool your employees use, transformative AI is the infrastructure your business runs on.
Most businesses today have gotten AI into employees’ hands, but far fewer have turned it into shared infrastructure. Eliassen’s latest research found that 66% of organizations bought and integrated an AI solution within the last year, but only about a third rebuilt a product, service, or business line around it or began offering new products or services as a result.
The transition from the former to the latter requires rethinking processes at a structural level. Rather than "How can AI make this task faster?" tech leaders should instead ask, "If AI could handle this process in its entirety, how would we redesign this workflow from scratch?"
Unlike chatbots that simply respond to prompts, agentic AI systems can take initiative, make decisions, and execute complex workflows with minimal or no human intervention. Agentic AI systems are key to unlocking AI transformation, but McKinsey's 2025 State of AI report found that only 23% of organizations have begun scaling AI agents, and most are doing so in just one or two functions.
The gap between where agents are and where they're going is exactly the window enterprises need to move through now.
AI transformation should change what you deliver, not just how you deliver it. Refer back to the statistic above: two-thirds of organizations procured an AI solution in the last year, but only about a third used it to meaningfully transform an aspect of their business. While that number will inevitably climb over time, for the near term, it’s clear that organizations have learned to optimize with AI, but very few have learned to grow with it.
To cross the gap between chatbots and AI transformation, organizations need to move beyond just thinking in terms of efficiency gains and cost savings and begin thinking about ways AI can deliver meaningful strategic outcomes.
Knowing what AI transformation looks like is the easy part. Building the path to AI transformation is where most organizations struggle.
Decidr's report uncovers a striking statistic: 73% of businesses say they experience difficulties or delays because only a small number of employees know how to complete key workflows and processes. At first glance, this seems like a people problem. In reality, it’s a problem of planning and preparation.
You can't automate what you haven't mapped, and AI can't scale inside an operating model that isn't legible — to leadership, to systems, or to the AI itself. Without first mapping key processes, understanding dependencies, and having a clear picture of who owns what at which stage, there’s no way to truly determine where AI can deliver the most value and begin transforming a process or business line.
A 2025 survey from the Intelligent Enterprise Leaders Alliance (IELA) found that 75% of enterprises say they have an AI strategy — but only 10% have fully integrated AI into their broader data strategy. As one senior data executive surveyed in the IELA study observed: "Many organizations are data-rich but insights-poor.
Without a strong data foundation, AI cannot deliver its promised value. It's why 85% of the AI-ready organizations surveyed are prioritizing improvements in data governance as their primary readiness investment.
As AI moves from experimentation to deployment, governance is the difference between scaling successfully and stalling out. Deloitte reports that enterprises where senior leadership actively shape AI governance achieve significantly greater business value than those that delegate governance to technical teams alone. And yet, only one in five companies currently has a mature model for governing autonomous AI agents — at precisely the moment those agents are becoming central to transformation strategies.
It’s important to note that governance here means defining where humans remain in the decision loop, how automated outputs get audited, and what accountability looks like when AI drives a business-critical process.
Talent readiness is the weakest link in enterprise AI preparedness, and relatively few organizations have sophisticated (and mandatory) AI training programs. Eliassen found that while 70% of organizations say they require workers to complete AI training, only 25% report that at least half of their workforce has actually completed that training.
But training is only a part of the equation, and in many organizations, there's a deeper issue at play: Most companies have focused on educating employees about AI rather than restructuring how work gets done around it. Leading organizations — that elite portion of companies that are radically transforming their companies around AI — are creating new roles that fundamentally shift how work is organized, not just how it's performed. AI operations managers, human-AI interaction specialists, and quality stewards are a few examples of the new roles emerging at organizations that fall into this category.
Crossing the gap between a general-purpose AI assistant and a fundamentally different way of delivering your products or services won’t happen by adding more tools. That gap can only be crossed by doing the harder, slower work of building the organizational foundations that allow AI to run inside your business, not just on top of it.
That work requires strategy, data infrastructure, governance discipline, and — for most organizations — some outside perspective on where to start. This is why the businesses pulling ahead today aren't necessarily the ones with the largest AI budgets.
They're the ones that built the right foundations early, and they’re starting to see the compounding effects emerge as we speak.
To get additional best practices and real-world guidance on AI transformation, innovation, and more, visit our resources page today.