Despite the hype and the headlines, most companies are still stuck in AI limbo. According to McKinsey, 77% of organizations are exploring the use of AI—but only 40% have actually implemented it.
This gap—between interest and action—is what we call the Enterprise AI Chasm.
The term originates from the Technology Adoption Lifecycle, a model popularized by Geoffrey Moore in his book Crossing the Chasm. It describes the critical phase where new technologies move from early adopters to the early majority. In this stage, the enthusiasm of innovators meets the caution of mainstream buyers, and many promising technologies fail to scale beyond that divide. We've seen this before: from the slow uptake of personal computers in the 1980s, to the uncertain early days of cloud computing, to the rocky road of mobile-first digital transformation. Each of these now-ubiquitous innovations once hovered at the edge of mass adoption, held back by confusion, fear, and organizational inertia. Today, enterprise AI stands at the same precipice—ready to transform business, but still waiting on strategy, clarity, and leadership to get it over the edge.
And crossing it? That’s where the real value begins.
Leaders generally understand that AI holds transformative potential—from automating time-consuming workflows to surfacing insights humans could never catch. And yet, even with that promise, adoption remains slow.
At our recent webinar, A Practical Roadmap to AI Integration, we explored why.
Here are the most common blockers that keep enterprises from moving beyond experimentation:
Many executives are asking, “Where do we even begin?”
They’ve seen AI tools flood the market, but haven’t identified how AI applies to their actual business workflows. Without a clear, business-aligned use case, AI remains a cool idea—not a strategic initiative.
Solution: Start with process mapping. Where are the bottlenecks, repetitive tasks, or decision points that could benefit from automation or better data access?
AI integrations don’t happen magically. They require cross-functional expertise—product owners, data engineers, AI architects, and operations leaders who understand risk and ROI. Most enterprise teams don’t have this combo in-house, and hiring takes time.
Solution: Find the right integration partner—not just a vendor, but a team that can guide your roadmap, build responsibly, and move fast without compromising security or governance.
Even with powerful AI models, garbage in still means garbage out. Disorganized data, duplicate systems, and inconsistent formats make it hard to build the kind of context-aware systems that AI thrives on.
Solution: Begin with a readiness assessment to understand where your systems, data, and processes stand. You may not need a full data transformation—just a clear plan for integrating what matters most.
As AI systems touch sensitive data, from customer records to strategic documents, concerns about misuse, access, and compliance grow. Many teams delay AI projects out of fear of unintended consequences.
Solution: Build AI with responsibility baked in. That includes role-based access controls, privacy impact assessments, and transparent monitoring. Responsible AI is a critical layer of enterprise trust and adoption.
The Enterprise AI Chasm isn’t a technology problem—it’s a strategy and execution problem. The tools are here. The use cases are proven. What’s missing is a clear path from potential to performance.
That’s where we come in.
At The Opening Door, we specialize in helping enterprises move from exploration to implementation—with AI systems designed for impact, privacy, and scale.
-We start with your goals
-We guide your roadmap
-We embed responsible AI practices from day one
Let’s turn AI from hype to help in your business.
