Breaking the Enterprise AI Stalemate
How vendors and businesses can collaboratively navigate the chicken-and-egg dilemma of AI adoption
There's an uncomfortable reality in enterprise AI today: we're stuck in a classic chicken-and-egg dilemma.
On one side, business leaders see tremendous potential in AI but aren't entirely sure how to define their requirements or measure their success. They're looking to technologists for guidance, but without clear business direction, technology teams hesitate.
On the other side, technology providers recognize this massive opportunity. However, without clearly articulated business cases, they're cautious about investing in fully developed solutions. They're waiting for businesses to provide clear signals, while businesses are waiting for technology leaders to show them what's possible.
This stalemate is fundamentally different from the cloud adoption era. When cloud technology emerged, it primarily involved moving well-understood business processes and workloads into a more flexible and scalable environment. The use cases were clear, the requirements well-defined, and the value relatively easy to calculate.
AI, however, introduces an entirely new dynamic. The use cases are still evolving, the ROI is harder to predict, and the patterns for success are not yet established. Companies aren't simply migrating familiar processes—they're inventing entirely new ones.
Major technology vendors find themselves frozen, trapped between these two uncertain realities. They have the resources, tools, and expertise, but they're cautious about developing overly prescriptive solutions that may not align with eventual business needs. Meanwhile, enterprises hesitate to commit without clear guidance or proven success patterns.
Breaking this deadlock requires courage and initiative. It demands that vendors move from being passive providers to active facilitators of conversation between business leaders and technologists. For instance, vendors could offer workshops that explicitly aim to surface business challenges and collaboratively define specific, achievable AI experiments. Rather than delivering a polished product, they can provide sandbox environments and pilot programs that encourage businesses to test ideas incrementally.
Companies like IBM and Google have begun experimenting with this facilitator role by offering structured "AI discovery sessions" where business leaders and technologists jointly explore use cases and feasibility without immediate pressure for significant investment.
Internally, enterprises face similar challenges. Often, business units struggle to clearly communicate their AI needs to internal IT and data science teams, who in turn wait for precise requirements before moving forward. Companies finding early success typically bridge this internal divide through cross-functional teams that jointly own AI projects, sharing accountability for both technical feasibility and business outcomes.
Early adopters navigating these challenges typically have strong data foundations, high-risk tolerance, or pressing industry-specific needs—such as healthcare providers leveraging AI for diagnostic accuracy or retail businesses using AI-driven analytics to optimize supply chains. These companies measure success incrementally, focusing initially on insights, operational improvements, agility, and learning outcomes rather than immediate ROI.
Ultimately, solving this chicken-and-egg problem requires proactive engagement from both vendors and enterprises. Businesses must embrace experimentation and iterative learning, while vendors must transparently guide and support that process.
The companies that step boldly into this facilitator role, helping businesses define their questions and collaboratively explore solutions, will break the stalemate and create the momentum everyone is seeking.