June 24, 2025
It’s one of the biggest paradoxes in today’s enterprise landscape. Artificial intelligence is everywhere. It’s seen as the future of competitive advantage. And for many, it has become non-negotiable.
Yet when it comes time to invest—not in theory, but in practice—the momentum slows.
This isn’t about venture capital or public excitement. It’s about internal decisions within enterprises. Should we approve this AI project? Is this the right use case? Can we integrate this into actual workflows without disruption?
These aren’t easy questions—and they’re becoming harder, not easier. The deeper organizations go into AI conversations, the more complexity emerges around making confident, aligned investment decisions.
Over the past two years, interest in AI has surged across enterprise settings — from boardrooms to factory floors, nearly every organization feels compelled to “do something with AI.” On one hand, this enthusiasm is backed by a clear adoption uptick: 78% of organizations reported using AI in 2024, up from just 55% in 2023.
But defining what that “something” should be—and how much to invest in it—has become increasingly difficult.
The pressure is real. Teams are expected to stay ahead of competitors, make bold moves, and avoid falling into irrelevance. At the same time, they’re cautioned not to waste budget on trends that might not stand the test of time.
This combination creates a high-stakes environment. In many cases, it leads to hesitation—not from lack of ambition, but from fear of choosing the wrong direction. Even well-defined opportunities can stall, simply because the risk of moving forward feels greater than the risk of standing still.
Despite rising interest, many enterprise AI initiatives struggle to get past the planning phase. Underneath that hesitation, several recurring patterns continue to surface:
It’s common to see organizations launch multiple isolated AI pilots—each addressing a different problem, in a different department, with little coordination. Most of these remain stuck in experimentation mode.
Without shared success criteria, teams face fragmented results, unclear ROI, and no scalable learning across the organization.
Furthermore, a 2024 AI adoption study by O'Reilly reveals that only 26% of AI projects progressed beyond the pilot phase. Meanwhile, the majority of AI projects faced significant obstacles, including failure to meet business goals, underperforming models, and inadequate infrastructure.
The AI narrative evolves quickly—LLMs, GenAI, multi-agent systems, digital twins. With each wave of innovation comes a wave of new terminology. But without a clear link to a specific operational problem, these terms create noise rather than clarity.
This makes it difficult for stakeholders to discern whether they’re evaluating a real opportunity or just the next hype cycle.
AI doesn’t fit neatly into traditional organizational charts. Is it the responsibility of IT, R&D, operations, or a digital transformation office?
Without a clear owner—someone empowered to align budgets, teams, and infrastructure—AI projects tend to lose momentum before they begin.
- ROI frameworks that don’t quite fit:
AI isn’t like traditional software. It often learns, adapts, and improves over time. But most enterprise investment frameworks are built around fixed-cost, fixed-output models.
As a result, AI projects that involve real-time systems or edge deployments are often deprioritized—not because they lack value, but because they don’t fit the conventional investment lens.
Despite widespread interest and non-stop headlines, enterprise AI adoption has not accelerated significantly in the past two years. According to McKinsey’s 2023 Global AI Survey, about 55% of organizations report using AI in at least one function. That figure has remained largely unchanged since 2022, signaling a plateau in new adoption.
Meanwhile, Generative AI has reshaped the investment landscape. Stanford’s 2025 AI Index reports that private GenAI funding grew from $4.5 billion in 2022 to $25.2 billion in 2023 — a nearly sixfold increase. However, this surge happened alongside a decline in total private AI investment, meaning GenAI is attracting a larger share of a shrinking pie.
This shift reflects a growing imbalance. While GenAI dominates boardroom discussions, investment in traditional enterprise AI use cases — such as computer vision, predictive maintenance, or operational intelligence — is struggling to keep pace.
In short, AI has never been more talked about, but actual funding and deployment decisions are becoming more selective, more concentrated, and, in many cases, more cautious.
For enterprises navigating uncertainty around AI investment, the path forward doesn’t have to be about picking the next big breakthrough. In our experience, the most successful organizations share a few grounded principles that consistently help them move from hesitation to meaningful progress:
Caution is justified. The pace of AI innovation is relentless, and not every new breakthrough will be relevant or reliable for your operations.
But in many cases, the real risk isn't making the wrong investment—it's standing still for too long. Organizations that delay action indefinitely often find themselves with teams that are curious, but not enabled; data that is collected, but not used; and use cases that are talked about, but never deployed.
AI isn’t just about models or algorithms. It’s about building organizational capability—the ability to consistently identify, validate, and deploy intelligence across workflows, machines, and systems.
That capability doesn’t come from jumping on trends. It comes from building readiness: clear goals, aligned teams, and infrastructure that can support not just one initiative, but many.
So yes—be selective. Ask hard questions. Start with real problems.
But don’t stay frozen.
