The enterprise AI gold rush is in full swing.
Every week, a new agentic AI use case hits the headlines. Digital assistants write emails, resolve claims, review contracts - and the promise of intelligent agents working alongside humans feels tantalizingly close. At least that's the sense we all get reading posts on LinkedIn, listening to analysts and so on.
With all the hype, business leaders are watching this unfold and wondering: which tech investments will ensure success without getting left behind?
Here's the uncomfortable truth: for many enterprises, the returns still aren't showing up. Despite soaring experimentation, agentic AI often fails to deliver meaningful transformation.
Why? Because most organizations are digging without a map.
In this AI rush, the most immediate beneficiaries are the infrastructure players - the "shovel makers" of our time. Cloud vendors, chip manufacturers, and model providers rub their hands every time an enterprise runs an LLM experiment. But for the enterprise itself, the gold remains elusive.
That's because many AI initiatives rest on flawed assumptions: that layering foundational models onto existing tech stacks will magically deliver intelligence, efficiency and productivity. But foundational models, however powerful, don't understand your business. They lack visibility into how your teams actually work.
Work itself is opaque. It happens between systems, across apps, and in the nuanced decisions humans make daily. In fact, we often liken real workflows to a mysterious black box, where you don't fully know or understand the contents. And when agentic AI is deployed without truly understanding that terrain, it doesn't just fail to optimize - it risks reinforcing inefficiency.
Agentic AI is not just another automation tool. It represents a step-change - AI systems that don't wait for commands, but take action. They make decisions, learn from feedback, and dynamically manage workflows. But that power, without precision, becomes dangerous.
As my colleague and co-founder Manish Garg recently wrote in Forbes, "generic models can't grasp enterprise nuance." Without first-party data to reflect domain-specific conditions, agentic AI will inevitably act on incomplete understanding, reinforcing inefficiencies rather than resolving them.
Enterprises that skip the data foundation risk building blind pilots - sophisticated tools acting on faulty assumptions.
To avoid that fate, you need a radically different approach to data. Specifically: first-party data - your organization's own telemetry of work. This is unique to your organization, which is both a benefit and potential stumbling block.
Because this is the only data set that reflects how your business actually functions. Not in theory. Not according to process documentation. But in real time, on real desktops, across real workflows.
It's the difference between buying a map and surveying the land yourself. And in enterprise AI, that difference determines success.
This is where Process Intelligence enters the picture. Not as another AI tool - but as the connective tissue that enables every AI initiative to be grounded in truth.
At Skan AI, we specialize in capturing the telemetry of work - the observable reality of how tasks are performed, decisions are made, and outcomes unfold. By analyzing these human-system interactions, we build digital twins of operations that are:
With this foundation, enterprises can finally do three things that matter:
C-level leaders face a critical inflection point. The tools are ready. The hype is real. But the winners of this era won't be those who move fastest - they'll be the ones who move with clarity.
The gold is out there. But digging without a map is a fast path to wasted investment, reputational risk, and stalled transformation.
Agentic AI isn't the gold. It's the drill. Your enterprise's unique processes are the gold. And first-party data, captured through process intelligence, is the only map that can show you where to strike.
Because in this new age of intelligent automation, the future belongs to those who don't just build AI. Instead, they build it on truth.
Let's work together to shape this new reality.