AI is now a priority for nearly every company, with most launching some form of generative AI (GenAI) initiative. The expected benefits, like faster decisions, sharper forecasts, fewer support tickets, and cost savings, were clearly defined. But behind the scenes, expectations are colliding head-on with operational realities, and the gap is exposing the real constraint: broken workflows and fragmented systems.
For most companies, data resides in disconnected systems, processes are manually stitched together, and tools fail to communicate with one another. Leaders are betting on AI to solve problems that are fundamentally about workflows and systems, but that rarely works. AI can’t thrive in environments riddled with data gaps, manual workflows, and a patchwork of tools that do not connect.
Even Salesforce, arguably one of the most aggressive AI implementers, recently shrunk its organization by roughly 4,000 support roles (approximately 45% of its customer support employees) as AI handled a growing share of the work. Slack is now central to their agentic AI strategy because it’s embedded in the work employees already do. That’s where operational change actually begins.
So where exactly is enterprise AI going off the rails? What needs to be true for it to finally deliver?
1. AI Needs Clean, Source-Level Data. Most Companies Don’t Have It.
AI systems are only as good as the data they’re fed. But in most organizations, the inputs are incomplete, inconsistent, and scattered across the places work actually happens. Data lives in PDFs, Slack threads, rarely updated CRM fields, and meeting notes that are never logged. Procurement decisions are hidden in emails, risk disclosures are buried in Zoom calls, and the systems being asked to “act smart” are left guessing.
The result is predictable: AI makes suggestions based on partial truths. Decisions are informed by guesswork, and leaders lose confidence in the tools they’re being asked to trust. According to McKinsey, 63% of executives say poor data quality is actively undermining the value of their AI investments. That is not a small problem.
Agentic AI flips the model. Instead of waiting for someone to manually log a conversation or flag a risk, it captures structured, source-level data at the moment the interaction happens, then routes it into the systems of record. A pricing shift during a vendor call? Logged and benchmarked. Legal red flags from customer negotiations? Routed to review before the meeting ends. No dashboards. No delay.
This type of capture changes everything. Organizations stop relying on memory and systems stop running on stale inputs. AI becomes actionable because the foundation is trustworthy from the start.
2. Automating Actions Over Alerts
GenAI tools are great at generating content, including meeting notes, email drafts, and task suggestions. But most of them stop there. They don’t actually take action, don’t move deals forward, don’t unblock revenue, and they certainly don’t resolve issues when no one’s watching.
In short, they assist and rarely execute. That’s a big problem.
Agentic AI does what GenAI doesn’t: it operates. It embeds directly into business systems like Salesforce, Slack, and Coupa, and initiates next steps based on real-time context and policy. If a vendorquote exceeds a benchmark, it gets flagged and routed to finance. If risk language appears in a customer call, the renewal workflow can be held for review. No waiting for manual follow-up.
Execution is the missing piece. Most companies don’t have it. Agentic AI does, by embedding directly into day-to-day workflows and initiating the next step when conditions are met, rather than stopping atrecommendations. That’s why the conversation is shifting from copilots that suggest work to systems that can carry it forward inside the tools teams already use.
3. AI That Doesn’t Depend on Human Behavior
One of the quiet truths of enterprise AI is that it depends far too much on humans doing the right thing.Employees are expected to prompt the AI, update dashboards, tag conversations, and know when to ask for help. But most frontline operators aren’t prompt engineers or AI experts. And they’re not going to change their behavior unless the tool makes their job radically easier.
That’s where traditional AI falls short, and where Agentic AI changes the equation. Agentic systems observe work as it happens, interpret context, and act within existing workflows without needing a human to initiate the process. They integrate with Slack, Zoom, CRMs, and ticketing systems, so line-of-business operators don’t have to leave their workflow. There’s no learning curve. No need to “remember” to use the AI. It’s just there, doing the work in the background.
Chat-style tools still depend on someone asking at the right moment, in the right way. Agentic AI removes that dependency. That’s the difference between automation that gets used and automation that gets ignored.
Why So Many AI Projects Still Fall Flat
Currently, a disconnect is playing out across companies.
Leaders were promised disruption and what they got was a slightly faster way to write meeting notes. They hear about transformation, but internally, everything still runs on Slack reminders, manual handoffs, and data that no one trusts. McKinsey has a name for this: the “GenAI paradox.” More than 80% of companies report using GenAI in at least one function, but only 30% say it’s delivered real business impact. Fewer still can tie it to EBIT gains, showing that adoption alone does not create value.
The core issue isn’t with AI itself. Broken operations and outdated systems are the real barrier to transformation, not limited AI capabilities. Most companies are layering advanced models on top of operational debt, such as disjointed CRMs, siloed systems, and processes that rely on memory and motivation instead of fixing the foundation first. Without source-level structured capture, execution inside the systems of record, and adoption that does not require behavior change, even the smartest model becomes just another tool that looks impressive and gets ignored.
Final Take: AI Doesn’t Fix Bad Operations. It Exposes Them.
The idea that AI will “solve” inefficient systems is backward. AI doesn’t patch over cracks in your workflows; it shines a spotlight on them. Unless those cracks are fixed, all the GenAI in the world won’t move the needle. Real transformation begins with the environment in which the model operates rather than the model itself. Until companies fix operational weaknesses, AI cannot succeed.
The companies that understand this are already moving. They’re embedding intelligence into workflows, not layering it on top. They’re measuring execution, not engagement, and designing operating models where AI can capture what matters at the source, initiate action in real time, and deliver outcomes without asking employees to become AI experts.
The rest? They’ll be left behind and continue to wonder why all that AI spending still feels like hype.
