Monisha Somji | Senior Digital Transformation & AI Executive.
The enterprise AI conversation has entered a more uncomfortable phase. The excitement is still there. The budgets are still there. But inside many organizations, a quieter question is surfacing: If AI is so powerful, why is it so hard to turn it into measurable business value?
That gap between capability and operational trust is where many AI programs stall. Leaders are not short on tools or pilots. They are short on repeatable outcomes, accountable ownership, clean data, redesigned workflows and confidence that AI can withstand production.
The ROI problem is not really an AI problem.
BCG recently reported that “just 5% of companies achieve substantial value from AI, while 60% report no material value at all.” McKinsey similarly found that more than 80% of respondents were not seeing tangible enterprise-level EBIT impact from generative AI, even as adoption rose.
Those numbers should not be read as an indictment of AI. They should be read as an indictment of shallow implementation.
Too many AI programs begin with the technology and then search for a business problem. Each may produce a useful proof of concept, but few are anchored to a baseline, a business outcome, a workflow owner or a path to scaled adoption.
The result is a familiar pattern: a compelling demo, a promising pilot and then very little movement in revenue, margin, cycle time, customer experience or risk reduction. That is not transformation. That is activity.
Production AI has a higher reliability bar.
Businesses run on consistency. In brainstorming, two different answers can be useful. In finance, compliance, healthcare, legal review, tax or supply chain execution, two different answers can become a control issue.
This is why the neurosymbolic AI conversation matters. Large language models are excellent at interpreting language, summarizing information and working through ambiguity. But enterprises also need rules, constraints, auditability, policy enforcement and deterministic logic.
The future of enterprise AI will not be one giant model doing everything. It will be a coordinated architecture: neural systems for flexibility, symbolic systems for rules, knowledge graphs for grounded context, workflow engines for execution and governance layers for accountability.
AI value is created when an answer is trusted, acted on, embedded into a process and measured against a business outcome.
Data and workflow readiness decide the return.
AI leaders often talk about model performance, but many of the biggest blockers sit underneath the model. Dun & Bradstreet’s 2026 survey of 10,000 businesses found that 97% report active AI initiatives, while only 5% say their data is ready to support them.
That finding should stop executives in their tracks. If nearly everyone is pursuing AI but almost no one believes their data is ready, then the enterprise problem is not experimentation. It is foundational.
In complex organizations, customer, supplier, product, employee, financial and operational data often live across fragmented systems. Definitions differ. Ownership is unclear. Business rules are buried in spreadsheets, institutional knowledge and exception handling.
AI does not magically fix that. In many cases, it exposes it. An AI agent cannot reliably recommend an action if it does not know which record is authoritative, which policy applies or which exception path should override the standard process.
The same is true for workflows. PwC found that stronger outcomes come from “redesigning workflows, modernizing operations, and embedding AI into core business processes instead of layering AI onto legacy ways of working.”
If AI is bolted onto a broken process, it may make that process faster. But it will not necessarily change the economics of the work.
AI needs portfolio discipline.
Many organizations manage AI as a set of experiments. They need to manage it as a portfolio.
Every AI initiative should have a clear investment thesis. What business outcome will improve? What baseline are we measuring against? Who owns the result? What dependencies and risk controls must be addressed? What is the funding gate to move from pilot to production?
BCG found that “successful companies prioritize just three or four use cases on average, compared with six or seven for less-successful organizations.” That is a governance lesson as much as a technology lesson. Focus beats fragmentation.
AI initiatives should not be approved because they are exciting. They should be approved because they are strategically aligned, adoptable and economically measurable. They also need kill criteria. If a pilot cannot show value or a path to scale, it should not live forever under the banner of innovation.
Go from AI theater to AI returns.
There is also a human reason AI ROI remains elusive. AI changes roles, decision rights, routines, incentives and confidence. If those elements are ignored, adoption stays superficial.
According to BCG, “In successful AI-driven transformations, 70% of the value is derived from people-related action rather than technology-related action.” Training cannot be a generic webinar. Change management cannot be a launch email.
The next phase of AI will separate companies that bought tools from companies that changed how they operate. The winners will move from pilots to portfolios, demos to production reliability, model obsession to data and workflow readiness, and AI activity to measurable outcomes.
So yes, there is a lot of AI. But in too many organizations, there is still not enough ROI.
The executive mandate now is to close that gap. Not with another proof of concept, but with the operating discipline to turn AI into a measurable, trusted and repeatable source of enterprise value.
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