The future of enterprise AI that M&A should build towards

Enterprise IT has entered a new wave of consolidation. Organizations are re-evaluating their investments and M&A activity is picking up as software players maneuver to stay relevant in the age of AI.

For many executives, this urgency stems from the need to rebuild data architectures and business processes for AI tools, but so far, the results have been underwhelming.

A recent MIT study found that only 5% of enterprise AI rollouts have delivered meaningful value. The stakes couldn’t be higher.

The real question is whether today’s M&A strategies are pointing in the right direction. Simply bolting together a “complete” AI stack for IT teams misses the point.

True value won’t come from reorganizing legacy applications for technical users. It will come from empowering business users and analysts, the people closest to the work, to reimagine the processes they understand, own, and operate in a world with AI.

That requires data to flow freely and meaningfully across the organization and for new processes to be imagined, not bolted on top of old ones.

The Lakehouse Speed Bump

Many enterprises have already migrated business data into modern lakehouse architectures, enabling more centralized analytics and data-driven decision-making. But the AI era exposes new challenges for this status quo of data centralization.

Connecting AI models directly to vast stores of sensitive data is a governance nightmare for boards wary of risk. A better approach is selective: giving AI access only to the limited, highly relevant data needed for each specific use case.

This is data that needs to be separated from the lakehouse before being inputted into an AI model, as opposed to giving AI free-rein access to the whole lakehouse.

But the problem runs deeper. Data in lakehouses is often still shaped by the enterprise applications it came from: ERP, CRM, and beyond.

It’s not enough to centralize and standardize it; the data must be made usable by AI. That means embedding the business logic that underpins day-to-day processes with relevant data.

IT-led rollouts often fail to source this logic to link to data, because the relevant nuance lives with frontline teams. Sales leaders, for example, instinctively understand the context behind forecasts and can spot high-impact AI use cases.

Scaling enterprise AI means enabling these teams to inject that context-rich logic directly into AI workflows.

The Rise of The AI Data Clearinghouse

This is where the idea of an AI Data Clearinghouse comes in: a neutral, business-friendly software layer that connects disparate systems and allows business users to design AI workflows in a visual manner, with governance and process logic built in from the start.

This concept is resonating with business leaders because it addresses the friction points stalling enterprise AI. Drag-and-drop workflows democratize AI process creation for business users and teams beyond IT.

Built-in governance checks give compliance and risk teams visibility from day one, which subsequently speeds up the time to deployment for AI workflows.

And the nature of data visualization with workflows makes it easy for executives to understand data flows and quickly approve use cases.

Instead of AI being a mystery box, the clearinghouse turns it into a transparent enabler of decision-making and collaboration across the workforce that’s accessible to far more team members.

For CEOs still reluctant to feed first-party data into AI, this middle ground matters. Data is often a company’s most valuable asset, and hesitation is reasonable. But without a clearinghouse-like approach, AI will remain trapped in pilots and proofs of concept, never scaling to real impact.

This is the exact situation that MIT’s recent study pointed to. It would be a mistake for all the attention and industry debate those findings ignited to not be followed by action to change course and draw meaningful value from AI investments.

Empowering Business Users with Data

Too many vendors are pitching data platforms and copilots as the fast track for IT teams to bring AI success to the business. The reality is different: IT cannot reconfigure processes and drive AI adoption alone.

Embedding AI across organizations cannot follow the outdated model where business teams rely on business intelligence departments for every data-driven answer to a problem. That model is too slow and too disconnected from business context.

The future lies in putting intuitive, governed AI workflow tools directly in the hands of business users. When those tools serve the dual purpose of embedding compliance guardrails, leadership can draw on more confidence that AI is being deployed responsibly internally.

As AI moves from experimentation to enterprise-wide adoption, the winners will be the organizations willing to rethink both their data architectures and their assumptions about who owns AI.

By embracing the clearinghouse model, businesses can unlock the next wave of value: AI that is transparent, trusted, and driven by the very teams closest to the customer and the work.

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