Empower the Enterprise with an AI Platform

Across industries, I see the same pattern: the first valuable AI use cases rarely come from a steering committee. They come from the teams closest to operational friction.

I spend a lot of time with leadership teams fielding the same question: “We’re investing in AI, so where’s the business value?” It is a fair question, and the honest answer is that for most organizations, value is not showing up fast enough. RAND has estimated that more than 80% of AI projects fail, roughly twice the rate of non-AI IT projects. BCG’s research is just as sobering: by late 2025, 60% of companies were generating little to no material value from AI, and only 5% were creating value at scale. MIT’s NANDA initiative put it bluntly: only about 5% of enterprise generative AI pilots achieve rapid revenue acceleration.

The instinctive response is often to launch one massive transformation program and defer meaningful AI delivery until every foundational layer is complete. That is where momentum dies. Here is the core message: your data platform is essential for AI success, but waiting for the entire platform to be finished is the wrong operating model. Build the platform and create value in parallel.

Why the Frontline Is Where Value Starts

In any organization with a large knowledge-worker population, value is often easily found if you ask the right people, and it is bigger than asking a chatbot to summarize a document. The real opportunity is agentic AI embedded into operational workflows. We have used low-code platforms in accounting to accelerate invoice processing and automatically answer accounts payable questions. In customer support, an agent that triages an inbound case, pulls the customer’s history, and drafts a resolution for human review can materially improve response rates and customer satisfaction. These are multi-step workflows that span systems, require judgment, and sit closest to the people who already know where the friction lives. They do not need a six-month discovery phase to tell them what is broken. They deal with it every day.

There are two moves that work together. Empower knowledge workers with governed low-code/no-code AI tooling so they can improve their own workflows, and create an AI engineering function for business teams for harder use cases. Both create value, and the strongest organizations do both. But neither scales without a shared platform for identity, access, data, integration, and operations.

The Pilot-to-Production Gap Is a Platform Problem

Many organizations get stuck debating which model to use. That matters, but it is not the primary constraint. The bigger issue is platform maturity. Teams can prototype quickly, but they cannot deploy consistently across the enterprise when shared foundations are missing: identity and access controls business teams can actually use, clear data boundaries, reusable integration patterns, and a practical process for review, release, and monitoring. S&P Global found that the average organization scrapped 46% of AI proofs of concept before they reached production.

This is why the data platform matters so much. AI-ready data is not a nice-to-have; it is the key to production success. Without trustworthy, accessible, policy-compliant data, even strong use cases stall.

The Data Trap, and Why You Shouldn’t Wait

Here is what stops most programs cold: the data. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are not supported by AI-ready data. The usual reaction is to launch a massive data-platform program first, modernize everything, perfect lineage, and then do AI.

That sequence sounds disciplined, but it often delays value for years. Data platform modernization is real, necessary, and non-negotiable. It also runs on multi-year timelines in most enterprises. If every AI use case is forced to wait for end-state architecture, executive confidence erodes before meaningful outcomes arrive.

The better approach is parallel execution: continue building the enterprise data platform while prioritizing AI enablement in process areas with the highest operational friction.

Just-in-Time, Just-Enough Governance for High-Friction Workflows

Start with the process pain, not the technology roadmap. Identify where friction is highest and business impact is clearest: delayed invoice cycles, slow case resolution, exception-heavy approvals, repetitive data lookups, or manual reconciliation loops. Then target those workflows first for AI enablement.

Use just-in-time governance to make that possible. Instead of trying to govern everything up front, govern the specific data needed for each prioritized use case when the use case is ready to move. A small cross-functional team can adjudicate ownership, access, quality requirements, and controls in days, not months, in committee cycles.

Apply just enough control to match real risk. A use case handling public product content does not require the same guardrails as one handling customer personally identifiable information. This risk-proportional model protects the organization while maintaining delivery speed, and it reduces the shadow AI behavior that emerges when formal paths are too slow.

The Backlog Is the Bridge to Full Data Readiness

Each frontline AI deployment should generate structured learning: what data was used, what quality defects surfaced, what transformations were required, and what governance decisions were made. Capture each gap as a backlog item for platform remediation.

That backlog becomes a demand-driven map for data modernization. Instead of guessing where to invest first, you improve the datasets and data products that are already tied to measurable business outcomes. Over time, this creates a reinforcing loop: frontline AI use cases expose the most important data gaps, platform teams remediate them, and the next wave of AI solutions ships faster with lower risk.

Final Thought: Platform Is the Decision

The decision is not “pilot or no pilot.” It is whether you will build a governed AI and data platform that lets your organization learn, ship, and scale safely.

Treat AI-ready data as mission-critical. Build the data platform as a strategic program. But do not wait for the whole thing to be done. Prioritize the highest-friction process areas, enable them with AI, and govern data just in time. That is how you create value now while building the foundation that sustains value at scale.