Explainable AI With Real-World Returns: Inside The 'Enterprise Grade Intelligence' Framework

Despite massive investments, most AI projects never progress beyond the pilot stage. The promise of intelligent automation is significant, but the struggle to translate that experimentation into tangible ROI is far more common. Without the ability to explain how an AI model arrived at an answer, trust evaporates. And without trust, the technology is all but useless for high-stakes business decisions.

But the issue isn't limited to technology, according to Raman Mehta, a multi-time Chief Information Officer for global manufacturing giants like Johnson Electric, Visteon, and Fabrinet. With a track record of leading digital transformations, modernizing enterprise systems, and implementing software-centric strategies to improve business outcomes, Mehta is a three-time CIO 100 award winner, a published author, and a keynote speaker on technology and innovation. Today, he believes the standard approach to AI has significant gaps.

"Everybody has the same large language models. But the trick is you need to teach them the nuances of your business, the context of your business, and the regulatory environment of your business. Once you start to do that, your AI becomes exponentially more powerful," Mehta said. His solution is a framework called Enterprise Grade Intelligence (EGI): a three-layer architecture designed to turn "systems of record" into "engines of action." To facilitate deployment at scale, the blueprint begins with a sound master data strategy, taxonomies, and knowledge graphs.

• Getting layered: The subsequent layers introduce what Mehta called Enterprise Language Models (ELMs) and an orchestration layer where intelligent agents can coordinate actions. "The journey is about turning systems of record into engines of action with the Enterprise Language Models, or ELMs. These are language models that understand the context of your business. You then expose the functionality of your core systems through what I call the Model-Context Protocol (MCP) tools. Finally, an agentic layer can reliably orchestrate those tools using natural language to get the job done. Not the task, but the job."

Most AI initiatives fail when they're built on disconnected data systems, Mehta explained. In his experience, the first warning signs are usually clear. "The big red flag is a master data strategy that isn't working. You have multiple definitions for the same entities, like customers, suppliers, and products, and the organization is holding it all together with a legacy fragile data lake—or even worse, spreadsheet—layer that acts as a kind of scaffolding where tribal knowledge gets stuck."

• Mindset matters: But the biggest mistake is treating AI as just another technology to be bolted onto existing processes, Mehta continued. From his perspective, implementation calls for a new way of thinking, driven from the top down. "Don't focus on the task. Focus on the job to be done."

• It takes a village: Rethinking entire workflows rather than automating them is the foundation of his approach. "This should be treated as an infrastructure imperative at the leadership level. The CEO and CFO must be fully behind it. Otherwise, it becomes just another IT initiative, where success will be quite limited beyond the pilots."

Designed to fuel an "AI to ROI" mindset, Mehta's approach aims to rethink how work gets done. Achieving this requires both a strong enterprise integration strategy and a commitment to redesigning core processes from the ground up.

• Human work, machine work: For example, Mehta described a sales team using agents to synthesize data, which frees up humans to focus on what they do best. "An agent can scan the news and external feeds for industry changes, like new regulations or tariffs, and curate that information for the salesperson. This combines internal and external assets into an extended workflow, freeing up your sales team from mundane information synthesis to focus on the distinctly human part of their job: building relationships. That is the game you want to change."

• Clean data, clear ROI: That "job to be done" philosophy also applies directly to operational efficiency, Mehta explained. However, this powerful capability also comes with a significant caveat: the promise of AI is heavily dependent on the quality of the data it’s fed. "With clean data, you can give an AI agent a strategic goal, such as driving profitable growth, protecting a market segment, or maximizing capacity to avoid CapEx. An agent can then analyze that humongous amount of data to provide you with the optimal parameters. It presents the results in an explainable format, giving you full traceability into the agent's decision-making."

Ultimately, strong data governance must become part of the organization's DNA to prevent these new systems from amplifying existing errors at scale. For Mehta, this effort requires a modern approach where AI is not just the end goal, but also a tool to enforce high-level quality control. "It's a team sport. The first step is to put safeguards in place for data lineage and implement guardrails to prevent duplicating existing parts, customers, or suppliers. Then, you can use AI itself to flag potential data issues and provide recommendations, making the job of data stewardship easier for your teams."

The endgame for EGI is a future defined by greater autonomy, where agent-run workflows continually improve, Mehta concluded. The final component of this vision is "digital exhaust," or the trail of data left by every execution step, which can be fed back into the model. "The future is moving towards autonomy, where enterprise workflows are executed by agents in an explainable, traceable way. When an agent goes off track, it creates 'digital exhaust,' which is a data trail from every execution step. That data can be fed back into the model to make it better the next time. This self-improving feedback loop is the biggest change that's coming."