In the rush to adopt AI, many organizations skip the most important step which is asking what problem they’re actually trying to solve. It can be tempting to just move forward with an AI solution. But, organizations who do this, quickly realize things can go off the rails quickly.
Vijay Mehta, Chief Data & Technology Officer at Experian, argues that the real work of enterprise AI isn’t about the model, but rather it’s about the plumbing. From ensuring model drift detection and compliance automation to managing prompt injection risks and user governance, the future of responsible AI depends on what’s happening behind the scenes.
When I spoke with Vijay he stressed that success starts with solid workflows, clarity of purpose, and a culture willing to fail fast and learn faster. Whether it’s teaching non-technical teams how to safely use AI or defining what AI project success really means, Experian’s approach underscores a fundamental truth: you can’t scale AI until you can trust its foundations.
Before You Build AI, Know Why You’re Building It
Enterprises are racing to integrate AI into every corner of their operations, but too often they skip the most critical first step of clearly defining the problem they’re trying to solve. For organizations to be successful, it’s important to take a “plumbing-first” approach. Simply put, this means that instead of starting with a model or a tool, organizations must start with intent. They need to understand their data flows, workflows, and decision points before deciding where AI fits. It’s not about slowing innovation, but about grounding it in purpose.
When I asked Vijay about this, he said, “It’s easy to get caught up in the excitement of AI and start building models before you’ve clearly defined the problem. But real success starts with a disciplined, plumbing-first approach. That means focusing on data, infrastructure, and governance before diving into algorithms.”
It’s always critical to start with the why and don’t shy away from getting real answers. Vijay stressed it’s important to ask what decision are we trying to improve, and how does it tie to business value?
Vijay continues “AI isn’t magic. It’s an engineering discipline. Strong plumbing ensures your models are auditable, explainable, and scalable. It’s not glamorous work, but it’s what separates organizations that experiment with AI from those that operate it successfully.”
His perspective underscores a simple truth: without clarity and structure, even the most advanced AI efforts risk collapsing under their own complexity. Defining the “why” before building the “how” may not make headlines, but it’s the foundation that determines whether AI delivers lasting impact or fleeting hype. In other words, success in enterprise AI doesn’t begin with technology, it begins with intentionality.
Building Continuous Compliance Into the AI Pipeline
As enterprises move beyond AI experimentation and embed models into production workflows, the challenge shifts from building models to maintaining them responsibly. Model drift, version control, and audit tracking aren’t back-office details. They’re boardroom concerns.
In industries like banking, finance, and healthcare, model risk management isn’t just a best practice. It’s a mandate. That means designing systems where governance happens automatically, audits never stop, and transparency is baked into every layer from the moment data enters the pipeline to the instant a decision is made.
As AI becomes further woven into enterprise workflows, the focus on model risk management grows even more critical. When I asked Vijay about this he shared that “Model risk management is one of the most important aspects of deploying AI responsibly. As models become part of critical decisioning systems, we have to make sure they remain fair, compliant, and effective over time.”
At Experian, Vijay shared, they take a platform approach. He explains “We’ve built governance and monitoring directly into our model lifecycle, from development through deployment. Every model is versioned, traceable, and continuously monitored for performance drift and data-quality changes. We recently launched the Experian Assistant for Model Risk Management, an AI-powered solution that helps organizations automate documentation, validation, and compliance audits. It aligns with global regulatory standards like SR 11-7 and SS1/23, and it drastically reduces the manual overhead of managing hundreds of models at scale.”
These regulatory standards set the global bar for responsible model governance. SR 11-7, issued by the Federal Reserve, focuses on lifecycle-wide principles for managing model risk, while SS1/23 from the Bank of England’s Prudential Regulation Authority provides more prescriptive guidance for modern AI and machine learning systems. By aligning with both, organizations are building infrastructure that can scale responsibly across jurisdictions, which is incredibly important for multinational organizations.
Bridging the Gap Between AI Pilots and Production Reality
I’ve recently heard the term “pilot purgatory”which is used to describe the stage where AI projects get stuck in perpetual pilot mode. These pilots show promise in controlled tests but never fully scale into production or deliver measurable business impact. Unfortunately, this is happening far more often than organizations would like.
Across industries, companies are discovering that building an AI proof of concept or pilot isn’t the hard part, operationalizing it is. AI initiatives falter not because the model fails, but because the surrounding infrastructure and processes aren’t ready. Scaling AI requires more than technical prowess. It demands disciplined engineering, robust governance, and a culture willing to evolve how teams work and measure success.
In our discussion, I asked Vijay to share his thoughts on why so many AI initiatives stall between pilot and production and what shifts are required to bridge that gap.
Vijay shared “The biggest reason AI pilots fail is that the fundamentals aren’t in place, things like clean data, version control, and operational pipelines. It’s easy to get a model working in a lab. It’s much harder to make it work reliably in production, day after day, with messy, real-world data.”
Beyond the technical challenges, Vijay noted that organizational silos often stand in the way of progress. Data science teams may build something great, but without alignment across operations, compliance, and IT, those efforts rarely translate into business impact. Another common pitfall, he added, is optimizing for model accuracy instead of real-world outcomes. “A model that’s 2% more accurate but impossible to deploy isn’t progress,” he said.
To overcome this, Vijay shared, “organizations need to make a few key shifts. Move from a project to a product mindset, and treat models as living assets that need maintenance, monitoring, and iteration. Build cross-functional collaboration early, and embed compliance and governance from day one. Our most successful AI projects are the ones where data scientists, engineers, and business leaders work as one team, because AI at scale is a team sport, not a solo act.”
Escaping pilot purgatory requires alignment, ownership, and trust at every stage of the AI lifecycle. The lesson is clear that until enterprises master the basics, scaling AI will remain just out of reach.
Measuring AI Success Beyond the Metrics
It’s tempting to define AI success through dashboards and KPIs. However, the real measure of value goes beyond the numbers. True AI success is reflected in adoption, trust, and tangible impact. It’s measured in the moments when people closest to the work can clearly articulate how AI is helping them. Equally important is recognizing when an initiative isn’t working and having the discipline to course-correct early.
I discussed with Vijay what AI success and failure in an AI project looks like to him. He shared that for him metrics, accuracy, ROI, and stability are all important, but they don’t tell the whole story. For him, success in AI means it’s driving measurable business outcomes and becoming a natural part of how decisions are made.
Vijay went on to say, “You know you’re succeeding when the business starts to rely on AI without even thinking about it, when it’s invisible because it’s embedded in everyday workflows or reinvents a workflow so it becomes AI native. Maybe it’s detecting fraud faster, expanding credit responsibly, or improving customer experiences. That’s when you know it’s creating real value.”
On the flip side, knowing when to stop is just as critical. Vijay shared that, “If a model isn’t delivering, or if its complexity outweighs its benefit, don’t be afraid to pivot or retire it. Responsible AI is about knowing when to double down and when to walk away. It’s not about deploying AI everywhere; it’s about deploying it where it truly makes a difference.”
That mindset represents the ultimate maturity in enterprise AI. True progress isn’t defined by how much AI an organization uses, but by how intentionally and responsibly it’s applied. The organizations that get this aren’t just scaling faster; they’re scaling with purpose. They’re turning pilots into real outcomes. And these organizations will be set up for long term success because they understood what real problems AI was solving for their organization.
