I’ve spent over a decade watching how organizations approach governance—sometimes with rigor, sometimes with reluctance. But over the past few years, one truth has become impossible to ignore: the way we govern data and AI is overdue for a fundamental reset.
AI is not some distant experiment. It’s already here—powering customer decisions, filtering candidates, flagging fraud, and even grading students. And yet, the frameworks overseeing this technology often look like they’re stuck in the past. You know the kind—bulky, checkbox-driven, and indifferent to context.
Here’s the problem: These traditional management and governance models weren’t designed for AI’s pace or unpredictability. They weren’t built for models that learn on the fly, or for data ecosystems that stretch across continents: due to reasons like drift in data, adaptive learning in real-time, and bias in edge cases, to name a few. And they certainly weren’t made to answer the tough questions—like who’s accountable when things go wrong.
That’s where the Contingency and Evolutionary Governance comes in. These aren’t just frameworks—they’re approaches built to adapt without losing grip, and most importantly, align deeply with how real organizations work.
What Recent AI Failures Have Taught Us (If We’re Paying Attention)
Let me be blunt—some of the loudest governance alarms have already gone off. And in most cases, they weren’t about managed risk. They were painfully real.
One healthcare model, trained on skewed historical data, deprioritized care for patients of specific demography.
A financial services tool gave significantly lower credit limits to women. Same income, same score—different results.
Hiring systems used AI to streamline applications. In theory. In practice? They filtered out top-tier candidates based on patterns from past bias.
Social media platforms tried to automate content moderation, but failed to stop harmful misinformation during global crises.
Educational bodies deployed grading algorithms in a rush, only to discover they penalized students from lower-income backgrounds—again, based on the past, not potential.
Now, none of these organizations set out to cause harm. But the damage happened just the same. Not because AI failed. But because governance didn’t keep up.
Why a Contingency Model Makes More Sense Than a Single Blueprint
Let’s stop pretending that one framework can fit all. An early-stage fintech working on internal automation doesn’t need the same governance depth as a multinational retail firm managing customer data across jurisdictions. Yet that’s often how policies are rolled out—uniformly, rigidly, and without consideration for context.
A Contingency Model, at its core, accepts that organizations are at different levels of maturity. It acknowledges that culture is different, risk varies, impact is situational, and that governance should be aligned to the situation. This model lets you prioritize controls where they matter most, instead of spreading efforts thin across areas that don’t need it.
It’s not about doing less. It’s about doing what counts.
In practical terms, that means aligning governance with business strategy, making data ownership real (not just aspirational), and ensuring that AI decisions are monitored, challenged, and explainable by design.
Governance That Evolves—Because AI Does Too
Here’s something we don’t say often enough: governance should be capable of learning. When we treat governance as a project, something to document and shelve, we miss the chance to adapt. The benefits are time bound and are lost. We fail to respond to shifting risks, or worse, to warning signs that the system isn’t working.
The Evolutionary Model is built on this insight. It encourages organizations to treat governance as a living system—one that changes as data flows shift, as models evolve, and as feedback loops reveal what’s working and what isn’t.
Some of the most mature organizations I’ve worked with are doing this already. They don’t wait for audits to rethink controls. They build in periodic assessments. They run retrospectives after governance failures. They treat policies like they treat software: versioned, reviewed, iterated. That’s not a future concept. That’s what modern governance looks like today.
Governance and the Boardroom: Closing the Gap
One of the biggest misconceptions I see is this: that AI governance is a tech issue. It’s not. It’s an enterprise issue. It belongs in the boardroom just as much as financial risk or brand strategy.
Boards today need to understand what AI is doing—not just what it’s capable of. That means asking new questions:
What decisions are being delegated to machines?
What risks are emerging as data grows?
How do we hold the system accountable if no one person is making the call?
The Contingency and Evolutionary Models bridge the space between operational risk and strategic oversight. They make AI governance visible to leadership, actionable for business teams, and aligned with broader principles of accountability, transparency, and trust.
In that sense, they aren’t just about protecting the organization. They’re about enabling it to move faster, confidently, responsibly, and with integrity.
A Closing Note: Governance as a Strategic Advantage
We’re moving into a world where trust is currency. And in that world, governance isn’t a back-office burden. It’s a front-line enabler. The right governance doesn’t slow innovation—it allows it to scale, safely.
The Contingency and Evolutionary Models are not about adding layers. They’re about building the kind of flexibility and foresight that modern organizations need to thrive.
* These are personal views of the author and doesn’t resonate any firm
Author Bio
Tejasvi Addagada is a data and AI strategist, speaker, and inventor of the Contingency and Evolutionary Models for Data and AI Governance. He advises leading banks, enterprises, and government programs on ethical AI design, enterprise risk, and governance maturity. Tejasvi’s work bridges strategy with execution—helping organizations embed trust into every layer of data and technology use. He is the author of two best-sellers on Data management and governance – simple and effective approaches, Data risk management – essentials to implement an enterprise control environment.