AI governance requires an integrated offensive and defensive approach
If you treat AI governance purely as a compliance exercise, you’re leaving value on the table. What works is a two-sided approach, defensive and offensive. You manage risks, yes. But you also unlock growth opportunities and build capabilities that evolve with your business.
The defensive side addresses the basics, regulations, data protection, ethical safeguards, and making sure you’re using the right data in the right way. This is foundational. Without it, you’re exposed to reputational damage, legal risk, or worse, stalled innovation. But if you stop there, you miss the point. AI isn’t stable, it’s evolutionary. The offensive side of governance is about positioning your organization to move faster, learn faster, and create better experiences and products with AI. That’s how you stay ahead.
What helps here is aligning AI governance with business strategy, not running it as a parallel track. When governance feeds into digital transformation, it becomes a lever for progress. It means designing AI systems that are scalable, reusable, and built with production use in mind. Think long-term.
Kurt Muehmel, who leads AI strategy at Dataiku, put it clearly: governance should be used to align AI with company objectives and design systems with efficiency and reuse in mind. When you do that, governance becomes a competitive edge.
The Chief Data Officer (CDO) is central to AI governance
The CDO’s job has expanded. They’re no longer just managing databases and setting data standards. They’re steering the ship when it comes to AI governance, defining structure, driving execution, and enabling trust as AI becomes business-critical. Today’s CDO must combine a strong technical foundation with strategic alignment across the executive team.
At the core of this renewed role is visibility, auditability, control, and automation. Data is training AI models, building customer experiences, and influencing decisions at scale. That means governance has to scale too. The CDO owns this responsibility, to reduce risk, and to establish systems where trust is built into every AI asset the organization develops.
Kjell Carlsson, Head of Data Science Strategy at Domino, said it best: governance means reducing ethical or regulatory risks and enabling transformation by earning trust. Henry Umney from Mitratech outlined clear priorities, define AI clearly, build an inventory of models with risk profiles, and benchmark using frameworks like NIST’s AI Risk Management Framework.
And to keep that edge, you’re going to need more than risk checklists. Jeremy Kelway from EDB points out that GenAI adoption shouldn’t compromise data sovereignty. Jurisdiction matters. Data observability matters. That’s how offensive strategy becomes real, by building trusted systems that scale internationally, adapt fast, and deliver value with less friction. That’s CDO-level impact.
Business engagement and clear communication are key to successful AI governance
If data leaders want AI governance to matter across the business, they have to make it matter to people who don’t live in data. That means making the strategy visible, the goals practical, and the value immediate. Governance doesn’t succeed in a vacuum. It needs executive alignment, funding, and time. Without communication that ties technical work to business priorities, it doesn’t get any of those.
The basics are straightforward: business leaders need to know why data platforms, observability tools, catalog systems, and data fabric infrastructures are being introduced or upgraded. More importantly, they need to know why now. It’s not enough for data teams to recognize the critical path, communicating it is just as critical.
Creating a vision statement for AI helps frame governance as a capability, instead of a constraint. Once aligned with a broader data strategy and roadmap, it becomes easier to build cross-functional trust. That includes breaking down silos with governance councils and shared terminology. Shared definitions enable people in different departments to collaborate on AI systems without second-guessing the data or outcomes being produced.
Ana-Maria Badulescu, Senior Director of the AI Lab at Precisely, advises building governance into your AI strategy from day one rather than bolting it on later. The integration must include observability, data quality, enrichment, and privacy, not as separate checklists but as a unified solution. Heather Gentile from IBM adds that governance enhances transparency and explainability, which speeds up AI project scaling and strengthens business results. When executives see that clean data drives quality models, they start connecting governance directly to outcomes.
AI-Specific governance practices enhance oversight and innovation
AI governance isn’t a copy-paste job from standard data governance. The models are dynamic, the systems are constantly learning, and the stakes, financial, reputational, operational, are high. Keeping pace with this evolution requires AI-specific governance practices that can deliver oversight at speed. That includes model operations (modelops) and visibility into all data feeding AI models through tools like an AI Data Bill of Materials.
Modelops is about real-time model monitoring. It tracks how machine learning models perform over time and flags when performance drops or conditions shift. This helps in deciding when it’s time to retrain or replace models. Without it, systems that once delivered strong results start drifting. That drift erodes trust, accuracy, and performance.
Kapil Raina, Data Security Evangelist at Bedrock Security, drives home the importance of an AI Data Bill of Materials. It documents the entire data supply chain, training, fine-tuning, and inference. This creates transparency around what data your AI models are accessing and generating. Without this level of visibility, organizations risk falling out of compliance and misusing sensitive or untrusted data. AI DBoMs also speed up project cycles because teams don’t waste time clarifying what’s powering their AI systems.
Rahul Auradkar from Salesforce points out that inconsistent controls and fragmented governance structures are increasing technical debt. That slows down innovation and increases cost. Jon Kennedy from Quickbase explains that when information is buried in disconnected tools, teams lose time hunting it down instead of acting on it. Consolidating governance and improving access reduces those inefficiencies, and the ripple effect improves customer experience.
Leaders who implement AI-specific governance practices are building safer systems and setting their organizations up to adapt faster, innovate confidently, and scale with clarity. That’s execution at the level today’s market demands.
Offensive AI governance drives business value and market differentiation
If executed with intent, governance becomes a clear driver of growth. That’s where the offensive side of AI governance comes in. You’re lowering risk and designing governance to fuel innovation, automate operations, improve customer engagement, and open new revenue streams.
This begins with shifting how organizations treat their data. When data is seen as an asset, one that’s governed, transparent, and structured for reuse across teams, it turns into a repeatable engine for value creation. That includes building data and AI products that serve internal needs and make services better, faster, and more tailored to the customer. Good governance supports that shift by embedding control and quality into data products from the start.
Scaling personalization is a strong example. When governance establishes trust in how data is used and shared, businesses can confidently deploy AI-driven engines that adapt to customer behavior. Predictive models are another. When data is reliable and usage is tracked, you can anticipate needs and reduce churn more precisely. These are direct paths to measurable business impact.
Ed Frederici, CTO at Appfire, reinforces this outlook by arguing that governance enables seamless interoperability and should be treated as a foundation for monetization. Srujan Akula, CEO of The Modern Data Company, takes it further, pushing for governance to become part of the product itself. Internal and customer-facing data products built with embedded governance controls generate faster returns and lower compliance exposure at the same time.
Jason Smith, Senior Principal of Strategy and Transformation at Conga, points out that when governance is extended to revenue operations, like sales or pricing workflows, departments benefit from cleaner pipelines and tighter collaboration. This turns governance into a workplace multiplier, where data flows predictably and friction is minimized.
Offensive governance shortens time to value, increases agility across the organization, and contributes directly to market positioning. For leaders, this is a practical route to both scaling AI and building long-term differentiators.
Key takeaways for leaders
- Prioritize both defense and offense in AI governance: Leaders should treat governance as both a safeguard and a strategic asset, using it to manage risk while accelerating business outcomes, efficiency, and scalable AI deployment.
- Empower the CDO to drive strategic AI alignment: CDOs must lead governance initiatives by embedding transparency, control, and auditability into AI programs while aligning governance priorities with overall business impact.
- Align AI governance with business strategy and communication: Executives should ensure AI governance is supported by strong cross-functional collaboration, clear business-language communication, and a unified roadmap that ties technical investments to measurable value.
- Invest in AI-specific oversight frameworks: Leaders must adopt AI-specific capabilities such as model performance monitoring and AI Data Bills of Materials to ensure transparency, compliance, and agile innovation across AI lifecycles.
- Use governance to fuel growth and differentiation: Governance should be embedded in data and AI products to streamline operations, accelerate innovation, and unlock new revenue opportunities, turning governance into a core business enabler.
