Under the theme “Find it. Fund it. Fix it,” a hot agenda topic was how to strike a balance between compliance and value creation. Regulation often arrives with tension. The concern is that rules stall innovation, slow investment, or add bureaucracy. Senior data and AI leaders challenged this perception demonstrating that the reality is more nuanced. Done well, responsible AI becomes a catalyst rather than a constraint.
Here, we explore how AI governance is being reframed as a business asset, and how data leaders are using it to drive enterprise value.
1. AI Governance Is Now a Strategic Priority
Governance has moved beyond technical teams to become a board-level topic. With regulators watching, boards are asking more detailed questions, and executives are looking for assurance that AI is being used responsibly.
CDOs are responding by building formal Responsible AI Frameworks that define how AI is developed, tested, and deployed in line with values such as fairness, transparency, and accountability.
One data leader shared how they secured board sign-off for their AI principles, turning them into a mandatory gate for all AI projects. Whether it was a marketing automation tool or a third-party recommendation engine, every use case had to align with the organisation’s ethical standards.
This gave the data team a clear mandate to embed responsible AI into enterprise risk processes with the authority to review, refine or reject initiatives. In addition, it meant leadership had full visibility and confidence in the AI portfolio.
2. Responsible AI Builds Trust
Customers and regulators increasingly expect organisations to demonstrate how they are using AI.
For data leaders, this means showing how AI aligns with the brand promise and being explicit about the safeguards in place. In publishing, for example, one CDO described how their organisation committed to human-written journalism, even as they explored AI to support editorial processes. By making this position public, and backing it with clear governance, they preserved credibility with readers and editorial staff alike.
In another example from the energy sector, a CDO focused early AI efforts on customer experience. Their team built a single customer view and applied AI to analyse over 20 million service calls. This insight was used to personalise support, resolve issues faster, and improve Net Promoter Score (NPS) all while being transparent about how customer data was being used.
In both cases, responsible AI created visible benefits for both the organisation and the customer strengthening trust and reinforcing the value of the data function.
3. Regulation Can Unlock Investment When Framed Strategically
Rather than viewing regulation as a constraint, successful CDOs are using it to unlock investment in data foundations.
One leader shared how their organisation’s preparations for the EU AI Act became the trigger for funding improvements in metadata management, model documentation, and lineage tracking. By framing these activities as essential for both compliance and innovation, the data team secured budget to mature their AI infrastructure.
Crucially, this argument worked because it focused on value, not just risk. The business case showed how governance improvements would speed up deployment, reduce duplication, and enable more reliable decision-making. In short, governance was positioned as an enabler of performance rather than a cost of doing business.
4. Strong Governance = Control Over the AI Ecosystem
As organisations adopt more AI from third-party vendors, maintaining control becomes harder. Without clear standards, external tools can introduce risk, bias, or black-box behaviour.
To prevent this, many CDOs are building AI governance into procurement and vendor management. Instead of assessing tools after implementation, they are setting requirements upfront, demanding documentation, model explainability, and adherence to the company’s ethical AI policy.
This front door approach ensures external partners are aligned from the start, and gives the data team a central role in shaping the enterprise AI landscape.
5. Constraints Can Spark Innovation – Not Kill It
A recurring message from The Discussion was that regulation and creativity are not mutually exclusive. In fact, guardrails can encourage better design, more responsible experimentation, and stronger alignment between AI and business goals.
Take the challenge of generative AI and copyright. One CDO from a creative industry shared how their team was working with legal and editorial colleagues to explore responsible use, ensuring that the rights of content creators were respected, while still enabling innovation. The solution was not to block progress, but to define where and how it could happen safely.
This mindset of treating regulation as a framework for innovation is gaining traction. Rather than waiting passively for legal certainty or fearing a compliance backlash, CDOs are designing governance models that are adaptable by design, drawing lessons from earlier regulatory cycles such as GDPR where those who acted early gained operational readiness, board credibility, and smoother implementation down the line.
DataIQ enables data and AI leaders to drive impact. One way we do this is by helping our clients make smarter decisions by connecting them to our community-powered intelligence. Click here to find out more about how your organisation could benefit from our highly-curated services tailored explicitly to help you overcome your challenges.
