CIOs, take note: Your bosses aren’t convinced you’re ready to tackle enterprise AI. A recent Gartner survey shows that only 44% of CEOs consider their CIOs “AI-savvy.”
The perception? CIOs lack the knowledge, urgency, and capabilities to scale AI successfully.
The reality? CIOs aren’t lagging out of indifference or incompetence. The real obstacles stem from organizational readiness gaps, a lack of governance policies, and poor data quality (according to IDC). CIOs navigate deeply rooted challenges, from legacy systems to cultural resistance and budget constraints, while managing pressure to deliver quick wins and sustainable results.
Equipped with the right playbook, CIOs can lead by building sustainable AI systems that are ethical (bias-aware, explainable, and fair), responsibly deployed (aligned with organizational values and regulatory requirements), and built for long-term viability (secure, maintainable, and cost-effective). Here’s how to start:
1. Take a slow and deliberate approach
Leave the rapid innovation and AI centers of excellence to your CTOs — CIOs play a fundamentally different role. A CIO’s responsibility is not to race ahead, but to step back, evaluate, and build systems that last — exercising deliberate restraint to ensure that AI integrates properly into the enterprise with oversight, security, and scalability in mind.
Consider this: When GenAI tools exploded on the scene in late 2022, many CIOs hit pause instead of rushing in. Concerned about data leakage and trust, some blocked GenAI until the right guardrails and infrastructure were in place. Only then did they begin introducing low-risk tools like meeting transcription, using early wins to build both confidence and compliance.
This cautious approach mirrors what we’re now seeing more broadly: CIOs who may have initially lagged are catching up strategically. Rather than jumping in headfirst, they’re prioritizing data readiness, governance, and internal education, focusing on small, low-risk pilots to build momentum before scaling organization-wide.
This is where CIOs really shine: not by moving faster, but by moving smarter.
2. Lead with policy and guardrails
Only 13% of organizations have established shared AI guidelines, and fewer than one-third maintain a formal AI strategy. Policy isn’t just a guardrail; it’s a multiplier. Shared standards let teams move faster with confidence.
CIOs should lead the effort to introduce guardrails early by:
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Defining how AI will be used across the enterprise
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Setting ethical boundaries
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Identifying risks to avoid
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Determining who is accountable
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Aligning with emerging global frameworks, such as the EU AI Act and emerging US standards
CIOs can also introduce the concept of AI “green zones” to safely pilot low-risk use cases, while “red-zoning” high-risk areas like customer data or financial modeling until proper controls exist. A slower, more intentional rollout anchored in clear policy provides the structure organizations need to scale AI safely and reduce risk.
3. Modernize infrastructure with security in mind
Scaling AI across the enterprise requires technical readiness. CIOs should work closely with their CISOs, CTOs, and platform engineers to assess how current infrastructure supports AI workloads and where investments and modernization are needed.
Expanding AI use cases and new emerging threats require an even more scalable, secure foundation. CIOs must ensure that AI environments — whether cloud, edge, or hybrid — remain trustworthy, secure, and compliant. Data governance is critical here. Auditability, lineage tracking, and access controls must be ingrained from the start.
4. Enable your workforce via education and access
One of the biggest barriers to sustainable AI adoption isn’t technology; it’s literacy.
Only 18% of organizations have conducted any formal AI training, and just 4% offer certification programs. Most organizations regret not training employees before deploying AI tools, which costs their companies tangible business value.
The importance of training shouldn’t be overlooked. A well-trained workforce doesn’t
just reduce errors — it accelerates safe adoption. CIOs should treat AI education like cybersecurity awareness: repetitive, layered, and enterprise wide. This means ongoing, role-specific training; clearly communicating what AI tools are approved for use; and providing channels for employees to ask questions and offer feedback.
5. Communicate early, often, and cross-functionally
No AI strategy can succeed in a vacuum. CIOs must maintain frequent, transparent communication across departments — not just about AI tools themselves, but about how their impact will be measured.
Framing AI as a partner rather than a replacement eases. Collaborating with legal, HR, compliance, and security teams builds trust and ensures AI initiatives align with broader business goals. This is increasingly important as new use cases and frameworks like agentic AI emerge. No need to be complicated; simple mechanisms like weekly newsletters, “lunch and learn” sessions, or dedicated Slack channels can reinforce expectations.
6. Prove AI savviness through strategy
Only 1% of organizations consider their AI deployments to be mature. This isn’t due to a lack of ambition. It’s a lack of readiness.
This is where CIOs can step in. Be vocal about your organization’s AI readiness, not just its aspirations. Partner with other stakeholders to create frameworks for ethical use. And lead the cultural transformation required to help teams understand, trust, and succeed with AI.
CIOs may not be the loudest voices in the AI conversation, but they are the most disciplined. By focusing on a sustainable AI strategy rooted in governance, infrastructure, and training, CIOs can scale AI responsibly and securely. After all, a CIO’s long game lays the foundation for real AI success — proving you are quite savvy after all.