AI Leadership: A Guide to Building AI Strategy and Governance

AI success usually involves a dedicated leadership team — an AI steering committee or a chief AI officer, for example, reporting to an organization’s CEO or board. These leaders should articulate a clear AI vision tied to business objectives and ensure that resources including budgets, data and technology are committed accordingly.

They also set priorities on where AI should drive value, such as in customer experience or operations, and how it aligns with the overall strategy. “In well-led companies, everyone from the CEO to the CIO is on the same page,” Juneja says.

Get Wide-Range Buy-In

Securing buy-in from internal stakeholders for AI initiatives starts with trust, according to Forrester Principal Analyst Carlos Casanova: “The tech leader has to design for transparency and make sure the systems explain their reasoning, show their data sources and have strong governance in place.”

Governance should not be seen as a purely technical function, but rather a strategic one that involves the entire organization, Casanova adds. That includes setting clear policies for data ownership, quality and privacy to ensure reliability and compliance with relevant regulations.

Just as critical is building adaptability into AI systems so business leaders can act quickly. “When you see a new market opportunity, you don’t want to hear it will take six months to re-architect the system,” Casanova says. “AI-driven leaders must be agile and design solutions that scale with those asking for them, without violating compliance or trust.”

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Failing to establish that confidence, he warns, can slow decision-making or push stakeholders to seek alternatives. “You have to instill trust so leaders can move freely and run the business,” he says.

Casanova says that AI-augmented decision-making combines domain knowledge with organizational context to produce meaningful, trustworthy insights. “Generic models aren’t enough,” he says. “You need to layer in organizational data, history and industry context, so the recommendations align with business value.”

Transparency and explainability are critical so leaders can understand why an AI model recommended a particular action. “AI can’t be a black box,” Casanova says. “If the system suggests a change in code or infrastructure, it has to explain why — just like a human decision-maker would.”

Build Cross-Functional Teams

AI projects flourish when handled by diverse, cross-disciplinary teams — not isolated data scientists in a lab, says Jabez Mendelson, research manager with Frost & Sullivan. “Success requires blending technical expertise with domain know-how and operational skills,” he says. “The most impact comes when AI teams include data scientists and engineers working side by side with business owners and IT leaders.”

AI leaders or managers set the strategy and roadmap, acting as the bridge between executives and technical teams, while AI builders — including machine learning engineers and data scientists — focus on developing models and solving complex technical challenges. Business executives and domain experts provide essential context on customer needs, compliance requirements and operational realities, ensuring that solutions address real-world use cases.

Finally, IT and data architects should make sure the infrastructure, data pipelines and cloud resources are in place, enabling models to scale securely and efficiently into production.

Casanova says building AI-first business models requires more than simply layering AI onto existing workflows; it demands a fundamental rethinking of how the enterprise operates. This includes cross-functional teams that bring together technical experts, business leaders and operational staff to redesign processes around AI from the ground up.

“AI can’t just be a technology capability,” he adds “It has to become an enterprise capability.”

IT Leaders Manage AI Risk

Boards and senior leaders must treat AI risk with the same gravity as financial or cybersecurity risk, Juneja says. AI must be integrated into enterprise risk management capabilities, with formal assessments addressing privacy, fairness, security and compliance, supported by human review for sensitive use cases. “There should be regular review of major AI initiatives, approval of high-risk projects and ensuring alignment with corporate values,” she says.

Organizations should define clear, ethical AI policies, including transparency and nondiscrimination standards and involve cross-functional teams.

Further, staying ahead of evolving regulations such as the E.U. Artificial Intelligence Act is essential, as are documenting decisions, assigning business sponsors and publicly reporting on AI use to embed trust and accountability.

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Cultivate and Retain AI Talent

As AI has become more widely deployed, organizations are taking steps to hire and retain workers with the skills needed to succeed with this technology. Retaining top AI talent requires creating an environment where employees feel empowered, valued and encouraged to experiment, Casanova says.

Leaders should invest in upskilling and reskilling, tie AI initiatives to clear business value and foster a culture of collaboration where lessons learned can be shared openly. The goal is a controlled but not restrictive environment that rewards curiosity and continuous learning. “You have to build a culture where people aren’t afraid to try, fail and learn — and where their success directly accelerates the success of the business,” he says.

He suggests establishing a corporate culture where AI literacy is cultivated through clear strategy and alignment to business goals, so employees see AI as purposeful rather than a passing trend. “AI leaders must act as translators, helping technical teams understand business objectives and ensuring business leaders grasp technical capabilities and limitations,” Casanova explains.

Tools including knowledge graphs can make these connections visible and accessible, fostering shared understanding.

“The tech leader’s role is to translate, to make sure business and technical teams speak the same language, understand the strategy and can move forward together with confidence,” he says.