What it takes to lead in the age of AI

15 Apr 2025

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Artificial Intelligence (AI) is not just another technological advancement—it is a paradigm shift that requires leaders to rethink how their organizations operate and make decisions. Some people compare it to the technological revolution two decades ago, which led to the rise of the internet and the emergence of companies like Amazon.com and Facebook. Significant shifts are rare in a lifetime, so we must be prepared for this change. AI-first leadership is about using AI to guide decisions across the organization while ensuring a strong data foundation to support these initiatives. It involves being forward-looking, willing to experiment, and embracing ambiguity and complexity.

Building an AI-Ready Culture

AI-first leadership is not only about technology; it’s about transforming the organizational culture. Leaders must foster an environment where teams are comfortable adopting AI tools and processes. This requires significant reskilling and upskilling of employees to work alongside AI. Establishing cross-functional teams and implementing governance frameworks ensures that AI initiatives are deployed and aligned with organizational goals and ethical standards. Cross-functional teams bring diverse perspectives, blending technical expertise such as data science, machine learning, and software engineering with domain-specific knowledge in areas like finance, healthcare, or marketing. These teams also require strong product management and communication skills to clearly define the problem, coordinate efforts across disciplines, and understand ethical considerations to ensure responsible AI deployment. Meanwhile, governance frameworks provide a structured approach to managing risks, ensuring compliance with legal and ethical guidelines, and fostering accountability. Together, these elements create a robust system for integrating AI into organizational workflows, maintaining transparency, and driving meaningful results.

Data as the Foundation of AI

AI cannot succeed without a strong data foundation. High-quality, clean data is essential, as is a formalized data governance framework to manage this data effectively. Leaders must prioritize investments in data infrastructure and processes to enable their organizations to benefit fully from AI’s capabilities. This is harder than it sounds because many organizations still struggle to extract value from the data they produce as part of their business activities. The key to addressing this challenge is to start by clearly defining high-impact use cases that align with strategic business goals. Once the use cases are prioritized, leaders should focus on collecting and organizing high-quality data while establishing robust pipelines and governance protocols to ensure consistency and scalability.

Identifying high-impact use cases

AI-first leaders must quickly identify areas where AI can bring the most value to their organization. This could be through reducing costs, improving productivity, or increasing revenue. While AI co-pilots can enhance individual productivity, they also offer exciting applications in companies across various industries that showcase the transformative potential of AI. For example, technology companies can leverage AI-enhanced and conversational log searching to streamline troubleshooting, allowing engineers to identify and resolve system issues quickly. Anomaly detection could play a crucial role in payment and retail companies by proactively identifying unusual patterns to prevent fraud and system errors, ensuring smoother operations, and safeguarding customer trust. And consulting firms like KPMG are already using Generative AI to develop budgets and forecasts, produce financial commentary and presentations. Leaders must prioritize such use cases based on their potential to reduce costs, improve operational efficiency, deliver measurable business impact, and start building!

Cultivating an experimental mindset

Leaders who prioritize AI need to foster a culture of experimentation in their organizations. This means creating an environment where teams can quickly test, iterate, and scale AI ideas and initiatives. Providing the necessary tools and platforms for experimentation is crucial. Given the rapidly evolving AI technology landscape—where every vendor seems to be “AI-washing” their solutions—leaders must embrace experimentation to determine the right AI stack for their organization. Companies that fear failure will struggle in this dynamic environment. As the saying goes, “If you’re not trying, you’re not buying.”

Human-AI collaboration

One of the key principles of AI-first leadership is understanding that AI is a tool to augment human capabilities, not replace them. For example, AI coding co-pilots can propose innovative solutions to software problems but often struggle to deliver these solutions within the broader context of a product. Solid engineering skills are still required to integrate and refine AI-driven solutions fully. Organizations can achieve better outcomes and avoid over-reliance on AI tools by fostering strong collaboration between humans and AI.

Measuring Success

“What gets measured gets improved.” AI-first leaders must define success in terms of measurable business outcomes. For example, they can evaluate the complaint resolution performance of customer success teams using AI co-pilots versus those who don’t while monitoring customer satisfaction metrics. Other relevant metrics could include productivity improvements, cost reductions, or fraud prevention outcomes, depending on the specific use cases.

Staying Ahead of the Curve

AI-first leaders must stay informed about new developments in AI, such as generative AI and artificial general intelligence (AGI); Agentic AI; Model Context Protocol (MCP), and many others that come up weekly! This requires continuous learning and a commitment to keeping their teams updated. Leaders can encourage growth by sharing educational materials, scheduling training sessions, and supporting attendance at AI workshops where employees can learn from peers in their industry.

Conclusion

AI-first leadership is about more than adopting new technology; it’s about reshaping how organizations operate and innovate. By fostering a culture of experimentation, ensuring a strong data foundation, prioritizing human-AI collaboration, and staying ahead of emerging trends, leaders can position their organizations to thrive in the AI era. With a clear focus on measurable outcomes and a commitment to continuous learning, AI-first leaders can unlock unprecedented value and drive their organizations toward a successful future.