In leadership roles, especially technical-leadership roles, there are few subjects you will be asked about more often than AI.
But what if, like me until recently, you have lots of technical experience but have yet to dive meaningfully into AI development?
While working as the SVP of Product Engineering at Slack, I was repeatedly amazed at how rapidly Notion launched and evolved its AI offerings. As I began talking with Ivan, Akshay, and the leadership team about possibly joining the company, I wondered to myself, How can I be an AI leader without deep knowledge of AI?
I knew everyone would have questions.
Shareholders and investors want to know how we're staying ahead of the curve. Employees want to know how AI will alter their work. Journalists want to know how it will change our society and economy. All this curiosity—some might say pressure—increases when you're responsible for teams actually developing large-scale AI products.
1. Embrace your role as a translator
One of the most valuable things you can do as a leader is to translate complex technical concepts into clear, actionable insights for different audiences. This doesn't require deep expertise in AI algorithms, but rather an understanding of how AI impacts your business and stakeholders.
When technical teams explain their work, help them frame it in terms of business outcomes. When business stakeholders express concerns, help them understand the technical constraints. Your role is to facilitate understanding across these different domains.
2. Focus on outcomes, not implementations
As a leader without deep AI expertise, your strength lies in focusing on what AI should achieve rather than how it works. Ask questions like: What problem are we solving? How will we measure success? What are the ethical implications?
This outcomes-focused approach allows you to provide valuable direction without needing to understand every technical detail. It also helps ensure that AI initiatives remain aligned with broader organizational goals.
3. Build a diverse team of experts
Surround yourself with people who have the technical expertise you lack. This includes AI researchers, engineers, ethicists, and domain experts. Your job isn't to know everything but to bring together the right people who collectively do.
Create an environment where these experts can collaborate effectively and where diverse perspectives are valued. This approach not only compensates for your knowledge gaps but often leads to more robust and innovative solutions.
4. Develop a learning mindset
While you don't need deep expertise, you should commit to continuous learning about AI. Stay informed about major developments, understand the basic concepts, and be familiar with the terminology.
This ongoing education helps you ask better questions, evaluate proposals more effectively, and communicate more confidently with both technical and non-technical stakeholders.
5. Lead with ethical considerations
AI raises significant ethical questions around bias, privacy, transparency, and societal impact. As a leader, you should prioritize these considerations even if you don't understand all the technical details.
Establish clear ethical guidelines for AI development and use. Encourage your team to think critically about the implications of their work. This ethical leadership is valuable regardless of your technical expertise.
Conclusion
Effective AI leadership doesn't necessarily require deep technical expertise. By focusing on your strengths as a leader—setting direction, asking the right questions, bringing together diverse perspectives, and maintaining ethical standards—you can successfully guide AI initiatives even as you continue to build your knowledge in this rapidly evolving field.
Remember that many of the core principles of good leadership remain the same, regardless of the technology involved. Trust your experience, be honest about what you don't know, and approach AI with the same thoughtful leadership you've applied to other challenges throughout your career.
