The New Technical Leadership: Embracing AI/ML as a Core Competency

Discover how tech leaders can transition to AI leadership, build essential AI/ML skills, master foundational machine learning concepts, foster team growth, explore real-world AI applications, and champion responsible, ethical AI practices for sustainable organizational impact.

When I started my journey in AI/ML in the early 2010s, the landscape was dramatically different from what we see today. I began as an intern at a New York startup, building face detection and tracking systems in an era when deep learning was just beginning to emerge. After grad school, I joined Qualcomm to develop and optimize computer vision and machine learning primitives for Snapdragon processors. Both experiences taught me the same thing: AI was highly specialized back then. The tools were limited, compute power was constrained, and optimization felt like solving complex puzzles daily. While the work was cutting-edge, AI thrived primarily in academic circles and large companies investing in its potential. It wasn’t a technology most engineering teams deploying code to production needed to worry about!

Throughout most of 2010s, success in engineering and leadership did not require AI expertise. This decade was dominated by mobile app development, big data, and cloud computing. AI was seen as a “nice-to-have” skill, a specialized interest rather than a core competency required for success. As engineering leaders, one could excel in traditional domains without engaging with AI whatsoever!

The landscape has fundamentally shifted. AI has evolved from specialized applications to become the driving force of innovation across industries. I’ve experienced this transformation firsthand, from a student researcher tinkering with image segmentation algorithms to an engineering leader driving AI adoption at scale. Along this journey, I’ve had the privilege of helping many talented engineers and leaders navigate their path into AI/ML, learning valuable lessons together.

In this article, I’ll share insights on why this shift matters now more than ever, how it reshapes your role as a leader, and practical strategies that have helped others succeed in this transformation. If you’re a tech leader today, understanding and leveraging AI isn’t optional — it’s imperative.

Why AI Skills Matter Now

If you’re leading a technical team today, you’re probably witnessing how breakthroughs in foundation models, massive gains in computing power, and advances in training methods have transformed AI. These capabilities have moved from research labs into your developers’ hands through simple cloud APIs, revolutionizing how you can approach product innovation and development workflows.

A significant part of my responsibilities as an org leader involve identifying opportunities where AI can drive innovation, making compelling cases to leadership, and successfully launching AI-powered features into production. This means motivating my teams to embrace new technologies, evangelizing AI capabilities to product teams, and carefully managing resources to balance innovation with delivery. Sound familiar? Nearly every tech leader I speak with is on this same journey, whether they’re just starting or deep into implementation!

Even if your product doesn’t directly integrate AI, its impact on team productivity and workflows can’t be ignored. AI is transforming how engineering teams operate, from code development and documentation to automation and knowledge sharing. When you integrate these tools well, your team can focus less on routine tasks and more on what truly matters: solving complex challenges that differentiate your product in the market.

Finally, as engineering leaders, you stand at a critical intersection of technical understanding and ethical development in AI. Your unique combination of hands-on expertise and strategic oversight makes it your duty to ensure AI is built responsibly. A key part of your role is to review the technical choices that affect bias, create transparency, and build safeguards. When you champion responsible AI, your teams build enduring, trusted systems that maintain user confidence.

How Engineering Leaders Can Transition to AI/ML

For leaders who already have extensive experience in AI/ML, this is an exciting time to make an even bigger impact. You’re in the right field at the perfect moment, where innovation is happening at an incredible pace. Focus on scaling your influence by driving organizational strategy, mentoring the next wave of AI talent, and staying ahead of the curve. You have the expertise to shape the future — lean into it!

For the rest of this article, I’m going to focus on leaders and aspiring leaders in the tech industry who are new to AI/ML. Stepping into this field might feel overwhelming at first, but with the right mindset and approach, it’s completely doable. The approach I’ll outline focuses on three key areas:

• Building Your Foundation: This is all about getting the basics right. You need to understand the core concepts of AI/ML so you can guide your team and make informed decisions.
• Learning with Your Team: Work alongside your team as they explore AI. This builds trust, encourages collaboration, and helps everyone grow together.
• Learning from Real-World Applications: See how AI is being used successfully by others. Real-world examples will help you figure out what works and where AI can actually make a difference.

1. Building Your Foundation

AI introduces unique challenges that demand a significant shift in mindset compared to traditional software development. Concepts like model behavior, latency trade-offs, and ML lifecycle complexities aren’t just nice-to-know, they are fundamental to understanding how AI systems work. From my experience, building this foundation takes more than casual exploration — it demands structured learning.

I have found that graduate-level courses from reputed universities, either online or in-person, are one of the best ways to build a strong foundation. These programs dive deep into key concepts, offer rigorous curricula, and provide accountability through deadlines and structured evaluations. If you need something more flexible, MOOC (Massive Open Online Course) platforms like Coursera, edX, and Udemy are excellent for learning topics like machine learning, deep learning, and AI systems. That said, MOOCs are highly self-driven, and I’ve personally struggled with staying consistent without external accountability. They require discipline, so carving out specific time and sticking to a schedule is key.

So, here’s how you can build your foundation:

• Start with Graduate-Level Courses: If possible, pursue online or in-person programs that cover essentials like a Statistics refresher, Machine Learning 101, and foundational Deep Learning. These courses provide both structured learning and hands-on experience with ML tools and frameworks.
• Use MOOCs as a Supplement: I’ve found MOOCs to be great for picking up trending topics or exploring new tools, but they work best when treated like formal courses. Set deadlines and maintain a schedule to stay on track.
• Read Case Studies, Whitepapers, and Survey Papers: Whitepapers and case studies are excellent for learning how AI is applied in real-world settings. I also recommend survey papers to gain a broad understanding of the research landscape, without getting lost in the technical depth of individual papers.
• Consider Pursuing Another Degree: If you’re deeply committed to making a long-term impact in this domain, consider pursuing a professional master’s with AI/ML specialization, even if you already hold advanced degrees! Many world-class universities now offer part-time or online programs in this specialization, tailored for working professionals, enabling you to advance your knowledge and skills without pausing your career.

2. Learning with Your Team

Your success as a leader is closely tied to how you and your team grow together, especially in the rapidly evolving world of AI. From my experience leading teams at Amazon, I have found that creating opportunities for hands-on exploration, such as hackathons, proof-of-concept projects, or internal learning programs, is one of the most effective ways to foster innovation and build trust. Especially in the quickly advancing world of AI, these activities provide a low-pressure environment for both you and your team to experiment with new tools and frameworks. They help your team develop confidence and creativity while giving you the chance to stay connected to emerging AI technologies, observe how challenges are tackled, and contribute collaboratively to innovative solutions.

Here are some actionable tips:

• Host Hackathons or AI Challenges: These give your team practical experience with AI/ML technologies and also allow you to work with them on creative solutions.
• Establish a Learning Culture: Build a shared library of resources and encourage regular upskilling in this domain. Participate in learning initiatives yourself to reinforce that growth is a team effort. This collaborative approach creates a culture where learning feels shared and supported.
• Facilitate Knowledge Sharing: Attend lunch and learn sessions and technical demos, not just as a listener but as an active participant. This helps you stay connected to the details while encouraging collaboration.
• Tie Learning to Business Goals: Collaborate with your team and product partners to align learning and AI experimentation with organizational objectives, like improving CXs or optimizing workflows. This alignment ensures that efforts are not only educational but also impactful for the business!

3. Learning from Real-World Applications

Learning from real-world applications is your most powerful tool for separating AI’s real potential from the hype. In my journey from AI engineer to leader, I’ve found that theoretical knowledge crystallizes only when you see it solving actual business problems.

Start by analyzing AI implementations within your organization — look for patterns in successful deployments. Which problems yielded the highest ROI? Where did teams struggle? The answers help you anticipate challenges before you encounter them. Beyond your company, study how different industries leverage AI.

But reading about AI applications isn’t enough. You need to actively test and use AI-powered tools and applications in your environment. Try popular AI coding assistants in your development workflow. Test different LLM/LMM APIs to understand their strengths and limitations. Use AI-powered analytics tools to see how they process data. Experiment with AI-enhanced productivity tools your team might adopt.

Here are some actionable ideas you can try!

• Observe and Learn Within Your Organization: Look at how other teams in your company are using AI. Study their successes and challenges to uncover opportunities for collaboration or improvement.
• Explore Use Cases Across Industries: Learn how companies in other sectors are solving problems with AI.
• Stay Updated with Tech Blogs and Articles: Follow reputable engineering blogs and case studies from leading companies. These often share insights into how AI systems are designed, deployed, and scaled.
• Experiment Extensively with Tools: Test AI frameworks, APIs, and platforms. Evaluate their practical use cases and limitations to identify how they can improve your team’s workflows or offerings.

Looking Ahead

This guide reflects my journey of helping leaders navigate the AI transition, but it’s just a starting point. Your path into AI leadership will be unique, shaped by your team’s needs, your industry’s demands, and the rapidly evolving AI landscape. The key is to begin. Pick one approach from this article that resonates with you, whether it’s taking an online course, experimenting with AI tools, or studying successful implementations in your industry. The field will keep evolving, but the fundamentals of thoughtful adoption and responsible leadership we’ve discussed will serve you well as you move forward.

In upcoming articles, I’ll share hands-on guidance for Software Developers wanting to move into AI/ML — a transition I help with constantly. I’ll also focus on fresh graduates starting their AI careers. There is so much more to uncover, and I am excited to continue this journey with you!

Related Reading

• From Code to People: Choosing the Management Path: Navigate the transition from coding to management. Learn how engineering and research leaders can excel in people management while driving technical impact.
• Being Technical as an Engineering Manager: Evolving Beyond the Code: Explore how engineering and research managers can stay technical, drive innovation, lead teams effectively, and balance coding with strategic leadership.
• Staying Technical as a Leader: Practical Strategies: Learn practical strategies for engineering leaders to stay technical while balancing leadership responsibilities. Stay relevant, lead effectively!
• AI for Software Engineers: Evolve, Don’t Restart: Explore how software engineers can evolve into AI/ML roles, leveraging existing skills with practical insights and guidance.
• Land Your Dream AI/ML Job: A Practical Guide: Want to land your dream AI/ML job? This practical guide breaks down key strategies, from mastering technical skills to optimizing your resume and networking effectively, helping you stand out in the competitive AI job market.

About the article

Disclaimer: 

The views and opinions expressed in my articles are my own and do not represent those of my current or past employers, or any other affiliations.

Publication Note:

This article was originally published on my Medium publication on Dec 29, 2024. I’ve republished it here as part of my ongoing effort to centralize my insights

For the original post and additional context, please visit the original article.