Leaders' Hands-On AI Skills Drive Rapid Prototyping and Strategic Advantage

The C-Suite Command Line: Why Technical Depth is the New Strategic Imperative in the AI Era

This conversation with Humza Teherany, Chief Strategy and Innovation Officer at Maple Leaf Sports & Entertainment, reveals a critical, often overlooked, implication of the AI revolution: the necessity for senior leadership to possess genuine technical fluency. While many leaders delegate AI to specialists, Teherany argues that hands-on engagement with AI tools, even at a deep technical level, is not a nice-to-have but a fundamental requirement for strategic advantage. This episode is essential for C-suite executives, CTOs, and innovation leaders who want to move beyond superficial AI adoption and unlock truly transformative business outcomes. Understanding the "how" behind AI, not just the "what," provides a unique lens to identify opportunities and navigate the rapid technological shifts that will redefine industries.

The Unseen Advantage of the Technical Leader

The prevailing wisdom often suggests that as leaders ascend, their role shifts from "doing" to "delegating." For AI, this often translates to entrusting the technology to specialized teams. However, Humza Teherany, Chief Strategy and Innovation Officer at Maple Leaf Sports & Entertainment, presents a compelling counter-argument: a leader's genuine technical depth is not just beneficial, it's becoming a strategic necessity. His personal journey back to hands-on development, spending at least two hours daily engaging with AI models and tools, underscores a profound shift. This isn't about micromanaging developers; it's about cultivating an intuitive understanding of AI's capabilities and limitations, which directly informs strategic decision-making and innovation.

Teherany’s experience highlights that the opportunity cost of a leader’s time, often cited as a reason to avoid deep technical engagement, is precisely why they must engage. By personally building full-stack applications within MLSE’s Version One (V1) Lab, he demonstrates how this hands-on approach can accelerate innovation from weeks to days, or even hours. This capability allows ideas to be rapidly prototyped and validated, moving beyond theoretical discussions to tangible results.

"I think these technologies have changed the world and they will continue to change the world. Included in that are legacy businesses that have been around forever, and it doesn't really matter if you're a food service company or cleaning company or sports and entertainment organization or a TV network, these things are fundamentally going to change and have changed how business is done."

-- Humza Teherany

The V1 Lab, where ideas can be dropped on a Monday and a full-stack app can emerge by the end of the week, exemplifies this accelerated innovation cycle. This isn't about replacing traditional development but augmenting it, allowing for near-instantaneous testing of concepts. This rapid prototyping capability, driven by Teherany's technical engagement, allows MLSE to quickly determine the viability of new ideas, whether for margin improvement, revenue growth, or enhancing fan experience. The alternative, as Teherany implies, is to be left behind as competitors leverage these accelerated cycles to their advantage.

From Consumer Tools to Command Line: Bridging the AI Chasm

The journey into AI engagement for leaders often starts with consumer-facing tools like ChatGPT. Teherany advocates for mastering these "consumer stuff" first, understanding their capabilities and limitations. This foundational step is crucial for building confidence and an intuitive grasp of AI. However, he stresses the importance of crossing the "chasm" to more technical tools, such as VS Code, Cursor, or Claude Code, and even the command-line interface (CLI). This deeper dive allows for a more profound understanding of how LLMs work and how to leverage them for complex tasks.

The business case for this deeper engagement is rooted in tangible outcomes. Teherany’s personal development of three to four full-stack platforms within the V1 Lab, despite not being a full-stack developer 25 years ago, illustrates the power of this hands-on approach. These platforms, including MLSE AI (an AI homepage with multiple apps) and Ideagen (an idea submission and voting platform), demonstrate how AI can democratize innovation and streamline business processes. Ideagen, for instance, allows anyone in the organization to submit ideas, fostering a culture where the best concepts rise to the top, regardless of origin. The subsequent "Build in a Day" initiative, where top ideas are rapidly prototyped, transforms abstract concepts into working applications, creating a tangible feedback loop and demonstrating the organization's commitment to innovation.

"For me personally, I've now been able to build full-stack apps for MLSE. We have a, I developed a group about a year ago called our V1 Lab, our Version One Lab, which is our agentic development lab. Our V1 Lab at MLSE allows us to drop in an idea on a Monday and have a full-stack app latest by the end of that week."

-- Humza Teherany

The pivot from building numerous "micro-apps" to more integrated "software as a service" experiences, like MLSE Deep Dive, further illustrates a systems-thinking approach. Deep Dive allows users to brainstorm and research complex ideas within a contained environment, generating contextualized reports and marketing materials. This evolution shows a progression from discrete AI tools to integrated solutions that solve broader business problems, highlighting how understanding the technical underpinnings enables the creation of more impactful and scalable applications.

The "AI-Adjacent" Advantage: Solving Problems, Not Chasing Buzzwords

A critical insight from Teherany is the strategy of downplaying the "AI" label within the broader organization. While technical teams and leaders might be fascinated by the cutting edge, the focus for most employees should be on how these tools solve their day-to-day problems. This "AI-adjacent" approach, where the technology is embedded within user-friendly tools that simply make jobs easier, is far more effective for broad adoption. Instead of asking, "How do we use AI?", the question becomes, "What problem are we trying to solve, and can this tool help?"

This perspective is crucial for understanding the ROI of AI. When tools enable faster research, analysis, or content creation, freeing up 80% of an employee's time for more value-added activities, the business impact is clear. This is particularly true for non-technical users who are less concerned with the underlying technology and more with its practical application. The success of platforms like the agentic media access platform, which won an NBA Innovation Award for dramatically reducing video production time, exemplifies this by focusing on the outcome -- faster content delivery -- rather than the AI itself.

"It's almost like the AI, I think the AI for the technical people in there is really interesting. We could talk about it for a long time, but for somebody who's not technical, they just want to know, 'How's it going to help me do my job better?' If you could just almost take out AI from the commentary, and exactly as you said it, 'Here's a tool, use it. It should help you go faster.'"

-- Humza Teherany

Teherany’s approach suggests a strategic duality: deep technical mastery and hands-on experimentation for the leadership and innovation teams, coupled with a problem-focused, tool-agnostic rollout for the broader workforce. This ensures that AI is integrated effectively, driving efficiency and innovation without becoming a buzzword that overwhelms or alienates users. The goal is to empower individuals by providing better ways to do their jobs, leading to organic adoption and tangible business improvements.

Actionable Takeaways

• Embrace Hands-On Learning: Dedicate consistent time (e.g., 1-2 hours daily, outside core work hours) to personally engage with AI tools, from consumer-grade applications to developer environments. This builds intuition and strategic insight. (Immediate to Ongoing)
• Master the "Consumer Stuff" First: Become proficient with foundational AI models and platforms like ChatGPT, Claude, and Gemini. Understand their strengths and weaknesses for everyday tasks. (Immediate)
• Cross the Chasm to Technical Tools: Explore and utilize more advanced tools like VS Code, Cursor, Claude Code, and the command-line interface (CLI) to deepen your understanding of AI's inner workings. (Over the next quarter)
• Build Rapid Prototypes: Establish or leverage an "agentic development lab" (like MLSE's V1 Lab) to bring ideas to life within days or hours, enabling rapid validation and iteration. (This pays off in 3-6 months)
• Focus on Problem-Solving, Not AI Buzz: When introducing AI-powered tools to the broader organization, frame them around solving specific user problems and improving workflows, rather than focusing on the AI technology itself. (Immediate)
• Develop Integrated Solutions: Move beyond single-purpose "micro-apps" to create more comprehensive, service-oriented platforms that address complex workflows and user needs, like MLSE's Deep Dive. (This pays off in 6-12 months)
• Foster Idea Generation and Realization: Implement mechanisms like idea competitions and "Build in a Day" events to harness organizational creativity and demonstrate the capability to rapidly materialize promising concepts. (This pays off in 6-18 months)