Leaders Accelerate AI Adoption Through Hands-On Technical Fluency

In a landscape often dominated by the hype surrounding artificial intelligence, this conversation with Humza Teherany, Chief Strategy and Innovation Officer at MLSE, offers a potent antidote: a focus on practical application and immediate impact over abstract potential. The core thesis here is that true AI innovation within an organization isn't about widespread adoption of AI tools, but about cultivating a specific, technically fluent leadership cadre capable of bridging business needs with AI capabilities. This approach reveals the hidden consequence that many AI initiatives fail not due to a lack of technology, but a deficit in the leadership's ability to translate complex business problems into actionable AI projects and then deliver tangible results rapidly. Leaders who embrace this hands-on, technically grounded approach gain a significant advantage by building functional AI capabilities that solve real problems, rather than chasing the latest AI trend. This is essential reading for executives and technical leaders grappling with the question of how to move beyond AI experimentation to genuine organizational impact.

The Agitator, The Gravity, and The Bridge: Redefining AI Leadership

The conventional wisdom for rolling out AI often centers on broad training initiatives or democratizing access to tools. However, Humza Teherany's experience at MLSE, as discussed in this conversation, points to a more nuanced, systems-level approach. Instead of a top-down mandate or a bottom-up free-for-all, Teherany has cultivated a specific type of leader: one who possesses technical fluency, the ability to attract and rally talent, and the skill to connect these capabilities directly to business domains. This isn't about general AI literacy; it's about building a core engine of innovation.

The immediate benefit of this model is speed. Teherany's team can turn ideas into prototypes in just 24 hours. This rapid iteration cycle bypasses the lengthy planning and justification phases that often stall AI projects. The hidden cost of slower, more conventional approaches is the opportunity cost -- the problems that remain unsolved, the efficiencies that are not realized, and the competitive advantage that erodes while organizations deliberate.

"You need to have good technical understanding then you have to be able to basically get the people together you need to be able to attract people and then you have to bridge into the domains like the CMAs and..."

-- Humza Teherany

This structure highlights three critical leadership qualities: agitation, gravity, and bridging. "Agitation," as Jeremy clarifies, isn't about causing trouble, but about possessing the drive and technical capability to get things done. It's the energy to move from concept to reality. "Gravity" is the ability to draw people and resources to a central idea, essential for recruiting talent and securing sponsorship. Finally, "bridging" is the crucial skill of connecting technical possibilities with business needs, ensuring that AI initiatives are solving actual problems. This contrasts with a more entrepreneurial resourcefulness, which often focuses on achieving a lot with little, whereas Teherany's model emphasizes building a dedicated, capable team.

The downstream effect of cultivating these leaders is the creation of an internal AI engine that is both technically credible and business-savvy. This allows MLSE to move beyond simply experimenting with AI to actively shipping solutions. The immediate payoff is the rapid prototyping, but the lasting advantage is the development of a core competency that can be applied to a wide range of business challenges, creating a sustainable source of innovation.

The Unpopular Truth: Master AI, Downplay the Hype

One of the most striking insights from the conversation is Teherany's dual approach: mastering AI personally while downplaying its overt presence within the broader organization. This strategy sidesteps the common pitfall of overhyping AI, which can lead to unrealistic expectations and eventual disillusionment. Instead, the focus is on problem-solving, with AI as a tool rather than the headline.

"I think the danger is to fall in the middle where you overhype AI and you under master it right... but what he's done is he's mastered far more than most folks I see in organizations."

-- Jeremy (Host)

This approach creates a competitive advantage because it grounds AI in tangible business value. When leaders like Teherany can demonstrate AI's capabilities through concrete examples -- like adding a feature to an app on the fly during a sushi lunch -- it builds credibility and trust. The immediate benefit is that AI is seen as a solution provider, not a buzzword. The longer-term payoff is a more mature and effective integration of AI, driven by genuine utility rather than external pressure or hype.

The consequence of leading with "forget AI, what's the problem?" is that it naturally filters for impactful applications. It avoids the "AI for AI's sake" trap. This means that when AI is eventually introduced, it's already aligned with a clear business objective, making adoption smoother and benefits more apparent. This strategy requires a leader who can not only understand the technology but also navigate organizational dynamics effectively, ensuring that the AI capabilities are deployed where they will have the most impact.

The "Build in a Day" Engine: From Ideas to Impact

The conversation highlights a powerful mechanism for driving AI adoption and innovation: the "build in a day" concept, inspired by platforms like Dig. This isn't about asking everyone to become a prompt engineer; it's about creating a structured process where well-defined ideas can be rapidly prototyped by specialized AI builders, working alongside domain experts.

The initial step involves an idea generation competition, where anyone in the organization can submit an idea, and others can upvote or downvote them. This crowdsourcing aspect ensures that the ideas being pursued are relevant and have organizational buy-in. The hidden consequence of this approach, compared to traditional idea contests, is that it doesn't just generate ideas; it has a built-in mechanism for rapid realization.

"People listen to this once a quarter, we take the top three and we do a build in a day where each of the people who champion that idea gets to sit next to an agentically capable AI builder and they go from concept to actual realized thing in a day."

-- Humza Teherany

The "build in a day" event itself is where the magic happens. By pairing idea champions with capable AI builders, MLSE can transform concepts into tangible prototypes in a single day. This is followed by a demo day, which showcases the organization's commitment to innovation and validates the ideas that were voted on. The immediate payoff is the creation of functional prototypes and a sense of momentum. The lasting advantage lies in fostering a culture where ideas are not just submitted but are actively materialized, demonstrating the value of both innovation and the AI capabilities that enable it. This mechanism provides a clear pathway for ideas to move from conception to impact, creating social momentum and reinforcing the organization's commitment to AI-driven progress. It’s a stark contrast to initiatives that rely on organic, grassroots development, which can be slow and unpredictable.

Key Action Items

• Cultivate "Agitator" Leaders: Identify and empower individuals with strong technical understanding and the drive to execute AI projects. (Immediate)
• Establish "Gravity" for AI Initiatives: Create clear communication channels and sponsorship pathways to attract talent and resources to AI projects. (Ongoing)
• Bridge Technical and Business Domains: Actively foster collaboration between AI teams and business units to ensure AI solutions address genuine organizational needs. (Immediate)
• Downplay AI Hype, Focus on Problems: Lead with business challenges and introduce AI as a solution, rather than leading with AI as a concept. (Immediate)
• Implement "Build in a Day" or Similar Rapid Prototyping: Establish a mechanism for quickly turning top-voted ideas into functional prototypes with dedicated AI builders. (This pays off in 3-6 months as a capability)
• Run Regular Idea Generation and Voting Cycles: Create a structured process for employees to submit and vote on AI-driven ideas, fostering engagement and identifying high-potential projects. (Quarterly)
• Showcase Rapid Prototyping Successes: Use demo days or internal showcases to highlight the tangible outcomes of AI initiatives, building organizational momentum and trust. (Quarterly, following build days)