From Magic to Mechanics: AI Grows Up
How Product, Engineering, and Leadership Must Adapt for Lasting Impact
AI has moved past prototype stage, way beyond features hyped at a conference. It is now running inside the workflows of most enterprises, shaping everything from how code gets written to how supply chains get monitored. Yet here’s the hard truth: owning or developing cutting-edge models is no longer what sets companies apart. The real advantage lies in embedding AI into messy, human-centered systems where it can free up capacity and drive measurable business results.
In my experience incubating startups and scaling B2B SaaS companies into unicorns, I have seen the same pattern play out again and again. Flashy technology grabs headlines, but sustained success comes from applied execution. This article is all about what product, engineering, and leadership teams need to understand to thrive in the next era of AI.
Image by DC Studio on Freepik
The model itself is not the prize
There’s a misconception across the industry that value comes from building or owning proprietary AI models. In reality, the space is already filled with open-source leaders and infrastructure giants that are commoditizing access at breakneck speed. The companies that will win are those with proprietary data, existing customer pipelines, and the ability to weave AI into processes that actually matter.
When I helped build a supply chain risk platform that processed terabytes of data daily, the value wasn’t in the LLM we integrated. It was in connecting predictive signals to actual business actions—whether that was alerting a procurement team to a supplier disruption or helping an executive assess risk exposure across a multibillion-dollar network. The model was a tool, not the differentiator.
Why more knowledge leads to less awe—and that’s good
One surprising dynamic in AI adoption is that people with lower technical literacy are often more excited by the technology. No surprise, it feels magical to them. But as teams get closer to the mechanics—the data dependencies, the hallucinations, the trade-offs in accuracy versus explainability—that sense of wonder fades.
I have seen this firsthand, from working with teams integrating IBM Watson into healthcare tools 10 years ago to deploying GenAI agents for predictive analytics. Teams that once marveled at the outputs eventually had to wrestle with what to automate (RAG), what to supervise/modify (algorithms), and where human review was non-negotiable (HITL). This shift from hype to realism is not a failure. It’s where real innovation starts.
Build integrated systems, not shiny demos
One of the biggest traps product and engineering teams face is falling in love with standalone AI features. A chatbot here, a recommendation engine there, a dashboard widget on the side. None of these on their own change business outcomes.
What moves the needle are systems where AI works across layers: multimodal inputs, real-time decisioning, automated handoffs, and yes, human collaboration. When we launched new products in mental health and fintech, the breakthrough moments came not from an individual model, but from designing workflows where the machine accelerated what mattered—and where the people in the loop could focus on work that demanded context, creativity, or nuance.
Leadership’s human edge matters more than ever
There’s an understandable anxiety among some leaders about what AI will do to their roles. But the most critical leadership tasks are not going away. No model can frame the right problem, navigate a political impasse, or read the unspoken dynamics in a client negotiation.
In fact, as AI takes on more of the execution load, leaders are freed to lean into the work only they can do. When our teams faced tough calls, from negotiating roadmap priorities to managing stressed-out developers during high-stakes launches, it wasn’t automation that solved the problem. It was clear, human-centered leadership: listening, making trade-offs, and holding people together when the work got hard.
The future belongs to disciplined builders, not magic chasers
If there’s one lesson I would offer to product and engineering teams today, it’s this: the companies that will thrive are not the ones with the loudest AI marketing or the biggest patent portfolio. They are the ones doing the unglamorous work of integrating, testing, improving, and delivering value over time.
This means knowing when to deploy AI agents to speed up document analysis, when to pair a junior associate with an assistant to level up their work, and when to make the call that no machine can replace thoughtful human judgment. It means designing products for users across a range of AI literacy, balancing wonder with clarity.
As someone who has been part of teams building everything from offline government platforms to healthcare integrations and mental health SaaS, I have learned that success comes down to this: treat AI not as a shortcut or magic trick, but as a collaborative tool. Get that right, and you don’t just survive the AI wave. You shape where it goes next.
Attribution and Inspiration
Image by DC Studio on Freepik
HBR On Strategy / Episode 115, The Promises, Pitfalls, and Trade-offs of the Circular Economy, June 18, 2025
The AI Revolution Won’t Happen Overnight, by Paul Hlivko, Harvard Business Review, June 24, 2025
HBR IdeaCast / Episode 1034, How to Build an AI Assistant for Any Challenge, July 08, 2025
Why Understanding AI Doesn’t Necessarily Lead People to Embrace It, by Chiara Longoni, Gil Appel and Stephanie M. Tully, Harvard Business Review, July 11, 2025
How AI Is Redefining Managerial Roles, Harvard Business Review (July–August 2025)