Why Your Craft Is the One Thing AI Can’t Prompt Its Way Into

A man taking a picture with a camera they are aiming it and looking directly at us, the audience.

That answer may annoy you. It’s the kind of thing talented people say when they don’t want to explain themselves. But sit with it, because in an age when anyone can generate a passable photo, song, or paragraph with a prompt, “the shot finds him” might be the most important sentence in this whole conversation about AI.

Craft is Not Talent. It’s a Recipe, and You’re the Only One Who Has It.

Here’s what Russel actually does, stripped of mystique: he researches his subjects obsessively before he ever lifts the camera. He shows up to shoots knowing more about the person in front of the lens than most journalists would bother to learn. He gives away photographs—including the ones that ran in Time and Vogue—because the celebrities themselves often never got copies; the magazines owned those. Russel always made sure they got their own. People remember that. He gets the second meeting. He treats a photo shoot the way a chef treats a tasting menu: not “get to the shot,” but “build the experience that makes the shot inevitable.”

None of that shows up on a spec sheet. You cannot download it. It was built over forty years of decisions nobody was forcing him to make.

The Difference Between a Cook and a Chef

There’s a useful distinction buried in all this: a cook makes a meal. A chef creates an experience that could only have come from them. Same ingredients, same stove, wildly different outcome—because the chef has spent years developing a point of view about food that lives nowhere except inside their own trained instincts.

Generative AI is, right now, the greatest cook the world has ever produced. It can combine any ingredients you hand it, instantly, competently, and at a scale no human kitchen could match. What it cannot do is have spent a childhood sleeping under a hand-me-down guitar, or forty years learning to read a stranger’s face before a shutter clicks. It has no accumulated why. It has only pattern.

That’s the opening. Not a loophole to hide in, but an actual, durable advantage—if you consider to build it.

Howard Schultz didn’t invent coffee.

He didn’t even found Starbucks—he bought it from founders who went off to start Peet’s. What he had was a trained eye from years as a coffee buyer, sharpened until the day he sat in a café in Milan and noticed something everyone else walking past had also seen and ignored: people wanted to linger. That noticing was his craft, quietly built over years of unglamorous buying trips, and it’s the whole reason Starbucks exists.

Insight, in other words, isn’t a lightning strike. It’s well-practiced. Insight is hard work finally reaching your gut.

The Stevie Wonder test.

Jon Batiste once described watching Stevie Wonder walk into a room—before he’d touched an instrument—and feeling the air change. Artist and human, fused into one thing, arriving ahead of the performance itself. You can’t fake that with better equipment, and you can’t prompt an AI into having lived a life that produces it. That feeling is the compound interest of decades of an identity being poured into a single form of expression. It arrives before the work starts, and it’s the reason people will always pay a premium to be near it.

So, What’s Your Recipe?

Here’s the uncomfortable homework: your job is not your craft. Your craft is the combination of what you’ve obsessed over, suffered through, and quietly practiced—the thing that makes you unreachable by imitation or machine. Craft is rarely singular. It’s usually a strange, personal mixture that took years to notice you even had.

The danger of this AI moment isn’t that machines will out-create us if we live in the convenient. It’s that “good enough” will feel so convenient that most people quietly stop building their own recipe at all—and hand the whole kitchen over. The amateurs may let their instincts atrophy. The people who’ve actually done the work—who’ve stood in the studio for hours the way Russel did, will find that AI simply hands them a sharper knife.

The tool doesn’t make the chef. It never did. It’s still the person who spent years learning to taste.

Gen AI is creating producers and consumers in the work. If you build your craft, you will be the chef and not the meal.

In my next column, I will delve into how purpose brings out our insight when combined with craft. Happy innovating!

Click here for more columns from Mohan Nair.

Innovation Leadership: Building Beyond the Mindset

Blueprints with small model of a house on top of it, along with rulers, pencil, pliers.

Firing Up the Engines of Innovation Leadership

Innovation as a corporate capability is often swirling with uncertainty. Navigating through change and executing an ambitious strategy hinge on effective leadership. In Fast Company’s “6 Leadership lessons from large-scale business transformations,” author Anil Chintapalli, managing partner of Human Capital Development, explores leadership dynamics that can ramp up the engines of innovation.

  1. Transformation starts with clarity, not urgency. Many transformations begin with a sense of urgency—declining performance, competitive pressure, or technological disruption. While urgency can mobilize action, it is clarity that sustains momentum. Effective leaders take the time to articulate why transformation is necessary, what success looks like, and how the organization will get there. Clarity means defining outcomes, not just activities. Rather than focusing solely on implementing systems or restructuring teams, strong leaders frame transformation around business value.
  2. Execution matters more than the perfect plan. One of the most common transformation pitfalls is overplanning. While strategy and design are essential, no transformation unfolds exactly as expected. Markets shift, technologies evolve, and organizational realities surface only during execution. Successful leaders recognize that execution is a learning process. They build flexibility into plans. They empower teams to make decisions. And they create feedback loops so organizations can adjust quickly.
  3. Culture is the ultimate force multiplier. Technology and processes can be redesigned relatively quickly; culture cannot. Yet culture often determines whether transformation efforts succeed or fail. Leaders play a decisive role in shaping cultural change through their actions, not just their words. In large-scale transformations, employees watch leadership behavior closely. Are leaders open to new ideas? Do they reward collaboration or protect silos? Do they tolerate short-term disruption in pursuit of long-term value? The answers to these questions shape how people engage with change. High-performing transformation leaders invest in communication, capability building, and trust. They acknowledge uncertainty. They listen actively. And they involve employees in the journey.
  4. Integrate AI with business transformation. Artificial intelligence has rapidly evolved from a futuristic concept to a key driver of enterprise transformation. Organizations are leveraging AI to optimize operations, enhance decision-making, and create new business opportunities. Leaders who understand how to integrate AI strategically can unlock measurable value and sustain competitive advantage. AI adoption is most effective when it aligns with an organization’s strategic objectives. This means identifying areas where AI can enhance efficiency, reduce operational costs, or improve customer experience. By embedding AI into core business processes, companies can transform traditional operations into intelligent systems that adapt and learn over time.
  5. Operationalize AI for measurable transformation outcomes. Implementing AI requires more than technology deployment; it demands operational integration. Organizations must restructure workflows, train teams, and establish monitoring mechanisms to ensure AI models deliver consistent performance. By operationalizing AI, businesses can translate insights into actionable outcomes.
  6. Drive continuous innovation through AI. AI is not static—it evolves rapidly. Enterprises that treat AI as a continuous innovation engine can explore new applications, refine models, and adapt to changing market demands. Leaders must foster a culture of experimentation and learning, enabling teams to test new algorithms. It also allows them to optimize models and scale successful pilots.

FEI Session Spotlight: Leadership Lessons from the Street

The FEI: Innovation Summit will be held October 5-6, 2026, at The Colorado Convention Center, Denver. The summit will be co-located with TMRE.

The keynote, “Leadership Lessons from the Street: Commonalities Between Hacking the Culture of Criminal Organization and Fortune 500 Companies,” will be presented by Antonio Fernandez, aka “King Tone”, former leader of the largest U.S. Hispanic Street Gang, Founder & CEO at Grow Up Grow Out Jr.

What parallels can be drawn between managing a street gang and managing an innovation team? Explore some of these commonalities from someone who lived them. Antonio Fernandez, aka “King Tone” the former leader of the largest Hispanic Street Gang in the U.S., explores underground leadership, creativity, innovation, and the art of hustle. What can innovation directors and entrepreneurs learn from drug dealers about overcoming bureaucracies and entrenched power systems? What can black market innovators teach entrepreneurs and intrapreneurs about pushing your idea forward? What commonalities do we see between driving change within a Fortune 500 company and hacking the culture of criminal organizations? What leadership lessons from the underground can we apply to formal institutions?

Click here for more information about the FEI: Innovation Summit

Staying Consistent

Breakthroughs and transformative innovation should be tied to long-term innovation and strategic goals. Equally important is consistency when it comes to innovation and leadership, notes Chintapalli.

“Large-scale transformations take years, not quarters. Leaders who frequently change priorities or messages undermine trust and slow progress. Those who remain anchored to a clear vision—even as tactics evolve—create stability in the midst of change,” says Chintapalli. “They understand that transformation is not an event, but a continuous process of learning, adaptation, and value creation.”

As Chintapalli notes in his Fast Company article, AI is playing a larger role than ever before when it comes to corporate innovation. Leveraging AI in corporate innovation is also a key part of this learning and adaptation process. As Chintapalli points out, organizations that adopt AI as an ongoing innovation capability can maintain relevance, adapt to disruption, and create sustainable growth.

Video: “What Makes a Great Leader?” courtesy of Harvard Business Review.

Scale Meets Speed: The Strategic Case for Corporate-Startup Collaboration

A person in background holding out a key in outstretched hand.

I’ve seen this dynamic from both sides. During the past six years, I’ve worked as the Chief Technology Officer for a materials science startup, a spin out from Kimberly Clark. My prior 14 years were spent working at Dow Chemical in R&D, nine in Corporate R&D. I evaluated various startup technologies and my group partnered with a startup, which Dow later purchased.

Finding the Perfect Match

The corporation-startup dynamic is full of challenges, but also rich with opportunity. Let’s unpack some effective win-win strategies for corporations to employ when engaging with startups.

Having sat on both sides of the negotiation table, I have observed a cultural and operational mismatch:

  • The Startup Profile: Unencumbered by bureaucratic corporate structures and slow internal rigor, a startup operates with a singular, high-velocity drive to solve specific technical and commercial problems. Because there is no corporate fallback option, this survival instinct forces them to dismantle roadblocks rapidly. However, they frequently lack the comprehensive tools and breadth of wisdom to formulate and fully validate their hypotheses.
  • The Corporate Profile: Corporations need to incubate, stress test, and de-risk emerging technical concepts without disrupting steady financial performance or baseline manufacturing stability. Crucially, they possess the comprehensive tools and expertise that startups desperately need.

A startup partnership lets a corporation take a small risk for potentially big rewards, while the startup gains resources it could not otherwise afford.

What Startups Need

Lacking comprehensive tools to test hypotheses, startups rely on creative entrepreneurial methods that can yield incomplete or incorrect beliefs, hampering the innovation journey.

Startups will need some or all of the following:

  • Advice and wisdom from business and technical experts and executives.
  • Connections – introductions to key industry players.
  • Expertise – in limited doses when stuck, or collaboratively on an ongoing basis.
  • Resources – access to specialized testing, access to equipment, analysis, prototyping, information, sales and marketing insights.
  • Public relations – partnership with a bigger company gives credibility and upside momentum to a startup. Corporate partners can also help create pull within a supply chain.

Although cash is generally constrained in a startup, corporations can exchange these non-cash assets for access.

Exploring Four Types of Corporate-Startup Ventures

There are four primary ways that corporations can work with startups, beyond simply acquiring the product and evaluating it in a silo.

  • Type 1: Projects – Pay for Work
  • Type 2: Investment only
  • Type 3: Joint Project
  • Type 4: Joint project tied to an investment

Type 1: Projects — Pay for Work

Type 1 is the lowest level of financial and resource commitment — essentially “try before you buy.” It typically includes:

  • A scoped, chartered, and funded project with results delivered in a report and maybe samples.
  • Milestone based payment.
  • Minimal collaboration, to protect each party’s IP.

The pitfall: the technology may not be ready yet. Stay in contact with the startup to know when to re-engage.

Type 2: Investment Only

A corporation writes a check and takes a stake, without a working relationship attached. This is the lightest touch of the four types where the cash investment comes with near absolute trust that the startup has the ability to incubate and launch the technology.

Type 3: Joint Project

Both entities actively scope a development pipeline of mutual interest. The startup shares proprietary product designs or formulations. In exchange, the corporation deploys its engineering staff, specialized analytical testing facilities, and large-scale operational capabilities. Each party shares data collaboratively.

Type 4: Investment With a Joint Project

Before diving into an investment (Type 2) consider a joint project (Type 3) with structuring an investment on the back end, tied to specific milestones and outcomes. This approach strongly incentivizes the startup from the outset.

Key elements include:

  • Strengthening your investment through support: Financial investment from a corporation should come paired with access to the corporation’s resources.
  • Aligning CVC structure with operational support: Corporate venture capital arms should structure investments with commitment from a business unit and/or corporate R&D. The goal is not to manage the startup by proxy, but to back the investment with tangible support along the way.

Dismantling the IP Roadblock

Partnerships often die fast through legal gridlock over intellectual property. Corporate legal teams routinely demand total control over derivative IP. However, startups must maintain clear revenue growth to satisfy investors; surrendering their core IP rights outright is a complete non-starter.

Locking horns over a complex master joint development agreement drains startup resources. Follow this approach instead:

  1. Deploy a Validation Project: Initiate a small, highly restricted verification project first. This gives both technical teams a low-risk way to confirm structural alignment and prove program viability before spending thousands on legal fees.
  2. Build Trust Through Early Wins: Securing fast technical wins gives both leadership teams clarity on technology value and gaps, providing a realistic baseline for what they are negotiating.
  3. Establish IP & Licensing Framework: Shortly after validation occurs, agree on a framework for IP ownership. Arrange licensing terms that still let the startup meet its investment thesis, while allowing the corporation to not entirely lose its investment.

How Corporations Can Help Themselves

Unlock Resources: A corporation should consider their internal accounting and management practices for effectively supporting startups using company resources. Creating a mechanism for leveraging internal resources unlocks greater potential with the startup. Now the relationship can go beyond a simple product/technology evaluation.

Expand Market Impact: Startup technology can bring value across several markets. Corporations sometimes view technology in a silo: What’s in it for us? Consider bringing other corporate partners to the table. A successful startup may provide valuable technology that is useful for several corporations across non-competing markets. Strategic moves like this can be complex but can exponentially increase the startup’s value and chance of success.

Ultimately, bridging the corporate-startup divide is about exchanging scale and wisdom for velocity and focus. When corporations back their engagements with tangible operational support, they mitigate risk and turn structural mismatches into powerful competitive advantage.