There’s a common myth that you need to be an “IT whiz” to play a role in AI. Reality check: fancy tech only gets you so far… And the bigger the ambition, the clearer it becomes – one overwhelmed “techie” won’t save the day alone.
New AI tools often sparkle – alone in the dark. But when applied at scale – esp. in larger organizations – cracks start to show… Gaps appear around will (motivation, trust, incentives) and skill (integration, adoption, governance). That’s why, when it comes to AI transformation, teamwork does make the dream work.
In practice, I see four complementary archetypes – let’s call them the “AI-vengers” – carry the weight along innovation processes. Each brings something vital to overcome those recurring hurdles. For every role, I have at least one real face in mind from my own projects. And for the ones that didn’t work out, I can now tell who was missing…
I’ll introduce each role, how they accelerate change and which “assets” they bring to the table. I’ll keep it tangible with real-world examples and tips from my own experience.
Table of Contents
Strategic Innovators – “Why this, why now”
They secure the right to play and set the direction where AI creates real impact. They link the technology’s potential to relevant stakeholder value and longer-term priorities. Without them, teams chase shiny toys instead of solving meaningful problems…
Think e.g. of Steve Jobs – not for his coding skills, but for turning vision into clear, contagious stories. He let people “taste the possible” and aligned teams around selected bold bets. This archetype overcomes “status quo bias” (“why change?”) with persuasive storytelling linking the change to individual or organizational goals. When people better understand what’s in it for them, reservations and fear fade.
What they bring: strong intuition, creativity and pattern recognition; visionary storytelling and rhetoric; courage to say no to the trivial many to focus on the vital few; proximity to authority and resource orchestration; option thinking (“what if”) and “lean startup” methodology for swift idea validation. Few things translate into change like such laser-like focus…
System Architects – “Where to put the lever”
System architects plan how AI best fits the organization’s bigger picture. They see where small changes create bigger impact. They care less about flashy demos and “spot fixes” and more about holistic systems, processes and structures that scale.
Picture “The Architect” (The Matrix) – not for the “éminence grise” vibe, but for how core design choices shape everything downstream. In companies, this means guiding sustainable choices on tech selection – data pipelines, AI models, build/buy/partnering etc. They make sure AI doesn’t stay a lab toy. They balance the visions above with “reality checks” on often overlooked success factors: data readiness, implementation constraints, lifecycle management etc.
Their superpowers include thinking in systems and scenarios and translating that in plain words; prioritizing what to fix first like a truffle pig for leverage points and vulnerabilities (“5 whys”); understanding the “tech stack” end to end from UX to models to data to infrastructure etc. They help organizations avoid pilot purgatory by designing for scale from day one.
Technical Anchors – “Make it real, make it robust”
These are the engineers, data scientists and compliance experts who turn ideas into real products. Their work isn’t always “glamorous” – deployments, testing, documentation, guardrails. But it’s what keeps promises alive long after the demo’s glow fades…
For instance, think Hermione Granger: precise, calm under pressure, the execution backbone keeping the trio alive… They instill confidence by proving systems are stable, safe and measurable in real environments. They run tightly scoped POCs to surface the truth fast – whether something is ready to scale or not yet (and why). That clarity prevents costly “zigzags” and lets the team either double down or pivot with eyes open.
Their “toolkit” spans AI engineering and evals, a user-oriented mindset, agile software development with relevant programming languages and frameworks, usable documentation and an ability to navigate complex governance requirements without stalling progress.
Change Motors – “How people can win with it”
They focus on humans and relationships – the most critical aspect of any change process. Their mission: turn “interesting pilots” into daily reality. They coach teams to shed the old and master the new ways of working. (This is also the bucket where I personally feel most in my comfort zone – next to the first one. Leading AI innovations in organizations, I often switch between these roles though.)
Think, for example, of “Mr. Miyagi” here – i.e. teaching by doing, shaping mindset and skill in unison. Outside-the-box thinking can overcome any resistance; patience and tiny rituals culminate in lasting habits. They facilitate engagement by co-creating solutions with teams and letting them shape what they’ll later use. That “IKEA effect” – seeing one’s own fingerprints in the outcome – is a powerful motivator.
Core capabilities include empathy (e.g. creating safe spaces) and design thinking; change management (Kotter’s 8-step model etc.); individualized communication; (informal) influence; stakeholder and force field analysis; knack for cultural psychology and human nature.
Wrap-Up – Are Your AI-vengers Assembled?
Mastering the impending changes around AI calls for diverse skill- and mindsets for individuals, companies and societies. Like a good fruit salad… The good news is no one needs to wear all those hats alone. Now, look at your own situation – your team or even company board. Which of these roles are already covered and which ones still need a named owner for your AI transformation?
On a last note, keep in mind that each of these roles alone is “fragile”. Vision without execution is “slideware”. Innovation without change is theater. Tech without adoption is just a flashy demo. You get the point…
While you need all roles throughout, there’s a “logical chain” where each has the lead at different stages: Innovators pave the direction (“always start with why”). Architects pinpoint the leverage points (“where”). Techies secure the “what” – scalability. Change agents empower the “how” – adoption. Together they can remove all those stubborn will/skill blockers.
That’s why – happy to repeat myself – AI transformation is not an “IT project” but a cross-functional team sport. “Fancy” may open the door… But it takes a full “fellowship of the ring” to walk through it! (If I haven’t overdone it with analogies yet… 😉) I’m curious what you think: Which role do you identify with? Did I miss any roles you see? Please drop your ideas below or get in touch and spread the word with fellow innovators.
Cheers,
John
What do you think?