When companies get serious about AI, the talent conversation often turns outward. Hire an engineer. Find a consultant. Recruit someone with AI in the title.
The instinct makes sense. AI transformation requires technical skill. But technical skill alone does not tell you which workflows matter, where the real friction lives, or which changes employees will actually trust.
Good Karma Brands found another source of transformation talent. When the sports media and marketing company created full-time innovation specialist roles, it elevated employees from marketing and sales support who had already started redesigning their own work with AI.
Their advantage was not a formal AI title. They knew the work, spoke the company's language, and were curious enough to challenge processes they had lived with every day. Good Karma's experience suggests that before leaders launch an external search, they should look closely at who is already doing the job in pieces.
Good Karma did not start with a polished innovation-specialist job description and then search for candidates. The role followed the work, says Ty Bauschek, Good Karma's Senior Director of Innovation and Sales Development, who now leads the company's innovation specialists.
Gina, a marketing design coordinator in New York, built an AI system that helped assemble partner campaign decks. A process that had taken three hours took about 10 minutes in its first demonstration. She later traveled to Chicago and helped that market build its own version.
Max brought the same instinct to sales support. He helped automate the process for identifying missing advertising copy, creating dashboards and scheduled alerts around work that employees had previously handled manually.
These were not assignments handed down by an innovation department. They were employees looking at recurring work and deciding there had to be a better way, after the company began rolling out AI to most employees. Their results led Good Karma to ask a different question: What could these people accomplish if innovation were their full-time job?
That sequence matters. The company identified its first transformation leaders through demonstrated behavior, then gave that behavior a formal role, additional time, and broader reach.
AI expertise can be hired. Deep knowledge of how a company actually operates is harder to import.
Good Karma's innovation specialists had worked inside the functions they were now helping improve. Gina knew marketing. Max knew sales support. They understood the formal process, the unwritten workarounds, the recurring frustrations, and the point at which a theoretically elegant solution would become impractical.
They also had credibility with the people doing the work. They were not an outside team arriving with an abstract mandate to apply AI. They were teammates who understood why the process existed in the first place.
Today, at Good Karma, these new innovation specialists now sit with teams, learn how the work moves, and decide whether a process needs a major redesign, a smaller improvement, or no intervention at all.
Tenure alone does not make someone an AI transformation leader. Neither does heavy use of ChatGPT.
The stronger signal is how someone responds when AI reaches their own job. Good Karma's first innovation specialists did not protect their existing task lists. They looked for work the technology could remove so they could spend more time creating, collaborating, and helping partners.
"They look at their career description and, as opposed to saying, 'What can I keep AI from touching?' they went, 'What can AI do that can get me to do the stuff I actually want to do, which is be creative, which is help partners, which is be teammate facing?'"
Ty Bauschek, Senior Director of Innovation and Sales Development at Good Karma Brands
Elevating internal employees does not eliminate the need for technical specialists. It changes how the two groups work together.
As Good Karma's innovation specialists moved from prototypes toward more durable systems, they encountered unfamiliar territory. They began working in GitLab, branching projects on the back end of the campaign system, and raising questions about Microsoft authentication and penetration testing. That was the point to involve the company's senior software engineers.
This division of labor is one of the more useful parts of Good Karma's model. The innovation specialists bring workflow knowledge, business trust, and a sharp eye for pain points. Engineers bring the depth required to make systems secure, reliable, and ready to scale.
In other words, finding internal transformation talent is not the same as telling an enthusiastic employee to build whatever they want. The role needs authority, focus, governance, and access to people who can turn a promising workflow into dependable infrastructure.
Good Karma's experience does not prove that every company's best AI transformation leader is already on payroll. The word probably matters.
Bauschek is already thinking about how difficult the original model may be to replicate. Gina and Max brought an unusually useful combination: company knowledge, curiosity, AI experience, and a willingness to embed themselves in workflows. Other divisions may require specialists to spend more time learning the work before trying to change it.
Good Karma may also invest more heavily in entry-level employees who grew up with AI. Bauschek says the company is seeing "people who are AI hungry and curious and generalists" learn its workflows faster than some employees already inside those workflows learn AI.
However, it’s clear that there’s an opportunity here:
Start by looking for employees who are already rebuilding their own workflows, teaching peers, and pulling useful ideas from unexpected corners of the company. Give the strongest candidates time, a mandate, a way to prioritize, and an engineering backstop. If that search comes up empty, recruit hungry generalists and embed them deeply enough to earn the context insiders already possess.
Many companies will begin by searching for a transformation leader. The better first move may be noticing who has already started transforming the place.
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