Copyright thenewstack
Building an AI team should start with hiring people who exhibit natural curiosity, grit and technical versatility across AI, machine learning and software engineering. If you hire the right people, your team will be able to navigate the bleeding edge, maintain expertise, stay current on advancements and separate signal from hype. Surprisingly, once you’ve hired the right people and have a clear center of AI excellence in your organization, this is when your problems actually start. The Talent Paradox In my experience, the same traits that make talented AI engineers invaluable also make them nearly impossible to lead. Ten experts means 10 brilliant solutions to every problem, and 10 debates you will need to referee before anything ships. The irony is that these are precisely the people you want on your team. They bring depth of experience you can’t find anywhere else. They also bring strong opinions, which lead to disagreement loops and competing solutions where everyone is technically correct, but you still have to choose one direction to go. All of this can get in the way of velocity. And in the age of AI, if something takes longer than two months from conception to production, it’s already stale. A large language model (LLM) will probably beat you to the punch. Not all engineers can keep the pace, stay updated on research while shipping code and remain aligned with objectives when direction keeps shifting. As a leader, you have to keep the team moving at speed, make decisions that don’t get stuck in endless feedback loops and identify whether you still have all the right people in place. In this environment, the leadership frameworks that worked for traditional engineering teams fall apart. Here’s what actually works. Four Frameworks for Consensus and Speed Start with the basics by flattening your organizational structures so extra layers don’t turn decisions into multiweek exercises. Shorten your timelines to match the pace of innovation and use the pressure of a looming deadline to figure out when to fail fast, when to uplevel talent and when to provide an off-ramp. Set unapologetically high standards for adaptability and on-time delivery. Once you’ve done that, give your experts these four frameworks for making decisions and moving at the pace of AI. One DRI (directly responsible individual) owns every decision. After input is gathered, one person makes the call. Discussions are timeboxed with clear success criteria. No parallel debates should take place in different channels. Separate ideas from execution. Commit to one direction for a fixed period after making a decision. During that time, questioning the approach is temporarily suspended. Theory will compete with theory infinitely if you let it. So set a direction and gather real data before considering a change. Use only evidence to pivot, not just a new idea. The bar for success does not have to be perfect; it can simply be “better than it was.” If a new approach shows improvement in your evaluation metrics, consider it seriously. If it doesn’t, move on immediately. Meet your experts where they are. When you’re talking to people who think in terms of model architectures, embedding dimensions and evaluation frameworks, don’t force everything into business metrics and OKRs. Business impact matters, but these are technical people solving technical problems. Speak technically when necessary, speak strategically when it matters and know the difference. These frameworks will help you ship faster and make better decisions. But the reality is that even if you implement all of this perfectly, you’re still operating in an environment where competitors announce breakthrough features every few weeks, and your competitors are constantly trying to recruit your best engineers. The frameworks get you velocity. Retaining your talent is what keeps you in the game. Sustaining Velocity Building the team is only one-third of the battle. Managing them is the second third. And then, after you’ve hired brilliantly, established frameworks and started shipping, you actually have to retain them. Give them problems worth solving. Lack of compelling vision, unappealing issues to solve and nonstop debates that never resolve into action kill AI team engagement. Show them how their work connects to something bigger and make decisions that allow them to actually do the work. Create a path for career progression. AI roles have increased in complexity faster than most companies’ career frameworks have kept pace. Define what senior AI leadership looks like in your organization. Create clear advancement opportunities with milestones that recognize both technical depth and strategic impact. Top AI talent will choose organizations where they can see themselves growing, not just working. Prioritize continuous learning. The chance to work on the cutting edge, learn constantly and stay ahead of the curve is what attracted them to your team in the first place. Make space for it by allowing time to attend conferences, do research and experiment. This isn’t a nice-to-have perk. It’s how your tiger team stays effective in a field that is in a state of perpetual reinvention. Technology will continue to advance, whether we’re ready or not. The models will continue to improve, and the competitors will continue to ship. But it’s the people who will ensure your company excels. You win when you have engineers who move fast without sacrificing quality, leaders who align brilliant minds without crushing their creativity and teams that ship consistently in an environment designed for chaos. Be there for them, and you will stay on the bleeding edge of innovation.
