As AI takes on more of the innovation process end-to-end, the question isn’t just what the technology can do. It’s what happens to the teams. When agents connect the full arc from early signals to go/no-go decisions, roles shift, skills change, and the org chart starts to look different.
In this session, Berto Mill of Aucctus and Harry Laplanche, formerly a leader at Panasonic, Walmart, and Verizon, dug into how forward-looking teams are restructuring and reskilling for a world where AI runs the connected process — the full innovation cycle as one continuous AI driven workflow — and what it takes to stay ahead of it. The session looked at how roles, skills, and team structure change once AI links all of the steps of that process.
Here are five tips that Mill and Laplanche shared during the webcast.
1. Design AI Around Human Judgment, Not Human Replacement
Laplanche argues that the most valuable role for people is not execution but decision-making. As he puts it, “judgment… becomes a really critical skill in the AI era,” and teams should avoid “outsourcing your thinking and outsourcing your judgment to AI.” He recommends intentionally designing workflows where AI generates analysis while humans retain responsibility for strategic choices, because innovation ultimately depends on making sound decisions under uncertainty.
2. Use AI for Synthesis and Structure, but Keep Creative Prioritization Human
Rather than asking AI to invent the next breakthrough on its own, Laplance recommends assigning it the work it does best. He describes being thrilled when AI handled research synthesis because “I didn’t have to sit and read… Gartner Industry reports for four hours,” but found that unconstrained ideation produced “900 ideas to review” and “dragged everything to the middle into the average.” His advice is to let AI synthesize information and enforce process discipline, while people focus on prioritization, creativity, and selecting the few ideas that truly matter.
3. Measure Business Value Instead of AI Activity
Laplanche cautions against evaluating AI initiatives with superficial metrics. He advises organizations to “start clean on your metrics” because “it has never been easier to fall into vanity metrics,” such as counting ideas or tracking how quickly work moves through stage gates. Instead, teams should work backward from the business outcomes they want—growth, customer satisfaction, quality, or profitability—and judge AI by whether it meaningfully improves those value drivers rather than simply increasing activity.
4. Treat Organizational Context as a Strategic Asset
Both speakers emphasize that AI only becomes a competitive advantage when it understands an organization’s unique context. Harry notes that “the biggest missing piece… is that organizational context,” while Mill adds that competitive advantage comes from “capturing the decisions you made, capturing the outcomes” in an end-to-end system. Their shared advice is that companies should continuously curate high-quality institutional knowledge instead of simply connecting every available data source, since relevant context—not raw volume—is what enables better decisions.
5. Reshape Teams and Leadership for an AI-Native Organization
Laplanche believes AI requires organizations to rethink roles instead of merely layering technology onto existing processes. He argues that “everyone is having to become a leader in having to plan, delegate and orchestrate work and not execute it,” while Mill echoes this by stressing the need to “rethink the structure of your team and rethink your processes, not just strap AI onto what you’re currently doing.” Together, they suggest that the future belongs to teams built around orchestration, systems thinking, and collaboration with AI rather than narrowly defined job functions.
You must be logged in to post a comment.