Wolf&Grayson EditorialAs AI tools make work faster and more cross-functional, legacy, department-led structures increasingly block growth by slowing decisions and blurring accountability. Early stage companies offer a counterexample, showing that when teams are organized around clear projects with a single owner, alignment sharpens, incentives line up and progress accelerates.
“The goal of a company is to develop a product, not to advance a discipline,” says Finith Jernigan, a technology strategist and Founder and CEO of Finith Capital. “When reporting structures prioritise departments over outcomes, incentives become misaligned very quickly.”
After years applying advanced computing, AI and systems thinking to some of the most complex problems in science and business, Jernigan’s work shows that innovation tends to move faster when teams are organized around projects and outcomes, rather than job titles or departments.
Why functional structures slow progress
Structural misalignment, he argues, remains one of the most persistent and underestimated barriers to innovation across industries. Jernigan’s early career in pharma and biotech exposed him to highly specialised organizations modelled on academic departments. Biology, chemistry and physics teams operated as distinct silos, each reporting up its own hierarchy. While logical in universities, the model falters in commercial environments where success depends on delivering a product rather than publishing knowledge.
In practice, project teams are assembled from these departments, but the reporting lines remain elsewhere. “People say they are doing everything they can to push a project forward,” Jernigan says, “but they are doing it within the constraints of what is best for the department they report to.” The result is political friction, delayed decisions and diluted accountability.
He has seen situations where team members report to leaders running rival initiatives, forcing project leads to navigate internal politics rather than focus on execution. Over time, this dynamic rewards those adept at organizational maneuvering rather than those best equipped to solve hard problems.
Building teams around outcomes, not titles
The alternative is to organise teams around projects and make the project leader accountable for results. Reporting structures should flow through that leader, regardless of individual disciplines. This mirrors how early stage companies operate by necessity.
“A startup cannot afford a chemistry department or a data department,” he says. “One person becomes the department. Everyone is aligned to shipping the product.”
Critics of project-based structures often raise concerns about oversight, particularly when a leader manages specialists outside their own domain. It is a solvable problem, however. Targeted mentorship, external consultants and light-touch peer review can maintain professional standards without reintroducing rigid hierarchies. “Science and technology are messy,” he says. “Organizations need to be comfortable with that messiness if they want real breakthroughs.”
Communication as an innovation multiplier
Jernigan places equal emphasis on communication and trust. “Some of the best ideas start with bothering someone down the hall because you are excited about something,” he says.
Remote work can replicate this only if teams invest deliberately in relationships and responsiveness. Cultural norms matter. Quick replies, informal messaging and shared platforms reduce friction and keep momentum high. “If people trust each other and respect each other, they do not leave questions hanging,” Jernigan observes.
He is particularly critical of environments where political behaviour suppresses open exchange. “I have seen great ideas destroyed by politics,” he says. “When people know they can execute ideas and be rewarded for them, innovation becomes cultural, not forced.”
AI and the rise of the project-centric leader
Jernigan believes AI will reinforce, not replace, this project-centric model. Drawing parallels with his early exposure to instant messaging, he sees current AI tools as another step-change in speed and access to knowledge. “You can now get answers to almost any question instantly,” he says, describing how AI agents supplement, rather than replace, human experts.
The implication is a new kind of leadership. As AI agents become embedded in workflows, everyone becomes a manager of both people and systems. “Building an agent is not enough,” Jernigan argues. “You have to know how to manage it effectively, just like a human team.”
Those who master this hybrid management skill set will move faster, make better decisions and reduce dependency bottlenecks across functions. It also lowers the cost of experimentation, allowing teams to test ideas before escalating to scarce human expertise.
Why this matters now
As industries face accelerating technological change, rigid organizational models are becoming liabilities. Jernigan’s perspective challenges leaders to rethink not only how teams are composed, but how authority, incentives and communication are designed.
Innovation, in his view, is less about hiring exceptional individuals and more about removing structural drag. When teams are aligned around outcomes, supported by trust and augmented by AI, even problems once considered impossible become tractable.
