When AI can produce answers in an afternoon, the value of an innovation team lies in the problems it chooses to work on.
Most innovation roadmaps across Europe share the same structure: supply chain resilience, digital transformation, cost discipline. Paqui Lizana, an independent strategic advisor on the future of innovation and co-founder of Brilliant Failures AI Studio, wanted to see exactly where those roadmaps converge. Her work focuses on the foundational questions about the role of innovation management in large organizations. So, she scanned the innovation roadmaps of Europe's 20 most innovative companies and listed the 20 top questions they are working on over the next five years.
Every single one pointed inward, built around one basic priority: how does our company win?
Paqui argues the innovation function should aim higher. She proposes three questions none of them asked: what do our people need to flourish, what does our ecosystem actually need from us, and what does the living system we are part of require from us?
Twenty Roadmaps, One Direction
The 20 questions came from various industries, such as manufacturing, energy, infrastructure, financial services, and technology. Among them were: How do we scale AI across the organization? How do we prepare for autonomous AI agents? How do we build cyber resilience? How do we close the STEM and AI skills gap? How do we use M&A to achieve competitive scale? How do we manage energy price volatility? How do we accelerate biotech through AI?
They shared a single direction. "They are all egocentric questions," Paqui says. "How do I compete? How do I survive? How do we scale? How do I automate?"
That matters more now than it did five years ago, because AI is making answers cheap. Any team can generate a market analysis, a technology scan or a strategy draft in an afternoon, and so can every competitor. AI delivers the roadmap faster, but it does not make it any more distinctive.
That puts the pressure on leadership. What CEOs need from their innovation teams is no longer more options, more pilots or more reports. They need clarity: which problems are worth the organization's time, energy and talent, and which ones it is uniquely placed to solve. AI cannot supply that clarity, because it depends on knowing what the organization is for.
This is where the innovation function earns its place today. Its job is less about finding answers and more about framing the questions leadership cannot afford to ignore. The questions the 20 plans left out, about people, ecosystems and living systems, are where the clearest value is waiting.
What Happened When Schneider Electric Published Its Real Problems
The reason people, ecosystems, and living systems keep falling off the roadmap is the same reason most cross-functional work stalls: egos, inertia, and silos. Paqui saw this firsthand when she started a new role as innovation leader. Her CEO invited her into a virtual conference with specialists in robotics, quantum computing, and advanced materials. While she wanted to work with every person on that call, it actually took her four months to sit down with any of them, blocked by large silos and constant inertia.
This inertia also shows up in numbers. Gallup's State of the Global Workplace report, published in April 2026, found that global employee engagement fell to 20% in 2025: the lowest figure since 2020 and the second consecutive year of decline. In Europe, engagement sits at 12%, the lowest of any region. Each percentage point represents roughly 21 million workers. The estimated cost: more than $10 trillion in lost productivity in 2024 alone, approximately 9% of global GDP.
That 20% figure reveals the reality of today's organizations. In the past, they used to hire people to solve problems they could see and care about. As companies grew, the work got sliced into tasks, hierarchies assigned those tasks, and the original problem disappeared behind systems and processes. People show up, do their work, and have lost sight of what the work is for, but that's not an individual's fault.
"People are not broken," Paqui says. "The systems are."
Schneider Electric, one of Europe's largest industrial companies, experienced this in 2024. The company's leadership identified that roughly half of employee departures were driven by the same issue: people had lost the connection to the problem their role was supposed to solve. Compensation was adequate, career paths existed, and employees could describe their tasks, but they could no longer explain what those tasks were building toward.
To resolve this, Schneider Electric built an internal system that surfaced the challenges the organization was facing beyond each employee's day-to-day responsibilities: the cross-functional problems that sit between departments with no owner, the strategic priorities that leadership was wrestling with, and the operational issues nobody had picked up. Leadership published every challenge where any employee could find it and sign up. Within two months, 50% of the entire workforce had signed up to work on at least one challenge. There was no mandate, no incentive program, no requirement. Employees chose to engage because the problems were real and the invitation was genuine.
"People were hungry to do something that matters," Paqui notes.
Paqui calls this room the Agora, after the Greek public gathering space where citizens came to debate and solve shared problems. The principle behind Schneider Electric's results is the same one the Agora is built on: when organizations make their real challenges visible, people volunteer to work on them.
The Agora asks organizations to make three shifts:
Problems, not positions. From the org chart to what is worth solving. Instead of organizing the company around roles, departments and hierarchies, the shift is toward organizing it around the problems it wants to solve.
Chosen, not assigned. From what people have done to what they want to become. Employees choose challenges based on curiosity, rather than their CV, role or title. Neol, a company based in London and Istanbul, is designing an AI-orchestrated system where employees upload their vision of where they want to grow, their curiosity areas, and the skills they want to develop. The system then matches them to teams and projects based on interest and aspiration, rather than waiting for a manager to assign the work.
Problem ecosystems. From inside the company to whoever can solve it. Most companies treat their problems as unique, but they rarely are. Solving them takes system thinkers, teachers, designers and engineers from outside the organization, and sometimes competitors too.
Schneider Electric proves that engaged people will show up when the problems are real. But can those people access the knowledge they need to actually solve them? In most large organizations right now, that knowledge is concentrated in a generation that is about to leave.
The Knowledge That Built the Company Leaves by 2030
Four generations are working side by side in most large organizations right now: baby boomers, Gen X, millennials, and Gen Z. By 2030, baby boomers will have left the workforce, and by 2035, Gen Z and Generation Alpha will make up the largest share of employees.
The generation heading for the exit carries the most institutional knowledge: the unwritten rules, the trade-offs learned across decades, the relationships that explain how the company actually operates. The generation replacing them, on the other hand, grew up with AI and can produce answers to most problems at speed. The judgment that only comes from years inside a specific organization — knowing which stakeholder to call first, knowing why a previous initiative died in a way the documentation missed, knowing the founder's original intent three strategy rewrites later — lives in the outgoing generation and has no backup copy.
In a recent project, Paqui led a strategic initiative alongside a colleague who had spent decades at the same company. She asked him things that fell outside any standard strategy template: What was the soul of the company before it was written into any PowerPoint? What was the legacy the founder actually wanted to leave? Together, they built a technology strategy rooted in 80 years of institutional history, something qualitatively different from a standard AI strategy with the company logo on top, because it drew on knowledge that only existed in one person's memory.
Her grandmother Pepa holds a similar kind of knowledge. Pepa lost her parents during the Spanish Civil War and went on to raise an entire family and give purpose to a community in southern Spain, in part through an extraordinary ability to cook from almost nothing. Paqui and her mother are documenting everything: the ingredients, the processes, the mistakes, the stories behind every recipe. The project captures knowledge, care, and culture across generations. The same principle applies inside companies. The data that gives an organization its identity, its edge, and its meaning lives in people, and it leaves when they do.
The challenge, then, is how to transfer that knowledge before the people who carry it walk out the door. Paqui's framework addresses this through what she calls the Kitchen: the space where, in every culture, old knowledge meets new ingredients and learning happens by standing next to someone who already knows.
The framework structures that transfer around three shifts:
Reciprocal pairing. Traditional mentoring flows in one direction: senior teaches junior. Reciprocal pairing runs both ways. The senior brings judgment, trade-offs, and relationship capital. The junior brings fluency in new tools, new platforms, and new ways of working. The relationship itself becomes the infrastructure for knowledge transfer.
AI as the capture layer. Once the pairs are working together, AI records and organizes what surfaces: voice, text, dialogue, tacit knowledge made explicit. The knowledge moves from one person's head into a system the organization can search after that person leaves.
Wisdom as intellectual property. Every time an employee contributes institutional knowledge to the system, that person receives formal recognition, similar in structure to how an artist receives royalties. This may sound futuristic, but companies are already testing it. Ferrovial is putting a system in place to do exactly that, because it has thousands of employees holding tacit knowledge it doesn't want to lose.
Baby boomers start leaving in volume by 2030, which gives most organizations a four-to-five-year window where all four generations still overlap. After that, the transfer gets much harder.
Engaged people with institutional knowledge can solve most problems an innovation team faces today. However, the problems arriving tomorrow look different: biological computing, legal personhood for natural systems, gene editing decisions with consequences that stretch across generations.
Corporate Decisions That Stretch Across Generations
The first two gaps Paqui identifies, people and knowledge, are challenges innovation leaders already recognize. The third gap, living systems, takes the innovation function somewhere a bit less familiar.
Paqui's father tends more than 900 olive trees in northern Spain. Some are more than 1,000 years old. They have survived every shift in climate and continue to produce because they are part of a living ecosystem that regenerates, signals, and holds the ground. Four billion years of trial and error have taught the natural world how to manage resources, build resilience, and recover after collapse. Yet, most companies still innovate with two forms of intelligence, human and machine, without considering a third: nature itself.
Today, some organizations are starting to build with nature in mind. This summer in Singapore, Melbourne-based Cortical Labs, together with NUS Medicine and DayOne, switched on a prototype server rack built from living human neurons grown from stem cells, with the potential to run on a fraction of a conventional data center's energy. Technology companies are spending billions on data centers that consume enormous amounts of water and energy, and Cortical Labs is betting that radical efficiency will come from biology, rather than more silicon.
At Brilliant Failures AI Studio, Paqui built VIVA, a model trained to think the way nature thinks (in cycles, in regeneration, and in what happens after collapse) rather than trained about nature. The model surfaces the long-term consequences of decisions, looking seven generations ahead, the kind of output that standard AI tools ignore. For an innovation team evaluating a new venture or technology investment, a tool that models consequences that far out widens what the team can see.
The room built around these ideas is the Garden, and it asks organizations to make three shifts:
Belonging. Most companies treat nature as something separate from themselves. The shift is toward seeing the organization as part of the living system, which changes how resources, waste, and long-term impact get measured.
The long now. Today, most R&D decisions are evaluated on product cycle timelines: can we build it, and will it sell? The shift is toward evaluating consequences across generations: should we build it, and what happens in 50 years if we do? Companies can already edit genes without touching them, extend human lifespan, and bring back extinct species. Innovation teams may increasingly find themselves in the room when these calls get made.
Authority. Today, humans make all the decisions. The shift is toward giving non-human entities a voice. In 2022, Mar Menor, a lagoon in southeastern Spain, received legal personhood, and this summer it went to court to sue a company for dumping wastewater upstream.
Where the Mandate Is Heading
Innovation teams are tasked with executing on their companies' roadmaps, and the short-term wins still need to be delivered. But as AI takes on more of that delivery, the time it frees up can go to the three gaps: people, knowledge, and living systems.
Of the three, the people gap is the most urgent. The Gallup data is clear, the Schneider Electric results are concrete, and any innovation team can start surfacing real challenges to the wider organization this quarter. The knowledge gap has a deadline: baby boomers leave by 2030, and every month without a structured transfer program is a month of institutional knowledge gone for good. The living systems gap is further out, but the courts and labs building the infrastructure are moving fast.
Which of these gaps is already costing your organization? Paqui's advice is to carry that question with you, because the chance to raise it always comes, whether it's an elevator ride, a chance meeting, or a budget round.
Most innovation roadmaps point in the same direction. The leaders who start their roadmap with the bigger question are the ones who will shape what world we’ll live in.
Because as Paqui says, "The questions you ask determine the future you see."
