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The Global Lighthouse Network is a World Economic Forum initiative cofounded with McKinsey. It examines the future of operations and considers how Fourth Industrial Revolution (4IR) technologies are shaping production. This growing community of organizations is setting the trends of the future with their use of digital and analytics tools across the value chain to drive growth and productivity, improve resilience, and deliver environmental sustainability.

The Global Lighthouse Network marks seven years of progress in industrial transformation. Since its launch in 2018, the network has grown to more than 220 Lighthouses across 35 countries and more than 30 industries, spanning manufacturing and end-to-end supply chains. In 2025, 36 new sites were awarded, joining the network and reflecting continued momentum and rising ambition across the industrial landscape.

The 2025 Lighthouses demonstrate a clear step change—moving from isolated digital initiatives to enterprise-wide, AI-driven transformation. Recognized across five categories—productivity, supply chain resilience, sustainability, customer centricity, and talent—these sites are scaling analytical AI, agentic AI, and generative AI across capital-intensive operations, embedding intelligence directly into core processes and decision-making. This cohort is progressing decisively from pilots to enterprise-wide deployment, reinforcing resilience across the organizational ecosystem. AI, machine learning, and robotics are embedded in many of their highest-value use cases, with generative AI scaling rapidly year over year and moving beyond experimentation.

By prioritizing vertical, domain-specific applications, they overcome the “gen AI paradox,” translating experimentation into tangible performance gains. Leaders are focusing investments on three AI-enabled priorities: building scalable digital and data foundations for network agility, empowering people through hybrid human–AI ways of working, and amplifying impact through collaboration and purpose. As these sites evolve from smart factories into cognitive networks, they master critical trade-offs—balancing speed and standardization, autonomy and visibility, and connectivity and cybersecurity—to ensure AI delivers business value at scale.

The impact is tangible. Lighthouses deliver meaningful gains in productivity, lead times, quality, and energy efficiency, while strengthening their ability to operate through ongoing disruption. At a time when many industrial organizations struggle to move beyond pilots, Global Lighthouse Network members are showing what it takes to scale change—combining technology, people, and execution discipline to turn ambition into sustained performance.

The Global Lighthouse Network spotlights companies that have achieved exceptional productivity and sustainability outcomes through digital transformation.

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For the past two decades, there’s been a clear first step for anyone who starts experiencing new medical symptoms: Look them up online. The practice was so common that it gained the pejorative moniker “Dr. Google.” But times are changing, and many medical-information seekers are now using LLMs. According to OpenAI, 230 million people ask ChatGPT health-related queries each week. 

That’s the context around the launch of OpenAI’s new ChatGPT Health product, which debuted earlier this month. It landed at an inauspicious time: Two days earlier, the news website SFGate had broken the story of Sam Nelson, a teenager who died of an overdose last year after extensive conversations with ChatGPT about how best to combine various drugs. In the wake of both pieces of news, multiple journalists questioned the wisdom of relying for medical advice on a tool that could cause such extreme harm.

Though ChatGPT Health lives in a separate sidebar tab from the rest of ChatGPT, it isn’t a new model. It’s more like a wrapper that provides one of OpenAI’s preexisting models with guidance and tools it can use to provide health advice—including some that allow it to access a user’s electronic medical records and fitness app data, if granted permission. There’s no doubt that ChatGPT and other large language models can make medical mistakes, and OpenAI emphasizes that ChatGPT Health is intended as an additional support, rather than a replacement for one’s doctor. But when doctors are unavailable or unable to help, people will turn to alternatives. 

Some doctors see LLMs as a boon for medical literacy. The average patient might struggle to navigate the vast landscape of online medical information—and, in particular, to distinguish high-quality sources from polished but factually dubious websites—but LLMs can do that job for them, at least in theory.

The release of ChatGPT Health, and Anthropic’s subsequent announcement of new health integrations for Claude, indicate that the AI giants are increasingly willing to acknowledge and encourage health-rlated uses of their models. Such uses certainly come with risks, given LLMs’ well-documented tendencies to agree with users and make up information rather than admit ignorance. 

But those risks also have to be weighed against potential benefits. There’s an analogy here to autonomous vehicles: When policymakers consider whether to allow Waymo in their city, the key metric is not whether its cars are ever involved in accidents but whether they cause less harm than the status quo of relying on human drivers. If Dr. ChatGPT is an improvement over Dr. Google—and early evidence suggests it may be—it could potentially lessen the enormous burden of medical misinformation and unnecessary health anxiety that the internet has created.  Pinning down the effectiveness of a chatbot such as ChatGPT or Claude for consumer health, however, is tricky. 

Even if ChatGPT Health and other new tools do represent a meaningful improvement over Google searches, they could still conceivably have a negative effect on health overall. Much as automated vehicles, even if they are safer than human-driven cars, might still prove a net negative if they encourage people to use public transit less, LLMs could undermine users’ health if they induce people to rely on the internet instead of human doctors, even if they do increase the quality of health information available online.

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In an environment of constant change, projects are how businesses evolve, adapt, and grow. They’re how strategies are executed. They’re how innovation is delivered.

That became abundantly clear in 2020, when the Covid-19 pandemic was upending old ways of doing business around the world. Organizations everywhere were scrambling to set up digital infrastructures, reconfigure global supply chains, and launch new services—in days, not years. Transformation was urgently needed, and projects were the only way it could be carried out. Virtually everyone in that era, from entry-level employees to the CEO, became a project leader, whether people recognized it or not.

Around that time the author argued, in “The Project Economy Has Arrived” (HBR, November–December 2021), that we were at a turning point in the history of business: While companies had traditionally created value through their operations (by focusing on scale, efficiency, and service excellence), now for the first time projects were the primary engines of value creation. That idea resonated across industries, and in the past four years many companies have tried to adapt to this new reality, with some promising results. They’ve trained more project managers, created centers of excellence and project management offices, upskilled middle managers, and introduced better governance. Execution is now more consistent, frameworks are better understood, and timelines are clearer.

Nevertheless, failure rates remain stubbornly high, and value creation is elusive. Why? Because too many organizations today approach projects with an operational mindset that prioritizes hierarchy, control, stability, and efficiency. And they continue to make a host of missteps, among them: committing to too many initiatives and spreading resources too thin, mistaking output for value and measuring what’s easy, not what matters, failing to deploy fully dedicated cross-functional teams, relying on long, complex transformation efforts rather than short, manageable ones, not appointing active executive sponsors who commit enough time to projects and own their outcomes, outsourcing transformation to consultants instead of building internal capabilities, and not killing underperforming projects quickly—or at all—because of the fear of being blamed

Those are all project management problems, of course. But they’re also evidence of something deeper: To meet today’s demands for constant transformation, we can’t simply focus on managing projects better. We also have to think differently about organizational design, leadership, and value creation.

The authors introduces the idea of the project-driven organization: a new enterprise model that places projects at the center of how companies are structured and led and generate value.   It’s a natural next step beyond the agile organization, a 20-year-old idea that, while groundbreaking in its time, no longer can fully address the scale and complexity of transformation today.

To become project-driven enterprises, companies need to pull eight key levers. The first three—changing culture, changing structure, and changing governance—concern organizational design. The second three—changing the approaches used to set strategic priorities, deploy human resources, and manage performance—concern leadership. And the final two—changing operations and changing execution to facilitate fast, high-impact delivery—concern value generation.

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In 2016, Wells Fargo found itself embroiled in scandal when headlines revealed that its employees, under pressure to meet aggressive sales targets, had opened millions of unauthorized customer accounts. The root cause wasn’t just unethical behavior but a flawed approach to performance measurement. When Wells Fargo’s leadership incentivized employees to sell eight financial products per customer, they inadvertently encouraged gaming behaviors that harmed customers, employees, and ultimately the bank itself. The metric had become more important than its underlying business purpose.

Although Goodhart’s law in action only sometimes rises to the level of scandal, companies everywhere fall prey to it: “When a measure becomes a target, it ceases to be a good measure.” Despite decades of warnings against metric fixation, leaders continue to build incentives around narrow indicators, which results in gaming and ethical lapses and harms business performance.

Traditional solutions to overcoming Goodhart’s law, like balanced scorecards and KPIs, often fail because they remain vulnerable to narrow optimization and gaming behaviors in the absence of careful oversight. The persistence of metric fixation signals a need for a more sophisticated approach to address the problem.

A New Lens: Insights From AI Training.  Leaders increasingly view organizations as systems to be optimized for specific outcomes, much as machine learning researchers optimize algorithms. Since both contexts involve optimizing proxy measures that can diverge from the true goals, solutions from AI research could help solve persistent organizational measurement problems. Of course, organizations consist of people with the agency and complex motivations that algorithms lack, so these techniques provide frameworks that require human adaptation, not mechanistic solutions.

AI researchers have long studied a phenomenon called overfitting, where machine learning models perform well on training data but fail when faced with real-world scenarios because they can’t generalize to predict outcomes with the new data.

The Metric Intelligence Framework in Action.  Machine learning has evolved four strategies to combat overfitting, each with direct implications for organizational design.  These are:  A)  Early stopping: Prevent overoptimization through timely reassessment.  B) Noise injection: Build robustness through controlled randomness.  C)  Capacity alignment: Match metric complexity to organizational capabilities.  And D) Regularization: Create balance through simplicity incentives.

The combination of artificial intelligence research and organizational design offers practical solutions to metric fixation. By applying techniques that keep AI models aligned with real-world goals, managers can improve how organizational performance is measured.

These approaches must work in concert rather than isolation. Organizations adopting metric intelligence treat measurement as an ongoing practice requiring constant refinement, not a “set it and forget it” solution. Successful implementations involve those who will be measured in designing the measurement system, resulting in both better metrics and greater buy-in for the new practices.

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As 2026 unfolds, many business leaders are taking a hard look at the patterns that may be limiting their effectiveness. Even well-intentioned leadership habits can lead to reactive decision-making and burnout-driven behaviors, quietly undermining organizational trust, growth and performance. The challenge isn’t just to identify what to stop doing, but to also make meaningful changes that actually stick.  Members of Forbes Coaches Council share the behaviors they believe leaders should leave behind this year to break unproductive cycles and adopt more sustainable approaches.

1. Replace Quick Fixes With Questions.  Many leaders jump too quickly into solutions. It’s well-intentioned, but it cuts off learning and reduces team ownership. A more effective shift is moving from “answering” to anchoring: Before offering any advice, ask one clarifying question. It may feel uncomfortable at first because the instinct to fix things is strong. But each time you want to give a solution, pause and ask a question instead.
2. Eliminate Device Distraction In Critical Meetings.  Stop the addiction and distraction of your personal electronic devices during meetings that matter. It erodes trust. Leadership teams need to mutually commit to being fully present—closing the laptop, listening and being focused during important meetings. The payoff is better decisions, more aligned teams and shorter meetings.
3. Trade Control For Clear Delegation Boundaries.  A common bad habit: defaulting to control—stepping in, second-guessing and “rescuing” work instead of building leaders’ capacity. Make it stick by naming one delegation boundary (decisions you won’t touch), defining success and running a 10-minute check-in to cover outcomes, obstacles and next steps. Track rescues as a KPI and replace each with a coaching question. Coach the process; don’t reclaim the task.
4. Choose Empowerment Over Doing It Yourself.  Under pressure, leaders often default to the law of least effort, doing the work themselves to save time. It’s like tying their team’s shoelaces: fast, but stunts growth. This year, resolve to pause. Delegate, coach and let your people tie their own laces. It may feel slower, but it builds capability, confidence and culture. Growth doesn’t come from ease; it comes from empowerment.
5. Protect Focus By Saying ‘No’ With Intention.  A common behavior leaders want to stop is overcommitting—saying “yes” too often, spreading themselves thin and wasting their most critical resource: time. To make the change stick, they can adopt a simple rule: every “yes” must have a clear purpose and visible cost. Practicing saying “no” intentionally protects focus, models healthy boundaries and enables more deliberate, high-impact leadership.

The others are:  Lead With Curiosity Instead Of Certainty.  Shift From Firefighting To Strategic Thinking.  Develop Talent By Letting Go Of Ownership.  Replace Performative Busyness With Calm Leadership.  Seek Support To Expand Leadership Impact.  Stop Softening The Truth For Comfort.  Prioritize Well-Being To Sustain Performance.  Respond From Stability, Not Emotion.  Instead Of Communicating Urgency, Design Clarity.  Invite The Team’s Voice Into Real Decision-Making.  Pause To Discern Instead Of Judging.  Focus On Priorities Instead Of Motion.  Practice Responding Rather Than Reacting.  And Slow Down To Respond With Purpose.

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Established industries are filled with processes that work, but are barely held together by manual labor, legacy systems, and decades of accumulated workarounds. These aren’t unsolved problems. They’re poorly solved problems. And AI gives entrepreneurs the ability to reimagine how entire industries operate rather than incrementally fixing broken foundations.

According to the author he has spent three decades watching technology transform industries—first as a tech founder who scaled a software company onto the Inc. 500 list, and now as a coach helping companies navigate AI implementation. The pattern he is seeing today is different from anything before. The entrepreneurs creating the biggest opportunities aren’t trying to solve problems no one has solved. They’re looking at problems with messy, complicated solutions and rebuilding them from scratch using AI-first thinking.  Here’s how they’re doing it.

Approach familiar industries with a beginner’s mind.  The first step isn’t mapping AI capabilities. It’s forgetting everything you think you know about how an industry operates. Most people look at established industries and see fixed constraints. AI-first entrepreneurs look at the same industries and ask why those constraints exist in the first place. They question every assumption about how work gets done, who does it, and why processes evolved the way they did.  A financial analysis firm assumes you need teams of analysts to review data and develop recommendations because that’s how it’s always worked. An AI-first entrepreneur asks whether the analysis itself could be automated, freeing humans to focus on higher-level advisory work. The beginner’s mind reveals opportunities that industry veterans can’t see because they’ve stopped questioning the fundamentals.

Map current AI capabilities to real pain points.  Once you’ve identified the assumptions worth challenging, the next step is understanding what AI can actually do today—not in theory, but in production. Too many entrepreneurs either overestimate AI’s capabilities and build products that can’t deliver or underestimate them and miss obvious opportunities. The practical approach is mapping specific AI tools to specific industry pain points.  Where does the current solution require expensive human judgment that AI can now replicate? Where does manual data processing create bottlenecks? Where do customers tolerate friction because they assume no better option exists? A commercial real estate firm might discover that AI can now analyze lease documents, market comparisons, and property data faster and more accurately than junior analysts, not replacing expertise but amplifying it.

Anticipate where capabilities are heading.  Today’s AI capabilities matter, but tomorrow’s prospective capabilities create first-mover advantages. The entrepreneurs building the strongest positions are tracking AI developments six to 12 months ahead of general availability. They’re watching research announcements, beta programs, and emerging tools to understand what will soon become possible.  This isn’t about speculation, it’s about positioning. When a new capability becomes accessible, the entrepreneur who anticipated it can move immediately, while competitors are still evaluating. The window between “technically possible” and “widely adopted” is where outsized opportunities exist.

Find the leverage points for entry.  You can’t reimagine an entire industry overnight. The strategic question is where to start. AI-first entrepreneurs identify the segments where current solutions create the most friction, where customers are most underserved, or where incumbents are least likely to respond quickly.  These entry points let you establish traction and credibility before expanding. A legal technology entrepreneur might start with contract review for mid-sized companies or a segment too small for major firms to prioritize but large enough to build a meaningful business. Once established, expansion into adjacent services becomes possible.

Iterate rapidly and compress the startup phase.  The traditional startup playbook assumes months or years of development before meaningful market feedback. AI-first approaches compress this dramatically. Entrepreneurs can build functional prototypes in days, test them with real customers, and iterate based on actual usage rather than assumptions. This speed changes the risk calculus entirely. Instead of betting everything on a single product vision, you can run multiple experiments, learn what works, and refine your approach continuously.  One entrepreneur I’m working with launched three different AI-powered service offerings in one month, learned which resonated with customers, and doubled down on the winner—a process that would have taken years using traditional approaches.

The entrepreneurs creating the biggest opportunities right now aren’t inventing new categories. They’re taking industries filled with friction, waste, and outdated assumptions and rebuilding them with AI at the core. That’s where the real leverage exists.

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Ask any business leader what keeps them up at night, and they will likely cite challenges like market conditions, revenue growth, cost control or talent retention. But if you dig deeper, the root cause is often operational — issues around process, people, systems and serving clients.

Teams want to deliver gold-standard results, but they’re often hampered by manual processes and administrative friction. And those issues may silently grow and intensify until they begin to impact every aspect of the business.  Test the followings:

1. Architect the change with your team, not for them.  Studies show that less than one-third of organizational transformation projects succeed. That’s because change is hard. For the author, managing the burden of transformation comes down to balancing the volume and speed at which it occurs — both factors that matter immensely when teams are already at their limits.  But there’s a third reality that’s just as critical: it’s nearly impossible to overhaul operations without buy-in from the people doing the work.  It’s about opening up lines of communication and encouraging participation in decision-making.
2. Standardize the routine to make space for the personal.  Standardizing workflows brings clarity and consistency, and it also helps surface operational insights that might otherwise be hidden in day-to-day variability.  Of course, certain situations require walking a fine line between standard protocols and individual preferences.  You never want to become so beholden to your processes that they negatively impact the quality of service. But by standardizing workflows and tracking key metrics, you can reduce disorder, optimize training, decrease mistakes and make space and time for the kind of personal touch that truly elevates the service you provide.
3. Embrace AI as an empowerment tool.  Let’s face it: in spite of its current shortfalls, AI has the potential to lighten the load significantly. From documentation and communicating with clients, AI can help.

In the end, it’s the organizations that are willing to invest in finding the right solutions — not just automating what should stay in human hands, but streamlining workflows and aligning systems with outcomes — that will reap the benefits. Because when operations work well, everything else tends to fall into place.