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The 2026 edition of the Global Cooperation Barometer shows that overall cooperation is largely unchanged from previous years, but its composition appears to be changing. Metrics relating to multilateralism weakened the most. Metrics in which more flexible and smaller arrangements of cooperation can operate—in data flows, services trade, and select capital flows, for example—have continued to grow, including in 2025.  These dynamics are visible in each of the five pillars of the barometer (Climat and natural capital, Innovation and technology, Trade and capital, Health and wellness, and Peace and security):

Trade and capital cooperation flattened. Cooperation remained above 2019 values, but its makeup is shifting. Goods volumes grew, albeit slower than the global economy, and flows are shifting to more aligned partners. Services and select capital flows show momentum, particularly among aligned economies, especially where they can contribute to bolstering domestic capabilities. While the global multilateral trade system faces rising barriers, smaller coalitions of countries are cooperating through initiatives such as the Future of Investment and Trade Partnership.

Innovation and technology cooperation rose to unlock new capabilities even amid tighter controls. IT services and talent flows are up, and international bandwidth is now four times larger than before the COVID-19 pandemic. Restrictions on flows of critical resources, technologies, and knowledge expanded—especially but not only between the United States and China. However, new cooperation formats are increasing, with instances of cooperation on AI, 5G infrastructure, and other cutting-edge technologies among aligned countries.

Climate and natural capital cooperation grew but is still short of global goals. Increased financing and global supply chains stimulated deployment of clean technologies, which reached record levels in mid-2025. While China accounted for two-thirds of additions of solar, wind, and electric vehicles, other developing economies stepped up. As multilateral negotiations become more challenging, groups of nations—for example, the European Union and the Association of Southeast Asian Nations—are combining decarbonization with energy security goals.

Health and wellness cooperation held steady, with outcomes resilient for now, but aid is under severe pressure. Top-line cooperation in this pillar did not fall, in part because health outcomes continued to improve after the end of the COVID-19 pandemic. Although health outcomes have stayed resilient, the stability masks growing fragility. Pressures on multilateral organizations have eroded support flows, and development assistance for health contracted sharply, with further tightening in 2025; this affected low- and middle-income countries most acutely.

Peace and security cooperation continued to decrease, as every tracked metric fell below pre-COVID-19-pandemic levels. Conflicts escalated, military spending rose, and global multilateral resolution mechanisms struggled to de-escalate crises. By the end of 2024, the number of forcibly displaced people reached a record 123 million globally. Still, growing pressures are creating an impetus for increased cooperation, including through regional peacekeeping mechanisms.

Since key challenges and important opportunities cannot be addressed by individual countries alone, leaders should anticipate shifts and move proactively to “remap” international engagement, strengthen resilience by building new capabilities, and find new forums to cooperate—matching the right format to the right issue.

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MIT Technology Review’s reporters and editors constantly debate which emerging technologies will define the future. Once a year, they take stock and share some educated guesses with their readers. Here are the advances that they think will drive progress or incite the most change—for better or worse—in the years ahead.

1. Sodium-ion batteries.  Sodium-ion batteries, made from abundant materials like salt, are emerging as a cheaper, safer alternative to lithium. Backed by major players and public investment, they’re poised to power grids and affordable EVs worldwide.
2. Generative coding.  AI coding tools are revolutionizing how we write, test, and deploy code, making it easier and faster to build sophisticated websites, games, and other applications than ever before.
3. Next-gen nuclear.  Nuclear power already provides steady electricity to grids around the world, without producing any greenhouse-gas emissions. New designs rely on alternative fuels and cooling systems or take up less space, which could get more reactors online faster.
4. AI companions.  Every day, millions of people interact with AI chatbots. Some of them form what feel like close, personal bonds with the bots. There’s mounting evidence that this can be dangerous, and politicians are finally waking up.
5. Base-edited baby.  When he was just seven months old, baby KJ became the first person to receive a personalized gene-editing treatment. A clinical trial is now planned, and bespoke gene-editing drugs could be approved within the next few years.
6. Gene resurrection.  Growing banks of gene information on extinct creatures are providing clues to new treatments and suggesting solutions to climate change—and may help save endangered species.
7. Mechanistic interpretability.  Nobody knows exactly how large language models work, which means we don’t have a clear idea of their limitations. But that’s changing: Clever research techniques are giving us the best glimpse yet of what’s really going on inside the black box.
8. Commercial space stations.  Space tourism may seem fanciful, but this might be the year paying customers can finally check into a room with a galactic view. The shiny new modules will also support research missions by scientists and space agencies.
9. Embryo scoring.  Screening embryos for genetic diseases is relatively common practice in fertility clinics today, and it can give potential parents some peace of mind. Now, though, new startups are making bold claims about using similar techniques to predict certain traits, including intelligence.
10. Hyperscale AI data centers.  The race for AI supremacy has supercharged data centers. Hyperscale AI data centers pack powerful computer chips into synchronized clusters that work like giant, high-speed supercomputers—sizzling hot, power-hungry behemoths pushing infrastructure to its limits.

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The fundamental appeal of restaurants was once that they produced flavors you couldn’t find inside your own home. If you wanted what the premier red-sauce joint in America was selling, you would have to find your way to one of eight global cities to finagle a reservation at Carbone. To experience White Castle, you would have to go, in person, to White Castle. And should you have a craving for TGI Fridays’ signature loaded potato skins, there was no choice but to make a pilgrimage to TGI Fridays.

Now restaurants are invading the grocery store. They’ve captured the freezer aisle, colonized the pastas, conquered condiments and spreads. 

Restaurants have been creeping into grocery stores since the middle of the last century, when the beloved Ohio-based restaurant chain Stouffer’s arrived in the freezer aisle. “It’s been a long, slow burn to get to this point,” says Elly Truesdell, founder and managing partner at New Fare, an investment fund specializing in early-stage food and beverage businesses, including Tacombi. White Castle has been selling microwavable sliders in the grocery store since 1987, when, after being turned down by external suppliers, it developed its own manufacturing process in-house. Over the next decade and a half, Taco Bell’s branded taco shells, salsas and sauces hit supermarket aisles, and California Pizza Kitchen brought its barbecue chicken pizzas to the freezer case. This gave the brands not only new revenue streams but also exposure to new customers. 

What’s different today is the sheer number of brands. “I do feel like there are new restaurants or chefs launching food products almost every week,” says Truesdell, who was once a product scout for Whole Foods Market. Restaurants have always been precarious businesses, but Covid-19, and the ensuing shutdowns, illustrated just how fragile they were. “By necessity, a lot of chefs and restaurants were scrambling, and so they started launching products,” she continues, and while some of these moves were short-term pivots, many of those businesses transitioned to become “full consumer brands.” 

There was an age when a restaurant would have to be a corporate giant before venturing into retail. Now, there’s the internet. Restaurants have Instagram accounts; chefs are celebrities. “Ninety percent of the people who followed Dave and Momofuku on socials did not live in cities in which we operated restaurants,” Mariscal says. Introducing a successful consumer packaged-goods brand is notoriously difficult, but the barrier to entry is also lower than ever. It’s easier for smaller brands to communicate directly with manufacturers, many of whom are now willing to take a chance on smaller runs. Meanwhile, specialty shops and online marketplaces allow chefs “a way to do this without immediately launching at Kroger nationwide,” Truesdell says.

Part of the sell to consumers is that restaurants have credibility that big food brands do not; the grocery store is an extension of this already-built world. 

Meanwhile, frozen food is having a moment. If you look at pandemic-era survey —somewhere from 33% (per the US Energy Information Administration) to 55% (per Tyson Foods) of Americans have a second freezer? Home consumers have better setups, too, says Brandon Hoy, co-founder of Roberta’s. “The microwave sucks, right?” he says. “Rarely can you make anything good in a microwave.” But now 60% of households have an air fryer.

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After taking the software industry by storm, generative AI is now moving into a broad set of industries, including manufacturing, where it is helping manage unpredictability and support real-time decision-making. Gen AI’s ability to codify, automate, and distribute organizational expertise may eventually reshape work structures from the shop floor to the C-suite. Already some companies are using it to analyze the flood of information generated in factories and to predict problems, simulate complex scenarios, and optimize processes in real time. By working with a wide range of manufacturing industry data—from maintenance manuals and machine automation code to complex diagrams, 3D drawings, and process data—gen AI has the potential to establish new ways for people and machines to collaborate.

But who will benefit from these changes, and how quickly? That’s not a simple question. Like electricity and the printing press, gen AI is a general-purpose technology—the adoption of which, history teaches us, is rarely straightforward. Managers often fail to recognize the true economic potential of new technologies and struggle to reorganize tasks, skills, and workflows to suit them. As a result, performance gains typically lag behind technological diffusion, giving rise to what’s known as the “productivity J curve”: an initial dip in productivity as organizations adapt to a new technology, followed by sustained gains once complementary investments pay off.

Because it’s not clear how firms will adopt gen AI, managers face a strategic dilemma: Wait for more clarity and risk falling behind? Or act too soon and invest in applications that don’t deliver?

To address this tension, leaders need to think about gen AI adoption not as a single decision but as a portfolio of organizational experiments. Like A/B testing in digital-product development, these experiments should aim to isolate causal effects—focusing not just on whether gen AI works but also on how it works, for whom, and under what conditions. By testing gen AI applications before scaling them up, managers can reduce risk, refine their strategies, and build internal momentum for change. Experts have been advocating for this approach, but many firms are struggling to implement it. Experimentation therefore remains a relatively novel practice in many organizations.

That needs to change. Experimentation allows companies to transform gen AI uncertainty into a strategic advantage. It helps firms move through their own adoption phase more successfully than their competitors do. And the knowledge generated through experimentation can be leveraged to reinforce existing relationships—or create new ones—within their ecosystems. In this article the author describe how firms are getting better at adopting gen AI through experimentation—within their own organizations and across entire ecosystems.

Engines of Learning and Adaptation.  At its core an organizational experiment is an application of the scientific method. In a real-work setting, it establishes a treatment group (for example, employees or teams using a new AI system) and a control group (those operating as usual, without the new system).  When randomization is difficult, some firms implement staggered rollouts, phasing an intervention in to different teams over time to create natural control groups.  Another approach is to create a “lab in the field”—that is, a controlled environment where interactions with the new technology can be observed.

When conducted properly, organizational gen-AI experiments can produce a host of benefits, including the following:  Causal insights, granularity, risk reduction, and strategic learning.  

Ecosystem Experimentation.  Gen AI experimentation does not simply benefit potential adopters. Innovators can see even greater returns: They can apply the insights gleaned to help prospective buyers understand which gen AI use cases really matter for them or which challenges might prevent them from integrating the technology into existing processes. Some innovators with large user bases are leading the experimentation surrounding new gen AI applications outside their own organizations—for example, in partnership with current or prospective buyers. In these cases, innovators orchestrate ecosystem experimentation.

To become an organizational experimenter successfully, you’ll need to focus on several critical areas:  Customer needs.  Usable prototypes.  A learning mindset.  Experimental expertise.  And partnership capabilities.

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As new employees enter an organization with their own work norms, values, and assumptions about how things should be done, the cohesion of the once unified team often begins to strain. This transformation introduces two key challenges that we have observed firsthand in our work with growing businesses.

The first challenge is the rise of division among employees in the form of subgroups. Employees break into factions based on shared experiences or characteristics. In many of the organizations the authors studied, these fault lines emerged prominently between early joiners and newcomers.  The second challenge is a lack of shared understanding of the organizational reality across early joiners and newcomers.

Those two challenges pose significant risks to organizations navigating the transition from small, cohesive teams to larger, more complex ventures. As the authors have observed in numerous cases, this transition phase often comes with high costs, including the departure of both early joiners and promising newcomers, a loss of momentum, and a diminishment of the very innovation and agility that growth is meant to amplify.  For organizations to thrive amid rapid growth, they must address these challenges head-on. Here’s how.

1. Create a Shared Language.  Founders often believe that newcomers will intuitively pick up on informal norms and implicit rules, and they avoid formalizing processes to maintain what they see as a culture of flexibility. However, as the workforce grows, this approach frequently leads to misunderstandings and conflicts. Managers often turn to informal bonding activities, such as offsite retreats or social events, hoping to spark new cohesion. While well meaning, these activities can unintentionally amplify cultural and behavioral differences, further deepening divides.  Instead, the first priority for managers should be to establish a shared organizational language that aligns employees around consistent understandings of processes, norms, roles, and goals. Much like an architectural blueprint enables varied professionals to collaborate on a construction site, a shared language ensures that early joiners and newcomers can work together effectively.  Various tools to do this could be: develop lists of appreciated actions and unwelcome actions; employ three C’s planning approach i.e., records processes, goals, and priorities in a way that is comprehensive, complete, and consistent.
2. Foster a Shared Identity.  The fast-paced nature of high-growth organizations often pushes issues of cohesion to the back burner until a crisis demands attention.  To avoid such scenarios, managers must address their audiences in distinct ways.  First, as the shared vision that once defined the organization gives way to growth and change, managers should reconnect with early joiners and recognize their frustration. Managers must address this situation by crafting a refreshed story that reflects the company’s larger, more heterogeneous structure.  Second, managers need to focus on integrating newcomers. Managers should emphasize the unique value newcomers bring, framing their differences as assets that enhance team success. Integration begins with creating deliberate opportunities for cross-functional collaboration and shared work-related rituals that unite employees around common goals.
3. Encourage a Culture of Dissent.  A shared understanding of organizational reality and a shared identity lay the foundation, but to benefit from the full spectrum of ideas that heterogeneity brings, organizations must go further.  Harnessing differences requires a culture of dissent, where disagreements are not only respected but encouraged and rewarded. Such a culture brings differences to the forefront, transforming them into opportunities for growth and innovation.  Promoting a full range of conversation, including views opposed to those expressed by the stronger voices in the room, requires intentional leadership. Managers must lead by example, actively seeking out and welcoming a variety of opinions. Structured feedback mechanisms, such as anonymous channels or dedicated forums, enable employees to voice differing views. Recognizing and rewarding thoughtful disagreement, such as by publicly acknowledging employees who challenge ideas constructively and by incorporating such behavior into performance reviews and promotion criteria, signals that such contributions are both valued and essential to organizational success. Transparency in decision-making is equally important.

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Ramana Thumu, the chief technology officer at Expedia Group, says there’s no “AI Center of Excellence” at the online travel agency that sits in an ivory tower and mandates how everyone should be using artificial intelligence.  “We are democratizing AI across the entire company,” says Thumu. “Every employee, every team, and every workflow.”

Some examples of how this plays out include the creation of Expedia’s “AI playground,” which gives employees access to more than 60 different large language models—including from OpenAI, Google’s Gemini, Meta’s Llama, and Anthropic’s Claude—to build their own AI agents. Since January 2025, employees have built more than 1,500 different AI agents and around 6,000 monthly sessions occur within the secure AI agent builder environment on a monthly basis. 

Around two-thirds of Thumu’s software developer workforce have embraced AI coding assistant tools including Copilot, Claude Code, and Cursor, often resulting in an estimated 20% productivity lift. Thumu says he wants to bring more “joy” to coding by giving them a broad set of AI tools to infuse in their workflows. He vows that more efficiency won’t necessarily mean fewer jobs. “That’s not how I see it,” Thumu adds. “It’s an improvement which means you can get more work done, much faster, and higher quality work.” 

And last year, Expedia began to embed AI “squads,” teams of around four to six AI engineers that work with various business divisions ranging from legal, procurement, human resources, and marketing, collaboratively working alongside AI “champions” for each of those segments to figure out where AI can possibly be implemented to handle some manual tasks. 

Every one of these AI investments are being measured, ranging from velocity and cycle time for the AI coding assistant tools, and within customer service, tracking how AI is speeding up the time it takes to resolve a customer query. For the broader employee population, Expedia is measuring usage and the impact on workflows. If the company doesn’t see the right adoption or outcomes for these tools, Thumu says Expedia is quick to reassess and adjust its approach.

“We are absolutely testing a lot of experiences and placing a lot of bets to make sure which ones stick and where we scale, and when the test is not wildly successful, fail fast and take those learnings to do something different,” says Thumu.

External applications of AI include Expedia Trip Matching, which debuted in June and allows travelers to share any publicly available travel reel that caught their attention on Instagram and then share that reel directly with Expedia. In October, Expedia also served as a pilot partner—along with Booking.com, Zillow, and Spotify—for what’s known as “Apps in ChatGPT,” a feature that aims to further integrate the chatbot with external brands.  The company also developed an AI customer service agent that’s built on Amazon Web Services, which can help travelers with simple tasks including cancelling a hotel booking, getting a refund status, or changing their books so they don’t need to call a human agent. 

As Expedia moves forward with embracing more agentic AI capabilities, Thumu stresses that the company will prioritize stitching together the technology in a manner so one AI companion can help customers effortlessly through all touchpoints. He doesn’t want to build one AI companion that helps book hotels, another to handle discovery for excursions, and yet another to address customer service questions. 

“We are trying to bring those together,” says Thumu. “To make sure that from a customer standpoint, it’s one agentic experience. There is a lot of work to be done, but we are in the initial stages.”

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When 60 Minutes interviewed NVIDIA CEO Jensen Huang, they didn’t meet the head of the world’s first $5 trillion company at its gleaming new headquarters. Instead, they met at a nearby Denny’s, where Huang’s journey began.  Huang recognizes a good setting because he’s a storyteller at heart. Huang told Wired Magazine that he didn’t sell the idea of NVIDIA from a pitch deck. “It was really about telling a story,” he said.  

The ability to tell a compelling story is a crucial skill for today’s leaders who need to clarify complex information, win support for their ideas, and align teams around ambitious goals.

Storytelling is a skill anyone can learn.  In his advanced communication classes for global executives at Harvard, the author introduces a framework called The Hero’s Journey, adapted slightly to make it easier for business professionals to follow.

In the 1940s, mythologist Joseph Campbell uncovered a common pattern that heroic stories follow across time and cultures: an ordinary person accepts a challenge, faces tests along the way, and is transformed by what they learn.  Filmmakers and storytellers like Star Wars director George Lucas and screenwriters Christopher Vogler and Blake Snyder later adapted it for modern cinema. At Harvard, we condense it to nine stages for anyone who wants to build a blockbuster business pitch that inspires and persuades.

9 Stages of The Hero’s Journey for Leaders

1. Ordinary World.  Most great stories start in an ordinary setting. It makes the story relatable and provides insight into the hero’s values. That’s why Huang often shares the story of his first job, cleaning dishes and clearing tables at Denny’s.  “I washed the living daylights out of the dishes,” Huang told Stanford business students. “And then they promoted me to busboy. I’m certain I was the best busboy Denny’s ever had. No task was beneath me.”
2. The Catalyst.  A catalyst or ‘inciting incident’ is absolutely required to kick off the adventure. If Luke Skywalker never sees Princess Leia’s hidden message, there’s no Star Wars and no multi-billion-dollar franchise.  Think of the catalyst as the moment when you realized the status quo was no longer good enough.  For Huang, the catalyst was meeting two former co-workers at, you guessed it—Denny’s. His friends proposed an ambitious plan to start their own company.
3. The Debate.  Since most people get comfortable with the status quo, a hero should show relatable human impulses like hesitating before making the leap into the unknown.  It’s natural for people to go back and forth before making a big decision. Huang was no different and was reluctant to join his friends. “I was gainfully employed and happy doing what I was doing,” Huang said on the podcast Crucible Moments. “I told them I’d wish them well.”
4. Crossing the Threshold.  Once the hero decides to take the leap into the unknown, there’s no turning back.  Huang eventually decided to take the courageous step of leaving the safety of a steady paycheck, even though he had two young children at the time. But he didn’t take the leap blindly.  “I believed in my co-founders, and I believed in myself,” Huang told students at the Haas School of Business at UC Berkeley. “The only thing that really matters is, are you going to love the people that you work with, and are you going to love the work that you’re going to do?”  This stage raises the stakes and encourages audiences to lean in. They’re invested in what happens next.
5. Fun and Games.  This is the stage where momentum starts to build, the time when leaders look back in disbelief at the crazy events they experienced.  Huang often gets a laugh when he reminds audiences that NVIDIA’s strategy to make graphics chips for PC games was like entering “a zero billion market.”  Leaders are more relatable—and entertaining—when they look back with humor on days money was tight, ambition was high, and excitement was in the air.
6. Tests, Allies, and Enemies.  This is the messy middle where the hero is tested, allies emerge to offer help or guidance, and the world pushes back.  “NVIDIA didn’t almost fail once; it nearly failed repeatedly,” Huang says. “We were 30 days from going out of business. More than once.”  Failures, mistakes, hurdles, and challenges are part of being human. Leaders who excel as storytellers don’t avoid such stories. They celebrate them.
7. The Supreme Ordeal.  Just when you think it can’t get much worse, it does.  When NVIDIA was locked into a contract with Sega to build the NV2 chip, the computer industry’s underlying architecture shifted.  “I was confronted with a situation where we would finish the project and die or not finish the project and die right away,” Huang recalled in Crucible Moments. NVIDIA’s strategy would have led to ruin, but by abandoning a flawed plan (with the help of an ally in Sega), the company managed to pivot and survive.
8. The Reward.  In the reward stage, the hero ‘seizes the sword’ symbolically, taking possession of the treasure they seek. In NVIDIA’s case, the reward was a moment of clarity and transformation.  After surviving failures and ordeals, Huang realized that accelerated computing, not just graphics, would define the future. Years before ChatGPT arrived, NVIDIA was perfecting the technology that would make AI a reality.
9. Return with the Elixir.  Heroes return from their adventure, not as conquerors but as guides with newfound insights or wisdom that transform their lives or better their communities. In this stage, leaders turn hard-won insights into shared benefits.  For example, in his keynote at CES 2026, Huang announced new AI models to help partners accelerate the development of AI applications. According to Huang, NVIDIA now builds “completely in the open so that we can enable every company, every industry, every country to be part of this AI revolution.”

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Consulting is experiencing a boom right now. More seasoned professionals are pivoting to self-employment as consultants.  This is a response to the unemployment rate being the highest in part due to an uncertain economies and the adoption of AI and automation altering industry dynamics.

The shift is also driven by opportunity, as accessible and affordable freelance marketplaces, business tools, and global clients allow professionals to manage their practices with fewer barriers to entry. Because of this, the competition for consulting jobs has also gotten more fierce.  

1. Choose a focus based on demand.  What should you offer to clients as a consultant? You’ll want to focus on where there’s demand to hire you right now, given you’re just getting started. But that doesn’t mean taking on any project that comes across your desk haphazardly. This is a process of reverse engineering what you’ve done in your past positions to pinpoint the tangible value you can offer clients externally.  That might be delivering improved reliability and security across retail infrastructure as an IT consultant or higher employee engagement and retention as a fractional HR leader.  Determining the result of your contributions from the customer’s perspective helps ensure you’re not just creating a practice around your key strengths, what you enjoy doing, and the services you think are valuable to offer, but prioritizing what outcomes clients will actually hire you for.  Your niche can involve associating your consulting with a certain tool, job function, type of client, company maturity level, or industry, all in the service of delivering a specific set of outcomes.  While not every gap is an opportunity, thinking through a niche that’s recognizable in your industry and at least somewhat unique to you is a good way to find traction.
2. Productize your services to capture more business.  Consulting is a service offering, but it should be packaged like a product so what you’re offering to freelance clients is clear, like a menu of options, and while also protecting your bandwidth.  Creating packages to treat your services like a product helps to clearly document the work you offer in terms of the deliverables, project timeline, expected outcomes, and associated pricing.   This way you can convert more business by creating packages aligned to clients with different budgets or organizational complexity without stretching yourself too thin.
3. Turn past employers into your first clients.  Landing your first clients is a challenge as you’re not yet known as an independent consultant and you’re still building trust in what you offer. One of the best ways to attract the initial set of clients is pitching past colleagues and partners at former employers.  Best case scenario is there’s alignment to hire you or refer you to others as a trusted vendor. At a minimum, each interaction is an opportunity to do research and get feedback on what you’re offering as a consultant.
4. Market your business in a way that energizes you.  You can’t rely on referrals alone to build your customer base, as there’s a definitive limit to how much word of mouth will convert to clients. Investing in marketing as a routine part of your consulting business is necessary to reach new, qualified prospects outside the people you already know.
5. Complement advising with execution support.  I’ve seen an uptick in companies saying, “I’m looking for a consultant to do more of the work, not just tell me how.” In many circumstances, there’s an expectation you’ll be providing implementation in addition to your advising support.
6. Capitalize on AI to meet demand and scale your productivity.  Being mindful to avoid the hype, it’s necessary to incorporate AI into your client offerings as companies are asking for it and also to scale your own processes as a solopreneur.
7. And Be an adaptable expert for longevity.

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Anna Lenhardt is the founder and CEO of Lenhardt Partners, a company whose mission is to help venture-backed startups build people systems that scale. “We come in when things are moving fast and founders realize their structure, leadership, and culture are starting to lag behind the business,” she told Entrepreneur.

Lenhardt believes that HR will be “one of the biggest transformation agents” in the age of AI. “That’s why my work focuses on what I call Human Intelligence — designing how people, teams, and technology work together so companies can grow without burning out their people or breaking what makes them special,” she explains.

Lenhardt knows firsthand about the real threat of burnout and shared her experience of building a company that aims to help other entrepreneurs overcome the many challenges that successful startups will face.

The following are some of the insights from her interview:

She began her career at McKinsey & Company, then refined her HR expertise as a consultant at Deloitte and went on to help Oscar Health and Hippo Insurance scale and prepare to IPO on the New York Stock Exchange. Starting her own business, Lenhardt Partners, was risky. But she moved ahead anyway because she trusted the pattern she had seen over two decades: when founders invest in people and structure early, they move faster and break less. That conviction has guided the firm from day one and shapes how she advise leaders when they are tempted to chase trends instead of building solid foundations.

Last year, she realized she was working 100 hours a week and had become burnt out.  It was a wake-up call—especially because sustainability is something she advises her clients on every day.  How she was operating was not sustainable, to say the least. So she  hired more leaders, established a new partnership for the business, delegated more work, and established a clear roadmap for growth, roles and company evolution. That shift allowed her to double capacity without losing quality and reminded her that even as tools and automation improve, the real lever is still how we design roles, teams, and decision-making around human capacity.

Earning her master’s degree in counseling psychology fundamentally changed how she lead. It gave her a deeper understanding of how people think, react, and make decisions, which is essential when you are advising founders and executives in high-pressure environments. She also hold a master’s in the arts and am a classically trained opera singer, which means she takes preparation and performance seriously. You do not step on a stage unprepared, and the same is true for leadership moments.  A simple daily practice that supports all of this is evening meditation. It helps her slow down, reflect, and stay clear-headed so she can make sound decisions and show up steadily when things are difficult. That mix of psychology, discipline, and reflection is the backbone of how she design people strategies that work in real life, not just on paper.

HR is not a “nice to have” function. It is the operating system for how work actually happens. If you are entering this field, know that your job is to understand business and people deeply. It requires courage, clear judgment, and a strong spine.

An entrepreneur is the person who signs their name on the bet and then looks their team in the eye when it is time to deliver. It means taking ownership of your vision and standing behind it completely. Entrepreneurs move first, and they are responsible for what happens next.

At first, success meant security for my family. Now it means building things that last. That could be a company, a culture, or a team that continues to thrive long after I’m gone. It means creating a meaningful life where I have a positive impact on others and on society.