Informed i’s Weekly Business Insights

Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life.  

For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. 

Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access.  For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.

Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.

Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s.

The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce.

That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match.

This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline.   Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset.

Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems.

What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought.  The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it.  To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product.

For decades, organizations have relied on early-career talent to do the routine, lower-risk work that supports the business and to serve as a training ground for future leaders.  Nevertheless, advances in automation and AI are changing the composition of entry-level work itself. Tasks such as research, documentation, data cleanup, basic coding, and preliminary analysis are being streamlined or absorbed into AI systems. These are precisely the activities through which young employees have traditionally built instincts, developed judgment, and earned the right to take on more.

At the same time, fears are growing about the impact of AI on jobs. In the 2025 Women in the Workplace report by McKinsey and LeanIn.Org, entry-level workers, particularly women, reported feeling the most worried of all age groups about how AI use will affect their jobs. The anxiety is broad based: Among graduating seniors, pessimism about starting a career climbed to 62 percent, from 46 percent, in just two years, and three-quarters of the pessimists pointed to firms hiring fewer entry-level workers as the reason.

The outlook for entry-level hiring is still evolving, but early indicators suggest a tightening market.  How much of this softening is attributable to AI remains genuinely contested: Federal Reserve Bank of New York economists estimate that the rise of remote work—which makes it harder to train novices at a distance—accounts for much of the increase in young-graduate unemployment, and Yale’s Budget Lab finds no clear economy-wide AI fingerprint yet, even as it flags the growing divergence between younger and older graduates as consistent with early-career effects.

Whether the apprenticeship channel is being eroded by algorithms or by distance, the implication for employers is the same: The informal mechanisms that once turned novices into experts can no longer be taken for granted. These shifts point to a narrowing set of traditional entry points just as the nature of early-career work is being redefined.

For organizations, this is a moment of both uncertainty and choice. As AI reshapes how work gets done, the question is no longer how many entry-level roles to hire, but what those roles are designed to do. Companies can use this transition to rethink entry-level work as a foundation for building expertise in an AI-enabled environment—equipping early-career employees to design, develop, and steer AI systems, not just operate within them.

Realizing the potential of early-career employees is not automatic. It requires intentional design in four areas:

1. Knowledge management. Codify how your best performers think—their frameworks, decision rules, and past judgments—and structure knowledge so AI systems and junior employees can draw on it directly. Build in expert validation, feedback loops, and clear ownership, and weight inputs so accumulated judgment outranks isolated experience. The system should improve as it is used and continually refining what good looks like.
2. Workflow and role redesign. As you redesign workflows around AI agents, treat entry-level roles as a deliberate part of that redesign rather than a residual. Rebuild those roles around working with, supervising, and improving AI outputs. Hire for judgment over tool fluency—curiosity, adaptability, problem-solving, and emotional intelligence—and widen the aperture to include strong candidates without formal training in the given field. The goal is to hire a “general athlete” who can apply, connect, and extend knowledge across a workflow, not a narrow specialist.
3. AI-based learning design. Embed learning into real work so employees build judgment while producing genuine outputs, with AI acting as an embedded guide and reviewer. Anchor the design in the attempt-then-check loop, and treat the narrowing gap between an employee’s independent work and the AI model’s output as direct evidence that judgment is forming. Design and dose these workflows deliberately rather than bolting them on.
4. Manager upskilling. Treat coaching as a core capability, not an afterthought, since junior employees now engage senior stakeholders and interpret potent data far earlier than before. Equip managers to coach judgment, context, and influence rather than supervising task mechanics. Consider formalizing the role through a preceptor model, giving structured mentorship the institutional weight that informal apprenticeship has lost.

Organizations that continue investing in early-career talent while redesigning roles to work alongside AI are more likely to develop the skilled workforce they’ll need in the future. Indeed, one of the most notable findings of the early AI era may be how little the hierarchy of essential skills has changed, even as the bar for entry-level work rises.

All companies recognize the need to adapt as technology, society, and geopolitics collide to produce multiple competing futures. But attempts at systemic change often fail. Typically, a CEO sets a bold, monolithic transformation goal and then launches multiple initiatives to achieve it. The trouble is, that goal is constantly destabilized by shifts in the environment. As a result, early projects fail to deliver expected results, new initiatives proliferate, and the transformation journey becomes increasingly fragmented, leading to employee fatigue and shareholder frustration.

The root cause of these failures, we believe, is the senior leadership team’s insistence on managing transformation as a large, complex project with a detailed road map to a fixed goal and results-oriented targets. While a disciplined, planned approach is understandable given the coordination complexity and budget demands of driving change in a large corporation, it’s ultimately unfeasible. The gaps between the knowledge and capabilities of the firm’s current business and those of its future business, combined with inevitable environmental turbulence over a multiyear project, hamper the organization’s chances of success.

A better approach is to manage transformation as a learning journey. By that, we mean a process with an incompletely defined goal that evolves as the organization makes decisions, learns from outcomes, and adjusts accordingly. At the outset the senior leaders need to explicitly acknowledge that while they have some idea of where the company ought to be, a final outcome can’t yet be fully known. With no fixed end point, the road map similarly is not fixed. The leadership team must proceed by selecting a coordinated portfolio of projects that will generate the greatest momentum and learning. Insights from the first wave of projects will then shape the priorities for the second wave, and so on, dynamically advancing the organization toward an evolving goal.

The authors draw on their research and experience as consultants illustrate their recommendations by comparing GE’s more traditionally managed and ultimately unsuccessful transformation with that of DBS Bank, which unfolded as a cumulative learning journey that continues today.

Charting the Course.  A clear sense of a desired destination is the starting point of every journey. That’s why all transformations need a vision. The vision must be ambitious enough to inspire employees to work on the transformation while remaining concrete enough to make employees and stakeholders believe it is attainable.  Setting a vision requires accepting that it will evolve as the organization learns and redirects its energies to reflect its learning. The only permanent characteristics of the vision are that it must inspire a collective endeavor focused on creating as well as capturing value and that it must unite employees from all parts of the company.

The Transformation Journey.  Each transformation requires the company’s leadership to determine which projects and initiatives to launch in pursuit of the vision and how best to orchestrate them. Traditionally, initiatives are treated as steps toward the goal, and success is measured in terms of revenues and profits. However, the yardstick for success should be how much the company will learn from a project: Will it reduce critical strategic unknowns and close gaps in key capabilities? Of course, leadership must also consider the probable impact a project will have on momentum. Will it improve performance in established businesses and tangibly help scale up new opportunities?  As companies travel along on their transformation journey, they can pursue four types of projects. The first two serve as engines of learning, either as controlled experiments designed to test specific hypotheses or as investments in new capabilities. The second two integrate the ideas and practices that arise from earlier projects to drive value in established and new businesses.  

A) Pilots.  These projects test critical assumptions about the business opportunities and threats embedded in the transformation’s vision.  They are essentially structured experiments—ranging from new products and processes to different sales channels and monetization models—that should generate essential learning even when they don’t yield immediate business results.  

B) Options.  This type of project focuses on building the capabilities—in both people and technology—needed to scale the future business and the operating models that the pilots are testing. These investments should close critical capability gaps, but they typically do not yield direct business benefits. Their success should be measured by how widely and effectively the new capabilities are adopted or deployed. To maximize learning, option projects must be tightly coordinated with the pilots, which often reveal the capability gaps that targeted options can address. Conversely, and even though they are not explicitly designed to deliver business results, options can spark ideas that shape future pilots.

Established business improvements (EBIs).  Projects in this category apply new strategic insights from pilots and capabilities from options to drive top- and bottom-line growth in core operations. While most organizations do not consider EBIs to be part of the transformation journey, the authors strongly recommend including these projects in the transformation portfolio. Their success can be measured using traditional ROI metrics, and they are a powerful way to demonstrate early wins and sustain momentum.

Ventures.  The fourth type of project focuses on scaling validated business models by leveraging insights gleaned from pilots and capabilities acquired from options. These ventures target new markets, customer segments, and business models to generate fresh revenue streams. Their success is measured by financial and growth metrics. Scaling up a new business depends on both validated assumptions and well-developed capabilities.

The Challenge for Leadership.  Managing transformation as a long-term journey is inherently difficult. The road map is bound to shift over time, which can cause employees to become disoriented or fatigued, and that can slow momentum. It is the responsibility of leadership to maintain stakeholder enthusiasm along the journey. That can be done in three ways:  Provide discipline.  Welcome bottom-up ideas. And incorporate pivots into measurement systems.  

CEOs are expected to articulate and then achieve goals. And it’s natural for them to treat all challenges that way. But large transformations involve deep architectural changes that unfold over long time periods and produce outcomes that are at least partly unforeseeable. Transformation goals cannot be achieved through decisive action and resources alone. Instead they must be guessed at, explored, revised, and continually reconnected to an evolving vision of what the organization should be. That’s why we contend that leaders must become intrepid explorers, ready to adjust course as their goals and environment evolve. Their fundamental challenge, perhaps, is to balance the humility that exploration requires with the confidence needed to inspire the crew.

Imagine deciding to spend the day at the U.S. Open tennis tournament in Flushing Meadows, New York. You don’t need a great seat. In fact, you don’t need a seat at all. You just want to walk the grounds, watch some tennis on the smaller courts, and soak up the atmosphere.

The listed price for a grounds pass might be $65. There’s just one problem: You may have to pay hundreds of dollars to get one.   That’s the issue economist and Yale Law School professor Natasha Sarin recently raised in a New York Times opinion piece about soaring U.S. Open prices. Secondary-market prices for the 2026 tournament have exploded, with grounds passes sometimes selling for several times their original price.  For business leaders, there’s a larger lesson here. Raising prices can increase revenue. There’s a point, however, where maximizing what customers will pay today can damage the relationship you need with them tomorrow. Here are seven things to keep in mind.

1. Don’t confuse demand with customer happiness. If customers keep buying as your prices rise, you may assume they’re happy. Don’t.  Sometimes customers pay more simply because they have no alternative. That’s very different from believing they’re getting good value. Pay attention to complaints, repeat purchases, referrals, and customer satisfaction — not simply whether the transaction went through. 
2. Remember your core customer. The U.S. Open has increasingly become a premium entertainment experience, complete with upscale food — including a $100 box of chicken nuggets dolloped with caviar — and hospitality offerings. The tournament is even in the middle of an $800 million renovation.   There’s nothing wrong with attracting wealthy customers. Just be careful that you don’t push away the customers who helped build your business in the first place.
3. Watch what happens after the initial sale.   You may sell something for $65, but if customers routinely have to pay $300 or $400 to actually get it, they’re unlikely to care much about the original price.   Your customer’s experience includes distributors, resellers, websites, service providers, and anyone else standing between you and them. You may not control every part of that chain, but customers will still associate the experience with your brand.
4. Don’t monetize everything you possibly can.   Businesses naturally look for new sources of revenue. That’s smart.   But squeezing every possible dollar out of every possible customer can eventually make people feel squeezed themselves. Sometimes leaving a little money on the table is a very good long-term investment.
5. Make affordability part of your strategy.   Affordability is understandably a hot button for many customers today. Other Grand Slam tournaments put greater restrictions on ticket resale, according to Sarin. That can help keep tickets closer to their original prices.   Consider doing something similar in your business. You might offer an entry-level product, grandfather longtime customers into older pricing, or reserve a portion of your inventory for lower-cost buyers.
6. Don’t let your best customers become your only customers.   Premium customers are attractive for an obvious reason: They spend more.   But if your business increasingly serves only the people willing to pay the highest prices, you may gradually shrink the pool of future customers. Today’s $65 customer might be tomorrow’s $1,000 customer — if you give them a chance to stick around. 
7. Think beyond today’s revenue.   Perhaps the most important question isn’t “How much can I charge?” It’s “What kind of relationship do I want with this customer five years from now?”   The U.S. Open is enormously popular, and strong demand is a problem most business owners would love to have. But success creates its own temptations.   You can raise prices. You can add premium products. And you can maximize revenue from your most enthusiastic customers. Just don’t price the people who love what you do right out of the game.

In trading, certainty is rare. You make decisions with incomplete information, changing conditions and outcomes that are never fully under your control. According to the author, over time, he has found that the same reality applies to business development.  A potential client can appear ready to move forward and suddenly disappear. A partnership that seems insignificant can become transformative. A promising opportunity can consume weeks of attention without producing a result.  The goal, therefore, is not to eliminate uncertainty. It is to build a process that performs well despite it.

According to the author, his work as a portfolio manager and trader has reinforced several principles that translate surprisingly well from markets to business development.

1. Think in probabilities, not predictions.  One of the easiest mistakes in business development is mentally converting interest into certainty.  A positive meeting becomes an expected contract. A verbal commitment becomes anticipated revenue. A promising introduction gets treated as a closed opportunity before anything has actually happened.  Markets punish that kind of thinking quickly. Business development may punish it more slowly, but the principle is the same.  Instead of asking, “Will this deal close?” I believe a better question is, “Based on what I know today, how likely is this opportunity to progress?”  That small change encourages better decision-making. You begin evaluating opportunities based on observable signals: responsiveness, urgency, decision-making authority, budget, internal alignment and the next concrete action.  A pipeline should represent possibilities, not promises.
2. Protect your time the way you protect capital.  Capital is limited. So is attention.  One of the most important disciplines in trading is deciding not only where to allocate resources, but how much to allocate. The same concept applies to business development.  Not every prospect deserves the same amount of time. If someone has expressed mild interest but repeatedly avoids taking a concrete next step, continuing to dedicate substantial resources to that opportunity can become expensive. The cost is not only the hours spent following up. It is the other opportunities you were unable to pursue during that time.  This does not mean abandoning prospects too quickly. It means allowing commitment to increase as evidence increases.
3. Define your downside before you chase the upside.  Business development naturally focuses on upside: revenue, partnerships, distribution, expansion and growth.  But some of the most important questions concern what happens if an opportunity does not work.  Before pursuing a major opportunity, establish what you are willing to risk in time, money, operational complexity and reputation.  Good opportunities should create attractive potential upside without placing the organization in a position where one unfavorable outcome becomes catastrophic.
4. Avoid becoming emotionally attached to an opportunity.  Trading can become dangerous when someone stops evaluating what is happening and starts defending what they hoped would happen.  Business development can create the same problem.  Once significant time has been invested in a relationship, it becomes difficult to walk away. People begin rationalizing delays, ignoring warning signs or allocating even more resources because they do not want their previous effort to have been wasted.  But past effort is not a reason to continue pursuing a weak opportunity.  It is useful to periodically evaluate major opportunities as though I were seeing them for the first time: “Knowing what I know today, would I still pursue this deal with the same level of intensity?”  If the answer is no, something needs to change.
5. Build a portfolio of opportunities.  Perhaps the most important lesson is diversification—not necessarily in the investment sense, but in how an organization approaches growth.  Depending too heavily on one prospective customer, one strategic partner or one distribution channel creates vulnerability. Business development becomes stronger when several independent paths can produce growth.  That might mean cultivating large strategic opportunities while simultaneously developing smaller partnerships, strengthening existing customer relationships and creating new channels for inbound demand.  The objective is not to pursue everything. It is to avoid constructing a growth strategy where everything depends on one outcome.

If you’re an entrepreneur, you know how often you can feel like you have no one who understands you.  Statistics illustrate an important truth: Founders need to surround themselves with supporters, mentors, peers and likeminded entrepreneurs. In other words, they need both the intrinsic and corporate value that comes from being involved in communities. This support helps them overcome the harsh truths about entrepreneurship that rarely get discussed.

Indeed, being a part of many communities can help startup owners feel less lonely. These networks also provide other benefits, including the ability to gain insights, stay ahead of trends and keep a competitive edge.  That said, you may be unsure which types of communities to join and leverage. Below are three options to guide your next moves.

Peer advisory-style networks.  Peers typically foster camaraderie between members by staying small so meetings can be very focused. Ideally, any peer group you enter should have some kind of structural element to it. Otherwise, you’ll just feel like you’re attending a freestyling network event. Those can be great, but they’re not designed to improve your skills and relationships at the same level of continuous focused feedback and support.  As you might imagine, these types of groups are unique and some serve distinct populations.

Entrepreneur incubators.  Many founders find it valuable to join startup incubator communities. These networks explain why smart startups are partnering with larger entities to unlock seed funding, future partnerships and talent pools. It’s not unusual to find incubators attached to academic institutions or government agencies. However, they can be privately run as well.

Digital-first communities.  In the digital era, founders may want to take their community-building into cyberspace. Specifically, becoming visible on social media channels — including LinkedIn — can give them and their businesses a spotlight. They can also start to build relationships with influencers who may become valuable marketing partners later.

It’s worth noting that you shouldn’t shy away from the idea of building a community around one of your passions. Maybe you’re an entrepreneur who overcame a learning disorder, and you want to raise awareness. In that case, you might want to launch a podcast or start publishing articles online to start conversations with other founders like yourself.

You don’t have to feel like you’re all alone as a founder. Involving yourself in communities ensures you’re surrounded by people who can serve as coaches and sounding boards. With them on your side, you’ll be better positioned to navigate your business in a positive direction.

What’s wrong with loyalty? Isn’t it a virtue to display steadfast allegiance to something or someone other than oneself? After all, loyalty has been celebrated as a virtue across many cultures for millennia.  

As economic headwinds and a tough job market appear to be empowering more authoritarian styles of leadership, it’s a good time to look more closely at loyalty. We must ask whether good leaders can fairly demand it of their followers and whether it’s an idea that modern management should finally discard.

Taking Stock of Your Organization.  What types of relationships define your unit or organization? Presumably you’d prefer not to be seen as coercing allegiance or rewarding sycophants. But how can you know?  Your first step needs to be an honest assessment of what the people around you see as the primary currency of social exchanges. Below are some hard but important questions to ask. And, given that the answers may well be unflattering and thus risky for those whose truth you most need to hear, you need to be sure to ask everyone for their input and do so in a way that guarantees they will suffer no consequences for their honesty throughout the process.

What kind of behavior is most likely to get you ahead around here? Unwavering commitment to specific people, sycophancy, or silence? Clear commitment to the mission and the truth, even if it upsets people sometimes?

Does our organization have “loyalty tests”? Loyalty to whom or what?

What’s the implicit definition of disloyal held by those in power? What happens to those deemed disloyal?

Do people with power show as much concern for and commitment to the individuals they lead as they expect from them?

Are most people willing to make sacrifices for the greater good, or are they ultimately transacting in a self-interested, self-promotional way?

You’ll also want to ask people whether the pattern of social exchanges seems to be changing or has changed in ways that are undesirable. Such shifts can happen in reaction to perceived signals about what behaviors are rewarded or punished. You might learn, for example, that what was once primarily principled commitment has drifted into less-healthy idealized devotion. Or what began as strong mutual trust may have shifted toward imbalanced allegiance because leaders have centralized authority and now routinely punish dissent.

We suspect that in most organizations, the honest answers to these questions will reveal gaps between what actually seems virtuous and suitable in organizational life and what’s playing out around you.

Building Culture Based on Shared Commitment.  It’s easy to say that shared principled commitment is the most desirable kind of relationship we’ve described. But doing the work to get there and maintain it is hard; it’s work that will likely demand personal sacrifice and new behaviors as you commit to putting mutual regard in pursuit of the organization’s purpose over personal agendas. If you’re serious about earning the upsides of shared principled commitment — discretionary effort, trust, high morale, sustained commitment — here are some ways to begin.  Live the organization’s values in everything you do.  Welcome challenge and dissent.  Remove the sycophants.  Demonstrate relational reciprocity.  And put limits on your leadership.