Informed i’s Weekly Business Insights

There are pieces of your life scattered all over the internet, and some of them are for sale. Data brokers amass web searches, financial records, and location data from millions of individuals and sell them to various clients, including the US government. Information on your recent online purchases or the route that you take to work could be sitting on hard drives around the world, waiting to be used.

While reassembling those pieces isn’t trivial, there is early evidence that LLMs might make it far easier. LLM agents could potentially do the work of intelligence analysts in a fraction of the time and for a fraction of the cost, which would enable the state to aim its all-seeing eye toward anyone, not just its highest-priority targets.

Worries over how LLMs could facilitate mass surveillance recently made headlines around the world. According to reporting from the New York Times and the Atlantic, contract negotiations between Anthropic and the US Department of Defense fell apart in late February because Anthropic balked when the DOD demanded leeway to use the company’s models to analyze commercially available data on US citizens. When Anthropic’s rival OpenAI agreed to a DOD deal mere hours later, OpenAI faced an immediate wave of public backlash for apparently swanning past Anthropic’s red lines. Under pressure, OpenAI and the DOD later revised the contract terms. 

For avid followers of Anthropic CEO Dario Amodei, the company’s firm stance probably didn’t come as a surprise. In a lengthy essay published to his personal website in January, Amodei had argued that AI-enabled mass surveillance could constitute a crime against humanity. The core concern underlying his dispute with the DOD was that the government might use LLM-based systems such as Claude to analyze reams of data obtained from brokers and build detailed profiles of individual Americans at scale.

There’s plenty of precedent for AI being used for mass surveillance: Most notably, governments worldwide use facial recognition to track citizens and noncitizens alike, and recent reporting indicates that US Immigrations and Customs Enforcement (ICE) agents have leaned heavily on facial recognition apps in order to carry out the Trump administration’s mass deportation campaign. While there’s not yet any smoking-gun evidence that the US government (or anyone else) is using LLMs to conduct surveillance in the way that Amodei warns about, there’s a clear appetite for such capabilities.

Few organizations would choose inefficient procedures of their own volition, but Congress could force the government down that path. Shortly after the Anthropic debacle, a bipartisan group of senators and representatives introduced a bill that would require the government to obtain a warrant before purchasing data from data brokers. Public outcry, too, seems to have had an effect: After OpenAI was overwhelmed by opprobrium for accepting DOD contract terms that Anthropic had rejected, the company and the Pentagon modified the contract to include additional surveillance protections.

But government surveillance is not the only concern. Private companies could just as easily purchase bulk data and analyze it with LLM agents, and they are less subject to legal constraints and public opposition, especially if they aren’t household names.

In the absence of legislation preventing such uses, we might need to rethink how we understand our own privacy. It has always been possible that someone online might unearth your address or connect you with your pseudonymous accounts, but given the effort that would take, it was easy to feel safe. Even in the wake of Edward Snowden’s 2013 revelations about the National Security Agency’s extensive surveillance of US citizens, many people reassured themselves that their privacy was still intact because the government had no reason to look into their lives.

That kind of privacy depends entirely on friction: the time and effort required to link a secret social media account with its real-life owner, or the skill and resources needed to analyze bulk datasets. Stay under the radar, and no one will care enough to overcome that friction. But LLM agents could lessen that effort, or remove it entirely. If the government and other organizations can construct detailed profiles of millions of people at the drop of a hat, no one is beneath their notice.

A house is only as strong as its foundation. That’s what companies are quickly coming to understand about agentic AI as well. Nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10 percent have scaled them to deliver tangible value.  Shaky data is often to blame; eight in ten companies cite data limitations as a roadblock to scaling agentic AI. Addressing this issue is a core element of building a solid capability foundation—and that’s what distinguishes companies that create value from AI from those that don’t.  While companies have often muscled through issues of fragmented and siloed data, those issues are impossible to manage at scale. Inconsistent governance has just increased the challenge of preserving data context while enforcing access control, lineage, and auditability.

Success with agentic AI depends on a data architecture that can support increasing levels of autonomy, coordination, and real-time decision-making. This often looks like modular, interoperable frameworks that give agents reliable access to the data they need to operate safely (see sidebar, “Seven data architecture principles that enable scale”). While gen AI has already shown the need for data access control, lineage, and traceability, agentic platforms place greater operational pressure on these foundations. Because agentic AI coordinates multiple models and data sources continuously, often without human intervention, it requires tighter, more automated governance to ensure reliability and control at scale.

Two agentic archetypes are emerging: single-agent workflows, where one agent uses multiple tools and data sources sequentially; and multi-agent workflows, where specialized agents collaborate through shared knowledge graphs and fine-grained data access. Both require consistent, interoperable data, without which agents could break down.

How to prepare data for agentic AI.  To enable a scaled transformation into an agentic organization, companies can start by building foundational data capabilities. This requires not just a technology reboot, but also an organizational one. That’s because a company’s data strategy and operating model is just as important as its underlying data quality and architecture. Success depends on taking four coordinated steps that link strategy, technology, and people:  

Step 1: Identify high-impact workflows to “agentify.” Organizations can identify a small number of high-value, end-to-end workflows where increased autonomy could unlock impact. Building on established approaches to building data products, leaders can prioritize agentic use cases based on value potential, feasibility, and strategic fit before scaling more broadly.

Step 2: Modernize each layer of the data architecture for agents. Rather than rebuilding everything from scratch, leaders can modernize existing platforms to support interoperability and governance across systems. While some may be tempted to lean on advancements in AI to shortcut data architecture best practices, the strongest organizations build modular, evolutionary architectures with components that can be replaced as new technologies emerge.

Step 3: Ensure that data quality is in place. Organizations must move from periodic data cleanup to continuous, real-time quality management. They can do this while ensuring that both structured and unstructured data, as well as agent-generated outputs, meet consistent standards for accuracy, lineage, and governance.

Step 4: Build an operating and governance model for agentic AI. Scaling agentic AI requires rethinking how work gets done. Human roles are shifting from execution to supervision and orchestration of agent-driven workflows. In a hybrid human–agent work environment, clear governance is essential to allow agents to operate transparently and safely at scale.

It happens in every boardroom. Hours into a marathon meeting, the conversation on the critical strategy topics has not yet started, and that one director won’t stop circling around a minor issue no one else finds relevant. As discussions continue, the same director pushes back on every idea. Momentum stalls, focus blurs, energy dissipates, and frustration mounts. Good governance becomes harder than it needs to be.

The article highlights the characteristics of the most common types of difficult board members. Followed by an outline of a practical framework to help chairs and directors spot early-warning signs, redirect unproductive behaviors, and restore a healthy board dynamic. Finally, it offers what happens when the chair is the problem and describes how the board can respond to protect its processes and its ability to make sound decisions.

Difficult Types.  Three main types of difficult board members: passive passengers (who stay silent and hope to go unnoticed), dominators (who take control of every discussion), and misguided experts (who focus too much on details). Although they behave differently, they create the same issues: Decisions slow, dynamics are strained, and trust erodes. All three types can make it hard for boards to stay anchored on their true mandates: serving as stewards for management and guiding companies’ long-term direction.

From Awareness to Action.  Diagnosing difficult behavior in directors begins with disciplined observation. Effective boards look for patterns, and they assess them through three simple but powerful lenses: engagement (Do directors come prepared, show curiosity, and lean into the conversation rather than hovering at its edges?), interaction (Do they listen, build, and challenge constructively, or do they derail, interrupt, or retreat into silence?), and impact (Does their participation strengthen debate, sharpen judgment, and help the board reach sound decisions, or does it slow progress and dilute focus?). The aim is not to police personalities but to read behaviors clearly, distinguish the occasional misstep from a persistent trait, and intervene early—long before unproductive habits begin to shape the culture of the boardroom.

Recognizing difficult behaviors is the starting point. What happens next determines whether a board regains its balance or slides into disarray. To deal with difficult directors, boards need to collaborate on the following actions:  Set clear expectations.  Give feedback early and directly.  Use structural and procedural levers.  And escalate when necessary.

Adjustments by Type.  Each of the actions described can and should be adapted to the specific type of difficult director.  Passive passengers often respond best to clear expectations and structured opportunities to contribute.  Dominators, for their part, require firm boundaries and consistent enforcement of meeting norms.  Finally, misguided experts benefit from targeted feedback and agendas that channel their expertise toward the right strategic priorities.  

A Balancing Act.  A well-functioning board can often regulate itself, but if emotions start to rise, the chair obviously has a crucial role to play. That role is not to take over the conversation or smooth over conflict. Instead, the chair should work to maintain a disciplined equilibrium in a way that allows disagreement without division, participation without chaos, and expertise without overreach.  This work starts with a simple truth: Chairs are more like conductors than referees. They know that most directors have special talents and want to contribute. They also recognize that directors need to be guided to act in alignment with the board’s collective purpose. Great chairs set the rhythm of discussion, anticipate problems and difficulties, smooth out frictions, and recalibrate tone in ways that maintain alignment. Doing so allows them to handle even the most difficult directors without humiliating them or escalating tensions.  Although chairs play a central role in guiding dynamics, they can’t carry the burden of culture alone.  Boards work best when every director pays attention to how the group functions: who speaks, who hesitates, when conversations drift, when the room needs a reset. 

But what happens when the very person tasked with setting the tone and maintaining alignment—the chair—becomes a problem?   Given the chair’s elevated position, addressing this dynamic is uniquely difficult. No single director can intervene effectively, and even collectively the other members of the board don’t have many options available for intervening. The best course of action is for senior directors or committee chairs to engage the chair with a private, candid conversation that frames the issue around the board’s effectiveness rather than the chair’s personality. Early dialogue often helps, but if that doesn’t lead to change, the board may need to coordinate a more structured process through the nomination or governance committee, ensuring that any action is handled with fairness, transparency, and a clear sense of fiduciary duty.  Should the chair resist feedback or continue to undermine trust, the board may need to consider a leadership change.

Florence Nightingale may be best remembered as the epitome of a kind, caring nurse, but she was also a force for disruptive innovation in health care. Three distinct elements of her work — communicating data compellingly, publicizing clear and simple instructions, and expanding professionalized training — carry timeless lessons for today’s leaders.

In 1854, as the Crimean War raged, Nightingale and a brigade of 38 nurses arrived at the war hospital in Scutari (now Üsküdar) in Türkiye. During the conflict, the first since the advent of the telegraph, newspaper reporters provided updates on the conflict in close to real time. In 1855, John MacDonald of the London Times reported on Nightingale, describing her as “a ‘ministering angel’ without any exaggeration in these hospitals. … When all the medical officers have retired for the night, and silence and darkness have settled down upon these miles of prostrate sick, she may be observed alone, with a little lamp in her hand, making her solitary rounds.”

Thus, Nightingale became “The Lady With the Lamp” — and, perhaps, the world’s first social media star. In 1854, 5,000 babies were named Florence. In 1855, after MacDonald’s article was published, 20,000 were.

Nightingale’s impact far exceeded her influence on baby names, of course. She and her fellow nurses encountered dire, squalid conditions and infectious diseases that ran rampant in military hospitals. The prime minister of Britain sent a sanitary commission to clean up the hospital after Nightingale telegraphed him for support, and she would continue to champion cleanliness in medical settings after the war. When she returned to England in 1856, she met with Queen Victoria to help spur the creation of a royal commission for hygiene in military hospitals.  Thus commenced Nightingale’s three-front disruptive battle in nursing and sanitation, using the tactics of data-driven communication, clear and accessible instruction, and standardized professional training.

Compelling Communication.  Nightingale’s experience convinced her of the importance of following proper hygiene and sanitation practices in hospitals. But how to make people viscerally feel that importance when germ theory hadn’t yet been widely accepted? The answer: through data, visuals, and stories. (“Whenever I am infuriated, I revenge myself with a new diagram,” Nightingale wrote.)  She collaborated with physician William Farr, one of the founders of the Statistical Society of London, crunching numbers to show the obvious impact of poor sanitation policies. Critically, they created powerful ways to communicate their findings.

Clear and Accessible Instruction.  In 1859, Nightingale released a groundbreaking book titled Notes on Nursing: What It Is, and What It Is Not. The first print run of 15,000 copies in England sold out within months. The book was quickly translated into multiple languages, and an American version was published in 1860.  In Notes on Nursing, Nightingale provided clear, practical guidance about how to care for patients. It wasn’t meant for someone seeking a career in nursing; rather, it targeted laypeople who might have to provide caretaking and similar services.  As usual, Nightingale stressed sanitation and prevention.  Her book enabled a broader population to learn to provide proper hygiene and ward off infectious diseases — classic disruptive innovation. In parallel, Nightingale turned her focus to increasing the number of skilled nurses.

Standardized Professional Training.  In 1857, the Nightingale Fund was established to oversee donations that had poured in, in support of Nightingale’s work, which had become widely known. She used a portion of the funds to help open the world’s first formal nursing school at St Thomas’s Hospital in London.  Prior to Nightingale’s efforts, training was disorganized and nursing was inconsistently practiced.  A key driver of disruption is allowing a broader population to do what once required specialized expertise. Nightingale herself had to receive one-on-one teaching to learn the art of being a skilled nurse. Her school played a pivotal role in turning such lessons from art to science, enabling more people to effectively provide nursing services.

Nightingale’s story has three timely lessons for modern leaders.  First, one of the powers of disruptive innovation is doing things differently, not just better. By educating a broader population about hygiene and nursing practices — which had previously been poorly understood — Nightingale enabled more decentralized and accessible health care.  Second, sophisticated technology is not required for significant impact. Nightingale and Farr used early adding machines for their groundbreaking analysis, but what’s striking about the story of their compelling “death wedge” diagram is how little technology was involved.  Third, disruption doesn’t require superpowers or a larger-than-life leadership presence. Nightingale demonstrated timeless qualities and behaviors that fuel disruptive success, such as curiosity, collaboration, and persistence.

As AI becomes more embedded in day-to-day business operations, many senior leaders are discovering the real challenge isn’t navigating the technology but managing the ripple effects its use has across critical areas like decision-making, accountability and trust-building. As teams move faster with AI than governance can keep up, roles are blurring and leaders are struggling to define where human judgment should still take the lead.

For leaders facing these challenges, it can be difficult to separate meaningful progress from noise. Here, members of Forbes Coaches Council share guidance on how to relieve the growing pains of AI implementation as a leader in one’s organization.

1. Reframe AI Integration As A Tech Implementation.  Stop treating AI like a panacea. It’s a new tool, not a solution to everything. Too many leaders struggle with implementation. They’re acting like it’s a grand solution to all their organization’s problems, rather than treating it as a tech implementation of a new tool. Bolting on AI just amplifies the challenges. Take the time for a proper tech implementation.
2. Foster Dialogue And Training To Build Confidence In Adoption.  AI continues to divide people; while some are excited by its potential, others are uneasy about the risks. Leaders can support integration by creating space for open conversation, offering practical training and setting clear, shared goals around the effort beyond just time or cost savings. Taking small steps together, recognizing that adoption will vary across environments, builds confidence.
3. Rethink Standard Approaches To Managerial Development.  A headache for leaders is, if you adapt AI to take over the more routine and early-career tasks, how do managers develop the skills they need to manage, regardless of whether they are managing agents or humans? They still need those basic skills to know how to adapt and flex.
4. Define Success Metrics Before Launching AI Pilots.  The challenge we often see is organizations launching many AI pilots without defining how success will be measured. Teams are eager to test different tools, but there are no shared criteria to judge the results. When it’s time to scale, everyone argues for their own experiment. Leaders need to set clear business metrics and evaluation rules before pilots start so that scaling decisions are aligned.
5. Determine Where AI Adds Value And Where Humans Decide.  Leaders are struggling with trust versus speed. AI moves fast, but teams don’t trust it yet. The headache is either overreliance or total resistance. They need to define where AI adds value and where humans make decisions, and build clear guardrails so adoption drives performance, not confusion.
6. Close The Gap Between Insight And Execution.  One headache leaders face with AI is the gap between insight and execution. Firms generate data and analysis but struggle to turn it into real decisions and action. Leaders must shift from tech fascination to operational discipline, clear ownership, simple processes and accountability, so AI becomes a tool that drives outcomes, not just more information.
7. Reset Expectations And Pace Integration Realistically.  A key headache is expectation inflation. Once AI tools are introduced, management expects immediate gains. What leaders need is guidance on pacing and framing. AI integration is not just a technology rollout. It is a change in workflows, decision rights and skill requirements. Leaders must set realistic horizons and clarify where human judgment still matters.

Other guidance are:

Address The Human Side Of AI-Driven Change.  Prioritize Meaningful Opportunities Amid AI Noise.  Challenge AI Outputs To Avoid ‘Phantom Confidence’.  Bridge Generational Gaps With Tailored Enablement.  Maintain Focus On Strategic Vision Amid AI Distraction.  Acknowledge Fear And Build Trust Through Clarity.  Clarify Value And Measure What Truly Matters.  Strengthen Operational Foundations Before Executing.  Redefine Talent’s Value In Response To Identity Disruption.  And Shift From Static Implementation To Continuous Orchestration.

According to the author he has spent years writing about how entrepreneurs can leverage AI in their businesses and the non-obvious ways AI is changing the game. But he has been lucky enough to spend the last two decades surrounded by entrepreneurs who look at massive industries and ask one simple question: Why does this still work this way?

According to the author his friend Trevor Sumner is one of those entrepreneurs. Trevor is the CEO of an AI company that’s shaking up the consumer market research industry—a space worth more than $30 billion that, until recently, still relied heavily on the same methods it used before the internet existed.  Trevor’s company uses AI to analyze millions of real consumer signals online—social conversations, reviews, search behavior—and turns them into the kind of insights that used to take months and cost a fortune. And they’re growing fast: revenue up significantly, team quadrupled in a year, working with major global brands across 30-plus countries.

But here’s what he finds most interesting. The lessons from Trevor’s journey aren’t just about market research. They’re a blueprint for any founder trying to build a company in an industry being disrupted by AI. And let’s be honest—that’s almost every industry right now.

1. Find the industry still running on fax machines.  Every industry ripe for disruption has a tell: the output is genuinely valuable, but the process is stuck in a different era.  The gap between how an industry operates and how the world actually works—that’s where the opportunity lives.  The lesson: Look for industries where the process is visibly broken but the need is undeniable. That gap is where AI creates the most dramatic ROI.
2. Don’t sell AI—Sell the outcome AI makes possible.  This one is huge, and the author sees founders get it wrong all the time. Nobody signs a contract because they’re excited about your algorithm. They sign because you can deliver a result they couldn’t get before—faster, cheaper, or more reliably. Trevor told the author that when his team pitches major brands, AI is never the headline. The headline is: What if you could understand what millions of consumers actually think about your brand—in real time, instead of waiting three months for a survey?  The moment you make AI the hero of your pitch, you’ve invited a procurement committee to debate whether AI is ready, safe, or overhyped. When the outcome is the hero, the conversation shifts to: Can you deliver this result? That’s a much better meeting.  The lesson: Position the result, not the technology. AI is how you do it. The outcome is why they buy.
3. Your first five clients should scare you a little.  Trevor’s company didn’t start by landing small, safe clients to cut their teeth. They went straight after some of the biggest consumer brands in the world—and they did it before they’d even raised outside funding or built a formal sales team.  That’s not recklessness. That’s strategy.  Big logos validate your product, compress future sales cycles, and set your pricing floor permanently higher.  The lesson: Don’t wait until you feel ready. Punch up. Your first five clients should stretch you and push your vision forward.
4. Context beats capability in a disrupted market.  Here’s something that keeps coming up in every AI-disrupted industry the author watchs: incumbents fight back by slapping the word “AI” onto their existing products. Traditional research firms are rebranding legacy tools as “AI-powered,” creating confusion for buyers who can’t tell the difference between a company built on AI and one that just bolted AI onto the side.  But here’s what separates the winners from the noise: deep domain expertise. Anyone can access powerful AI models these days. Not everyone understands the problem well enough to apply AI in a way that actually matters.  Trevor’s co-founders spent decades inside the world’s biggest consumer brands. They know how brand equity works, how category dynamics shift, what a CMO actually needs on their desk Monday morning. That kind of context can’t be replicated by fine-tuning a model.  This is the single biggest differentiator for AI startups right now. The founders who win won’t necessarily have the most powerful technology. They’ll be the ones who understand their buyer’s world better than anyone else.  The lesson: Anyone can access powerful AI. Not everyone understands the problem well enough to apply it. Domain expertise is your moat.
5. Build for the transition, not just the transformation.  This is the lesson most founders miss entirely.  Enterprise clients aren’t going to abandon their existing tools and processes overnight—no matter how much better your solution is. Trevor’s company was designed to complement existing workflows first, and replace them over time. They even provide playbooks for managing the internal transition—helping their clients navigate change management and stakeholder buy-in.  That patience, counterintuitively, accelerated their adoption.  The lesson: The boldest disruption often wins by moving slowly enough for the buyer to say “yes.”

Business owners spend a lot of time thinking about markets, products and growth strategies. And all of those are important. But, according to the author based on his experience of building companies, long-term success is driven just as much by learning and community as by revenue and technology.

Learning changes people, not just performance.  The author believes that in life, you are either growing or decaying. Staying the same is not an option. Five years from now, only two things will truly be different: the people you met and the books you read. Everything else is mostly noise.  Personally, learning changed the trajectory of his career.  The author’s organization supports learning in simple, practical ways. It offers Audible credits. It builds physical libraries in the office. It hosts leadership book discussions. These are not formal training programs. They are signals. They tell people that learning is part of how they operate, not an optional activity you pursue when things slow down.

Storytelling is how culture travels.  When teams are spread across countries, you cannot rely on proximity to transmit culture. You need stories.  Stories are more powerful than policy documents. They show people what “good” looks like in real situations. Stories also connect teams emotionally, even when they are not in the same room.  This is especially important in global organizations where cultural backgrounds differ. Shared stories create shared identity.

Community is not separate from business.  According to the author, he came to the United States with one suitcase and many dreams. Everything in his family and he has built has come from support systems, education and access to opportunity. He does not see success as something he achieved alone. It is something the community made possible.   Community does not only mean donations. It means creating workplaces where people feel seen, supported and valued. It means investing in benefits early, even when it is not financially convenient. It means showing up consistently and improving programs over time, rather than waiting for perfect conditions.  Entrepreneurs sometimes treat giving and culture as things to focus on after success arrives.  He believes this is part of how success is created in the first place.

Growth requires presence, not just effort.  Entrepreneurship is demanding. Long hours, constant decisions and financial pressure come with the territory. But he learned that worrying about what you cannot control only makes things harder. What helped him most was learning to focus on what he could influence each day and being fully present in those moments. Whether he is in a meeting, talking to his family or walking with a team member one-on-one, attention matters.  This mindset also applies to health.  Work and life are not separate systems. They are integrated. The goal is not balanced by the clock, but alignment with values.

Why this matters for entrepreneurs.  Building a company is not just about creating products or services. It is about shaping people and relationships at scale. Learning builds adaptability. Storytelling builds alignment. Community builds loyalty.  These are not soft concepts. They are operational advantages. They reduce turnover, improve collaboration and create resilience when markets change.  If you want your company to grow beyond what your current size suggests, invest in the things that scale with people, not just processes. Culture, learning and community do not slow growth. They make it sustainable.