Informed i’s Weekly Business Insights

The vibes were strong at Code with Claude, Anthropic’s two-day event for software developers in London that kicked off on May 19, the same day as Google’s I/O in Palo Alto.

“Who here has shipped a pull request in the last week that was completely written by Claude?” Jeremy Hadfield, an engineer at Anthropic, asked from the main stage. Almost half the people in the packed room—many sitting with laptops on their knees, coding or prompting as they watched the talks—raised their hands.

Pull requests are fixes or updates to existing software that are submitted for review before they go live. They are the bread and butter of software development, the chunks of code that most professional developers spend their lives writing—or did until now.  “Who here has shipped a pull request that was completely written by Claude where they did not read the code at all?” Hadfield asked next. Nervous laughter. Most of the hands stayed up.

It’s not news that LLM-powered tools like Anthropic’s Claude Code and OpenAI’s Codex have upended the way software gets made. Top tech companies now like to boast of how little code their developers write by hand. OpenAI, Google, and Microsoft make similar claims. Many others wish they could.

Even so, it is striking how normal this new paradigm already seems, and how fast it has set in. This was the second year that Anthropic has put on developer events, which also run in San Francisco and Tokyo. This time last year, the company had just released Claude 4. It could code, kind of. But with Anthropic’s latest string of updates—especially Claude 4.6 and then 4.7, released in February and April—Claude Code is a tool that more and more developers seem happy to hand their work off to.  

Anthropic says its goal is to push automation as far as it will go. Instead of using AI to generate code and then having humans clean it up and fix the mistakes, it wants Claude to check and correct its own work. “The default isn’t ‘I’m going to prompt Claude’—the default is now ‘I’m going to have Claude prompt itself,’” Boris Cherny, who heads Claude Code, said in the opening keynote.

Trivedi presented a new feature in Claude Managed Agents, Anthropic’s cloud-based setup for building and running multi-agent systems, announced two weeks ago, which the company calls dreaming. Claude agents write notes to themselves, recording and saving useful information about specific tasks. When another coding agent, say, starts to work on the same code that others have worked on, it can use the notes they left behind to get up to speed faster and learn from any errors those previous agents may have made.

Dreaming is a system that Claude agents can use to read through the notes and consolidate the information they contain, spotting patterns and common issues across different tasks. In theory, dreaming should help coding agents learn about a particular code base and get better and better at working on it.

And yet outside the conference there have been a number of reports that many coders are starting to question this bright new future. Some gripe in online forums like Reddit and Hacker News that AI coding tools are being pushed by managers chasing productivity gains, when in practice the technology makes software development harder because of all the extra code developers now have to review.  And yet as Anthropic and others push for greater automation and tools like Claude Code improve, the temptation increases to offload more and more tasks, including oversight.  Others claim that their coding abilities have fallen off as they hand more tasks to AI. And researchers have warned that AI tools can produce unsafe code that will make software more vulnerable to attacks.

If you’ve had the chance to ride in a Waymo, you’ve likely emerged from the vehicle amazed by its abilities.  

Waymo is a specific use case of a technology that’s maturing rapidly and set for significant deployment: robots powered by generative AI. Many companies are already using gen AI chatbots, agents, and related technologies to automate and scale up customer service, but in most of these cases customers interact with the technology on screens. Embedding gen AI into robots gives companies the chance to reinvent their interactions with customers in physical settings—restaurants, hotels, hospitals, retail stores, and other brick-and-mortar locations—where service has remained stubbornly human. Using large language models (LLMs), large behavioral models (LBMs), and agentic AI, this new generation of robots can better understand context, make inferences, and provide personalized experiences. They can converse like competent employees—following logic across conversational turns, clarifying ambiguity, and explaining complex ideas simply.

How to Deploy Gen AI Robots.  Bringing a robot into a workplace is more complicated than just unboxing a gadget, because many workplaces are unpredictable—waiters carry trays, doctors and nurses hustle from room to room, and so on. To convert potential into performance, leaders must carefully select use cases, communicate to customers and employees why and how they’re using robots, and set up guardrails. Four critical steps, based on the authors’ observations in the field, can guide them.  Start with use cases that address labor constraints.  Design robot interactions for customer acceptance.  Position robots as service enhancers, not workforce replacements.  And continually update responsible-use guidelines.

Gen-AI-enabled robots provide a practical path toward achieving cost-effective service in the physical world. They can deliver consistency and personalization at scale, which has long been the Achilles’ heel of physical service. But gen AI robots require a complex and lengthy implementation—and because it must take place in real-world settings, the stakes are higher, failures are public, and physical safety becomes a major concern.

If everyone is special, then no one is.” That line, adapted from the movie The Incredibles, captures the essence of a key issue with AI today. While AI adoption has exploded (nearly nine in ten organizations now use AI in at least one business function), most companies are deploying the same large language models (LLMs) to improve productivity. If everyone has the same advantage, it’s not really an advantage.

This is a trap we’ve seen before. During the digital-transformation wave, companies rushed to develop websites and apps, but competitive advantage—the distinct set of hard-to-replicate assets and operating models a company creates that earns superior returns over time—didn’t automatically follow.

The lesson: Apps and tools can be copied. Value comes from building advantages that are hard for competitors to replicate—that is, competitive moats. That’s a critical lesson to bear in mind as CEOs and boards consider how to capture their fair share of the enormous value just from generative AI that’s at stake.

To better focus that thinking, the authors identified moats—six strategies and three capabilities—that can provide a competitive advantage. These moats shouldn’t be particularly new to business leaders, but AI has shifted the dynamics in each of them. Understanding what that means is the path to leaping from AI as table stakes to AI as an abiding competitive advantage.

Strategic moats.  While a strategic moat is difficult for others to replicate, it should also represent an area where the business already has an advantage. This view can help CEOs determine which area to focus on and how to invest.

Economies of scale: Infrastructure to harness speed and scale.  If cognitive work represents high costs in your business, focus on what you need in place to build the infrastructure so economies of scale from AI work in your favor. Senior leaders should consider consolidating volume across business units or geographies, building shared AI platforms, or using M&A to push additional volume through the same AI stack. 

Privileged data: Treating data like an asset class.  Manage data as a strategic asset class. That starts with prioritizing the data that underpins your differentiation and instrumenting systems to capture, enrich, and maintain this data at scale. Demonstrate responsible data stewardship, such as stringent data protection measures to avoid future regulatory constraints.

Embeddedness: Making switching expensive.  What this means for you: For vendors, understand where the points of deep workflow integration are, and ensure services have feedback loops so performance improves and value increases with use. For customers, every workflow you hand over to an embedded AI system is a bet on the vendor’s road map, pricing trajectory, and continued existence. Negotiate data rights and portability up front, ensure you retain access to that learning in some usable form, and maintain data protection standards.

Network effects: AI as the network architect.  Look for opportunities to create network effects that you previously dismissed as too costly or risky. If you have an existing network, ensure your models improve matching quality, reduce noise, and increase trust with every transaction. As transactions increasingly flow through AI agents, be clear about who owns the agent and who captures value when agents transact.

Business model disruption: Shifting who owns the customer and how value gets priced.  If you’re at risk of being disintermediated, identify what your economic leverage points are to protect and enhance customer relationships. If you bill by time or throughput, identify how to switch to outcome-based pricing.

Constrained assets: AI meets the physical world.  As AI commoditizes knowledge, focus on where your company controls the physical systems that competitors cannot easily replicate. The goal is not simply to apply AI to existing assets, but to build physical networks whose value compounds when combined with intelligence.

Capability moats.  A capability moat is an organizational strength that is difficult to build but enables a company to repeatedly translate AI into sustainable advantage.

Rewiring for velocity: Increasing speed of learning and deployment.  Treat organizational velocity as a strategic differentiator, not an operational metric. Measure your clock speed from idea to proven value to scaled deployment—and remove the bottlenecks that slow it down. Companies looking to truly rewire their organization have to start by building conviction across the entire C-suite, target domains where they have economic leverage, and commit both real resources and their top people to lead the transformation.

Regulation and compliance: Integration into AI solutions.  Treat compliance as strategic infrastructure by embedding auditability, transparency, and governance directly into your AI systems. This helps regulatory compliance scale with your growth. Clarify which parts of your value proposition depend on regulated activities and where you may face gray-zone competition.

Trust: Your anchor for the customer relationship.  Winning companies treat trust as a speed enabler. Identify the core sources of trust in your businesses (safety, fairness, reliability, transparency) and embed them directly into your AI systems through automated governance and policy-as-code controls. Integrate risk and compliance guardrails early in AI solution development.

The forces unleashed by AI have shifted the locus of competitive advantage. For boards and CEOs, this suggests a clear agenda:  Align on your moats—and make trade-offs explicit.  Build the enabler systems that support your strategic moat.  And Govern the moat like a core business, not a set of experiments.

In the age of AI, competitive advantage won’t come from having the cleverest model. It will come from being the organization that turns common models into uncommon moats faster than anyone else.

It was March 2022, Citigroup CEO Jane Fraser was a year and a day into her job. She was the first woman ever to lead a major U.S. bank. And Citi was in a bad spot: Its stock had dropped 15% during her tenure, lagging behind the S&P 500’s 10% growth. It was the only big U.S. bank trading below its book value. There also had been a humiliating blunder in which the bank sent $900 million to the wrong place and struggled to get it back.

To make matters worse, just hours before Fraser strode onstage for the bank’s first investor day in five years, Citi had suffered another indignity: Two executives had caught COVID, and the entire event had gone virtual. Fraser was forced to deliver her remarks into a camera, eyes trained on a teleprompter in the largely empty auditorium.

“We have an urgent need to address the issues that have kept our firm from living up to its full potential,” she said, then spoke bluntly: “It’s frankly not a surprise that we’ve been outperformed by our peers and that we failed to meet the expectations of our investors.” She vowed to change how the bank was run, instilling crisp decision-making and real discipline on execution and delivering results.

Unfazed, Fraser ticked through her recovery plan with the ruthless precision of a seasoned McKinsey consultant (she’s an alum). Her vision for Citi: to be the preeminent banking partner for institutions with cross-border needs; a global leader in wealth management; and a valued personal bank in our home market. Anything that didn’t serve these purposes may end up on the chopping block. And the bank’s culture of mediocrity had to change: “Good enough was good enough for far too long,” she said.

Fraser’s no-nonsense strategy for Citi was a demonstration of the new kind of leadership she was bringing to Wall Street: historical by definition and displaying a vulnerability that bucked the stoic boys’ club culture that had always dominated banking. Here was a CEO who, when her appointment was announced in 2020, talked about empathy, balance, and her desire for a personal and family life—alongside results; one who wore a fuchsia scarf to match her suit. As she told the writer when we spoke again this April: “I think you can make tough decisions. It does not mean you need to be an asshole.”

Five years into her tenure, the grades for Fraser’s turnaround plan are in: The new Citi is very much here. In April, Citi logged its highest quarterly revenue in a decade, with all five of its divisions recording gains, led by services and markets. The bank’s return on tangible common equity hit 13.1% in the first quarter, the highest since 2021. Citi stock is up about 83% since Fraser took over as CEO. It has risen 7.8% this year, ahead of rivals JPMorgan Chase, Wells Fargo, and Bank of America, but slightly behind the S&P 500’s 8% growth. And it has largely addressed regulatory reporting issues, and shed management layers and bureaucracy.

“Turnaround” can be a loaded term when it comes to female leaders. The well-documented glass-cliff phenomenon—in which boards turn to women when the cleanup job is exceedingly difficult or impossible—can be a trap for female execs who accept a no-win challenge.

Fraser’s CEO appointment looked at first as though it largely fit that script, but then she flipped it: If she faced any sort of metaphorical precipice, she looks likely to stick the landing.

But the very metrics that vindicate Fraser’s turnaround also raise the stakes for what comes next. Cleaning up a sprawling bank is one job; growing one is another. She now faces the question dogging every CEO who inherits a fixer-upper: Can she shift Citi out of repair mode and into a genuine growth story—one that helps Wall Street believe Citi can lead again?

Citi’s investors had reason to be skeptical in 2022; they had heard promises of turnarounds before. For decades, Citi had tried and failed to shed its reputation as Wall Street’s slacker bank that had long trailed rivals in profitability. The board had tasked Fraser, a Citi veteran with a track record of reviving troubled divisions, with streamlining the bank, which had still not fully dismantled the unwieldy and lumbering financial supermarket former CEO Sandy Weill had bolted together in an ill-advised acquisition spree.

Fraser’s blueprint was classic consultant-style triage—divest the sideshow businesses, simplify the org chart, and redirect capital to the divisions that could actually win. Early on, she funneled Citi’s varied operations into five distinct business lines, flattened its management structure, and began exiting retail banking in 14 international markets. It’s now more specialty grocer than sprawling supermarket.

Lately, Fraser has used pointed language to signal a cultural reset. In 2023, she urged employees who weren’t on board with her overhaul to “get off the train.” In January, Fraser told her 226,000 staffers that she would no longer be grading them with A’s for effort; they would be judged on results. “I expect to see the last vestiges of old, bad habits fall away, and a more disciplined, more confident, winning Citi fully emerge in 2026,” she wrote.

Caterpillar’s CEO had a problem. Jim Umpleby had stepped into the chief executive role in 2017 with a vision of achieving more profitable growth by selling more services and parts to the company’s heavy-equipment customers. Because offering real-time fleet management information services and selling parts online would depend on digital technologies, he set up a new division called Cat Digital. But the division’s head soon had unwelcome news for Umpleby: The company didn’t know its customers well enough to deliver on its goal. Customer data was siloed, fragmented, and, in the case of secondhand equipment, often entirely lacking.  Once fixex, Caterpillar had grown its services revenue from $14 billion in 2016 to $24 billion in 2024.

That problem is one shared by countless leaders who see how digital can enable a growth strategy but are stymied by a legacy of fragmented, incomplete, and inconsistent data assets. They are frequently told that getting their data in order is a prerequisite for taking full advantage of new tools like artificial intelligence but too often see data transformation as an IT modernization project. They delegate the work to IT leaders and evaluate success based on cost, speed, and tool adoption. But when data is treated as IT infrastructure rather than as an enterprise asset, its impact is predictably limited. Companies that gain real value from their data actively involve the top management team in data transformation.

Caterpillar’s experience underscores a critical lesson: Data transformation is not a purely technical exercise. Top company leaders must set a goal for the transformation in terms of business outcomes; give executives responsibility for data; commit resources to building an enterprise data platform; give all stakeholders a voice in the transformation; and direct strategic investments that take advantage of new data capabilities including AI.

Every large organization today manages vast data assets, but few extract their full value. The difference lies in executive leadership. When CEOs talk about data in earnings calls, participate in governance discussions, and hold leaders accountable for data quality, the organization listens. In an era where competitive advantage increasingly depends on insight, integration, and intelligent automation, companies that treat data as chiefly the responsibility of the IT function will fall behind. Those that treat it as a corporate asset — and lead accordingly — will define the next decade of performance.

Can the fundamentals of strong leadership transfer across industries, or does deep specialization lead to a career ceiling? A 2024 TestGorilla survey of over 1,000 employers and 1,100 employees found that 94% of skills-based employers “agree that skills-based hiring is more predictive of on-the-job success than resumes.” Essentially, our ability to lead and build teams matters far more than our years of industry-specific knowledge.

Purpose needs to drive the decision.  The conventional advice for executives considering an industry change focuses on compensation and scope.  It should be the purpose that carries you through the transition period. When you’re learning a new industry and building new relationships while still delivering results, the work itself has to mean something to you, or the difficulty will outpace the novelty and ambition you felt when you first took on the challenge.

Your credibility doesn’t automatically transfer over.  According to the author, something he hadn’t expected about changing industries was how completely he had to rebuild his professional credibility. After 27 years in telecom, he was known. Whether people had worked with him directly or not, most understood what he stood for and what he’d accomplished. He walked into his new role assuming that his positive intent would be obvious, that people would see his drive and trust his approach the same way his teams had for decades.  For the first months, he was wrong. Every engagement with his team and his peers was being evaluated, as if he was a blank slate with no history within the organization or industry. He learned that expertise in a previous industry doesn’t buy you credibility in a new one. You have to earn it from zero.  The experience taught him that the leaders who succeed in these transitions are the ones willing to be vulnerable enough to say, “I don’t know.”  According to the author, he started asking questions constantly and admitting what he didn’t understand, and he let people see that he was there to learn before he was there to change things. That vulnerability, which can feel counterintuitive at the executive level, is what builds the two-way trust that makes everything else possible.

Fundamental leadership skills are valuable—no matter the sector.  According to the author, there’s a pattern he has seen among leaders who spend an entire career in one company or one sector. They can be brilliant at what they do, and the company might be a Fortune 100, but when they step outside that environment, their experience can be seen as narrow. They’ve operated within one set of industry norms and one regulatory framework. The value they bring, while likely significant, is just harder to translate.   But according to the author, having led in two different regulated industries with two different Fortune 100 organizations has given him something he is proud of: proof that his leadership, operational and transformational skills are transferable, that what he brings to a business is about how he builds teams and drive results, and that those fundamentals work across sectors. This kind of cross-industry experience also broadens what you can contribute in board service and senior leadership conversations where perspective across multiple industries is highly valued.

Inside one of Link Logistics’ industrial properties—warehouse space the author’s firm leases to businesses nationwide—you might see teams building airplanes, assembling drones, printing candy wrappers, or moving thousands of packages. Each facility is a vital piece of the broader U.S. supply chain and a window into how logistics real estate operations drive efficiency, speed, and growth.

Over the years, the author has come to see how much a smoothly operating warehouse resembles a well-functioning organization. The principles guiding effective industrial real estate operations offer valuable insights for leaders across industries.  Here are five lessons he has learned from spending time inside warehouses across Link Logistics’ national portfolio.

1. Put great people in the right seats.  In a well-designed warehouse facility, materials move without bottlenecks or wasted steps. The same principle applies to organizations: Great leadership is about removing barriers so people can do their best work.  That starts with decision-making. You’re trying to create an optimal way to make decisions quickly, whether that’s getting the right people in the room at the senior level or delegating authority to those closest to the work. The goal is empowerment: Put the right people in the right seats, trust them to make decisions, and get out of their way. If you do that well, those leaders, in turn, empower their teams, and decision-making becomes faster throughout the organization.
2. Design systems that scale and adapt.  Inside industrial facilities, businesses plan for both volume and flexibility. They need to handle seasonal spikes and shifting demand without collapsing under pressure. The same thinking should guide how leaders build organizational systems.  The foundation is investing early in systems built for scale, particularly around data. A strong data platform that can grow with your organization enables rapid decision-making and direct customer communication. In an AI-driven world, complex analysis that once took weeks can now happen instantaneously, allowing teams to focus on higher-level strategic work. Building agile, adaptable systems around data allows smart people to make great decisions while scaling rapidly.
3. Measure what matters.  In warehouse operations, you can’t just count pallets. You must measure throughput, turnaround time, picking accuracy, and dozens of other variables. The same discipline applies to leadership: You can’t improve what you don’t measure, and you must be smart about what you decide to measure.  You also need a culture that values measurement to drive excellence.  The key is developing a culture in which people constantly look for ways to improve not just what they do but also what they measure. If you run that cycle well, it leads to vast performance changes over time.
4. Build backup into your strategy.  Smart warehouse operators have backup systems: generators, multiple access points, and cross-trained staff. When disruption happens, these redundancies keep operations running. Resilient organizations operate the same way.  It starts with people. You want a deep bench of great talent, but you also need systems that ensure knowledge lives beyond any single person.
5. Trust the floor.  In any warehouse facility, the people closest to the work know what’s broken and what’s functioning well. The same is true in any organization, where strategy that ignores the field rarely works.

Almost every company today describes itself as “data-driven.” In practice, very few actually operate that way.  The difference usually isn’t about access to dashboards, AI tools or cloud platforms. Most organizations already have those. What separates high-performing companies from the rest is something much simpler: how leaders use information when it’s time to make decisions.

Many successful organizations follow a handful of consistent patterns. Six practical playbooks that show up again and again in companies that turn data into a real business advantage.

1. Stop waiting for monthly reports.  By the time those reports show a problem, the problem has usually been there for weeks.  Stronger organizations look for continuous signals instead. Modern analytics tools make this far easier than it used to be. Data pipelines, real-time dashboards and automated alerts can surface issues quickly instead of waiting for someone to notice them in a report.  The goal is not to react to every small fluctuation. What matters is spotting patterns early enough to respond.
2. Look for churn before it happens.  Customers rarely leave without warning.  Most churn follows a pattern. Usage declines. Engagement drops. Support tickets increase. Renewal starts taking longer.  Individually, these signals may not look dramatic. But together they tell a clear story.  Data-driven leaders pay attention to those signals early. Instead of waiting for cancel emails, they combine product data, support activity and billing trends to identify customers who may be drifting away.  Interestingly, this doesn’t always require complicated machine-learning models. Simple cohort analysis or trend comparisons often work just as well — and teams can actually understand the results.
3. Treat pricing as an experiment, not a decision.  Many organizations still treat it like a static decision — something set once and revisited occasionally.  The companies that perform well tend to approach pricing as a learning process.  Instead of relying on assumptions, they test ideas. They analyze how different segments respond to price changes. They look closely at discount patterns and conversion behavior.
4. Eliminate “whose numbers are right?” debates.  If you’ve ever sat in a leadership meeting where teams argue over whose numbers are correct, you already understand this problem.  The inconsistency slows everything down.  Organizations that operate well with data usually fix this early. They define a single source of truth for key business metrics.  This doesn’t require a massive transformation project. It often starts with a few important areas — revenue, churn and unit economics — and builds from there.
5. Understand where growth really comes from.  Growth can be misleading.  A company might increase revenue while quietly reducing margins. Or it may acquire many new customers who ultimately produce little long-term value.  That’s why strong data-driven leaders dig deeper.  Such analyses often reveal surprising patterns.  In many cases, a relatively small portion of customers or channels generates the majority of value. Once leaders see that clearly, resource allocation becomes much easier.
6. Put data where decisions happen.  One of the biggest differences between “data-aware” companies and truly data-driven ones is where insights live.  In many organizations, data sits inside dashboards. Teams have to go looking for it.  The strongest organizations bring insights directly into daily workflows.  Instead of asking people to check dashboards constantly, data shows up where decisions are already happening.  That shift sounds small, but it changes behavior dramatically. When insights are easy to access in the moment, people naturally start using them more.