In an era where AI is accelerating faster than most organizations can absorb, many IT leaders are grappling with how to move quickly without creating fragmentation. For Leigh-Ann Russell, BNY’s CIO and global head of engineering, the answer comes down to a single word: coherence.

For Russell, coherence isn’t a slogan. It’s a leadership discipline that connects strategy to execution, technology to trust, and ambition to sustainability. In a recent episode of the Tech Whisperers podcast, she discussed why coherence is so integral to the modern CIO playbook and how to leverage it to scale impact instead of chaos.

Russell’s perspective is shaped by a career defined by range and resilience, from her formative years in Scotland to leading complex, high-stakes transformations across industries and geographies. After the podcast, we spent more time exploring how that journey has informed her leadership operating system, how she translates coherence into enterprise-scale AI execution, and why IT leaders must learn to navigate the intersection of innovation, control, and reusability. What follows is that conversation, edited for length and clarity.

Dan Roberts: Are there experiences from your formative years growing up in Scotland that shaped how you lead today?

Leigh-Ann Russell: I credit my father as the biggest inspiration in my life. He worked seven days a week and had two jobs, yet he was always present. When I look back, I don’t know how that was possible, for him to have a 7-day-a-week job and still take me to the park and to ballet lessons, or even afford ballet lessons. He was this embodiment of a combination of work ethic and family, and that’s how I’ve led.

There was also a lot about my life growing up that I hid. I hid the fact that I was a single parent. I hid the fact that I grew up in a council estate, similar to public housing in the US. I hid a number of things about me. It wasn’t until I got to be a bit more mature in life that I realized these were not things to hide. These are the stepping stones that make me the person I am.

My father taught me to do more with less and the power of work ethic. My daughter taught me ninja productivity skills. These were not things you should be uncomfortable about. These are things you should celebrate. My whole virtuous feedback on that, as a leader, if you share what makes you human, then it will be much easier to connect with other leaders and other people in your team. And then they feel comfortable sharing what makes their human foundation.

When the environment is complex, fast-moving, and high stakes, like it is today with AI, what are the core operating principles you return to as a leader, when you’re tired, under pressure, or being watched?

My core operating model centers on two things: talent and clarity. My philosophy is, my job is to find great talent and help them be the best version of themselves. If you achieve that, then real, magical things happen, because it’s people who create magic, not technology.

The second part of my role is about creating clarity. Life is complex, leadership is complex, and what teams need is simplicity. It’s about trying to simplify the problem, understand the trade-offs, and align people. Take AI as an example: The technology can create enormous value while also creating friction at scale if we’re not redesigning the work thoughtfully around it. That’s why it feels like the next leadership challenge — it’s not just about deploying AI well but designing the system around it with clarity and consistency in mind.

During the podcast, we talked about how you were very intentional in choosing coherence as your word of the year. Can you give some examples of coherence applied as a leadership discipline?

Coherence is a hard thing to build and a fast thing to lose. It starts with humans. It’s something Robin Vince, our CEO, does really well in bringing our leadership team together. He talks publicly about the fact that we all have a coach, and we have the same coaches from the same company and come together around that. He’s very intentional about creating coherence as a leadership structure.

As you go vertically down to the different meanings of coherence, it also applies equally to technology. It’s very easy to chase the shiny thing, and there’s a very tenuous conflict between people being empowered to do great innovation but also doing it in a structured way so that you avoid lack of controls or duplication or issues with architecture and costs firing out of control.

That double meaning of coherence and being very tight on the balance between individual innovation and empowerment and leadership becomes critical.

Can you talk about what your AI strategy looks like across the enterprise?

We set up the AI hub in 2023, and when I came to BNY in 2024, we started thinking about adoption and enablement across the enterprise, guided by our mantra: AI for everyone, for everywhere, for everything. In 2025, we set the goal of having 65% of the bank trained on AI, but we made 100% as early as June, and we’ve had to rewrite the training program twice since then to enable our employees to continue deepening their proficiency.

That enablement was important and we’ve had an amazing uptake, with over 220 AI solutions now in production accessible in Eliza, our enterprise AI platform. Eliza is built on the premise of foundational, reusable capabilities and is designed to enhance client service and company operations and drive cultural transformation through the power of AI. We talk more about Eliza and how we are advancing responsible and ethical AI in financial services on our BNY website.

Over half the bank have built their own agents, and we also have digital employees at the bank — 140 autonomous agentic employees who work alongside our human employees and that have direct human managers who monitor what logic is applied to decision-making. This is truly agentic.

So, 2025 was about widespread adoption and literacy, and now we’re moving from AI adoption to AI at the core. Even in the most complicated use cases that we’ve put into production, I still think they’re somewhat at the edges — anomaly detection, pulling together client briefing documents, or looking at contract reviews. Very advanced use cases compared to most enterprises, but we are pivoting in 2026 to having AI at the core of everything that we do at the bank. That’s truly transformational, and it’s the next step in our journey.

You have 140 digital employees, an idea that wasn’t even on the radar at the beginning of 2025. How did your organization move and adapt to scale that up so quickly?

It goes back to the philosophy that leadership is all about having talent and enabling them. We have an amazing team in engineering — and across the bank, because this is not just an engineering piece. If you look at our first digital employee, it was in the payment space, looking at reconciliations, and that was born out of a collaboration between engineering and operations. Our first human manager of a digital employee was in the operations side of the business.

Reimagining how work gets done can’t just be an engineering issue. It has to be in partnership with the business. Those individuals in the businesses and in engineering who can think back to first principles about how work gets done are leaping ahead on their AI journeys because they’re not just thinking about adding in AI as an afterthought; they’re thinking about redesigning their workflows with AI at the core.

A great recent example of that is in our onboarding process. We now have a multi-agentic model that has taken the research part of the process down from double-digit hours to single-digit minutes. This partnership and ability for our people to reimagine how we work with AI now at the core is foundational.

The strategy you’ve laid out really is a journey, and it seems there are some key foundational steps that many organizations are trying to skip, which is creating all sorts of problems for them.

In the podcast, we talked about the flip side of coherence being chaos. If you have chaos, AI will just amplify that chaos. That’s at a stack level or a leadership level. As the pressure is on companies to go out and adopt, this is where Eliza, our platform, has been truly instrumental to us because everything AI-related at the company is centralized in Eliza. It’s our tech stack; it’s our governance framework. Having one place for AI so you’re not chasing multiple tools and multiple companies, and having that very clear AI strategy embedded in a single platform, has been really differentiating for us.

In truth, there has been no single silver bullet in our AI journey. We have a tech-enthusiastic CEO in Robin Vince, who realized very early on that AI would be transformative for our company and has been determined that BNY remain at the forefront. With his leadership from the top, we invested early in our people to cultivate the AI-literate workforce we have today. So the fact that it’s a CEO-led strategy, plus the platform, plus the enablement has really helped us get to the speed of having 220 AI solutions in production supporting the enterprise.

Considering the strategic priority you’ve placed on AI adoption, how do you balance innovation and control?

Our goal is to enable innovation across the firm, which speaks to this mindset that being “AI first” is more important than just control. Obviously, we need control from a risk, compliance, legal, resilience, and cyber point of view, but from a financial and leadership perspective, we’re not trying to control and damp down and make everyone justify every single use case. As a result, we’ve said no to a lot less than I think most other companies would have done.

When I think about the trade-offs of that, it’s understanding “what is innovation?” and making sure there’s a reusable core. Because people have in their mind, “If I just hire my own engineers, and I have my own architecture, and I have my own software, I’m really innovative.” People tie innovation to having the new shiny thing, and it’s very hard to shift to a mindset that understands sometimes innovation is reusing what other teams have already developed and building on that. So, sometimes the more painful conversations involve trying to help people reground in reusability, common architectures, and common data platforms, understanding that isn’t hampering their innovation, it’s providing a solid foundation they can build on to go faster.

That intersectionality of innovation and control and reusability is the difficult thing we have to get right, because if you have too little control, you have the chaos we spoke about, and we know that AI amplifies chaos. If you have too much control and too much centralization, then you do hamper innovation. It’s something I’m very conscious of, that we don’t want to do either.

Great leadership often comes down to managing those tensions. What is the central tension you’re learning to navigate right now and how is it shaping the leader you’re becoming?

It goes back to a quote I mentioned in the podcast about Atticus Finch: How do you maintain your convictions without being rigid? Because one thing I know for sure, there are no right answers right now. Does one discipline shift left and one discipline shift right? What is the role of engineering in the future? There is absolutely no right answer to that. There’s only a set of choices that’s right for your institution, and what might be right for a marketing company will not be right for a regulated bank.

I use my digital twin to make sure I’m not too over-convicted, because I have that tendency, in my past growing up in the oil field and it’s a slightly Scottish tendency. So that’s the thing I’m really pushing myself on: How do I retain my conviction, but not become rigid, and stay really open to what is coming at us, because the change is like we’ve never seen — in a very positive way as an engineer, but we have to remain convicted, not rigid.

In world of “double VUCA,” where the impact of external volatility, uncertainty, complexity, and ambiguity is being compounded by fragmented AI journeys, a lack of clear AI strategy, and ineffective leadership internally, Leigh-Ann Russell shows us why coherence is an essential strategic discipline. In the age of AI, it can spell the difference between leaders who scale impact and those who simply scale chaos. For more insights from Russell’s leadership playbook, tune in to the Tech Whisperers.

CIOs of B2B SaaS companies are just as responsible to represent technology as they are to run it. In an environment where the buyer is often another CIO, however, the role becomes something fundamentally different. It’s no longer confined to internal execution. It extends into the market, customer conversations, and the moments that ultimately shape revenue, trust, and long-term relationships. So the modern SaaS CIO operates as a true double agent, running the business from within while representing it to the market.

Box CIO Ravi Malick sits squarely in that duality. After serving as CIO of Vistra Energy, a company defined by legacy systems and industrial scale, he stepped into a digitally native, founder-led SaaS business in 2021 where technology is inseparable from the business itself. He now leads internal tech while engaging directly with CIOs of companies evaluating Box, bringing a perspective shaped by both worlds. What stands out in Malick’s perspective isn’t how different the role is, but how much more expansive it’s become.

What stays the same, what evolves

The core tension of the CIO role hasn’t changed. “There’s always more demand than you have the capacity or funding for,” Malick says. Prioritization, alignment to business strategy, and the constant need to modernize while operating at scale still define the job. The difference, however, is the environment in which those challenges now exist.

At Box, Malick operates inside a workforce where technology fluency is high and expectations are even higher. “I partner with 3,000 technologists who love to solve problems with technology,” he says. That creates a powerful advantage, but also a new kind of pressure. Demand for tools, platforms, and innovation is constant, and AI has only accelerated it.

That dynamic is further shaped by Box’s leadership. As a founder-led company, technology conversations extend well beyond the CIO’s organization. “It’s a different dynamic when your CEO is a founder and a technologist,” Malick says. “You’re as much a steward of incoming ideas as you are a generator of them.” That relationship creates both pace and perspective, requiring the CIO to operate as both orchestrator and partner in shaping how technology evolves across the business.

In that context, the CIO is leading within a highly informed, highly engaged organization where expectations for speed and innovation are constant. The challenge isn’t modernization as a one-time effort, but ensuring the tech stack continuously evolves and scales with the business.

Balancing the internal mandate with external pull

What truly differentiates the role in SaaS is what happens outside the enterprise, and the pressure that comes with it. The CIO is still accountable for running IT, ensuring security, and maintaining operational excellence. At the same time, there’s growing expectation to show up externally, engage customers, and directly support revenue.

Malick doesn’t present that balance as seamless. “It’s a daily challenge,” he says. “But sometimes not balanced so well.” There’s a constant push and pull between internal priorities and external demands, and in many cases, revenue pulls hard. The opportunity to influence deals, build relationships, and contribute to growth elevates the strategic importance of the role, but it doesn’t remove the responsibility for the day job.

What allows Malick to operate effectively in both worlds is the strength of the foundation behind him. He points to the maturity of his leadership team, operating model, and internal processes as critical enablers. With clear structures, strong leaders, and disciplined execution in place, he has the bandwidth to spend meaningful time externally. It isn’t always a perfect balance, but it’s a deliberate one.

From operator to peer in the market

Through Box’s customer zero program, Box on Box, Malick operates as both CIO and practitioner, bringing firsthand experience into customer conversations. “I can take how we build at Box to customer conversations,” he says. That perspective shifts the dialogue away from product positioning, and toward the realities of execution.

In a market where CIOs are constantly being pitched, that distinction carries weight. “They want to know how it works from the perspective of someone managing it,” he says, adding he leans into that by being transparent about both successes and missteps. “We share the challenges and false starts we’ve managed through.”

That candor builds credibility, and credibility builds trust. After all, people buy from people they trust, and in enterprise technology, says Malick, peer-to-peer conversations are a faster path to trust than demos. 

The external dimension of the role also holds a symbiotic relationship with internal responsibilities. Malick brings customer conversations back into Box, using them to inform how he thinks about technology decisions and broader strategy. He describes the CIO community as uniquely open, even therapeutic, where leaders candidly share challenges and exchange ideas. That openness creates a feedback loop where external insights sharpen internal execution, and internal experience strengthens external credibility.

What this means for the CIO role

What makes Malick’s perspective especially relevant is that the lesson isn’t limited to SaaS. As technology becomes more central to growth, customer experience, and business model change, CIOs in every industry are being pulled closer to the front office. The shift is about becoming more fluent in how technology translates into trust, speed, and commercial impact, not just becoming more visible.

For Malick, one of the biggest lessons is the role now demands a different kind of leadership than many CIOs were originally trained for. “Don’t make assumptions, and don’t assume something’s easy or intuitive,” Malick says. In a world where technology is reshaping how people work in real time, communication becomes a strategic discipline. CIOs have to explain change, absorb feedback, and keep translating between technical possibility and business reality.

The rise of AI adds another dimension to the double agent role. CIOs are building the content foundation that AI needs to be effective, and ensuring the organization can experiment with AI without sacrificing compliance or control. In a fast-paced technology company, ideas, opinions, and new technologies come from every direction. So the CIO isn’t simply the expert with the answers but often the one managing velocity itself, deciding where to push and where to hold.

“You have to figure out when you need to be in the fast lane and when you don’t,” Malick says. That kind of judgment is becoming more critical as technology moves to the center of the business, and it’s another reason CIOs are stepping into CEO and COO roles.

As AI accelerates the pace of change and creates the potential to decouple revenue growth from headcount growth, that ability to manage speed, scale, and tradeoffs becomes a defining leadership capability. That’s why the SaaS CIO should matter to leaders far beyond software. With AI transforming every industry, the role is becoming a preview of where the profession is headed — not just to run technology, but help shape how the company grows, how it shows up in the market, and how it earns trust. The double agent CIO may sound like a SaaS phenomenon. Increasingly, though, it looks more like the future of the job.

In an era of constant technological change, agility is more than a buzzword; it is the single most critical characteristic of a high-performing IT department. While C-suite leaders look to technology for a competitive edge, many CIOs find themselves wrestling with a fundamental challenge: Innovation is only as strong as a team’s ability to adapt. The most ambitious transformation roadmaps, from AI adoption to cloud migration, will inevitably stall if the workforce’s skills have not kept pace with the technology. This places a new mandate on the CIO, one focused less on managing technology and more on cultivating a culture of continuous learning.

CIOs need to think and act like chief learning officers, treating skill development as a core strategic function rather than solely an HR responsibility.

The urgency of this shift is clear in the data. The World Economic Forum’s Future of Jobs Report continues to list digital skills, cloud know-how and AI literacy among the fastest-growing capabilities. Meanwhile, research from CompTIA shows that nearly three-quarters of CIOs see skills alignment as the top barrier to realizing the value of their technology investments. This creates a dangerous gap between ambition and execution.

In its 2026 Global CEO Survey, PwC found that CEOs’ top concern is whether they are “transforming fast enough to keep up with technology, including AI”. Yet other PwC research on workforce hopes and fears reveals that only a small fraction of workers use generative AI daily. The pace of innovation is dramatically outstripping workforce readiness, creating an urgent mandate for CIOs to become agents of change.

From my perspective, AI upskilling must be treated as a strategic operating system for the entire IT department. Competitive advantage comes from a deep, holistic understanding of where AI fits, what business problems it solves and how humans and systems can work together effectively. It cannot be an afterthought or a hopeful assumption.

You can’t steer without knowing your starting point

To build an effective upskilling program, you must first understand your current capabilities. I advise CIOs to begin by mapping their existing IT, data and AI skills landscape to identify strengths and, more importantly, to expose blind spots or gaps before they become business risks. This requires a structured skills-mapping process that inventories both core technical skills and adaptive work behaviors. The former includes essentials like cloud fluency, modern software engineering, cybersecurity and data science literacy. The latter is about nurturing the human element and the skills that enable teams to thrive amidst change.

Using established competency frameworks, such as the NIST NICE Framework for cybersecurity roles, can provide a standardized language and structure to this process. These frameworks help create consistent job descriptions and clear learning pathways. This assessment should go beyond just listing skills. CIOs should understand how teams actually apply those capabilities in real delivery environments. Regular reassessment, ideally semi-annual, ensures your skills map stays current as technology evolves and business priorities shift, preventing your upskilling program from becoming misaligned.

Build a continuous learning system, not just a training program

The most effective CIOs I know treat learning like a living program, one designed to evolve as technology, roles and business priorities change. This means moving beyond sporadic, one-time training initiatives to build an ongoing capability-building system. Key elements include role-based, modular learning paths. For a cloud engineer, this might mean a path focused on advanced container orchestration and AI-powered observability tools. For a project manager, the path might focus on agile methodologies for running AI projects and data-driven reporting.

It is also critical to create safe environments for experimentation, such as internal sandboxes or pilot programs, where teams can apply new AI skills without operational risk. This fosters a culture where failure is seen as a valuable learning opportunity, not a mistake to be hidden. Furthermore, encouraging peer-led learning through internal workshops, hackathons and formal mentorship programs can accelerate skill transfer and break down the silos that so often hinder progress.

This is crucial because adoption alone does not guarantee success. For instance, new research from the Project Management Institute demonstrates this very point. In our Gen AI and Agility report, we surveyed 2,000 project professionals who use both agile practices and GenAI. We found that while adoption is growing rapidly, the actual value they realize varies widely depending on how the technology is applied and whether agile values are genuinely practiced. Simply giving teams new tools is not enough.

Governance also plays a critical role here, ensuring that learning investments stay connected to business outcomes. In practice, this could mean a small, cross-functional council that meets quarterly to review learning metrics, assess alignment with new business goals and make decisions on retiring old training modules and commissioning new ones. This keeps the program dynamic and prevents it from becoming obsolete.

Embedding this practice into your IT culture is what makes it stick. CIOs can use several tactics to weave continuous learning into the fabric of their departments. Link skill updates to project retrospectives. Tie career progressions and compensation to skills mastery in core areas like AI literacy and data integrity. I’m also a huge advocate for holding “innovation days” where teams can explore new AI tools and features, building confidence with the very technologies the organization is already investing in. Without this focus on adoption, even the best technology is wasted. A 2025 report on digital adoption from WalkMe found that enterprises wasted millions on underused tech last year alone because adoption was an afterthought.

Avoid the common detours on your upskilling roadmap

As you travel this path, be mindful of common pitfalls that can easily derail your efforts. One of the most frequent pitfalls I see is chasing a single trend, like GenAI, at the expense of foundational IT skills. I’ve seen organizations invest heavily in a single large language model API for all employees while their core network infrastructure remains outdated and vulnerable, creating a lopsided and fragile capability. Another pitfall is treating training and upskilling as a “one-and-done” event. Without continuous reinforcement and opportunities for real-world application, the natural “forgetting curve” takes over and knowledge quickly fades. Finally, a failure to apply governance leads to a “wild west” scenario. This results in one department becoming highly proficient in a specific AI tool that is incompatible with the rest of the enterprise, creating new, more complex silos instead of breaking them down. Upskilling for the AI era demands balance; you must build depth in core disciplines alongside adaptability for new technologies.

This AI era requires CIOs to be cultivators of talent, not just managers of technology. Our role is to model and encourage adaptability, continuous learning and disciplined experimentation across our entire IT workforce. To be truly agile, our teams must be empowered with the skills and the confidence to match their ambition. The time has come to move from a model of reactive training to one of intentional, strategic capability-building that becomes part of your organization’s very DNA. By leading this journey, you can ensure your teams and your organization are ready to meet the future with confidence.

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In the high-stakes world of enterprise technology, we are culturally conditioned to celebrate addition. We throw launch parties for platform modernizations, issue press releases for AI integrations and track delivery velocity as a proxy for progress. Roadmaps grow longer. Backlogs get fuller. Teams stay busy.

Yet in my work advising boards and executives on capital allocation, I have found that the most dangerous number in an IT organization is not how much you are building. It is how much you are refusing to retire.

I call this the innovation tax. It is the silent, compounding levy placed on engineering capacity by software assets that have outlived their economic utility. Unlike financial debt, which appears clearly on the balance sheet with a defined interest rate and maturity date, this tax hides in the margins of the P&L. It shows up as slower delivery, declining morale, rising operating expenses and an ever-present sense that, despite record effort, the organization is falling behind.

For CIOs attempting to position themselves as strategic business partners rather than delivery executives, this liability is existential. If you cannot explain where engineering capacity is being consumed, you cannot credibly argue for new investment.

The psychology of hoarding

To understand why this tax persists, we must look beyond the spreadsheet and look at the human brain. The barrier to decommissioning legacy software is rarely technical. It is emotional.

I experienced this firsthand during a portfolio review for a mid-sized logistics company. I had identified a legacy reporting module that cost $250,000 annually to maintain but was used by only three customers. The math was obvious. We should kill it.

I walked into the meeting expecting a quick approval. Instead, I faced a wall of emotional resistance. The VP of Sales argued that killing the feature would damage the relationship with those three customers. The Engineering Director, who had written the original code for that module a decade ago, argued that it was “stable core infrastructure” that shouldn’t be touched.

I realized in that moment that we weren’t debating economics. We were debating identity. Psychologists call this loss aversion. We feel the pain of losing something roughly twice as intensely as the pleasure of gaining something of equivalent value. When a product manager considers deleting a feature, they don’t see a gain in efficiency. They see a loss of optionality.

This is compounded by a deep-seated bias to overvalue things we created ourselves. That Engineering Director didn’t want to save the module because it generated value. He wanted to save it because it represented his contribution to the company’s history.

I eventually solved this not with logic but with process. I worked with the CTO to establish a quarterly “sunset committee,” an operational body with one KPI: code retirement. By formalizing asset destruction, we removed the emotional weight from the creators and placed it on a governance framework. In the efficiency economy, detachment is a competitive advantage.

The mechanics of carry cost

Consider the difference between a project and an asset. A project has a distinct end date. An asset has an indefinite carry cost.

I recently audited a portfolio for a logistics enterprise where leadership was baffled by their inability to ship new features. They were not suffering from a lack of talent but from asset congestion. Every feature they had shipped in the previous five years was still active and requiring security patching, infrastructure monitoring, compliance reviews and dependency updates.

This situation is often mislabeled as technical debt, implying a code-quality issue that can be resolved with refactoring. That framing is dangerously incomplete. This is a capital allocation problem. When you allow low-value assets to persist, you are effectively taxing every future initiative. New work must accommodate old assumptions that no longer serve the business.

Industry data supports this view. Gartner estimates that unmanaged technical debt and portfolio complexity can consume up to 35 percent of IT budgets, effectively crowding out investment in innovation. If a third of your technology spend is devoted to sustaining yesterday’s ideas, you are not underfunded. You are misallocated.

The zombie asset diagnostic

After seeing this pattern repeat across dozens of organizations, I developed a specific diagnostic to identify what I call “Zombie Assets.” These are features that are technically alive but functionally dead. They consume compute resources and engineering attention but generate zero marginal value.

I use a simple heuristic called the Rule of Two. When I audit a portfolio, I look for features that have not been touched by a user in two months or updated by a developer in two years. If a feature hits both markers, it is a candidate for the morgue.

I applied this Rule of Two at a healthcare SaaS company that was struggling with margin compression. We scanned their codebase and usage logs and found that nearly 22 percent of their features met the criteria. These weren’t obscure back-end scripts; they were customer-facing buttons and reports that had simply fallen out of fashion.

The engineering team was terrified to turn them off. They cited the “Sunk Cost Fallacy” in reverse, arguing that because they had spent millions building them, they had to keep them running “just in case.” To prove them wrong, we ran a “Scream Test.” We turned off the features in the staging environment and waited for someone to complain.

Silence.

We then turned them off in production. Still silence.

By decommissioning those zombie assets, we reclaimed 18 percent of the engineering team’s capacity in a single quarter. That capacity was immediately redeployed to a high-priority AI initiative that had been starved for resources. We didn’t need to hire more engineers. We just needed to stop maintaining the dead. This diagnostic is now the first step in every turnaround I lead.

The hidden talent drain

This compounding burden not only affects financial performance. It quietly destroys talent density. Top engineers want to work on meaningful problems. They want to build new capabilities, modernize systems and see their work drive measurable outcomes.

I recall a specific exit interview with a senior architect who was leaving a well-funded fintech startup. When I asked her why she was leaving, she didn’t mention money or culture. She said, “I spend 70 percent of my week patching a system that should have been turned off two years ago. I’m not an engineer anymore. I’m a curator.”

When most of an engineer’s time is spent patching brittle systems that exist solely because no one dares to retire them, disengagement follows. The cost of this churn is often invisible until it is too late. As top talent leaves, the remaining team becomes increasingly specialized in maintaining legacy systems and further cementing the status quo.

Research from McKinsey on developer velocity highlights that the top quartile of companies experience 4-5x faster revenue growth than their peers, largely because they minimize low-value toil. The correlation is clear. You cannot retain top talent if you treat them as museum curators for legacy code.

Richard Ewing

The innovation tax isn’t just a code problem; it’s a talent problem. The Audit Interview Protocol is designed to filter for “Capital Judgment”—ensuring you hire engineers who prioritize asset retirement and simplicity over blind code generation.

The portfolio surgeon’s playbook

Breaking this cycle requires governance rather than heroics. CIOs must institutionalize a governance of subtraction.

• Automatic sunsetting thresholds: Features should not live indefinitely by default. Adoption metrics must trigger impairment reviews automatically. When usage drops below a defined threshold, the burden of proof shifts. Product teams must justify continued investment by demonstrating positive margin contribution.
• Zero-sum roadmaps: In capital-constrained environments, complexity must be budgeted. Introducing new scope requires retiring equivalent legacy scope. This forces trade-offs before code is written, not years later.
• Maintenance margin reporting: CIOs should report the percentage of R&D spend devoted to defensive versus offensive work. Forrester research indicates that organizations allowing defensive spend to exceed 40 percent experience declining innovation velocity.

From code bloat to capital discipline

The innovation tax is not a failure of engineering. It is a failure of governance. Boards would never allow factories, real estate or inventory to remain on the books without periodic impairment testing. Software deserves the same discipline.

In the efficiency economy of 2026, leaders are remembered not for the volume of code they shipped but for the durability of the value they created. Sometimes the most profitable, strategic and courageous decision a CIO can make is to hit delete.

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Throughout his career, Sanjay Shringarpure has developed a reputation for doing what most technology leaders aspire to but few consistently achieve: using technology to reimagine what’s possible for companies and entire industries. He has successfully led transformation across multiple sectors and currently serves as CIO of The Freeman Company, a collective of brands that delivers complex live events, at scale.

Shringarpure is a master at the intangibles of leadership. In a recent episode of the Tech Whisperers podcast, we explored how he leverages those key differentiators — including his ability to see patterns early, move with healthy impatience, challenge people candidly but respectfully, and build belief in what’s possible — to become a true force multiplier for the companies where he’s worked.

After the episode wrapped, we spent some time exploring his playbook for reimagining and reinventing industries, a particularly timely conversation considering how fast things are moving today. In this Q&A, edited for length and clarity, Shringarpure shares how technology leaders can identify new opportunities, mobilize organizations around bold ideas, and turn innovation into real business impact.

Dan Roberts: What areas are you focusing on at The Freeman Company as you think about reinventing the experience itself, not just improving the technology behind it?

Sanjay Shringarpure: I’m primarily focused on three things: One, creating a digital experience through digital twinning. Two, simultaneously making sure the digital twin and the physical live event come together — through an ecommerce platform, from a purchasing perspective, execution perspective, all of that. And third, opening new avenues for efficiency and value driven by AI, whether that’s for insight generation or simply doing rote tasks better, or using Claude to rewrite applications that traditionally would be custom off-the-shelf applications that you’d buy.

Where AI code development is right now, we’re getting to a point where the barriers for custom software have dramatically disappeared. So the idea is, can I leverage this for the core competitive advantages that I have? For ecom platforms, demand engines, digital twinning, digital asset management — things that we can potentially build from scratch in weeks what would have taken nine or 10 months and millions of dollars previously. What we’re trying to figure right now is, how do you create that across our enterprise?

The Freeman Company is also at a unique point where the digital and the physical world are colliding, whether it be robotics, virtual events, virtual digital activations of customers who then translate into physical interactions, or vice versa. How do we create a technological foundation and a layer that accelerates that and makes it one seamless live event experience, even if it’s in the digital world?

There’s another component as well. Because of AI, the technological layer doesn’t have to be built for humans. That sounds a little bit weird, right? Everything we build in software today is because a human uses it. As agentic AI and synthetic capital start evolving, and synthetics start being built out, it’s synthetics talking to synthetics, avatars talking to avatars. You’re consuming the output of those interactions. When it comes to writing software, it needs to interact in that way. You can’t apply the same patterns that have been applied for the last 50 years.

How do you envision data and AI transforming the way events are designed, experienced, and measured in the years ahead?

In the age of AI, data is the currency; it’s how you measure value. And it’s not just raw data. It’s raw data plus context around that data — the data halo of context. Today, I think The Freeman Company is in that unique position to have all of this. We are now building out not just contextual capture across events through digital twinning, AI, and raw data coming in; we’re housing that inside our data lake, powered by Snowflake, and then applying AI models to generate insights out of that through my data analytics arm.

Is this going to be easy to achieve? No, because contextual data changes at speed. We’re hoping with the introduction of AI that speed will be handled and insights will be generated in a timely way for the action to be timely. It’s about how to compress data collection, insight generation, and action into days and weeks, rather than the months it takes today. Right now, what happens is the insight gets generated, then it takes forever for it to be actioned, and by the time it’s actioned, the insight is stale. You’re fighting last year’s war.

Do you see the industry evolving toward year-round experience platforms? What role will technology play in extending the value of events beyond the physical venue?

It would be easy to say everything’s going to be digital. But what I’m finding as I get into the industry more is that live events are becoming more important to every generation. The reason is, the digital has overtaken our day-to-day lives, and we crave the physical interaction, the experiential interaction. And we’re willing to pay more for that, because the value we get from that is crucial.

That is not to say we shouldn’t create simultaneous events for agents, or interaction for agents with the physical event. The folks who are going to win in this space are those who build the best physical, experiential events, layered with an interaction in the digital world. In the physical world, there are limitations. I can only attend so many booths, so many sessions, so many interactions. And yes, they’re now virtual, so I can record them and look at them. But by creating an agentic interface, you could have your agents attend all of these in real-time and provide you a day-to-day synopsis of how insights from every session can potentially be leveraged in your strategy.

This is probably sci-fi-ish right now, but I don’t think it’s that far away. The events company that can harness all of this together, and I think we’re well on our way to doing that, will have a moat and a competitive advantage and deliver incredible value to its customer base, whether it be associations, corporations, Major League Baseball and sporting events, whatever it is. The question is, how do we get there fast enough? I think the investments [CEO] Janet Dell has allowed us to make are getting us there. We just have to tell our story better, and we’re getting there.

Speaking of science fiction, just a few years ago concepts like digital twins of venues, AI-driven attendee journeys, immersive hybrid environments, and autonomous services sounded futuristic. Which emerging technologies do you believe will have the biggest impact on the future of experiences, and what is the new killer app that is going to completely disrupt this space?

I think what is going to disrupt the space is more foundational. One, I think custom software development at the speed of light that the AI has enabled will open new worlds we’ve never thought of. You’re not limited by your CRM or your ERP or your ecommerce engine anymore. You think it, it gets built within days, it gets moved to production within weeks. “Agile on steroids” is what AI has enabled.

Second, the killer app is the merger of the physical and the digital world together into a cadence of information flow to you to make decisions. It’s hard to envision that yet, because you go to a conference, association event, Major League Baseball event, or Cricket World Cup, and they give you an app. But they’re not merging your physical experience with the digital experience to a point where they’re now curating your journey. Today, they are point interactions. What would be ideal would be a curated journey that just knows you and knows what you like and helps you achieve your goals, whether it’s selling more of your product at CES, or making contacts at AWS, or generating a CMO network, or promoting what you’re trying to sell.

One of the themes you and I talk about often is the idea of being a “net giver” as a leader. Can you expand on what that means and how that mindset shaped the way you build teams and lead transformation, especially in high-stakes, fast-moving environments?

Servant leadership is a behavior I truly believe in. A true team can only be built when a leader focuses not just on business outcomes but on the development needs of each individual. Your success as a leader is ultimately amplified by the growth of your team. I make it a point to spend at least 30% of my one-on-one time focused on development plans, growth opportunities, and helping people stretch into what they’re capable of becoming.

But for me, this goes beyond traditional notions of servant leadership. Operating with a net giver mindset means investing in people without keeping score, creating opportunities before they’re asked for, and building belief in individuals sometimes before they see it in themselves. In high-stakes, fast-moving environments, you don’t have the luxury of carrying passengers; you need leaders at every level. That only happens when people feel genuinely supported, challenged, and trusted to step into bigger roles.

When you consistently show up this way, it creates a multiplier effect. Teams move faster because trust is already established. They take smarter risks because they know they’re backed. And they push beyond perceived limits because someone has invested in their growth along the way. In transformation, technology may set the direction, but it’s the development of people and the belief you instill in them that ultimately determines how far and how fast you can go.

During the podcast, you emphasized that the most effective CIOs don’t just deploy technology, they use it to redefine what’s possible for their organizations and industries. What advice would you give the next generation of CIOs who aspire to lead that kind of transformation?

First is make the investment of time in learning. You have to learn the patterns of the industry that you’re in, the give and take, the execution patterns, the way-we-make-money patterns. Then you have to apply a base philosophy of how you’re going to help the transformation. I start with, how do I want to organize my department? How do I create focus? Then I add the guiding principles of transparency, accountability, surprise and delight, all of those basic things — build that into cadences of interaction that create a self-fulfilling, virtuous cycle. And then rinse and repeat every week.

Build your network, but don’t build it at scale. Build your network incredibly choice-fully. Pick people who make you smarter. There’s a reason why I spend time with you or certain other folks. I’m very selective. You don’t see me out there at industry events at scale. I’m not networking every two minutes. Because I’m not looking for my next job. What I’m trying to do is build something great, and then the next job automatically comes. You don’t have to go find it; it finds you.

Focus on building. Focus on you. Focus on your team. Focus on driving value for the company. If you do it, the outside marketing happens by itself. And don’t try to chase the dollars. If you continually chase the dollars every three years, you’ll have two gigs, maybe three, and then you’re done.

I also think the crucible projects, crucible events, are important. You’ve got to run to them. Not, “Okay, I’m gonna do it.” No, you’ve got to create the craving for them. And be okay with the consequences. I’ve failed many times in my career, and it’s okay. You deal with a consequence, knowing that this isn’t forever, and your next win will wipe all that away.

Sanjay Shringarpure has developed a distinctive leadership playbook that has enabled him to thrive across multiple industries, building trust, unlocking belief, elevating teams to levels they didn’t think they could reach, and reinventing entire companies and industries in the process. For a true masterclass on the intangibles of leadership that matter, tune in to my conversation with Shringarpure on the Tech Whisperers podcast.