Earlier this year, Meta, one of the industry’s loudest AI advocates, was ready to slash up to 60% of the members of some teams and replace them with AI, as part of what it called Project OT (Organization Transformation), an initiative to make Meta “AI native.”

But it backed off at the last minute after internal data showed that the plan wasn’t working out, according to a Reuters investigation published Wednesday. For example, Reuters said, code changes made to the internal software platforms and infrastructure that employees used on the job were up 220% year-over-year, according to an early June post by Meta CTO Andrew Bosworth, yet changes that led to new or upgraded features reaching Meta users were only up 36%.

Meta executives also saw “’reliability warning signs’ caused by the AI coding surge,” according to an internal post, Reuters reported. “Another post, in April, said that unchecked AI agents were performing ‘large-scale, disruptive actions that humans are unlikely to execute.’ The result: Major technical and security incidents, such as service disruptions and possible data leaks, spiked 40% from the previous year, with the time staffers had to spend firefighting them up 70%.”

Reuters also noted that, in April, Meta had mandated that tracking software be installed on US employees’ devices to capture their keystrokes and mouse clicks to teach its AI agents to replicate how humans interact with computers. Believing they might be training their own AI replacements, employees rebelled.

To try and placate the workers, Meta promised to increase spending on travel and social events, and “to improve snack quality in office microkitchens.” Unsurprisingly, none of that seemed to help boost morale, and, Reuters reported, “Zuckerberg has stuck to the words ‘company-wide’ and ‘this year’ in discussing layoffs with employees, according to his internal communications. That has prompted some employees to speculate that he’ll continue trimming the ranks via team-specific cuts or performance-based dismissals – or delay company-wide headcount reductions until next year.” 

A cautionary tale

Consultants and analysts said enterprise IT executives should read the Meta story carefully, because it precisely illustrates what happens when AI marketing hype is not challenged aggressively.

Noted Sanchit Vir Gogia, chief analyst at Greyhound Research, “Meta trusted a forecast of AI capability before it existed in production, a different failure from trusting AI too much.”

He said, “Meta booked a forecast as capacity. Agents will improve, but the error was budgeting that improvement as production capacity before it arrived. Prove the action before widening the authority, and the authority before removing the human control. Only then is removing human capacity a decision, not a bet.”

Terra Higginson, a principal research director at Info-Tech Research Group, added that no experienced enterprise IT leader should be surprised by Meta’s experience. 

“Unchecked AI agents are a bad idea. Removing humans is a bad idea. That’s not the future anyone wants,” she said. “We want work to be reimagined so the human part still matters and technology makes it better. We are all still wrapping our heads around how AI and agentic AI will change the way we work.”

She noted, “what we are already seeing, though, is lots of output and action without always getting the outcome we actually want. We should not use AI output as a proxy for productivity. Humans bring judgment and friction before taking actions with significant consequences; agents can remove that friction. We don’t want easy outcomes, we want good outcomes.”

Tom Findling, CEO of Conifers.ai, also suggested that IT leaders should take the Meta report as “the best opportunity” to go to their board and argue that this is what happens with unchecked AI rollouts. 

“Tell them that we now have the opportunity to get it right. Say that you may not get a 500% productivity boost, but IT can show them a meaningful way to get 300%,” Findling said. “If you don’t want to end up like Meta, there is a way.”

Justin Greis, CEO of consulting firm Acceligence, added that he thinks that IT’s takeaway from the Meta situation is the disconnect between activity and actual value creation.

“AI can make an organization extraordinarily busy without necessarily making it more productive,” he said. “We have spent decades teaching technology leaders that lines of code, tickets closed, and projects launched are imperfect proxies for business value. AI makes that measurement problem much more acute because it can manufacture activity at machine speed.”

If an AI agent produces ten times as much code, analysis, or work product, that does not mean the enterprise created ten times as much value, Greis said. “It may simply mean the company created ten times as much material that somebody now has to validate, secure, integrate, maintain, or clean up. That is why I think the most important question for executives is not ‘How much work can AI produce?’ It is ‘What measurable business outcome improved because AI produced it?’”

This article originally appeared on Computerworld.

Mohan Sankararaman calls the old approach to technology-driven transformation — the kind of change that lands every few years and reshapes the organization in one push — a trap. As executive vice president and CIO of First Horizon, a regional bank headquartered in Memphis, he’s focused on driving digital transformation the way a bank funds risk: incrementally with room to pull back.

Every CIO is under similar pressure to rethink change management for the AI era. Wanda Wallace, managing partner at Leadership Forum, has advised CIOs on change management for years, and she thinks the job itself hasn’t changed much.

“The hardest and most critical aspect of making change happen and stick is convincing people to adopt a new approach,” she says. “AI doesn’t change that need or that process. It is a human-to-human dynamic.”

Talk to the practitioners and researchers closest to the work, and a version of her view emerges again and again. What has changed is how many things are competing for an organization’s limited capacity to absorb them — AI chief among them. Here are five hard truths IT leaders face about change management today.

1. There’s no finish line

Ashish Parmar, CIO of Standard Industries, a global industrial conglomerate with more than 20,000 employees across roughly 50 countries, has watched the nature of transformation shift beneath him. In the past, he says, change was treated like a project with a start date and an end date — whether the trigger was a new ERP system, a reorg, or a cost-cutting mandate. That model doesn’t hold anymore.

“Today, change is continuous,” Parmar says. “Our strategy is focused on building resilience and adaptability rather than getting to a single destination.”

AI is the clearest example of how the old model breaks down, says Fran Maxwell, who leads Protiviti’s people and change practice, though he’s quick to note it isn’t the only one. Unlike an ERP rollout, which lands as a discrete event, an AI transformation keeps moving.

“The technology evolves continuously, use cases emerge rapidly, and the impact on roles is often uncertain,” Maxwell says. The common misstep is treating any major shift, AI-driven or not, like a one-time project with a training curriculum and a communications plan, he says. The fix is building a permanent capability for adaptation rather than staffing up for a single push.

None of that continuous adaptation is possible if the underlying systems can’t support it, notes Manosiz Bhattacharyya, CTO of Nutanix.

“Technology is not the barrier to transformation; application modernization is,” he says. Years of accumulated dependencies, legacy integrations, and fragmented data are what actually slow an organization down. And layering new tools on top doesn’t make that debt disappear.

“Applying AI blindly does not remove technical debt,” Bhattacharyya says. “It amplifies it.”

2. Bandwidth isn’t just a network problem

A 2026 survey of roughly 3,000 HR leaders by talent firm LHH found that no single cause dominates why companies reshape their organizations: AI and automation, skills mismatches, M&A activity, and strategic shifts were each cited as drivers in the previous year by about a fifth of respondents. In other words, most organizations are contending with several forms of change at once, not just AI.

All that change at once runs into a hard limit: An organization can absorb only so much at a time.

“Every organization’s capacity for change is finite, so leaders cannot endlessly stack new initiatives on top of existing workloads,” Parmar of Standard Industries says.

Rather than treat that ceiling as a constraint, he argues CIOs should use it to force discipline. IT leaders should determine their non-negotiables and point the team’s energy there instead of spreading it thin across AI pilots, reorganizations, and everything else competing for attention.

Sankararaman arrived at nearly the same conclusion at First Horizon. Banking used to reward slow, occasional overhauls, the kind that could take years to prove out, he says. But that approach has become untenable.

“It’s tempting to treat transformation as one big initiative, but with technology evolving this fast, that’s a trap,” he says. Instead, Sankararaman releases funding in stages, each tied to a measurable result before the next is approved. Then, the organization can adapt and build confidence as it goes rather than betting everything on a single multi-year plan. “We reward progress, not perfection,” he says.

Kevin Martin, chief research officer at the Institute for Corporate Productivity (i4cp), has data that supports the value of incremental improvements. When leaders want to move faster, the reflex is to restructure: delayer, widen control, redraw reporting lines. i4cp’s research found no statistical relationship between those structural moves and organizational agility or market performance. What separates agile organizations are routines: scenario planning, faster resource reallocation, clear decision rights, continuous workforce planning, targeted reskilling, and disciplined execution.

“You don’t reorganize your way to agility,” Martin says. “You build it into how the organization operates.”

3. Shadow IT doesn’t belong in the shadows

Employees finding their own tools to get work done isn’t new. Shadow IT has taken on various forms over the years, from personal file-sharing accounts to unsanctioned SaaS subscriptions.

Today, it’s shadow AI, and Sankararaman argues most CIOs still treat it as a security or compliance issue rather than what it really is: information about what the organization needs and isn’t getting.

“Shadow AI is already happening in every organization. If you’re not addressing it through your change management strategy, you’re addressing it too late,” Sankararaman says.

Sankararaman’s approach starts with curiosity rather than restriction: understanding what employees are trying to accomplish with the tools they’ve found on their own, which makes it easier to agree on how the business should govern those tools.

“That’s a change management conversation, not just a policy conversation,” he says.

4. Trust has to be designed in, not repaired later

Every new system that changes how decisions get made must earn trust before it achieves adoption. Agentic AI raises the stakes because it doesn’t just inform decisions; it takes actions on its own within workflows.

That’s a fundamentally different dynamic from anything change leaders have managed before, Sankararaman says. The resistance it produces is often quieter, showing up in questions about how a system reached a conclusion, who’s accountable when it’s wrong, and whether it’s replacing what someone does. “Those questions deserve real answers, not reassurance,” he says.

At First Horizon, IT is building trust into the foundation with permissioned access, centralized guardrails, human oversight, and outputs that are consistent, reviewable, and explainable. “If people can’t understand how the technology reached a conclusion, you haven’t earned their trust,” Sankararaman says. “And without trust, adoption doesn’t hold.”

Employees today worry less about learning a new tool than about what it means for their role, their skills, and how their performance will be judged once a machine does part of the job.

“They worry about career relevance, accountability, job security, and how performance will be evaluated,” Protiviti’s Maxwell says. Closing that gap, in his view, takes more than a rollout plan. It demands transparency about what’s changing, what isn’t, and how people add value once the tool is in place.

5. Tired isn’t the same as unwilling

Ask IT leaders about change fatigue, and they frame it as a capacity problem rather than resistance. Late 2025 saw a wave of five-day return-to-office mandates that landed on top of continued layoffs. Tech companies alone cut more than 66,000 jobs between May and November, according to Newsweek and TechCrunch, exactly the kind of concurrent disruption that erodes an organization’s capacity for change.

Among what i4cp calls “coasting incumbents” — companies that still perform well despite low organizational agility — 51% of employees report finding change fatiguing, and just 8% say change management is an organizational strength. At “agile pacesetters” — the highest-agility, highest-performing organizations in i4cp’s research — only 12% report high fatigue.

“AI is an accelerant,” Martin says. “But organizational friction is the fuel.”

Protiviti’s Maxwell argues that change fatigue is often less about resistance and more about capacity. “Employees are far more likely to embrace change when leaders are clear about what matters most, what success looks like, and just as importantly, what is not a priority right now,” Maxwell says. His advice to CIOs: Empathize and prioritize before accelerating.

One structural fix most CIOs underuse is shared ownership. “Don’t go at it alone,” advises First Horizon’s Sankararaman. “Partner with others across the business, your CHRO, CFO, COO, and make them co-champions of the change, not just stakeholders who get updates.” A message that arrives from multiple leaders, he’s found, carries more weight and lasts longer than one delivered by IT alone.

The absence of fatigue, in Wallace’s view, is its own warning sign. “If your organization isn’t change-fatigued,” she says, “then I am worried about what you have been doing.”

When Sastry Durvasula joined TIAA in early 2022, he saw an organization fighting against outdated legacy technologies and in need of a major IT refresh.

Since then, the financial services organization has completed two phases of a comprehensive transformation initiative called Technology Ecosystem Transformation, or TETRIS, leading to a huge reduction in tech debt and a major expansion of functionality for customers.

The ongoing project, anchored in cloud and AI technologies, started in 2023 with phase one that modernized the core technology stack with 10 new enterprise platforms. Phase two, launched in late 2024, went further by enabling 87 use cases across all major lines of the business.

The project, for example, allowed TIAA to launch its MyChoice Multi-Year Guaranteed Annuity product, and helped create the TIAA Gateway portal, an API-based suite that integrates with partners in retirement and wealth planning using industry standards.

TIAA Gateway took home a CIO 100 Award in 2025, and phase two received a CIO 100 Award in 2026.

Durvasula, TIAA’s chief operating officer, pitched the multimillion-dollar TETRIS project to the board as a three-pronged strategy, with empowering business growth, fueling innovation, and transforming the IT core as its key goals.

Not only did TETRIS need to modernize the company’s IT systems, decommission legacy processes, and automate other processes, but Durvasula pitched it as the way to expand the reach of TIAA’s products and move the company into the future.

“As you expect in a company of our size, we have problems of yesterday, today, and tomorrow being solved at the same time,” he says.

Focus on business use cases

As TETRIS moved into phase two, project leaders shifted their goals from pure technology modernization to business outcome-driven prioritization. So once phase one delivered needed IT platforms like a data cloud and design studio, TIAA pivoted toward enabling business use cases.

This business-first approach ensured continuing executive support and clear ROI at every key milestone, TIAA says.

In 2022, just before the project launched, more than 80% of TIAA’s IT workloads resided in fragmented, end-of-life platforms, which created operational risk, compromised security and resilience, and constrained its ability to innovate. Through TETRIS phase two, however, the organization has cut that tech debt nearly in half.

And consolidating 17 design systems also led to digital products looking and behaving differently, depending on the team that designed them, and accelerated product launches by 35%, enabled multi-lingual capabilities, and increased accessibility to more than 185,000 customers who don’t speak English.

In addition, TETRIS allowed TIAA to combine multiple middleware systems and data lakes, Durvasula says, and the organization moved mainframe applications and data center infrastructure to the cloud.

A giant leap forward

TETRIS has been a huge project, with the company saying it empowered TIAA to have one of the largest leapfrog moments in company history in its submission for the 2026 CIO 100 Award.

Despite the reported failure rates of large transformation projects — some estimates suggest up to 95% fail to meet their goals — TETRIS was essential to keep TIAA competitive and move it forward in the market, Durvasula says.

A big part of the project has been workflow modernization, he says, because TIAA were using some technologies and workflows that were decades old.

“There’s your classical platform and application rationalization, and then there’s your end-of-support, end-of-life stuff that should’ve been remediated long ago,” he says. “Some of the processes we have, because we’re such a large, old company, were designed when the internet just came along.”

Stick to the metrics

Two keys to pulling off such a large project are establishing metrics for success and transparency with leadership, Durvasula says. Project leaders set milestones to indicate when things went well, and they planned for bumps in the road so the TIAA board knew when setbacks happened.

“Not everything is as pretty as it sounds in an awards application, but the success measures we established with our board were based on both phases,” he says. “For the first one, we said we’d deliver enterprise-grade platforms and accomplish migration objectives, but not tied to any specific business objectives.”

Phase two metrics focused more on business objectives, and the project team kept the TIAA board updated as TETRIS moved forward. Setting realistic goals was important, he says, with the team determined not to overpromise results.

“Large programs have a range of objectives, and if you publish the outcomes you’re looking for, people start looking for them, especially stakeholders, the C-suite, and board,” he says. “You have to be honest about which metrics or KPIs you can deliver in the first and second year, and when you’ll start seeing real business scale and impact, which definitely won’t be that soon in a large program like this.”

Goals also need to be flexible, Durvasula says, so transparency with leadership sometimes means telling them the project needs to reset. “If something doesn’t go well, what’s the level of fungibility you have?” he says. “We pick this tool, but what if it doesn’t work? You need to have a plan B.”

So TIAA’s IT team is heavily focused on flexible systems, and what was contemporary three years ago is probably legacy now, especially thanks to AI.

The power of change management

Another big lesson from a project of this size is the need to focus on change management. Retiring old IT systems requires the organization to bring employees along on the journey and convince them the changes are for the better.

TIAA established a multi-disciplinary team to implement a change management program focusing on breaking down silos and setting common adoption goals across the organization and its lines of business. Stakeholder forms and a huge focus on continuous collaboration helped employees understand the need for the changes.

“It’s a big organizational change,” Durvasula says. “If you’re working on a legacy system, and you think at some point it’s going to be modernized, then you become a legacy talent, and won’t have a job.” But the right change management program can convince these employees they can upskill and bring value to the new systems.

“You can bring your functional knowledge of the business and learn new technical skills,” he says. “It’s a massive culture- and people-change initiative as much as tech initiative.”

TIAA’s change management efforts were also made easier because TETRIS happened at the same time as the recent AI boom and involved AI elements. So it wasn’t hard to convince employees they needed to improve their AI skills.

“Because of AI, everybody woke up to this new reality,” he says. “We rode that wave when transformation drove from a cultural and organizational change management point.”

With vision comes perspective, and over a distinguished career, IT and digital transformation leader Niraj Bhatt has held may titles, and earned three consecutive CIO 100 awards since 2023.

As a storied advisor for startups and Fortune 500 companies, helping them navigate the unpredictability and fluidity of AI, Bhatt knows how emerging tech is rapidly reshaping the way organizations build products and deliver value, and how challenges shift as companies move from experimentation to real-world deployment.

AI, of course means a lot of different things to different people, and also for frictionless startups and large enterprises. For the former, speed is a huge asset, allowing them to punch above their weight. But it also means they need lightning fast reactions when landscapes shift. “The same speed can also hurt them when larger AI companies release new offerings that disrupt what startups are building,” he says, referencing recent moves by Anthropic and Google.

On the enterprise side, the conversation is more about scale and risk. Many large organizations have moved past the POC stage and now wrestle with the realities of putting AI into production.

Cost for both is naturally a recurring theme as organizations scale up AI efforts, and true expenses become clear only after the initial excitement fades. “Every input and output token, and the model you’re selecting, add up,” he says. Some customers like Open AI, he adds, get throttled because their usage, volumes, and costs are growing so fast, making planning, observability, and monitoring critical for any team moving beyond experimentation.

So understanding the full software development lifecycle is also vital. Therefore, before committing to production, he helps clients see the big picture, and make sure they understand technical requirements as well as operational and financial implications. “The cost picture isn’t just about usage, but scale and the model choices teams make,” he says.

Bhatt also discusses effective approaches to AI and enterprise IT, technology leadership, and the evolving role of today’s CIOs. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On AI hype: If you can’t explain something to someone who’s eight or 80, you don’t really understand it. It’s gone from LLMs, to RAG, to agentic AI, and now the essence is all about tokens. It’s predicting that next token and understanding that is key. So when LLMs came out, they were good at doing that on the data on which they were trained. When the enterprises looked at it, they wanted to make those LLMs work for their data. And the question became how to provide our data and context. It’s about building the right context for the LLM. Agentic AI is similar and that’s where the RAG evolution came in, in that I’ve got my data because every LLM has limitations in terms of how much context it can carry.

There are ranges of LLMs, where Google has the highest in regard to the context window size and what they support. Agentic AI is more action oriented, though. LLMs rely on the metadata you provide for the tools. Then they’re doing token prediction in that whatever I’m looking for, I should use a specific tool. Then it’s the infrastructure underlying which LLM it relies on to invoke the agent. So if you try to explain the microservices to a person, you’re going to struggle. But it’s very important to understand the evolution and that’s where you can cut through the hype. Understanding in this context is key.

On navigating challenges around talent: What I’m seeing on the IT side is there’s so much cognitive load, so how do we empower people to build solutions with the right mix of products and platforms? I think it’s about democratizing AI for the entire organization. Your talent strategy is everyone, all inclusive, starting from interns, the business and tech sides, CEO, everybody.Like your customer success or revenue officers, you need a talent strategy because in the end, IT alone isn’t going to be in a position to deliver for everyone in the organization.

AI has the potential to make everyone in the organization more productive. You have to plan that and facilitate broad innovation across the organization.That’s where the talent strategy, and working with HR and the people officer becomes very important providing those tools. One part of it is training, but how do I build an agent for a receptionist receiving calls, for instance?I’m not going to rely on vibe coding or things of that nature. But what are the tools? Where do I go, where do I host this? I think through that entire ecosystem beyond copilots. That’s where innovation can kick in, and that broader talent strategy is something I’m working with my customers on.

On collaboration: I heard a panel discussion recently, and a question was asked about what’s the number-one trait CIO needs to be successful at in the world of AI, and the answer was collaboration. You need to bring everybody together, move forward together, and make sure everybody’s on board. And in my mind, simplifying that is more like systems thinking when you operate, just bringing everybody along and ensuring they’re meeting outcomes.

But maybe what’s more important is managing expectations. Because if you’re a CIO, there’s a tremendous amount of pressure to deliver and have a rock solid AI strategy. So what I’m doing with my customers is get the board, CEO, and CFO into a room and help them understand what I’m talking about, the evolution, and what’s the art of possible. You don’t want to be a CIO who thinks I have a hammer and everything is a nail. Having buy in from the senior leaders is essential to know you’re headed in the right direction. You’re not reacting to pressure from top leadership, but driving and becoming the change agent for good for the company.

On navigating AI: It’s interesting times. I’m covering a spectrum of startups, non-technical and technical founders, and advising Fortune 500 companies. What I’m seeing is they love the velocity and momentum because that’s what they’ve always wanted, and AI is providing that. They’re able to bring their products to markets very quickly, so something that would’ve taken three years a couple of years ago is probably now taking them three months. There’s a lot of excitement there. But on the flip side, the same velocity is also hurting them. There are so many frontier AI companies getting disrupted. OpenAI, for instance, has offerings in sales and marketing, and Google has an interactive video model. So a lot of startups working in the marketing space are getting stuck. A lot of what I’m focused on is working with founders, helping them pivot in the gen AI space, ensuring their systems and products are built and structured in the right manner.

And on the enterprise space, what I’m seeing is the POC wave, and people have seen the value. There’s some excitement but now the struggle is getting them to production. That’s where you run into cost, latency, legal compliance, privacy issues, and customer concerns that if we get tickets to production, how’s it going to look and how are we going to scale. So engineering and product teams have to be ably supported by the enterprise architecture and R&D teams. I then help them get up to speed and build that internal platform product for the production workloads. It’s exciting times on both sides.

Petco Park consistently ranks among MLB’s top ballparks for fan experience. That doesn’t happen by accident, and it didn’t wait for a star-studded roster or a deep postseason run.

According to Padres CIO Ray Chan, the club made a deliberate choice more than a decade ago to run its tech organization as if every seat were full and the team was playing in October every year. The philosophy was simple — build a World Series-level digital foundation so when the on-field product caught up, the elite fan experience would already be there.

More than a ballpark

Most people know Petco Park as the home of the San Diego Padres, which it is, but the venue was designed to be more than that. In a typical year, it hosts 81 regular-season home games, plus potential postseason contests, and then adds concerts and private events in renovated premium spaces.

By Chan’s count, that totals to nearly 400 annual events, often with more than one on the property in a single day. Different parts of the venue may host different audiences simultaneously, with IT expected to turn spaces quickly and support the unique digital requirements of each event.

The multi-use model puts a premium on flexibility and speed. Spaces are designed to be reconfigured quickly, and the underlying technology stack must adapt just as quick.

An always‑on network

When Chan arrived 15 seasons ago, Petco Park looked very different from a connectivity standpoint. On sellout nights, fans often couldn’t place a call or send a text once they were inside the building. There was no real concept of a digital fan journey.

The first major shift came with deploying a full-venue managed distributed antenna system (DAS) from Verizon, and an Extreme Networks Wi-Fi solution, providing fans, staff, and baseball operations with reliable connectivity throughout the ballpark. That network has since become the converged backbone for almost everything that happens at Petco, including digital ticket entry via the MLB Ballpark app, security and operations, tech like instant replay and dugout tablets used by coaches and players, and in‑venue IPTV and signage, all riding on the same IP infrastructure.

For fans, the network is invisible. For Chan’s team, it’s non‑negotiable. “None of this stuff works without the infrastructure in place,” he says.

Consolidated convenience

The Padres have leaned heavily into the league-standard MLB Ballpark app, which provides a consistent digital experience across all 30 venues, while allowing clubs to customize the local section. At Petco specifically, that app becomes the fan’s control center for digital ticketing, ballpark navigation, and a built-in payments and discount wallets tied to offers like Padres Pay and contactless options.

The result is a highly digitized journey, and for many fans, their first and last interaction with the ballpark occurs on their mobile device, and that’s by design.

Toward frictionlessness

Chan and his team are already looking beyond digital barcodes to facial-authentication-based entry, leveraging MLB’s Go Ahead Entry program rolling out at several parks. In that model, fans enroll once in the app with a selfie, then simply walk through a designated lane while overhead cameras verify identity and automatically scan tickets.

The promise is a hands-free, eyes-up experience where fans no longer need to take out their phones at the gate. Chan says this is the most frictionless way to enter a ballpark, and it even enables personalized greetings by name at the turnstile, another small but memorable touch to create a World Series-caliber experience.

Concessions are another example of how Petco’s IT modernization seamlessly enhances the fan journey. Petco is now a fully cashless venue, so fans pay with credit cards, mobile wallets, or the dedicated Padres Pay capability integrated into the Ballpark app. This reduces transaction friction, speeds lines, and improves security by minimizing cash handling.

IPTV everywhere

The expanding IP television footprint is another hallmark of Petco’s fan experience strategy. New screens throughout the venue serve multiple roles to ensure game coverage is never lost, even when fans leave their seats.

They also show real-time updates and wayfinding, an L-bar format that combines live video with adjacent ad inventory and informational content, and full-screen takeovers during concerts or special events, letting the venue transform its look and feel to match what’s happening on the field or stage.

Because it’s all IP-based, game-day operations and marketing teams can reskin the park on the fly, turning screens into a flexible engagement and monetization channel rather than relying on fixed signage.

Constant modernization

Despite opening in 2004, Petco Park doesn’t feel like a 22-year-old venue. Chan says that’s intentional and points to a continuous program of infrastructure upgrades and capital projects to redo suites, unify premium spaces such as the Western Metal rooftop and loft, and find areas to transform into new experiences.

Beneath those visible changes lies ongoing modernization of the network and systems in terms of upgrading switches, faster Wi-Fi, and backend platforms to support the latest apps and services. As Chan puts it, the goal is to make the park look and feel no older than a couple of years, which requires consistent ownership commitment and alignment between IT and operations.

Perhaps the most important part of Chan’s playbook, however, is cultural rather than technical. He describes the Padres as a listening organization that actively solicits and incorporates fan feedback to refine the experience across seasons.

That mindset is shaping the club’s approach to AI, so instead of chasing it for its own sake, Chan is focused on use cases that improve customer service by using chatbots or AI-assisted voice lines to free staff for higher-value interactions, and solve specific operational problems such as using AI to match lost-and-found queries with a database of found items.

The bigger IT picture

The way Petco Park manages its technology operations offers patterns that can apply beyond sports venues, starting with establishing a converged, resilient backbone. Connectivity is a shared utility layer that everything else depends on, rather than a series of isolated projects. That makes it easier to add new capabilities later without rearchitecting every time.

Chan’s philosophy of building as if every seat were filled also applies across all e-commerce peaks, clinical surges, and manufacturing seasonality. Capacity planning, observability, and failover should be set at Black Friday, not an average Tuesday. And treat your environment as a multiuse and continuously modernizing platform. Petco’s “more than a ballpark” mindset reflects the shift toward mixed-use destinations or campuses that blend learning and events, and offices that evolve into collaboration hubs that chip away at legacy infrastructure. IT leaders across sectors can apply the same rolling-renovation model to networks, identity, observability, and edge infrastructure, keeping technical debt manageable.