Much has been made of Generation Z’s supposed inability to adequately conform to modern-day corporate culture.

A recent Business Insider article on Gen Z (born 1997–2012) workplace readiness contains some damning statistics. The piece cites a 2024 poll of nearly 1,000 business leaders, 75% of which described some or all of the recent graduates they hired as “unsatisfactory.” Arguably worse, it cites another 2024 study from Hult Business School, in which 37% of HR leaders said they’d prefer to have a robot or AI do a job than hire a recent new grad, while another 30% said they would rather just leave the job vacant. Quite damning, indeed.

Warranted or not, these complaints about Gen Z are not new.

NYU Stern professor Suzy Welch conducted a September 2025 study juxtaposing the values of incoming Gen Z workers with those of hiring managers. Welch’s study included survey data from 2,100 hiring managers and a behavioral assessment of over 45,000 respondents (7,500 of which were Gen Z).

In a Wall Street Journal Op-Ed, Welch writes, “A mere 2% of Gen Z members hold the values that companies want most in new hires, namely achievement, learning and an unbridled desire to work.” According to Welch’s study, Gen Z members mostly value well-being and self-care, helping others and authenticity and expression. Oh, heaven forbid.

Not to be outdone, in March 2026, another NYU professor wrote a Wall Street Journal Op-Ed on “Why Gen Z is unprepared for the workplace.” The one, from psychology professor Tessa West, laments Gen Z’s lack of communication skills and failure to understand older generations. West writes, “Younger generations—Gen Z specifically—have learned to communicate in a digital world, sending texts and instant messages instead of interacting in person.”

West attributes Gen Z’s poor communication skills to a combination of excessive screen time, online classes during the COVID pandemic and a general overreliance on technologies. I cannot definitively say whether these accusations hold water; however, I do believe that the answer doesn’t lie in replacing humans with AI or leaving roles unfilled. Hiring managers need to adapt to younger generations, whether they want to or not. And we’re seeing this in the help desk software space in real time.

Gen Z has little patience for traditional corporate bureaucracy

Aside from grievances from older folks like me, the fact remains that Gen Z has little patience for traditional corporate bureaucracy. When these younger workers experience a technology glitch, they don’t want to log into a clunky portal, fill out a 10-field form and wait two days for a level 1 support agent to respond. These kids expect instant, consumer-grade, self-service resolution. If the enterprise doesn’t provide it, they’ll just bypass IT entirely.

To overly generalize, most members of Gen Z are comfortable with AI chatbots and tend to search for their own solutions online before reaching out to another human directly. This tendency to rely on mediated communication as opposed to person-to-person communication makes sense given their life experiences with COVID, online coursework, the post-pandemic reevaluation of work culture and their status as a mobile-first generation.

Ramifications for the help desk software space

To retain young talent, enterprises should consider deploying conversational AI, automated self-service password portals and omni-channel support to match the speed at which Gen Z operates.

Self-service should be the first order of operation. Rather than forcing employees to create a ticket and escalate an issue to an agent, workers should have the option to remedy the situation themselves. Across the IT support industry, we’re seeing more emphasis on AI oversight and escalation handling, as opposed to level 1 IT support work.

Additionally, given Gen Z’s mobile-first status, it is vital to have mobile-native applications for internal IT issues. These shouldn’t just be desktop portals reconstructed to fit a mobile interface; these should be mobile apps with consumer-grade quality UX interfaces equipped with things like push notifications for ticket updates.

We’re also seeing more agentic chatbots in the ITSM space. Gen Z workers (and all generations, really) expect agents to be able to handle simpler requests, like access provisioning, password resetting and resolving calendar conflicts.

Omni-channel continuity has become vital too. When Gen Z’ers begin a conversation in Slack or Zoho Cliq, they expect that conversation to seamlessly flow into SMS, email, mobile apps and other corporate channels.

Key takeaway

When it comes to IT service management, Gen Z workers expect agentic self-service, a mobile-first UX, omni-channel support and the ability to resolve tier-1 support issues themselves. Despite the lamentations of some old curmudgeons like me, Gen Z isn’t going anywhere. And what’s more, Gen Alpha (2010 – 2024) is waiting in the wings. If we haven’t brought our service desk infrastructure up to Gen Z’s standards, now is the time.

It’s early days for AI-assisted work, and sentiments are mixed about its benefits: Does AI actually bring value and save employees time, or does it bog them down and add unnecessary activities?

Data from ActivTrak seems to vacillate between those two potential realities: The latest research from the productivity tracking software company says employees who use AI most heavily are working up to 90 minutes longer each day, yet its previous research, published earlier this year, found that, in general, all employees are actually working less.

The discrepancy may be due in part to the timespan and sample sizes of the data collection for the two reports, but it indicates that enterprises should still be wary of vendor claims about AI-related productivity gains. Measuring them remains incredibly difficult.

AI use compared

ActivTrak’s current findings are based on behavioral work data gathered from 9,140 knowledge workers across 467 enterprises over a five month period (April 1 to September 25, 2026). This sample is much smaller than that of its previous report based on 443 million hours of work performed by 163,638 employees across 1,111 organizations over a three year period (January 1, 2023 through December 31, 2025).

According to the latest findings, heavy AI users are working between 8.42 and 9.08 hours per day, compared to 7.58 hours for those not using AI. ActivTrak’s previous findings revealed that, overall, average workdays had decreased by roughly 9 minutes (a 2% decline).

On the productivity front, heavy AI users have 22% shorter focus sessions than light users (37.8 minutes versus 48.2), suggesting their work is more fragmented due to AI use; further, they switch apps 15% more frequently than non-users.

“Over time, those interruptions add friction to high-value work that requires concentration, critical thinking, and sustained attention,” Sarah Altemus, manager of the ActivTrak Productivity Lab, said in a blog post describing the latest findings.

ActivTrak’s previous report had found that over the period of the earlier study, focus efficiency, the percentage of work time spent in focused, uninterrupted activity, fell to a three year low of 60% with AI use, and average focused session time in 2025 declined by 9% from 2023. However, that same report found that daily productive time increased by 19 minutes, or 5%, to 6 hours and 36 minutes, with AI in the workplace. Furthermore, productive sessions grew by 13%.

The company’s latest report also tracked the productivity differences between users of tools with embedded AI and users of web-based AI tools. Those using embedded AI switched apps 0.6 times an hour, while those using AI on the web switched between 1.7 and 4.2 times per hour. That’s up to 4x more switching than by employees who don’t make use of AI at all, which creates what ActivTrak describes as a “swivel-chair tax” — the additional effort spent by employees due to the movement between apps.

Similarly, in its previous report, ActivTrak had found that, overall, task switching in general rose 12%, or 10-plus minutes during the survey period.

Interestingly, according to the latest research, light users spend more active work time in core job-specific apps (81.4% compared to 78%).  “In other words, more frequent AI engagement doesn’t necessarily translate into more focused work,” Altemus pointed out.

The larger issue is the way organizations manage the additional capacity that AI may create, Altemus noted. “When employees complete tasks faster, managers may assume they have room for more responsibilities,” she said. Without clear workload boundaries, time savings may be redirected into additional work that can be taxing.

The numbers often don’t tell the full story

ActivTrak has a useful view of how work moves across applications, noted Sanchit Vir Gogia, chief analyst at Greyhound Research, but he also pointed out that it sells the capability that produces these measurements.

Recorded use depends on what the instrumentation can detect, he observed. For example, an employee can use AI at work and still appear in the non-user group, and work on an unmanaged personal device sits outside the measured estate. In addition, an API or a background agent can finish work without producing a desktop session, and passive screen time, an offline meeting captured through a calendar, and paid hours are also different measures. Furthermore, switching apps is easier to observe than switching attention within an application.

Ultimately, he noted, those judging the value of AI need to establish what happened after the first task became faster. “An improvement per paid hour can conceal effort left off the timesheet,” said Gogia. “The [enterprise] board should be able to say whose day improved and whose work made that improvement possible.”

This article has been updated to clarify that some stats are not related to AI.

Organizations are rapidly implementing AI, but employees often feel left behind about how AI is changing their roles. While AI can help get work done faster, it won’t necessarily help produce better quality work if employees aren’t confident in their AI skills.

In fact, a recent survey from workforce analytics firm Visier reveals that 48% of employees report exaggerating their AI expertise at least some of the time, and 45% said they often feel pressure at work to use AI despite not feeling confident with the tools.

“The risk of underinvesting in training and upskilling is that the alternative can be much more expensive,” says Andrea Derler,  principal researcher at Visier. “Layoffs, rehiring, and competing for top AI talent all come with significant direct and indirect costs. Investing in the workforce organizations already have can be an important part of making AI adoption sustainable.”

When the pace of AI implementation is rushed, however, employees often default to AI compliance, meaning they’ll use AI tools as they’re rolled out, even if they haven’t received the proper guidance or training from their organization to make the most of the investment. So for companies investing millions into an AI future, adds Derler, it’s critical to ensure employee participation is part of the bigger picture to avoid performative AI usage.

Make the most of AI investments with training

Derler acknowledges that AI adoption goals vary widely from company to company. Some will need stronger investment in upskilling and training, while others might need to invest in new resources, talent, or tools to help employees get comfortable with the new pace of work, and to source the right skills for an AI-driven workplace.

Either way, the early stages of AI adoption should involve a basic framework to assist in employee adoption, one that involves open transparency and foundational guidelines for how AI will be implemented moving forward. At bare minimum, employees should be briefed on concepts around general data security, how to spot false outputs from AI tools, and expectations around AI’s impact on their job roles.

“A few foundational skills and concepts every employee should understand include protecting sensitive data and uploading information with the greatest care, understanding the basic capabilities of approved tools available at work, and checking the work before sending AI-generated output to others,” says Derler. “Most importantly, employees need to take full responsibility for the output. The AI isn’t responsible, the person using it is.”

Avoid setting blanket goals as well, where everyone is measured simply by a certain percentage of AI usage per individual or team. This can overlook reality, she says, creating a false narrative around AI usage and adoption, and a focus on quantity rather than quality. Instead, adds Derler, leaders need to strike a balance between mandating adoption and encouraging employees to explore, discuss, and learn together where AI makes sense.  

Embracing transparency around AI

When asked if they felt their organization had a clear plan for how AI affects employees and jobs, 59% in the Visier survey said they didn’t believe or were unsure that their company had a clear plan for AI implementation. And only 28% said they felt leaders in their organizations fully understand how employees use AI today, while 33% thought leaders understand how much training and support employees need.

Overall, employees are eager to embrace AI, but when asked what would boost confidence around using it at work, respondents first ranked AI training and upskilling opportunities as most important, then greater transparency from leadership, more opportunities for career growth and advancement, followed by better communication about how AI will impact employees.

Clarity from the top

Employees have made it clear they want training and transparency from leaders about how AI will affect workflows and processes. This is especially true when it comes to career paths, as AI stands to remove certain skills and expertise deemed irrelevant.

Workers know the future will look different in the wake of AI, but they want to understand exactly what that looks like so they can prepare and shift their own expectations and career goals. Part of keeping employees engaged is ensuring they’re invested in their own future at a company, but that’s difficult if the future is particularly unclear.

“Most organizations are still learning which parts of a workflow AI can take over or augment, so creating opportunities for experimentation and learning is an important step before setting formal adoption goals or metrics,” says Derler. “Many organizations, however, are still piecing together workforce data and don’t have a complete picture of their workforce, making it harder to understand where AI can create meaningful efficiencies.”

CIOs bring a lot to their new jobs: decades of leadership, transformation, pattern-recognition, and outcomes. But none of that matters in your new job. During a time of generalized uncertainty, your team — and every stakeholder — needs to trust you more than anything. But how do you build that trust in a new environment, and as quickly as possible? For Cengage CIO Ken Grady, the best approach is an architectural one.

What is a trust architecture, and why did you develop it?

I’ve been a CIO for almost 20 years, and when I joined Cengage six months ago, while I had roughly 7,300 days of evidence that I knew how to do this job, not one person in that building had ever seen any of it. The proof existed on my résumé. The trust did not.

On day one as a new CIO, you have no shared experience with your team, so you have no trust. Your team might have respect for your background, and hope you’ll succeed, but personal trust is something you must build from scratch.

So how do you do that when the currency of trust for your team can shift depending on the situation? Establishing trust with the people in your new business requires an intentional approach, an architecture.

What are the different currencies of trust?

The first is technical currency. My team needs to know that as a new CIO, I’ll respect their technical expertise, have enough of my own to be helpful, and engage in healthy technology discussions. To solve for that, I sat down and led an open conversation early on about whatever was on their minds, and I showed them that their technical expertise is valued.

The second is investment currency. Can I, a technology leader, speak to ROI or NPV? Can I explain why we should invest in this technology, or address this technical debt, or move from this version to that one? My team needs to respect my ability to guide our investments.

The third is human currency, which addresses the questions people are afraid to ask. Why did we move our offices? Why are we incentivized this way and not that way? IT people are people, and they have non-technical questions.

Technical, investment, and human currency get exchanged in a cadence of conversations, and that cadence almost matters more than the content itself. People value leaders who are steady.

How are you using the trust architecture now?

People have questions when a new leader comes on board, and they need a safe place to ask them. That’s why we built a monthly Enterprise Architecture Review, an open meeting with no agenda where technical people talk through hard problems concerning what we’re trying and learning, what’s working, and what isn’t.

We’ve also built a monthly portfolio review called Steps-to-Green. We talk about the yellow and red projects, and how we can help each other move them to green. There’s no punishment in this room, only encouragement. If your product is yellow, that means you’re one step closer to green.

But the fear is wondering if it’s okay to say what you really think, so the senior leadership team holds a monthly anonymous Q&A. Ask us anything, what you’re worried, frustrated, or even angry about. We might not have all the answers, but we’ll try. If you give your team that kind of safety, they’ll use it.

What’s the benefit of this safety?

You get a high-functioning team willing to challenge existing norms and continuously improve. There’s no such thing as a perfect organization, and safety creates an environment where anyone can suggest an improvement, try it, test it, and report back.

What you get with safety is democratic governance. If there’s a quality problem, anyone in IT can hit the big red button to say we’re not doing this release, we have a quality problem. Anyone on the team can know more than I do about this technology, migration, or this HR issue, and hit the button.

It doesn’t mean there’s no bandmaster. I still need to lead the band since that’s my job as CIO. But the trombone player has to trust me enough to tell me we’re not playing it right.

What advice do you have for CIOs building a trust architecture in a generally anxious workforce?

I cure prosciutto in my basement. Every time I start a new one, I remind myself it takes 18 months, regardless of how many years I’ve been doing it. Trust doesn’t care about your résumé. It’s earned, not given. Before your next conversation, ask yourself if you’re building trust in the currency this person accepts, or the currency I’m most comfortable spending.

Empathy should be your lens. With my team, I show up ready to learn something, connect with them, not to perform leadership. With my stakeholders, I understand they don’t care about my migration strategy. They care whether I’m making their life easier. With my board, empathy isn’t warmth. It’s precision on knowing they need confidence from me, not just an update.

Each direction takes a different currency for team, stakeholders, and customers. As a CIO, you can’t be comfortable in one lane and neglect the other two. You must invest in all three.

This summer, our big concern as IT leaders was figuring out how to drive adoption and ROI for our AI purchases while keeping shadow AI at bay. My advice then:  Give people transparent, sanctioned tools and put in place operational discipline to ensure the right tools are being deployed at the right times and in the right places. That advice still holds up.

But once again, the market shifted. Now, even the sanctioned tools are part of the problem, as we speed towards the autonomous, agentic enterprise.

Fortune reported recently that CIOs are “racing to put guardrails” around agents that one security chief called “a new kind of insider.” Gartner forecasts that 40% of enterprise applications will carry task-specific agents by the end of this year, up from under 5% last year. In the very same report, Gartner also predicts that 40% of enterprises will have to demote or shut down agents by 2027.

We’re building agents faster than we’re learning how to govern them.

Most leaders point to this as a visibility problem. We need to be able to see all the agents running and assign ownership to them. Fair points, but they only reveal the tip of the iceberg. The agents I’m concerned with are the ones nobody knows they’re creating.

Classic shadow AI is a tool someone chose: an employee connects a hyped consumer product straight into a corporate system, and now you have an unsanctioned agent running on unsanctioned software. That’s the fix I have written about before:  give people a sanctioned version good enough that they don’t need the workaround.

The harder case is when an employee schedules a recurring job with their sanctioned AI desktop harness. Perhaps they schedule an agent to scan their email at set times, draft replies that they can send later, maybe even grant approvals on some minor things like direct report PTO requests. 

There’s nothing shadowy about the process. It’s working within a fully sanctioned tool that is being used exactly as licensed. But that employee just built an agent. And most people aren’t calling it that.

For the use cases described above, that’s a productivity tool for a specific employee. If their computer is off or the employee leaves the company and this de facto agent doesn’t run, nothing happens. But what happens if the employee builds a process that reads emails from a shared inbox and updates the ERP based on these emails? Now you have an agent that is part of the back-office automation of the company, and if this employee is off, a potentially critical process is not working. There is also an attack surface that nobody with a security mindset is aware of, a malicious email read by the agent might cause data corruption in the ERP, for example.

The problem is, there’s no owner to name and no approval to audit, because we’re not looking for that as an agent to name and label. You can’t put something on an inventory you don’t know exists. That’s a different failure mode than shadow IT, and it needs a different fix than a better registry.

This is where we begin to see the full depth and breadth of the iceberg we’re dealing with. It opens up entirely new considerations for our governance practices as well. We used to be most concerned with the browser and what employees could be inviting into our networks that way. The bigger concern now with agents is the security of the desktop itself, because that’s where they’re actually built and run. The way to mitigate it centers on two things:  placing hard limits on what any of it can actually touch, and educating employees on what they’re actually doing, at the moment they’re most likely to do it.

Finding the balance point between control and innovation

Here’s where we also come up against the push and pull dynamic that rules our world as IT leaders, especially in the age of AI. Sometimes it seems like we have just two options:  We can lock down the enterprise and hinder productivity and creativity for our employees, or we can make things easy for our employees and put our enterprise security at risk.

The first rule I go by here is embracing the chaos and discomfort. We don’t have this all figured out yet and that’s okay. Where I see my peers fail is when they try to force a process into place in these situations. You end up pushing the behavior you’re trying to manage outside of your area of control, making your employees even more likely to turn to unsafe workarounds.

The better approach is to put in place guardrails that allow your employees some space to experiment but limit the risk. This allows you to have some control while also being able to observe the actions your employees take, and reach out to move local processes to the corporate level.

For example, you can put in place controls directly into systems like CRM or ITSM or the MCPs used to interact with them. For example, make them read-only by default, and put in place rate limits at a more “human level”:  a few hundred records a day, not the few hundred thousand that AI can process. Limit the fields and tables that can be edited, and rate limit them as well.

Then, if an agent misbehaves, whether due to malicious instructions or just a human guidance mistake, you catch the issue after a few dozen records get overwritten, rather than a thousand and retrace your steps to rebuild the files. You’re protected whether an agent is part of a central agent registry or not.

Governance is a literacy problem now, not a policy one

The other half of this has to educate the employees and designated power users, which we call AI Champions that are proxies of the CIO office in the various departments. You need to provide guidance the moment someone starts using it, not filed away in a policy no one reads. Every employee who gets a license for a sanctioned agentic tool at WalkMe takes a short course in basic agent concepts before their license activates.

Most organizations try to launch a full corporate AI training program, where research shows that only about a third of what employees learn is still being used a year later. The training fails because it teaches AI in the abstract, disconnected from the moment someone actually sits down to use it. Gate your training to the access itself, and it lands in the moment it’s most relevant.

The goal in the training isn’t to turn every employee into a developer. That’s not realistic, and I don’t think it’s the right goal either. What you’re aiming for is an understanding of what AI tool to use when: do you need a full-blown agent or just an agentic workflow, or maybe just good old deterministic integration, and the advantages, risks and limitations for each. They walk away knowing what an agent looks and acts like in the moment it’s most relevant, not months later when it’s done something they didn’t intend or expect.

Put those two fixes together and you’re solving a different problem than the one most governance conversations are stuck on. You don’t need a complete agent inventory to navigate this. You need defaults that are forgiving, and employees who recognize the moment they’ve crossed from using a tool to operating one.

AI didn’t just hand every employee a better tool this year. It handed a lot of them some of the responsibilities that used to belong to IT alone. Our job isn’t pretending that didn’t happen, or trying to claw it back. It’s making sure nobody has to become an IT admin to carry that responsibility safely.

Recently, I was confronted with a novel situation regarding a conscientious objector to AI. While working to formulate goals for our AI strategic initiatives, a teammate informed me they would refuse to use it as part of their role. Digital transformation is difficult to achieve on its own, let alone when members of your own team refuse to use new tools to get there. However, this situation is occurring across companies worldwide, as some employees decline to be onboarded with AI due to environmental or ethical concerns.

The conscientious objector on my team was largely concerned about future job displacement. I assured them that I didn’t require them to use something with which they disagree. However, I intend to hold that person to the same benchmarks as everyone else, and those benchmarks now assume the tool and its productivity gain exist. This may create a difficult situation for the employee as we adopt AI technology, but as a leader, I’m responsible for my teams’ performance and objectives.

AI has a PR problem

A healthy dose of skepticism always comes with the territory of any major technological shift. But as teams across industries race to embrace AI, consistent criticisms seem to be cutting through. My employee voiced most of them in our discussions.

The first was the biggest long-term fear: job displacement. The person refused to use the tool because they refused to “train their replacement”. The second was trust (or lack thereof). Their opinion is that AI is a machine-led tool that not only hallucinates but also can make the human in the loop increasingly lazy. The person stated that if the output is automatically presumed correct, we create a beginning-to-end resource with too much power and authority in decision-making. Third, there’s the moral objection (one shared by my wife) that data centers are springing up across the country, and small communities are pushing back against the strain on water and power.

This employee isn’t alone in their concerns. A survey in May by CNBC and SurveyMonkey found that nearly two-thirds (65%) of workers have at some point avoided using AI because of similar concerns, including that the technology can be inaccurate, a detractor to creative thinking and an eliminator of entry-level roles. Some experts perceive the discomfort in almost existential terms as an “attack on humanness.”

Others go even further than declining to use it. Nearly one in three (31%) employees report intentionally derailing their company’s automation roadmap by tampering with performance metrics to make it appear AI is underperforming and intentionally generating low-quality outputs. Interestingly, the number engaging in such practices jumps to 41% for millennial and Gen Z employees. This is a surprising statistic given younger employees tend to be more excited about technology adoption and innovation.

To their credit, my objector sat firmly on the honest side of the line. They weren’t breaking the machine but telling me, openly, that they didn’t want to use it.

Balancing ethical objection and technological progress

My initial response to this stance was to lead with honesty. I shared that AI is a tool, and your job is to get from A to B, while being free to choose the route there. At the same time, however, there are new business realities to keep in mind, particularly when generating specific goals about AI adoption.

At Paessler, like a lot of companies, we’re now setting internal AI benchmarks and moving a meaningful share of manual workloads onto these tools. Once that happens, declining it is no longer choosing a different path to the same result, but is actively stepping outside of something the whole team is now measured against. For example, other teams are using agents to automate routine tasks, share knowledge and plan work. If these cross-departmental efforts are rejected, there’s a risk that the employee will not be able to keep up with company-wide innovations. None of this is a personal criticism or a verdict on someone’s values, but the benchmark itself has shifted considerably in the last year.

There are also negative effects for leaders to consider as they navigate similar situations. If one person’s opt-out is tolerated indefinitely, others reasonably ask why they can’t do the same. Before long, there is a tangible risk that the team’s targets will slip, calling the leader’s tactics into question.

I suggested the employee focus less on the automation risk and more on the augmentation benefits. AI is a tool that can improve both your efficiency today and expand what’s possible tomorrow. For what it’s worth, I truly believe that AI job losses are much more likely to stem from a lack of retraining than from the technology itself. The employee who isn’t trained in AI tools is at much greater risk of losing their role than someone who can effectively supplement their work with the technology.

Encouragement can only go so far, though, when considering how AI has changed the landscape. Nearly every technology company is moving this way and, while it isn’t the posture I’d advocate, roughly 60% of executives say they’re considering laying off employees who won’t adopt AI. Of course, a hardline stance isn’t very motivating, so there needs to be a strategic bridge between “I respect your view” and “this is now a performance issue”. Our answer was to elect an AI ambassador on each team. This ambassador acts as both peer and champion who helps a hesitant colleague set up first use cases, find the value and establish the necessary KPIs. This works because a colleague allaying fears lands differently than a manager issuing mandates.

As a leader, I also couldn’t move forward without addressing trust. I agreed that we can’t just leave the tool to its own devices, but that demands best practices rather than abstention. For me, quality controls offer employee agency in this evolution. If your name is on the process, you own it, including anything the AI got wrong. Due diligence is more important than ever and it’s still up to the team to follow the links, check the conclusions and cut what doesn’t hold up. This is par for the course for any new tool, and I’ve previously caught AI misjudging a source and dropped it accordingly in my own work. Additionally, executives are monitoring output and intent, and they won’t simply accept AI slop. Checks and balances help build that trust and ensure the machine isn’t calling the shots.

Encourage agency, adopt consciously, educate along the way

AI is a massive capability jump and one that can be equal parts exciting and frightening. I think we’ve all had moments of uneasiness with this technology. For example, I remember modeling use cases and encouraging the security team in the early adoption phase. I watched one of my teammates start out with reservations, then be absolutely blown away by what he could suddenly do. Work that typically took weeks got done in hours or days, such as CMMC internal maturity assessments, penetration testing exercises and policy reviews. Once we entertained that curiosity and had team members thinking about how they could incorporate it, the results were remarkable.

For me, this is a reminder of how leaders need to approach this moment. Frame the technology as something that delivers a net benefit, removing rote work and freeing up time for higher-impact tasks. But, crucially, remind your team that the work still depends on them for oversight and guardrails. Where the objection is environmental, show some flexibility by encouraging leaner usage rather than a blanket refusal, because you can manage the footprint, but you can’t use it to opt out entirely.

Amidst the hype, many of us have forgotten that AI is a tool. It’s impressive, flawed and useful, but ultimately a tool. Refusing it outright is refusing a transformative technology, and putting your head in the sand isn’t a solution. At the same time, there are legitimate issues that employers and employees need to work out together.

This debate takes me back to the introduction of cloud computing and similar fears in the IT industry. Concerns such as the elimination of data-center jobs and the gutting of infrastructure teams were common. Long-term, though, it didn’t play out that cleanly, as there became a sudden need for cloud IT and security engineers. The cloud was soon understood as a different kind of data center requiring different skills, and the ones who refused were left behind. The best way forward was to continually adopt and adapt, and the same is true for any type of technical innovation.

Ultimately, for many of us working at technology companies, it’s hard to be a technologist when you refuse innovative technology.

A high school teacher had three girls sign up for her computer science class—three for the whole year. Then a group of ConocoPhillips technologists came in to talk about their careers, and more than 20 students registered. The school had to add a half-day CS teacher to handle them.

That is Girls Talk Tech working exactly as designed. A group of women in the ConocoPhillips IT organization formalized the program in 2018 with a clear mission: educate, encourage, and empower young women to embrace technology for the future. The gap it addresses is well documented. According to the National Center for Women & IT, women make up 57% of the US professional workforce and hold only 26% of IT roles, and the disparity widens in engineering and computing.

“It’s really an outreach education program,” says Billy-Joe Lafortune, general manager of Lower 48 Digital Technologies and Global Strategy at ConocoPhillips. “It’s about reaching out to young women in middle school and high school, and it’s about inspiring them and preparing them to consider or pursue a career in technology and STEM.”

Lafortune has been a co-sponsor since 2019, when the team asked her to take the role alongside the CIO at the time. Her day job spans two responsibilities: she is the business unit technology leader for ConocoPhillips’ Lower 48 segment, the largest piece of the portfolio, and she leads global digital strategy, deciding where the investments go and then delivering them. She reached that seat over two decades at the company, through IT leadership roles in the Canadian business unit and corporate functions. In April, the HoustonCIO chapter named her the 2026 Super Global ORBIE recipient, the category reserved for leaders at organizations above $7.5 billion in revenue with multinational operations.

How the program runs

Girls Talk Tech works in three tiers of depth.

School panels are the primary channel and the highest volume. Volunteers visit local schools, share their own career journeys, walk through the STEM pathways a student might take, field questions about college, and bring food and swag.

For a more immersive experience, students are invited to short on-campus events that typically span one or two days and provide hands-on time with real technology, breakout sessions and conversations with practitioners from cybersecurity, software development and other disciplines.

ConocoPhillips

Houston’s GTT Fest, a two-day event, draws close to 200 students. The deepest level of engagement comes through summer programs, where a small cohort of students spend about a week participating in mentorship, real-world projects and a capstone presentation back to the business. All programs are offered at no cost to participants.

Ninth year in, the numbers describe something that found demand rather than manufactured it. More than 7,000 girls reached. Three established affiliate programs in Houston, Bartlesville and Calgary. Pilots running in Midland and Equatorial Guinea. Twenty-plus global committee members and more than 150 volunteers.

“Schools want this, teachers want this, we’re seeing parents want this,” Lafortune says. “There’s this thing that’s drawing people in. And we’re not the only program like this out there. There are a lot of companies and nonprofits doing this, which I think is incredible, and the work has to continue. Clearly the gap still exists.”

She frames the case in pipeline terms rather than charitable ones. “It helps build the pipeline of talent needed for an AI-driven economy,” she says. “It’s making sure we have people equipped with the foundational skills, and that they participate rather than just consume.”

As AI plays an increasingly important role, it can help organizations improve productivity, decision quality and business performance. But the opportunity is not simply to use new tools. It is to help people develop the skills and judgment to apply them thoughtfully. “It’s more important than ever to bring young women on this journey so they can be part of building that future as we go along.”

The full loop

The story Lafortune returns to is Maddie’s. Maddie attended Girls Talk Tech events as a high school student, went on to earn a technology degree, and was hired into a ConocoPhillips rotation program. She is now a cybersecurity analyst at the company and sits on the Girls Talk Tech Houston committee. Her journey illustrates the human side of technology transformation: technical capabilities may continue to evolve, but expertise, mentorship and human judgment remain essential.

“She’s on the other side of the journey now,” Lafortune says. “She’s sharing her perspective, she’s mentoring students and she’s demonstrating what’s possible. For me that really hits home.”

Lafortune’s own role is sponsorship in the working sense of the word: air cover, prioritization and the executive weight that keeps a volunteer organization from drifting. That last piece is harder than it sounds. “It just keeps growing year over year, to the point where if we’re not careful this becomes a full-time job,” she says. “So, we really do have to prioritize how much we can do in a year and still have the impact we’re seeking. And everyone does it because they love it. They love to give back.”

Rationing volunteer capacity across three countries so a program stays effective while demand outruns supply is, functionally, portfolio management. It is the same work she does on the day job, in an arena where nobody has to do what she asks.

What leading something like this does for you

There is a secondary benefit here worth naming, because most technology executives underweight it.

Marketplace eminence is one of the dimensions we assess when we work with leaders on CIO readiness, and it is consistently the one they have thought about least. It is what puts you on a search firm’s short list before a role is posted, what makes a strong candidate return a recruiter’s call, and what earns you the benefit of the doubt in a board conversation.

Most of the ways leaders build it are good ways. Speaking on a panel puts your point of view in front of peers and gets it into circulation. Customer advisory boards, industry forums and analyst relationships all do real work, and the executives with the strongest reputations tend to do plenty of each.

Leading a community program complements those channels. It compounds, because you are there for years. It is genuine operating experience, since a program at this scale means governance, budget, volunteer labor and stakeholders across three countries who do not report to you. And it gives you something specific to say when the panel invitation comes, which is often the difference between an appearance people remember and one they do not.

Lafortune did not take this on as a career move. She took it on because she had seen what early access does. “It’s almost like you’re creating that spark to help people see what is actually possible,” she says. The standing came anyway, which is rather the point.

Call to action

If someone in your industry were asked tomorrow what you are known for beyond your own company, what would they say?

If the answer is thin, find the cause you would show up for even if nobody noticed and take a real leadership position in it. A STEM pipeline program. A workforce reentry initiative. A nonprofit that needs a technology strategy and cannot afford one. The cause matters less than the commitment, because the returns arrive slowly and they arrive as relationships, reputation and operating range you cannot buy or borrow.

Lafortune put it more plainly than I can. “The reality is that the future will be shaped by who shows up,” she says. “Programs like this help ensure who gets into the room, and whose voices are heard.”

That is true of the students. It is also true of the leaders who decide to show up for them.

For many years, Musinsa, South Korea’s premier online fashion platform and marketplace for Korean designer brands, streetwear, and beauty products, has described itself as a company dedicated to AI. They bring in AI-native talent and give employees room to experiment, yet meticulously manage token usage and work performance.

And instead of handing over AI completed by engineers to the field, they have the field restructure their own work using AI. So if external solutions are deemed lacking, they analyze the cause and create their own solutions.

Gil Gi-yong, the company’s director of core AI and CX engineering, began actively utilizing AI shortly after joining Musinsa last year when he saw AI evangelists, from the CTO to new employees, directly create results. He says that Musinsa changes faster than organizations he’s experienced before, so he’s diligent about anything new that comes along to augment its developing internal AI platform.

Director of operations Kim Dae-ho is on the same page, too. Kim, who joined in September 2022, currently leads the operations strategy team that’s responsible for planning and executing tasks to improve customer and partner experiences, and improve operational efficiency across Musinsa’s commerce ecosystem. Recently, most of his work has been closely related to AI, so he’s collaborating with the development team to lead AX projects.

Musinsa is investing so much in AI is because trends change rapidly and fashion consumers can be impulsive. So platforms have to be fast, anticipatory, and intuitive to meet demand. In addition, they should support areas such as sales management for partners, and AI is the means to meet these needs and boost business competitiveness. That’s how Musinsa has started solving unique fashion industry problems.

A prime example is how outfit photos are shot. To showcase products in the past, models were hired, they dress themselves, and it’s all shot in a studio. Now, AI is used as a coordinator to generate images and the goal, according to Gil, is to elevate visual AI tech to a top standard by H1 2027.

Plus, AI is used in the product registration process of partner companies, so when a product is registered, AI automatically infers categories, colors, materials, and more. In the past, these tasks were manually entered. Holistic AI has also been integrated into customer service centers. The AI chat agent operated by 29CM, a South Korean fashion and lifestyle platform operated by Musinsa, currently handles about 25% of all customer inquiries.

From business operations to ROI

Companies typically approach AX in two ways. One is for the engineering team to get the requirements of the field, develop the system, and then say try it, and pass it on. If it’s hard to solve internally, the other way is to leave it to an external vendor. But Gil sees that AX often fails in these two approaches.

“The reason is that engineers do AX, but they don’t fully understand the processes of the field,” he says. “That’s why we focus on creating an environment where field experts can directly improve the actual work processes.”

In fact, Musinsa’s AX-related ideas are directly proposed by each business team. For example, the planning and engineering departments review how to improve on repetitive tasks with LLMs by setting quantitative goals such as reducing tasks from 270 man hours per week to three.

The core of the approach Musinsa choses is next. Engineers don’t write scripts for them. Instead, the core AI team suggests guides until target figures are achieved through repeated testing. Gil says this process ultimately helps the business operator measure how to write prompts and what improvements they use to improve them.

This approach has also changed the process by which AI spreads. Instead of copying systems created by developers, colleagues directly use AI to improve actual work. As small success stories accumulate and are shared, overall AI utilization capabilities also increase.

Gil adds that this approach must align with the direction of leadership. Simply sending tasks down by saying try using AI and costs will be covered isn’t enough to drive adoption. At Musinsa, many leaders, including the C-suite, first experiment with AI and freely share results. This culture encourages voluntary experimentation and accelerates the spread of AI. 29CM’s AI chat agent was reputedly developed using this method.

In fact, this tech was developed in collaboration with outsourced companies, and produced a certain level of output, but the strategy was revised to in-house development. Those results were actually better, according to Gil, and they succeeded in launching quickly while reducing costs to about a third of the outsourcing method. The development period was also about a quarter of similar projects at other companies.

However, this speed isn’t simply due to AI. ROI verification is thorough and Musinsa first fixes timelines and budgets, then prioritizes requirements to implement within that framework.

Even before starting, cost issues are carefully considered. For example, they calculate a five-year TCO and first check when investment costs can be recovered as savings. When using external services, they don’t sign long-term contracts based on simple discount benefits. Kim says that starting a project based solely on rough cost estimates may result in significant investment but doesn’t achieve the expected efficiency.

For AI chat agents, they quantified the estimates generated by the technical team, and simulated how much customer satisfaction would change and inquiry rates decrease. Investment costs were also calculated by year and month.

After implementation, monthly tracking is conducted to see how costs differ from expectations, how the AI processes inquiries, and how close performance is to the original target. If differences from expectations occur, improvements are made, including which variables to add, if the scope of inquiries handled by AI should expand, and if agent workflows should change.

Generous support, but objective evaluation

For this approach to continue, a supportive organizational culture is necessary. First, Musinsa provides a substantial amount of tokens so its members can fully utilize AI. But token usage is actively managed, so each leader regularly checks it using separate monitoring tools. If too many are used for tasks that could be handled with fewer, it’s looked into. Conversely, tasks that could be completed much faster with AI are still subject to inspection even if they’re handled using traditional methods.

Kim says that ultimately, what matters isn’t whether AI was used, but what results were achieved and how many resources were invested in the process. If AI can improve the quality of deliverables and reduce time in the work, it should be actively utilized. But the costs invested and the results achieved should be considered together.

Naturally there are times when using AI when multiple rounds of testing are gone through before desired results are reached. This means more tokens and time are required, and costs increase. But Musinsa focuses more on what trial and error was experienced, what was learned in the process, and what can be done differently next time. Failure is also seen as data that increases the chances of success next time.

AI leader standards for talent

What kind of talent do AI-led companies need? Not so much proficiency in AI itself but the ability to properly define problems. Kim defines planning as a journey of problem-solving, meaning a series of processes to identify them, develop solutions, and execute. It’s become even more important for humans to define what to ask of AI. If the problem definition is ambiguous, no matter how plausible the AI is, it’s difficult to apply it to actual work.

Of course, acquiring these skills isn’t easy. Kim says that training to constantly ask why is helpful. He doesn’t just accept initial causes as they are, but searches until a real solution emerges.

“At first, it’s confusing,” he says, “But through intense discussions and debates, capabilities develop. If the opponent isn’t a human but an LLM, you can repeat the same process much faster.”

Gil’s standards to evaluate development talent are similar. He believes people who can think differently from traditional methods are now needed, and relying solely on experience and technical expertise is no longer a sufficient nor competitive advantage.

In fact, this is reflected in Musinsa’s AI native developer recruitment. The focus isn’t on simple algorithm proficiency, but on how well the applicant defines real-world problems, grasps the essence of the problem, and uses certain indicators to determine whether to solve the problem, even when freely using AI.

The principles of leadership aren’t much different. Kim defines his style as autonomy-based leadership, while Gil prefers to clarify responsibilities and authority. The common goal, however, is to have an organization that doesn’t rely on any single individual.

In terms of next steps in AI transformation. Gil believes that competitiveness depends on who’ll first leverage new technologies, and how quickly that experience will transform into organizational capabilities. But AI led by tech organizations has its limits. Models that worked in commerce don’t work offline as is, and a single model can’t transplant across all areas of the company. So each organization must handle AI based on its own domain of expertise.

This is where Musinsa is headed. It’s not a company that relies on a few AI experts, but a place where anyone can wield AI in their work. “I want to establish a company where every employee solves their own problems with AI, and accumulates that experience as an organizational asset to show we use it well,” says Gil.

Microsoft’s announcement that they would be cutting 4,800 jobs was met with confusion by many, who contrasted the layoffs with the company’s recent market success (Microsoft’s net income was $31.8 billion last quarter, up 23% year-over-year). But it’s the reality of business in 2026 – profitable public companies cut thousands of jobs, impacting the lives of thousands of real people to justify huge AI investments to Wall Street.

These layoffs by Microsoft and other corporate giants represent a turning point in business history in America: large corporations can no longer serve as the cultural leaders for American businesses; they’re structurally incapable. It’s time for small and medium businesses to take up the mantle.  

The doctrine that ate the workplace

Fifty years ago, Milton Friedman published his famous doctrine that the sole social responsibility of business is to maximize profits for shareholders. It became gospel — reshaping business schools, boardrooms, and eventually the personality of corporate America itself.

Over the last year, we’ve seen this idea taken to the extreme, as company after company has cut thousands of workers to justify AI spending and boost market performance. Amazon cut 16,000 corporate jobs in January (in addition to the 14,000 jobs cut in 2025). Block cut nearly half its workforce in February. And since then, several other American giants like Salesforce, Intuit, Oracle, GitLab, Meta, and Cisco have laid off several thousand employees as well. 

Big Tech has bent over backwards to live up to Friedman’s doctrine, continually justifying their reduction under the push for AI modernization and efficiency. They’ve ushered in a new and brutal era of corporate culture that looks unlike anything seen in generations past. 

The Detroit auto companies that mirrored their employees’ lifelong loyalty? Gone. IBM’s paternalistic career culture? Gone. Even Google’s once fabled cafeterias and “20% time” rules? Steadily eroding. 

Like offshoring, digital transformation, and SaaSification before it, AI is the latest excuse for business leaders to cast aside human talent for quick market wins. Executives point to these external forces as the reason layoffs are inevitable, and claim to be helpless in the wake of global and technological trends.

But the truth isn’t as cut and dry. According to  SHRM, only 6% of jobs are both highly automatable and free from the practical barriers that would actually let AI displace them. In other words, AI is transforming jobs, not eliminating them. Which means external conditions aren’t forcing these cuts: company leadership is choosing them. 

Small businesses are running a different playbook

With the resources and the talent to be experimental and push the envelope, large businesses — especially in the tech age — became the cultural leaders for America’s business environment. Small and mid-sized business leaders idolized public companies, as if they were the varsity team. Get there, and you’ve “made it.” Org charts were copied; perk playbooks were cribbed. Their roadmaps for innovation gave confidence to smaller companies that they had it all figured out.

But the brutalism of today’s corporate culture is shifting the dynamics and pushing small and medium-sized businesses into the driver’s seat. While the big guys were facing “unavoidable” layoffs, small and mid-sized businesses have largely been able to avoid mass layoffs. Instead, they used AI to change what their teams could do, invested in flexible talent, and emerged more resilient. Nearly half of SMB leaders reported maintaining high confidence throughout 2025’s economic shocks.

Employees have noticed, too. Employee happiness data shows companies with fewer than 25 employees scoring an average net promoter score of 51, while companies with more than 500 score 34. That gap has been widening since 2021.

This isn’t happening because private business owners are morally superior people who have transformation all figured out: it’s because they’re structurally free. 

There is no one-size-fits-all playbook that private companies have to follow — not with regard to profits, or managing downturns, or their business playbook. A multinational apparel company like Patagonia can dedicate 100% of its profits to fighting climate change, while a manufacturing technology company like Barry-Wehmiller can refuse layoffs, even with their backs against the wall. Without the obligation to translate every decision into a story that will move stock, these private business owners can afford to think in years, not quarters. They can decide that a person is worth more than a nominal point of margin, and decide for themselves what corporate culture should look like.

The success of public companies no longer grants them freedom or trust; the speed and values of the market have penned leaders in like a hog in a chute. Driven in a single direction, fattened up until they can no longer support their own weight, and then butchered.

While public companies may continue to lead and drive things like infrastructure, capital markets, and scale, it’s time for businesses (and business schools) to look towards a different set of leaders to set the tone of American business culture. Small- and medium-sized businesses, as well as private companies, can build a culture that continues to value human output and innovation in an era where it’s being minimized. 

Public companies have no choice. We do. We can choose to optimize for more than a monetary high score. We can structure our organizations to embody those intangible qualities we value most, and want to see grow throughout society. You don’t have to be a nonprofit to care about more than money. We don’t have to play the public companies’ game. We can play our own.

And we get to make the rules.

Improving employee productivity and efficiency is one of the top priorities for CIOs today. According to Gartner, 81% of CIOs say these areas will remain a focus for the rest of the year.

Hybrid work has also raised expectations for the office experience. Employees have gotten used to reliable technology and familiar tools at home. When they come into the office, they expect to find technology that’s simple and ready to use. 

However, many organizations continue to lose productive time to workplace technology that doesn’t run as expected. Employees might have a hard time finding the right space, starting a meeting, or connecting to the tools they need. Those disruptions frustrate employees and pull IT teams away from strategic work.

According to Cenero, 90% of people experience tech-related stress in meetings. Creating a more consistent experience across the workplace can help employees spend their time connecting and collaborating instead of dealing with technology.

Create a consistent experience throughout the day

Imagine you’ve just started working from the office a few days a week. Before leaving home, you reserve a desk and meeting room. When you arrive, digital wayfinding directs you to your workspace. Later, you step into a huddle room for a team call and join with the same familiar process you used in a larger conference room the day before.

Standardizing technology and workflows across spaces gives employees confidence to move through their day without figuring out how each room works. Continued training helps maintain that consistency as new employees join. 

Keep the technology simple

Start by understanding what employees need to accomplish in each space. “Workplace technology should support a business application, a person, and a purpose,” said Christopher Emminizer, president and chief revenue officer at Cenero.    

Employee interaction with the technology should also be minimal. Cenero works with Crestron to provide solutions that automate parts of the meeting experience and work across Microsoft Teams and Zoom. This gives employees a simpler experience while providing flexibility as an organization’s collaboration needs evolve. 

When architecture and design firm LS3P relocated its Charlotte office, the team worked with Cenero to create a simpler workplace experience. The project delivered flexible spaces that make it easier for employees to meet and share ideas, while proactive support gives LS3P insight into how its spaces are performing.

Let workplace data guide your investment

Ongoing support and monitoring can ensure reliability and functionality of your workspace technology. For example, real-time remote management helps identify and resolve technical issues before they disrupt meetings. This saves resources while improving the overall experience.

Leaders can also look at how employees use spaces to see where technology investments are delivering value. Room activity shows which spaces employees rely on, while service trends reveal which systems need attention. 

Collaboration technology typically has a five- to six-year lifespan. Combining activity and service data helps CIOs decide which systems to replace first and plan upcoming investments based on demand. 

Give employees a reason to come in

A successful hybrid workplace makes it easy for employees to connect and collaborate throughout the day. Creating a simple, consistent experience gives employees greater value from their time in the office — and makes the commute worthwhile.

Discover how to create workplace experiences that make it easier for employees to connect and collaborate. Explore Cenero’s digital workplace solutions.

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.