CIO Priorities

Companies today are facing disruptions on multiple fronts, from rising geopolitical tensions to ongoing supply chain turmoil, creating tension in the C-suite.

Almost three-fourths of CEOs reported feeling under pressure to accelerate innovation and adjust their business strategy while 67% of CIOs, CTOs and chief AI officers are feeling pressure to improve customer experience, according to KPMG’s Adaptability Pulse Survey. 

Many enterprises experiencing operational disruption tend to react rather than proactively enact a strategic plan, according to the report. KPMG surveyed 1,120 executives, many of whom indicated that their companies are stuck in a cycle of short-term fixes and putting out fires when faced with external pressures. Proactive strategies, on the other hand, are almost 1.5 times more likely to deliver better outcomes, the report said. 

“A proactive posture allows organizations to break free from the limitations of old playbooks, empowering them to adapt swiftly and confidently to new disruptions,” Andrew Getz, go-to-market enablement leader for deal advisory and strategy at KPMG, said in an accompanying survey report.

CIOs navigating mounting business disruptions can help establish a proactive approach that enables early, decisive actions, which KPMG argued is a critical component of success in conjunction with scale of implementation. 

Along with customer experience, almost two-thirds of technology leaders feel pressure to accelerate innovation while 63% said they felt pressure to reduce operating expenses. 

Technology leaders’ category rankings aligned with other members of the C-suite. Customer experience was ranked as the No. 1 pressure-inducing area collectively, with 70% of CEOs, 75% of CFOs and 68% of COOs pointing to this category. CX was followed by accelerating innovation, with 72% of CEOs, 68% of CFOs and 68% of COOs selecting the option. 

The global professional services provider suggested four tips on how executives can build a proactive strategy. The first is to pursue centralized and structured strategic planning that keeps businesses on a path to achieving objectives amid disruption, the report said. 

Second, C-suite executives must invest in structural changes that support continuous change and embed adaptability into everyday operations. 

“This means building mechanisms for continuous monitoring of market signals, emerging risks, and technological shifts, so teams are empowered to spot disruption early and act swiftly,” the report said. “Ultimately, proactive operating conditions are about creating an environment where agility, foresight, and decisive action are the norm — not the exception.” 

Third, building a network of trusted partners before they’re needed helps companies transition to a more proactive posture. Lastly, companies should engage in scenario planning. Instead of theoretical situations, scenario planning should focus on defining roles and establishing clear accountability, according to KPMG. 

Companies should focus on adopting a proactive approach at scale. Implementing a proactive approach enterprise-wide is nearly three times more likely to lead to successful outcomes than changes made on a smaller scale, the report found. 

“Rather than hesitating out of apprehension about making the wrong move, leaders who embrace proactivity build resilience by anticipating change and making bold, informed decisions before external pressures dictate their actions,” Getz said.

AI automation is expected to force meaningful change in operational capabilities for companies as they shift from digital to autonomous businesses, 80% of CEOs said in a Gartner report. The firm surveyed 469 CEOs and other executives worldwide in late 2025. 

Some CEOs said AI will negatively affect their profit models, as agents could disrupt existing intermediated systems or price negotiation processes. The shift could force executives to pursue recurring, outcome-based revenue models, the report said. 

The transition to autonomous businesses requires executives to prioritize how work gets done and how human value is delivered, Jennifer Carter, senior principal analyst at Gartner, told CIO Dive. They will be able to focus on the intrinsic, specialized value that employees can bring to their role. 

Enterprises are in the early stages of deploying autonomous AI at scale, but expect that they will ramp up their usage in the next several years. The long term effect on business models remains to be seen.

More than half of CEOs said automation was currently limited to specific tasks, but only 13% expect to remain at that level by the end of 2028. One-third of CEOs said their organizations will use self-learning and adaptable AI tools in human decision-making, and more than a quarter plan to use AI primarily without human intervention. 

“We're looking at our entire end-to-end operations and saying: How can we create entire workflows that, fundamentally, run themselves?” Carter said. “They can be managed by humans, but really turning inwards with the opportunity for automation.”

A recent Writer study found that tech leaders are feeling the pressure of AI adoption, with 61% reporting they fear losing their job if they fail to successfully lead their organization through AI deployment. Some say their own skills could become obsolete in the age of AI. 

Still, executives said they believe they can use AI to deepen relationships with existing customers and non-human, machine customers. Gartner expects enterprises will expand their use of dedicated business units for machine customers. 

The increased reliance on agents and autonomous workflows intensifies an enterprise’s need for trust, accuracy and data integrity, the report said. As adoption accelerates, the foundation on which companies deploy their AI becomes a core requirement, and poor AI hygiene will have lasting effects, Carter said. 

As CIOs take expanded responsibility over AI at their organizations, they must look inward at their operations and outward as the business landscape changes, Carter said. Leaders should be paying attention to market needs and understanding customers while assessing their autonomous ecosystem.  

Roles all across the enterprise will likely shift during this transformation, Carter said. It will become important for agent management to become a part of workflows, but it will also free up time for teams to focus on the uniquely human parts of business, she said. 

“We can get rid of some of the drudgery,” she said. “What's the specialty stuff that's left over?”

Sponsored content

By Atlassian

The old service playbook is broken

For years, service management meant the same thing: a queue, a form, a ticket, and a wait. Teams bolted AI onto legacy ITSM tools and called it transformation. But when work happens in real-time — across code deploys, SaaS apps, collaboration platforms, and hundreds of devices — funneling every request through a static portal isn't transformation. It's a bottleneck wearing a chatbot mask.

Employees now expect the same instant, context-aware help they get from consumer apps. And service teams are burning out trying to deliver it with tools designed for a simpler era. The result: rising ticket volumes, slow resolution times, and frustrated people on both sides of the conversation.

What "AI-native" actually means

There's a critical difference between AI-assisted and AI-native. AI-assisted means you've added a layer of intelligence on top of existing workflows — auto-classifying tickets, suggesting knowledge articles, maybe deflecting a few requests. Useful, but incremental.
AI-native means the entire system was designed around intelligence from day one. Routing, prioritization, resolution, even proactive outreach are powered by an AI that understands the full context of your organization: who works on what, which tools they use, how teams collaborate, and what's already been solved before.

That's the vision Atlassian is building toward with Jira Service Management, Rovo, and the Teamwork Graph.

The unfair advantage: Teamwork Graph + Rovo

Most AI tools are only as smart as the data they can see — and in most organizations, that data is siloed across dozens of disconnected systems. Atlassian's Teamwork Graph changes the equation. It maps the relationships between people, projects, goals, knowledge, and code across your entire Atlassian ecosystem, creating a rich, connected context layer that AI can actually reason over.

Rovo, Atlassian's AI engine, taps directly into this graph. That means when an employee asks a question — "Why is staging down?" or "How do I request a new laptop?" — Rovo doesn't just search a knowledge base. It understands who is asking, what they're working on, which team owns the answer, and what's already happening in related incidents or requests.

The result is faster answers, smarter routing, and — increasingly — problems resolved before they're even reported.

From reactive queue to proactive service

Imagine a world where your service team isn't waiting for tickets to arrive. Instead, the system detects an anomaly in a deployment pipeline, correlates it with a spike in "app is slow" messages in Slack, identifies the owning team, and auto-creates an incident with full context — before a single user files a request.

That's not science fiction. It's the direction Atlassian's Service Collection is heading: a unified suite that connects IT service management, customer service, and employee support on one platform, powered by AI that works across all of them.

Shatter the service quo

Legacy service desks were built for a world of predictable workloads and siloed departments. Today's organizations need something fundamentally different — service that's embedded in the flow of work, intelligent by default, and continuously learning.

Download the whitepaper to learn how Atlassian is enabling this new era of AI-native service — and why the combination of a connected system of work, the Teamwork Graph, and Rovo gives teams an unfair advantage.

Read the full whitepaper.

Boards and CEOs are redefining what they want from their technology executives. Rather than technology stewards, CIOs are more commonly operating under a broader mandate that includes measurable business outcomes — and their titles are sometimes reflecting the shift.

Executive IT search experts said they are seeing an uptick in expanding CIO titles that include another function such as digital, data, transformation and even product. The hybrid roles can help break down internal silos while moving tech executives closer to customers and to the CEO for greater accountability.

“More than ever, technology and the CIO are at the intersection of everything aimed at delivering business value,” said Megan McCann, CEO and co-founder of McCann Partners, an IT staffing recruitment firm.

While a two-titled tech leader is not the only way enterprises are reshaping technology leadership, the consolidation of tech silos is happening at major businesses and across industries.

One of the clearest examples comes from Delta, which announced CIO Rahul Samant’s retirement in January. Amala Duggirala, Samant’s replacement, took over as chief digital and technology officer for the airline.

“At Delta, they didn’t just replace the CIO, they upgraded the role,” said Jason Pyle, president and managing director at Harvey Nash USA, a technology recruiting firm. “They combined all of these pieces … it wasn’t so much a replacement as it was recognition that the role needs to transform forward.”

Examples also appear in telecommunications firms such as Lumen Technologies, which brought in veteran IT executive Jim Fowler to oversee technology and product, taking over for the company’s outgoing CTO. In consumer goods, Kimberly-Clark tapped Francesco Tinto as its chief information and global business services officer in March while General Mills added transformation to its chief digital and technology officer’s title. 

C-suite consolidation

Pyle, too, has observed a move from CIO roles to more hybrid titles; he attributed the shift to an urgency created by AI and pressure for tech leaders to create value. “It’s not just about tech strategy or infrastructure,” he said. “It's about how the business is monetizing all that it's doing across its products and digital space.”

An April report from Deloitte echoed Pyle’s observation. It found that 79% of the 660 tech leaders it surveyed cited driving business outcomes as their top priority, another data point underscoring the criticality of creating value for enterprise.

In a more recent report, IBM’s survey of 2,000 CEOs suggested that chief executives are reshaping the C-suite to capitalize on the technology, blurring the lines between business units and creating functional interdependencies.

While IBM documented an uptick in elevating or introducing a chief AI officer into the mix, it also noted role consolidation — including between talent and technology leadership — and highlighted the criticality for every member of the C-suite to work across their domains and become “cross-enterprise orchestrators.”

“The real difference won’t be the titles on the org chart but how those leaders work together — and how willing they are to challenge one another to evolve,” the report stated.

Plus, consolidating tech titles can create an opportunity to refresh the IT’s identity, signaling to employees, stakeholders and even potential job candidates that the company is thinking about technology differently.

“We are seeing a bit of evolution in the title construct that we're encountering in the market,” said Craig Stephenson, senior client partner for the tech, ops, data/AI and information security officers practice at Korn Ferry. “And I do believe some of that is … a bit of a brand refresh internally for how stakeholders and/or the C-suite and/or the board might interpret the functional domain that we're talking about.”

The change currently underway is part of a progression rather than a new development for CIOs, according to McCann. In recent decades, IT shifted out of the back office, shed its waterfall methodology in favor of agility, rolled into the cloud and is now changing again.

“Maybe at the heart of it all, the simplest way to think about it is they're changing the communication pattern, which is impacting people's roles,” she said. “I don't know that it's quite that simple, but a lot of it comes down to the value and understanding that you need to be … as astute at the business as you are with technology.”

CAMBRIDGE, Mass. — CIOs are well-versed in the interconnected dependencies of people, process and technology. These days, AI is compressing all three areas of the classic framework — challenging business operating models and pushing CIOs out of their technology orientation as they tackle a broader remit.

“At the end of the day, AI is going to scale with people, systems and workflows,” Mojgan Lefebvre, chief technology and operations officer at Travelers, put it simply during a panel discussion at the MIT Sloan CIO Symposium.

The people aspect garnered its share of attention at the event, as CIOs and other experts discussed not just autonomous systems and agents but the role of humans as enterprises press forward on AI. 

Technology leaders underscored time and again the criticality of being a people leader, which requires thinking through what skills to invest in, how to invest in them and even how to help employees overcome, as one panelist put it, FOBO — the fear of becoming obsolete. 

Monica Caldas, EVP and global CIO at Liberty Mutual, likened the people part to modernization initiatives during a digital transformation panel. “There’s not just modernization from the heavy-duty of the systems, but there’s modernization of how we work and even evolution in the skills, in the people,” she said.

Caldas referenced research that advises companies to think about tech and talent as a 1:3 ratio: For every dollar spent on AI investments, companies should spend $3 on people. “In the areas we’ve deployed [AI] that are most successful are where we’ve spent a lot of time on the culture, the skills, the people, the championing of the work,” she said.

Human-centric approach

The language of culture echoes prior technology shifts when mobile devices, cloud or digital transformation entered the vernacular. But it, too, carries a different weight with AI as employees speak directly to technology and as technology operates as a digital workforce, rejiggering departmental day-to-days and C-suite dynamics.

Take, for example, AI’s influence on skill sets and learning strategies. Rod Adams, principal for Advisory People & Inclusion Leader at PwC, said he now works closely with the consultancy’s CIO to help understand what’s needed to become an AI-native organization.

“That wasn’t the case prior to the last year or so, but it has certainly become the case,” he told attendees. 

While technical skills remain critical, PwC also developed a set of 15 human-centric skills needed to advance AI in the organization such as coaching, agility, judgment and empathy. Redefining sought-after skill sets required the organization to overhaul its recruiting approach as well as its development strategy for its 70,000 PwC U.S. employees. 

“And then also looking into the future, we typically hire 5,000 or so people a year,” Adams said. “How are we making sure that our learning platforms and our learning strategy and approach are developing those skills?”

Measuring performance can also be tricky, according to Adams. Determining how to hold employees accountable for meeting AI adoption and fluency expectations is still a gray area, and he called on IT leaders in the room to work with their people officers to think creatively and experiment on how to do this well. 

One conference attendee brought up the recent trend of tokenmaxxing, where AI adoption and productivity are measured based on token usage, as “an insane way of motivating people” and inquired about emerging best practices for performance management.

Adams was candid: “We’re figuring it out.” PwC started with usage then added impact measured by quantitative indicators such as productivity or qualitative indicators such as client experience as well as depth of the use of AI. But, Adams noted, it’s still a work in progress.

“Because of that, we haven’t actually introduced it as a performance metric,” he said. Instead, the company is aiming to be transparent by surfacing how it’s trying to measure AI fluency with the expectation that it will be a performance metric at some point.  

“We are trying to avoid fear but also motivate people to … get involved,” he said.

Acknowledging fear and understanding human concerns about where they fit into the new world order is critical to scaling AI — and another reason for CIOs to remember their role as a people leader, according to Irene Oh, CIO at Network Distribution, a supply chain management and distribution company. 

“The people side of change is so important,” she said during a panel discussion, adding that if employees aren’t on board, change isn’t sustainable. Oh encouraged CIOs to work with C-suite counterparts to ensure their teams have the right tools and training to be successful. Doing so also enables CIOs to better clock uncertainty, fear and doubt within an organization.

“Technology is evolving quickly and it’s affecting the entire organization, not just IT,” Oh said.

CAMBRIDGE, Mass. — Guiding an AI project from nascent idea to wider deployment is a complex task. For CIOs, navigating that complexity is critical, as companies have started measuring performance based on how well their IT leaders are able to complete — and repeat — the process.

To ease that burden, executives are developing ways to detect the potential for success in a given use case. Some are focused on how employees weave the tool into existing processes.

For Soumya Seetharam, SVP and chief digital and information officer at materials science company Corning, an early sign of pilot success came when staffers incorporated AI model outputs as a source to prove out their forecasting accuracy.  

“They actually did it in the operations review and said: ‘Our AI model said this, our internal legacy process said this, this is what we have come up with, and this is what we're forecasting,’” said Seetharam, speaking during the 2026 MIT Sloan CIO Symposium. “They're not blindly using that forecast that the AI model gave. They’re looking at it, comparing it against what the legacy process did and then putting judgment on top.”

MIT researchers found last year that the large majority of generative AI pilots fail before delivering on their stated outcomes. More than half of businesses, according to a Solvd report, will potentially shut down AI pilots this year due to poor performance. Surveyed tech leaders pointed to a lack of oversight as a key factor in derailed pilots. 

AI tools that can evolve past the pilot stage call for a championing leader in charge, said Mark Schmidt, CIO at Westlake, a manufacturer of petrochemicals, plastics, and building materials. Under the right leadership, one success can have a ripple effect and spread across the organization as others replicate what worked.

“You have to have the right sponsor,” said Schmidt, speaking on the same panel. “That sponsor has to have the drive and the vision to take it through.” 

Data and governance

Two other critical components help determine the success of an AI pilot in the enterprise: data and governance.

Corning has multiple layers of governance, Seetharam said. The company developed a governance council for AI and machine learning, which includes general counsel and several top executives along with Seetharam. The council approves what AI use cases to publish in a common marketplace, enabling other workers across the organization to leverage tools that both work and adhere to company standards.

Even with a governance strategy in place, pilots can still fall apart if they don’t have the access to the data. 

Without the right data sets to power the tools and an engaged sponsor to fuel adoption, “you’re picking something that’s probably too difficult at this point in time,” said Schmidt.

To aid in moving pilots forward, organizations must also learn from their previous mistakes, said Vipin Gupta, a former CIO turned advisor and board member.

“We tend to celebrate successes and document successes,” said Gupta, who moderated the panel. “But we don’t show the same discipline to documenting failures.”

Enterprises are transitioning away from single chatbots in favor of multiagent systems to solve customer pain points, with usage rising more than fourfold in four months, according to Databricks’ State of AI Agents report. The company gathered data from more than 20,000 global organizations using AI.

Customer-facing tasks such as customer support, customer advocacy, onboarding and personalized marketing content represent 1 in 4 AI use cases, according to the report.

Governance emerged as a key driver for AI adoption, Databricks found. Companies that actively use AI governance move 12x more AI projects from pilot to production than their counterparts. 

Multiagent systems, where several AI agents collaborate to plan, execute and coordinate tasks, are gaining popularity as enterprises move AI from isolated pilots to integrated systems. 

Customer engagement remains the primary testing ground for these tools. Databrick’s findings align with previous research from Deloitte Digital, which found nine in 10 customer experience leaders believe AI has the potential to improve CX.

For CIOs, early wins with AI are likely to come from customer-facing functions, where business value is easier to demonstrate and adoption barriers tend to be lower.

Yet while agentic tools can unlock new capabilities, they also bring inherent risks as enterprises grapple with the need to monitor systems’ autonomous controls. Databricks identified governance and model evaluations as keys to mitigating risks and advancing AI from pilot to production.

CIOs can help their companies scale AI use by deploying agents based on relevant data and business context, said Craig Wiley, VP of AI and product at Databricks. 

"Today’s frontrunners in AI are making a conscious effort to choose the right multiagent system to answer their business-specific needs, using AI to support mundane and repetitive tasks, and investing in governance and evaluation tools to ensure compliance and safety," said Wiley in an email to CIO Dive.

Gaps in data readiness hinder AI adoption for enterprises, sinking $108 billion in wasted annual spend, according to Hitachi Vantara data published last week. Still, companies plan to continue pursuing AI efforts, with spend set to rise by 76% in the next two years.

Securing AI has become a top priority for CIOs, according to Logicalis. The report, which surveyed more than 1,000 CIOs globally, found more than a quarter see AI as a significant source of risk, placing it nearly on par with traditional threats such as malware, ransomware and phishing. 

Employee misuse of AI is compounding concerns, with 57% of CIOs saying staff are putting data security at risk. Despite the mounting risk, AI governance measures remain limited, with just 37% of organizations saying they have visibility into the AI tools in use. 

The challenges posed by the advent of AI are significant enough that nearly half of respondents in the Logicalis report said they wish AI had “not been invented.”

While traditional threats remain the dominant concern for CIOs, AI is being increasingly cited as a risk as business leaders grapple with critical issues such as shadow AI, app sprawl and lack of oversight.

Security teams, already strained, are losing ground in the face of increased blind spots, with more than one-third reporting a reduced ability to detect breaches and worsening incident response times. 

At the same time, internal misuse of AI is introducing new workforce challenges. Two-thirds of respondents say employee training on AI risk management is insufficient, while 94% of CIOs report a cybersecurity skills shortage. 

While a renewed focus on upskilling and expenditure on post-breach remediation is rising in tandem with the threat, there is a growing need for more to be done to shift response from reactive to preventative.

“AI is a powerful force in cybersecurity, but without the right skills and governance, it can create more vulnerabilities than protection,” said Bob Bailkoski, CEO of Logicalis Group, in the report. “CIOs have the challenging task of defending their organisations against AI-driven threats, but also from the risks posed by the very AI tools meant to safeguard them.” 

In response, Bailkoski suggests CIOs integrate governance and transparency into AI initiatives from their inception to ensure long-term stability.

The findings align with broader industry concerns. Recent reporting from Cloud Security Alliance and Thales similarly highlighted how unstructured data and poorly governed AI pipelines are expanding the enterprise attack surface, with 68% of companies surveyed saying a majority of their data remains unprotected.

Reliable data, AI-specific upskilling and improved governance were similarly identified as key tools in mitigating risk.

Initiatives such as Project Glasswing, Anthropic’s recently announced effort to identify and remedy software vulnerabilities, further underscore a growing push to scale governance alongside tech adoption.

The initiative was launched alongside partners including AWS, Apple, Broadcom, Cisco, Google and others who will use Anthropic’s Claude Mythos Preview model to identify and fix vulnerabilities.