Technology leaders are being pushed beyond traditional IT responsibilities as organizations look to AI to drive business outcomes, but many are not yet structured to deliver on those expectations.
According to Deloitte’s 2026 Global Technology Leadership report, 79% of tech leaders now cite driving enterprise value as their top priority, marking a clear shift away from managing systems toward shaping business strategy.
The survey of more than 660 senior technology executives highlights a widening gap between ambition and execution.
While 81% of leaders say they are confident in their ability to scale AI, 75% also acknowledge that their operating models must fundamentally change to realize that value. That tension is emerging as a defining challenge for IT leadership in the AI era.
“This shift means moving from a defensive posture to an offensive one where a good day is no longer defined by system uptime, but by enterprise value created, such as productivity gains and net-new revenue growth,” says Anjali Shaikh, managing director, Deloitte Consulting, leader of Deloitte global CIO and US tech executive programs.
She notes the CIO role has fundamentally evolved from a technology steward into an enterprise architect of value creation.
Technical expertise alone is no longer sufficient, as leaders are expected to combine AI and data fluency with the ability to manage organizational change, build AI-ready teams and align technology investments with business goals.
For many, that means shifting focus from infrastructure and systems management to outcomes such as revenue growth, efficiency and competitive advantage.
“Ultimately, day-to-day leadership means balancing a complex triple mandate to run, grow, and transform the business simultaneously— a massive orchestration challenge,” Shaikh says.
IT Leadership More Distributed
At the same time, the structure of IT leadership is becoming more distributed. Seventy-one percent of organizations now have five or more technology leaders, reflecting a more complex, multi-stakeholder environment. In that model, success depends less on centralized control and more on coordination across the C-suite.
That shift is redefining how work gets done inside IT organizations. Leaders are being asked to connect strategy, execution and talent across business units, while navigating legacy systems, budget constraints and evolving governance requirements.
Shaikh says to maintain momentum across this distributed system, leaders must align around converging success metrics.
“For example, a CIO’s focus on AI adoption must seamlessly connect with a CISO’s focus on secure innovation and a CDAO's focus on trusted data governance,” she says.
She adds momentum is also sustained by actively managing the executive fluency gap; boards are reading the same headlines as everyone else and pulling the C-suite forward, meaning smart CIOs must spend their time between board meetings building AI fluency across the executive leadership team to keep pace with director expectations.
“Ultimately, coordination requires building formalized criteria to make explicit trade-offs across value, risk, and investment completely transparent, allowing the CIO to step into the crucial role of ecosystem orchestrator,” Shaikh explains.
Technical Knowledge, Business Understanding, Change Management
For IT professionals, the findings point to a broader change in career expectations. Leadership roles increasingly require a blend of technical depth, business understanding and the ability to influence across functions.
Skills such as change management, cross-team collaboration and AI literacy are becoming as important as traditional engineering expertise.
Shams Chauthani, CTO at Tempo, says the skill he didn't expect to need as much is what he calls "context design" — the ability to think about what your AI systems need to know about your organization and encode that knowledge in a way that scales.
“It's not enough to pick the right model or the right vendor. You need to be able to articulate your company's conventions, governance rules, and decision-making patterns in a way that machines can consume and apply consistently,” he says.
He points out that is a fundamentally different skill than managing infrastructure or running a dev team.
“My role has become orchestrator — I'm the connective tissue between business strategy and delivery outcomes,” Chauthani says. “Day to day, I'm not deep in architecture reviews anymore. I'm asking three questions constantly: Are we efficient? Are we leveraging AI fully? Are we delivering the right things?”
He notes those questions sound simple but they're deceptively hard to answer well, because the data to answer them lives across teams, tools, and workflows that were never designed to talk to each other.
“If I'm doing my job right, I'm making sure leadership can see the throughline from strategic intent to actual engineering output — and when those are misaligned, I'm the one surfacing it early enough to do something about it,” Chauthani says.
Orchestrating Enterprise-Wide Value
Michael Bevilacqua, vice president, AI product management at Adeptia, says organizations need to rethink both team structure and platform design if they want AI initiatives to deliver enterprise-wide value.
One of the biggest shifts, he says, is organizing teams around business outcomes rather than technical silos.
He also argues that companies should stop treating AI as an add-on layer and instead design platforms so AI agents can interact directly with systems through standardized interfaces.
“AI as a first-class user changes what’s possible,” he says.
Bevilacqua emphasizes that data readiness remains a major stumbling block, particularly as organizations try to scale generative AI initiatives on top of fragmented enterprise environments still dependent on spreadsheets, email and legacy workflows.
Without stronger integration, governance and validation processes, he says AI projects risk generating “fast confident wrong answers.”
He adds that organizations should shift performance metrics away from sprint activity and toward measurable business outcomes, such as completed integrations or validated datasets, to better assess the real operational impact of AI investments.
“Orchestrating value means designing the system, not running it,” he says. “That’s the shift.”
Chauthani says it’s important to get yourself into the rooms where investment decisions are being made — not to report on technology, but to bring the data that shapes those decisions.
“The shift from operator to orchestrator isn't a title change, it's showing up with the portfolio-level insight that makes you indispensable to how the business allocates resources,”.
