AI is prompting organizations to **rethink the core mandate of tech leadership**. Deloitte’s Global Technology Leadership Study, based on responses from **more than 660 technology leaders globally**, shows that: - AI has moved to the **top of CEO and board priorities**. - Tech leaders are now expected to **embed AI across the enterprise**, often in legacy environments and **without proportional increases in funding or support**. - **Over 7 in 10** surveyed leaders say they feel *inspired or determined* about the future of their role, even as expectations rise. This is creating a **dual mandate**: 1. **Deep technical expertise** – fluency in AI, architecture, cybersecurity, risk, and emerging technologies is no longer optional. 2. **Enterprise leadership** – shaping strategy, driving measurable business outcomes, orchestrating across a growing tech C‑suite, and leading transformation at scale. At the same time, there’s a **misalignment risk**: - Enterprises say their top priority is **measurable business outcomes through technology**. - Yet many tech leaders are **anchoring their own success metrics around AI-specific outcomes** (such as AI adoption, AI-enabled automation, or AI-driven innovation) rather than broader enterprise value. In short, AI is not just another technology initiative. It’s becoming the **primary lens** through which tech leaders are evaluated, even as their accountability spans far beyond AI alone. Successful leaders are those who can **translate AI ambition into enterprise-wide value**, not just deploy new tools.

The research suggests that many organizations are **not constrained by AI technology itself**, but by how the enterprise is structured, funded, and run. Three common constraints stand out:

1. Structural fragmentation in the tech C‑suite
   
   • In the survey, **71% of organizations** report having **five or more C‑suite technology leaders**.
   • In EMEA, respondents report an average of **six** tech C‑suite roles.
   • Alongside the CIO (present in **95%** of organizations), many now have CTOs, CISOs, CDAOs, and emerging roles like chief engineer or chief AI officer.
   This expansion can be powerful, but it also increases the need to **orchestrate shared authority** and maintain coherence under rising scrutiny. Without clear ownership and alignment, AI efforts can become fragmented.
2. Constrained and misaligned funding models
   
   • Technology investment averages about **6% of revenue in 2026**, projected to rise to **around 8%** over the next two years.
   • Yet **89% of tech leaders** say they allocate **no more than 25%** of their tech budgets to AI initiatives.
   • In 2023, tech budgets were split roughly **48% run / 31% grow / 21% transform**.
   • By 2026, spending is shifting toward a more even distribution across **run, grow, and transform**, with that trend expected to continue into 2028.
   This means leaders are trying to **fund operational stability, growth, and transformation simultaneously** without a major capital shift. AI is often treated as a **net-new cost**, rather than a lever to reduce “run” spend and free up capacity.
3. Operating models that lag AI ambition
   
   • **81% of leaders** say their current operating model can deploy and govern AI enterprisewide.
   • Yet **75%** also say they **must change their operating model within 12–18 months** to drive greater value.
   This tension suggests that while organizations can technically deploy AI, they are still working through **ownership, prioritization, and integration**—the elements needed to capture real value.

Across all three areas, the pattern is similar: **AI ambition is outpacing enterprise readiness**. To unlock impact, organizations often need to **realign structures, funding, and operating models** so AI becomes a core enterprise capability, not a set of isolated pilots.

The study indicates that the next generation of tech leaders will be defined by a **blend of technical depth and enterprise leadership**—a true dual mandate. In the near term (next two years), leaders say they most need to develop:

• AI and data literacy – **44%** of respondents cite deepening AI and data literacy as their top development focus.
• Leading human–AI collaboration – designing how people and AI work together in core processes.
• Developing next‑generation talent – building high-performing, AI‑fluent teams.
• Translating technology vision into enterprise strategy – connecting AI and digital initiatives directly to business outcomes.

Looking 3–5 years out, the capabilities tech leaders prioritize fall into two balanced groups:

1. Technical depth
   • AI architecture and governance
   • Cybersecurity and risk management for AI-enabled environments
   • Understanding emerging technologies (cloud, IoT, experiential tech, etc.) and how they intersect with AI
2. Enterprise leadership and orchestration
   • Building and leading high-performing, AI-fluent teams
   • Shaping enterprise strategy and capital allocation around AI
   • Orchestrating across the C‑suite, not just collaborating with other tech leaders
   • Communicating in terms of value, risk, and execution realities—not just technical detail

The research suggests that the **next standard for technology leadership** will not be defined by tools or platforms alone. It will be defined by leaders who can:

• Anticipate and shape what AI changes next—treating AI as a catalyst for continuous enterprise redesign.
• Make disciplined technology trade‑offs—using AI to improve decision speed and quality, then transparently funding what matters most.
• Navigate risk without stalling progress—balancing regulatory, security, and operational concerns with the need to move forward.
• Make AI an enterprise muscle, not an IT project—embedding AI into core platforms, processes, and operating models.
• Show up as business executives, not just technologists—tying technology choices directly to enterprise performance and long-term value.

In essence, tomorrow’s tech leaders will be those who can **reimagine how the business operates around AI**, pairing strong technical judgment with the ability to drive measurable, enterprise-wide outcomes.