Much has been written about how artificial intelligence can reshape business outcomes. Nowhere is the effect more immediate than across the end-to-end lifecycle of software engineering and the broader business of IT: generative AI can write code, run tests, and augment talent, speeding delivery and improving product-market fit. Deloitte research finds that companies leading this change are about twice as likely as their peers to report that generative AI is already transforming their organization or will within the next year.1
We argued in Tech Trends 2024 that improving developer experiences is essential for better IT outcomes. Today, the AI momentum makes that imperative broader: the tech function’s remit is expanding from leading digital transformation to leading AI transformation. Forward-looking CIOs and technology leaders are treating this as a once-in-a-generation moment to redefine roles, reset investment priorities, and make clear value expectations—so IT can both orchestrate and accelerate change across the organization.
After years of lean IT and as-a-service models, AI is prompting a shift in spending and priorities. Gartner projects worldwide IT spending at $5.26 trillion in 2024, up 7.5% from 2023, reflecting renewed emphasis on hardware, cloud, and data investments.2 To capture AI’s full potential, organizations need a clear strategy now: Deloitte recommends that over the next 18–24 months CIOs plan AI transformation across five pillars—engineering, talent, cloud financial operations (FinOps), infrastructure, and cyber risk—so investments in data and systems deliver measurable business value.
If citizen developers and digital agents make it easier to spin up apps, IT’s role may shift from hands-on building to orchestrating platforms and enabling innovation. In short: the reach (and remit) of the tech function is expanding—from a digital-transformation lighthouse to the lead for enterprise-wide AI transformation. What follows is a practical look at the five pillars leaders should prioritize to realize that shift.
Now: Spotlight—and higher spending—on IT
IT budgets are rising as companies place bigger bets on AI and data. Since 2020, investment in technology has increased to meet demand for digital collaboration and modernization. Deloitte research shows the global average technology budget as a share of revenue rose from 4.25% (2020) to 5.49% (2022), and in 2024 US companies are allocating roughly 7.5% of revenue toward digital transformation, with 5.4% coming from IT budgets.345
Hardware, cloud, and data modernization are top priorities. Gartner projects worldwide IT spending at $5.26 trillion in 2024 (a 7.5% increase from 2023), underscoring momentum behind infrastructure and cloud consumption as organizations pursue AI use cases that require greater compute and storage capacity.2
Investment follows expertise. Deloitte’s Q2 State of Generative AI report finds that businesses with very high generative-AI expertise invest more aggressively in hardware and cloud, and 75% of organizations surveyed have increased spending on data-life-cycle management because of generative AI—pointing to the need for faster cloud and data modernization if organizations want to capture AI’s cost and innovation benefits.67
What this means for leaders: Tech leaders who move early can shift IT from a cost center to a business differentiator. Deloitte research shows more than 60% of chief intelligence officers now report to CEOs—up by over 10 percentage points since 2020—highlighting that AI strategy is now a board-level conversation.9
Quick example: organizations that pair data modernization with targeted AI pilots tend to capture faster time-to-value—improving product development cycles, reducing manual workflows, and enabling richer customer insights. For practical guidance, see how these investments map to the five pillars covered below: engineering, talent, FinOps, infrastructure, and cyber.
New: An AI boost for IT
Over the next 18 to 24 months, CIOs should expect generative AI to reshape how IT delivers value. Deloitte’s foresight analysis indicates that by 2027 gen AI will be embedded across most companies’ digital products and software footprints—even in conservative scenarios—making it critical to align investments and strategy across engineering, talent, FinOps, infrastructure, and cyber risk.12
Engineering
Impact for business: AI-powered code generation, automated testing, and rapid analytics accelerate delivery and free engineers to focus on product strategy and innovation.
Example: coding productivity gains are estimated to be worth about US$12 billion in the United States alone, driven largely by reduced manual toil and faster iteration.13
Actions for CIOs: (1) Invest in integrated developer tools that embed generative AI and automated testing in the CI/CD pipeline; (2) formalize human-in-the-loop review and governance for generated code.
Example in practice: Google reports that about 25% of new internal code is developed with AI assistance, a pattern that tightens alignment between engineering output and business requirements and speeds feedback loops.14
Smaller-scale example: a health care team used COBOL code-assist to help a junior developer produce an explanation file with ~95% accuracy—demonstrating how AI tools can preserve legacy value while shortening onboarding time.15
Talent
Impact for business: Demand for AI and machine learning skills is outpacing supply, slowing projects and creating hiring gaps in security, ML, and architecture.
Actions for CIOs: (1) Prioritize targeted upskilling programs using AI-powered learning (skills-gap analysis, personalized paths, virtual tutors); (2) redesign roles so engineers move from rote coding to architecture, orchestration, and prompt engineering.
Example: Bayer uses generative AI to summarize procedural documents and generate animation-rich e-learning, improving training efficiency and accelerating employee readiness for new systems.19
Cloud financial operations
Impact for business: AI-driven FinOps provides real-time cost analysis, pattern detection, and resource allocation across systems—critical as AI workloads raise cloud consumption.
Actions for CIOs: (1) Deploy AI-enabled cost-monitoring and forecasting tools that surface optimization opportunities; (2) integrate FinOps insights into product and platform decisions to justify AI spend.
Example: applying predictive allocation and automated rightsizing can offset rising model-training costs and improve return on AI investments.23
Infrastructure
Impact for business: Autonomic infrastructure—systems that self-diagnose and self-heal—reduces manual toil and enables humans to remain in the loop for exceptions only.
Actions for CIOs: (1) Invest in monitoring and observability that support automated remediation and anomaly detection; (2) pilot generative-AI tooling to scale platform operations while preserving control gates.
Example: organizations like eBay are leveraging generative AI to scale infrastructure operations and extract actionable customer insights from large datasets, improving platform responsiveness and experience.29
Cyber
Impact for business: Generative AI expands the attack surface—phishing, deepfakes, and prompt injection become more sophisticated—so risk management must evolve.
Actions for CIOs: (1) Strengthen data authentication, masking, and incident response frameworks; (2) apply generative AI to automate policy generation and accelerate detection and remediation.
Example: SWEAR and similar solutions explore blockchain-based verification for digital media; pairing such approaches with AI-driven masking and automated response can reduce exposure to synthetic-media threats.31
Across these five pillars, the common thread is clear: successful AI adoption requires investment in data, tooling, and people, plus governance that preserves trust while enabling innovation. As teams adopt generative and traditional AI to streamline workflows and reduce manual intervention, IT leaders can reallocate effort toward higher-value initiatives that drive growth, customer experience, and product differentiation.33
Next step for CIOs: assess where your organization can pilot AI to reduce cost or time-to-market, then scale successful pilots by investing in platform capabilities, FinOps, and talent programs. For a practical checklist to guide decisions across these pillars, consider downloading the CIO AI playbook referenced below.
Next: IT itself as a service
IT’s future role will be to enable and package capabilities as services across the enterprise. As product managers, domain experts, and business-unit leaders rush to stand up AI proofs of concept, IT has an opportunity to move from being a cost center to a competitive differentiator—providing reusable platforms, APIs, and guardrails that let the business build safely and quickly.
Imagine the next decade when IT operates primarily as an internal service provider: low-code/no-code portals, catalogued AI components, and secure digital agents that deliver applications at a click. In that model, vendors may cede certain roles to internally trained AI agents that execute repeatable tasks—while IT focuses on architecture, governance, and enabling product teams.35
Practical examples already point this way: combining low-code tools with advanced AI can let nontechnical users compose workflows and request services from IT’s platform catalog—reducing time-to-market and increasing organizational agility. Over the next five to 10 years, whole applications could be provisioned with the same simplicity as opening a cloud block today, provided organizations invest in data, trust, and governance up front.36
This evolution elevates the tech function’s reach (and remit): IT leaders must add skills in journey and process knowledge, program and product management, business development, trust and compliance, and ecosystem management (including AI tools and shareability). They will also act as enterprise educators—driving adoption, training employees and citizen developers, and shepherding continuous learning across departments.
John Marcante puts it plainly: “AI capabilities may be democratized for the business and spur innovation, but tech leaders have to drive the agenda. There has to be a set of guiding principles and goals that people can point to globally to move their enterprise forward.”37
Three concrete actions for CIOs
- Prioritize data and platform modernization: invest in data lifecycle, observability, and modular platform components so AI services are secure, reusable, and cost-effective.
- Build talent and learning loops: create targeted upskilling, AI-led training, and clear career paths so employees shift from routine tasks to oversight, architecture, and product work.
- Establish governance and trust-by-design: set global principles, ethical guardrails, and FinOps practices so services scale safely and deliver measurable business outcomes.
Bottom line: Treat this as an opportunity to extend the reach (and remit) of the tech function—from digital-transformation leader to AI-transformation leader. Start with focused pilots that deliver clear ROI, then scale the platforms, governance, and training that let the rest of the business consume IT as a service.
For a practical checklist and implementation playbook, consider downloading the CIO AI playbook referenced earlier to help prioritize investments and measure success across engineering, talent, FinOps, infrastructure, and cyber.