IT, amplified: AI elevates the reach (and remit) of the tech function

Now: Spotlight—and higher spending—on IT

For years, IT has been under pressure to streamline sprawling cloud spend and curb costs. Since 2020, however, investments in tech have been on the rise thanks to pent-up demand for collaboration tools and the pandemic-era emphasis on digitalization.³ According to Deloitte research, from 2020 to 2022, the global average technology budget as a percentage of revenue jumped from 4.25% to 5.49%, an increase that approximately doubled the previous revenue change from 2018 to 2020.⁴ And in 2024, US companies’ average budget for digital transformation as a percentage of revenue is 7.5%, with 5.4% coming from the IT budget.⁵

As demand for AI sparks another increase in spending, the finding from Deloitte’s 2023 Global Technology Leadership Study continues to ring true: Technology is the business, and tech spend is increasing as a result.

Today, enterprises are grappling with the new relevance of hardware, data management, and digitization in ramping up their usage of AI and realizing its value potential. In Deloitte’s Q2 State of Generative AI in the Enterprise report, businesses that rated themselves as having “very high” levels of expertise in generative AI were increasing their investment in hardware and cloud consumption much more than the average enterprise.⁶ Overall, 75% of organizations surveyed have increased their investments around data-life-cycle management due to generative AI.⁷

These figures point to a common theme: To realize the highest impact from gen AI, enterprises likely need to accelerate their cloud and data modernization efforts. AI has the potential to deliver efficiencies in cost, innovation, and a host of other areas, but the first step to accruing these benefits is for businesses to focus on making the right tech investments.⁸ Because of these crucial investment strategies, the spotlight is on tech leaders who are paving the way.

According to Deloitte research, over 60% of chief intelligence officers now report directly to their chief executives, an increase of more than 10 percentage points since 2020.⁹ This is a testament to the tech leader’s increased importance in setting the AI strategy rather than simply enabling it. Far from a cost center, IT is increasingly being seen as a differentiator in the AI age, as CEOs, following market trends, are keen on staying abreast of AI’s adoption in their enterprise.¹⁰

John Marcante, former global CIO of Vanguard and US CIO-in-residence at Deloitte, believes AI will fundamentally change the role of IT. He says, “The technology organization will be leaner, but have a wider purview. It will be more integrated with the business than ever. AI is moving fast, and centralization is a good way to ensure organizational speed and focus.”¹¹

As IT gears up for the opportunity presented by AI—perhaps the opportunity that many tech leaders and employees have waited for—changes are already underway in how the technology function organizes itself and executes work. The stakes are high, and IT is due for a makeover.

New: An AI boost for IT

Over the next 18 to 24 months, the nature of the IT function is likely to change as enterprises increasingly employ generative AI. Deloitte’s foresight analysis suggests that, by 2027, even in the most conservative scenario, gen AI will be embedded into every company’s digital product or software footprint (figure 1), as we discuss across five key pillars.¹²

Engineering

In the traditional software development life cycle, manual testing, inexperienced developers, and disparate tool environments can lead to inefficiencies, as we’ve discussed in prior Tech Trends. Fortunately, AI is already having an impact on these areas. AI-assisted code generation, automated testing, and rapid data analytics all save developers more time for innovation and feature development. The productivity gain from coding alone is estimated to be worth US$12 billion in the United States alone.¹³

At Google, AI tools are being rolled out internally to developers. In a recent earnings call, CEO Sundar Pichai said that around 25 percent of the new code at the technology giant is developed using AI. Shivani Govil, senior director of product management for developer products, believes that “AI can transform how engineering teams work, leading to more capacity to innovate, less toil, and higher developer satisfaction. Google’s approach is to bring AI to our users and meet them where they are—by bringing the technology into products and tools that developers use every day to support them in their work. Over time, we can create even tighter alignment between the code and business requirements, allowing faster feedback loops, improved product market fit, and better alignment to the business outcomes.”¹⁴ In another example, a health care company used COBOL code assist to enable a junior developer with no experience in the programming language to generate an explanation file with 95% accuracy.¹⁵

As Deloitte recently stated in a piece on engineering in the age of gen AI, the developer role is likely to shift from writing code to defining the architecture, reviewing code, and orchestrating functionality through contextualized prompt engineering. Tech leaders should anticipate human-in-the-loop code generation and review to be the standard over the next few years of AI adoption.¹⁶

Talent

Technology executives surveyed by Deloitte last year noted that they struggle to hire workers with critical IT backgrounds in security, machine learning, and software architecture, and are forced to delay projects with financial backing due to a shortage of appropriately skilled talent.¹⁷ As AI becomes the newest skill in demand, many companies may not even be able to find all the talent they need, leading to a hiring gap wherein nearly 50% of AI-related positions cannot be filled.¹⁸

As a result, tech leaders should focus on upskilling their own talent, another area where AI can help. Consider the potential benefits of AI-powered skills gap analyses and recommendations, personalized learning paths, and virtual tutors for on-demand learning. Bayer, the life sciences company, has used generative AI to summarize procedural documents and generate rich media such as animation for e-learning.¹⁹ Along the same lines, AI could generate documentation to help a new developer understand a legacy technology, and then create an associated learning podcast and exam for that same developer.

At Google, developers thrive on hands-on experience and problem-solving, so leaders are keen to provide AI learning and tools (like coding assistants) that meet developers where they are on their learning journey. “We can use AI to enhance learning, in context with emerging technologies, in ways that anticipate and support the rapidly changing skills and knowledge required to adapt to them,” says Sara Ortloff, senior director of developer experience at Google.²⁰

As automation increases, tech talent would take an oversight role and enjoy more capacity to focus on innovation that can improve the bottom line (as we wrote about last year). This could help attract talent since, according to Deloitte research, the biggest incentive that attracts tech talent to new opportunities is the work they would do in the role.²¹

Cloud financial operations

Runaway spending became a common problem in the cloud era when resources could be provisioned with a click. Hyperscalers have offered data and tooling for finance teams and CIOs to keep better track of their team’s cloud usage, but many of these FinOps tools still require manual budgeting and offer limited visibility across disparate systems.²² The power of AI enables organizations to be more informed, proactive, and effective with their financial management. Real-time cost analysis, as well as robust pattern detection and resource allocation across systems, can optimize IT spending at a new speed.²³ AI can help enterprises identify more cost-saving opportunities through better predictions and tracking.²⁴ All of this is necessary because AI may significantly drive up cloud costs for large companies in the coming years. Applying AI to FinOps can help justify the investments in AI and optimize costs elsewhere while AI demand increases.²⁵

Infrastructure

Across the very broad scope of IT infrastructure, from toolchain to service management, organizations haven’t seen as much automation as they want.²⁶ Just a few years ago, studies estimated that nearly half of large enterprises were handling key tasks like security, compliance, and service management on a completely manual basis. The missing ingredient? Automation that can learn, improve, and react to the changing demands of a business. Now, that’s possible.

Automated resource allocation, predictive maintenance, and anomaly detection could all be possible in a system that’s set up to natively understand its own real-time status and then act.²⁷ This emerging view of IT is known as autonomic, in reference to the human body’s autonomic nervous system that regulates its heart rate and breath, and adjusts dynamically to internal and external stimuli.²⁸ As mentioned above, such a system would enable the change from human in charge to human in the loop, as infrastructure takes care of itself and surfaces only the issues that require human intervention. That’s why companies like eBay are already leveraging generative AI to scale their infrastructure and sort through troves of customer data, potentially leading to impactful changes to their platform.²⁹

Cyber

Although AI may make many aspects of IT simpler or more efficient, it certainly introduces more complexity to cyber risk. As we wrote about last year, generative AI and synthetic media open up more attack surfaces than ever for phishing, deepfakes, prompt injection, and others.³⁰ As AI proliferates and digital agents become the newest business-to-business representatives, these issues may become more severe. Enterprises should take steps to work on data authentication, as in the example of SWEAR, a security company that has pioneered a way to verify digital media through the blockchain.³¹ Data masking, incident response, and automated policy generation are all also areas where generative AI can be applied to optimize cybersecurity responses and defend against attacks.³²

Finally, as technology teams grow accustomed to the changes and challenges mentioned above, many will shift their focus to the innovation, agility, and growth that can be enabled by AI. Teams can streamline their IT workflows and reduce the need for manual intervention or offshoring, allowing IT to focus on higher-value activities.³³ Indeed, an entire reallocation of IT resources is likely to take place. As Ian Cairns, CEO of Freeplay, has noted, “As with any major platform shift, the businesses that succeed will be the ones that can rethink and adapt how they work and build software for a new era.”³⁴

Next: IT itself as a service

The current moment is like an all-hands-on-deck siren sounding for many IT teams, where product managers, domain experts, and business unit leaders are diving into the details of AI to stand up working proofs of concepts. If the bet pays off and companies are able to improve their margins with this new technology, IT may complete its transition from a cost center and enabler to a true competitive differentiator. By then, the role of the CIO and their management of the tech estate could be dramatically altered.

Imagine a scenario over the next decade where IT transitions from a centrally controlled function to an innovation leader, providing reusable code blocks and platforms that business units can use to develop their own solutions. While IT-as-a-service may not be new, the previous understanding was that several aspects of a company’s IT infrastructure would be handed off to a new vendor.³⁵ Looking forward, that vendor could be replaced by each organization’s internally trained and secure AI agents.

In this sense, IT itself could become a service run through online portals, where a combination of low-code or no-code technologies and advanced AI allows nontechnical users to create and run applications.³⁶ For example, the role of the chief architect could look very different with many legacy tasks performed by a digital agent. Just as cloud computing blocks can today be opened with a click, entire applications may be available at a click in the next five to 10 years. Continuous tech learning and fluency would become essential across the enterprise, not just in IT, as employees and citizen developers would be encouraged to adapt to the latest technologies. Trust and security responsibilities would also broaden, with technology teams retaining humans in the loop to review data privacy, cybersecurity, and ethical AI practices.

Though the advancement of AI may call into question the future role of IT, it actually elevates the technology function in the enterprise once it’s embedded everywhere. Savvy tech leaders will need to develop a bevy of skills as tech and AI become even more important in the enterprise. These skills include journey and process knowledge, program and product management, business development, trust and compliance expertise, and ecosystem management (including AI tools and shareability). Leaders may also need to take on a new role as the enterprise’s educator and evangelist of AI, in order to drive change management.

Marcante says, “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