Artificial intelligence opens up a new world of opportunity and complexity for CIOs and their organizations. Technology leaders are playing a larger role in business transformation as they drive growth and efficiency. They face the dual challenge of doing more with less while securing the resources to pilot and scale AI. Translating the promise of AI into performance without spiraling into cost and complexity is becoming essential to staying ahead.
As part of that push, many companies are already using AI to help control costs. For example, GPT, which categorizes spending data and tags invoices and maps them to general ledger entries, helps identify shadow spending and contain technology costs that are outside of the CIO’s control. Other uses include assessing applications for redundant functionality and underutilization to facilitate the retirement of low-value software and reduce associated costs by up to 30%.
But AI also creates complexity and new costs. In a recent Bain survey of more than 400 technology leaders, 69% expect spending on AI to increase by 5% or more (see Figure 1). The surge in business demand for AI is accelerating rapid technological change and large-scale process overhauls. At the same time, the underlying AI technology is changing rapidly, and the longevity of replacements is measured in months rather than years.
Beyond its direct costs, AI increases the operational complexity of businesses, supporting the ever-accelerating pace of change in technology, necessary architectural restructuring, and the development of new operating models. Businesses are layering AI models, agents, and platforms onto already fragmented digital ecosystems and aging core systems. This creates new integration challenges and, in some cases, increases operational costs (see Figure 2). It will also require more data collection and analysis, higher data storage costs, and new guardrails to track decisions and improve the effectiveness of autonomous agents and systems that operate with little or no human input. Some AI-enabled workloads, especially those leveraging large language models, can be significantly more expensive than the traditional technologies they replace, at least in the short term.
Given these additional cost pressures, it’s more important than ever for CIOs and other technology leaders to save as much as possible.
When AI reduces operational costs
AI is built to take on complex, knowledge-intensive tasks and perform them faster and more efficiently than ever before. Enterprise technology is a front-runner, with real opportunities to streamline operations, reduce waste, and significantly reduce costs.
- Smarter technology spending. AI provides near real-time visibility into IT costs, automatically tags expenses, and uncovers hidden expenses like shadow IT. This makes it easier to flag and control overspending, even when it’s beyond the CIO’s view. This is how a global media company leveraged AI to integrate data from more than 80 general ledger accounts and identify tens of millions of dollars in shadow IT spending. Increased transparency has allowed the company to categorize and benchmark costs, increase monitoring, and implement targeted controls, resulting in further savings and better management of IT resources.
- Get a clearer picture of resource usage. AI-powered usage analytics helps teams understand exactly what is being consumed and where. This means more accurate forecasts, less waste, and smarter infrastructure decisions, allowing businesses to pay only for what they actually need. A global life sciences company used AI to identify where it was overspending on cloud services, such as servers running when they weren’t needed and storage no longer being used. With a clearer view, we were able to scale our environment to meet our actual needs, reduce unnecessary spending, and introduce smarter controls. This resulted in ongoing savings and laid the foundation for future improvements.
- Rationalize your application portfolio. AI can help teams retire redundant software by identifying duplicate or underused tools across the application stack. These types of savings typically reduce software and maintenance costs by 10% to 30% and streamline the way you manage your technology. A specialty chemical company used AI to scan its application inventory, flag duplicate and underused software, map software costs and inventory, and identify nearly a quarter of its portfolio and nearly 30% of its spend as no-regret consolidation or retirement opportunities.
- Execute AI operations. By incorporating AI into operations, teams can predict and prevent incidents, automate remediation, quiet the noise of false alerts, and reduce change fatigue (fatigue caused by changing too quickly). This is a move towards a self-healing system that reduces outages, reduces manual effort, and simplifies complexity. For example, a content management software provider used AI tools to detect anomalies and alert teams before problems fully developed. This enabled the engineering team to respond more quickly, reducing the company’s resolution time by 15%.
- Accelerate software delivery. Generative AI coding assistants and automated testing tools help development teams work faster and smarter: generate code, refactor legacy systems, write tests, review code, and resolve bugs faster and more consistently. result? Software development cycles are shortened by 20% to 30%, reducing labor costs, improving quality, and reducing time to market.
How AI controls business demand for technology
Just as AI helps IT teams spend smarter, it can also help the broader enterprise manage spending. request Leverage technology more effectively. Now more than ever, IT leaders must be at the center of underwriting transformation, leveraging AI to transform more efficiently, embedding AI into their operations, and optimizing their people and operating models.
- Rewire the transformation lifecycle. Reducing IT costs means more than just software development. 50% to 65% of the work in technology transformation is administrative work (analysis, design, change management) rather than actual development work at the keyboard. AI can streamline every step, from preliminary research to automated design-to-code workflows. To derive maximum benefits, cost management must extend throughout the lifecycle.
- Incorporate AI into your operations. AI is changing technology support models such as conversational customer support agents and automated ticket resolution. Incorporating AI into daily operations reduces costs across model maintenance, data management, and vendor management. For example, airlines that implemented AI and other automation to assist customer support agents reported a 40% increase in productivity.
- Flex your employee and partner ecosystem. Large-scale transformation relies on a combination of internal teams and external partners. AI helps orchestrate ecosystems by matching the right work with the right resources, automating governance, and supporting better decision-making. The result is increased efficiency and higher quality execution.
AI is also reshaping IT service delivery. Some service providers, like Globant, are moving to an AI-driven outcome-based pricing model. In-house technology teams will also need to rethink how they procure, manage, and deliver IT services, and most will need to develop new roles, workflows, and ways of working that incorporate AI into their daily operations. (For more information on this, see Bain’s overview: AI Pods as a Service: Built for Modular, Scalable, and Speed.)
Manage costs while scaling AI
While digital demand continues to grow, AI has the potential to reduce overall technology spending. But influence only comes through disciplined scale. IT and transformation teams need to carefully consider how to deploy AI across the enterprise.
- We fund AI with AI. Investing in AI is essential, but it can also pay for itself. By using AI to streamline operations and reduce technology costs, IT can create a flywheel that offsets the cost of widespread AI deployment and funds transformation with unique efficiencies.
- Simplify with discipline. As AI agents proliferate, strong architectural governance becomes non-negotiable. Lean thinking: Simplify your architectural choices, set clear standards, and build in a controlled environment. One powerful example is the hierarchical AI model strategy. This means using small, fine-tuned models for high-volume, everyday tasks at low cost, and reserving large models for complex, high-impact applications. New supply chains are emerging, from optimized chips to multi-model platforms designed to help companies operate with cost-effective AI architectures.
- Embed AI into your operating model. The true value of AI comes when it is integrated into business operations, from delivery to governance. Companies that have already moved to cloud-based infrastructure tend to manage their AI costs more effectively. Extending AI across both IT and business operations means rethinking tools, updating governance and controls, and cultivating an AI-first mindset across your teams.
Executives know they can’t optimize what they don’t understand. Increased transparency into technology costs helps ensure that spending is focused on strategic priorities. Disciplined cost management ensures that these investments deliver the expected returns. AI can help with both.
