Chief information officers face a critical balancing act as artificial intelligence simultaneously drives up IT budgets whilst offering potential savings of up to 30 per cent on software and operational costs, according to Bain & Company research that reveals the technology’s dual impact on enterprise spending.
Sixty-nine per cent of technology leaders expect AI spending to increase by more than 5 per cent, creating pressure to fund rapid transformation whilst proving return on investment. The challenge for executives: how to scale AI deployment without letting costs and complexity spiral, whilst capturing the technology’s cost-reduction benefits to fund further expansion.
The strategic imperative centres on using AI’s own efficiencies to finance its adoption. Companies successfully deploying the technology to identify shadow IT spending, eliminate redundant software and automate operations are creating a “flywheel effect” where early savings offset expansion costs, according to Bain’s Technology Report analysing responses from more than 400 technology leaders.
“AI has the potential to reduce overall technology spend, even as demand for digital grows. But impact only comes with disciplined scale,” the research states. Technology and transformation teams must be deliberate in how they deploy AI across enterprises, funding AI with AI-generated savings whilst maintaining strict architecture governance.
The technology opens opportunities to cut IT costs by surfacing hidden spending, identifying underused software, improving operational efficiency and optimising infrastructure. However, AI simultaneously introduces new complexity and costs, including higher software and cloud spending, faster technology cycles, and more demanding data and architecture requirements.
Companies are already deploying AI to manage costs. GPTs that classify spending data, tagging invoices and mapping them to general ledger entries, help identify shadow spending and control technology costs outside CIO oversight. Other applications assess software for duplicate functionality or low usage, making it easier to retire less valuable applications and reducing related costs by up to 30 per cent.
A global media company consolidated data from more than 80 general ledgers using AI, identifying tens of millions of dollars in shadow IT spending. The improved transparency helped the company classify and benchmark costs, enhance oversight and implement targeted controls, creating additional savings and improved stewardship of IT resources.
Beyond direct costs, AI increases business complexity whilst supporting the technology’s accelerating pace of change, necessary architecture retooling and development of new operating models. Enterprises are layering AI models, agents and platforms onto already fragmented digital ecosystems and ageing core systems, creating new integration challenges and, in some cases, higher operating costs.
The technology also requires more data collection and analysis, higher data storage costs, and new guardrails to track decisions and improve efficacy of autonomous agents and systems acting with minimal human input. Some AI-enabled workloads, especially those powered by large language models, may prove significantly more expensive than traditional technologies they replace, at least in the near term.
AI brings near real-time visibility into IT costs, automatically tagging expenses and surfacing hidden spending including shadow IT. A global life sciences company used AI to identify overspending on cloud services, including servers running when unnecessary or unused storage. With clearer visibility, the company scaled its environment to actual needs, cut unnecessary spending and implemented smarter controls, delivering sustained savings whilst establishing a foundation for future improvements.
AI-powered usage analytics help teams see exactly what is being consumed and where, enabling more accurate forecasts, less waste and smarter infrastructure decisions so businesses only pay for actual needs. The technology can spot overlap and underused tools across application stacks, helping teams retire redundant software. Such consolidation typically cuts software and maintenance costs by 10 to 30 per cent whilst streamlining technology management.
A specialty chemicals company used AI to scan its application inventory, flagging duplicate and underused software, mapping inventory against software costs, and identifying approximately a quarter of the portfolio and nearly 30 per cent of spending as no-regret opportunities for consolidation or retirement.
With AI built into operations, teams can predict and prevent incidents, automate fixes, reduce false alerts and decrease alert fatigue resulting from excessive rapid change. A content management software provider used an AI tool to detect anomalies and alert teams before problems fully developed, helping engineering teams respond quicker and reducing resolution times by 15 per cent.
Generative AI coding assistants and automated testing tools help development teams move faster, generating code, refactoring legacy systems, writing tests, reviewing code and resolving bugs with higher consistency. Software development cycles shrink by 20 to 30 per cent, with lower labour costs, better quality and faster time-to-market.
AI is helping IT teams manage spending more effectively whilst enabling the broader enterprise to manage technology demand. Between 50 and 65 per cent of work in technology transformation is administrative, including analysis, design and change management, rather than hands-on development. AI can streamline every phase, from upfront research to automated design-to-code workflows.
An airline that deployed AI and other automation to assist customer support agents reported a 40 per cent increase in productivity. Some service providers including Globant are moving to AI-driven, outcome-based pricing models. Internal technology teams will need to rethink how they source, manage and deliver IT services, developing new roles, workflows and working methods that embed AI into daily operations.
Surging business demand for AI is fuelling rapid technological change and major process overhauls. Underlying AI technologies are changing quickly, with replacement lifespans measured in months rather than years. Talent and data represent top challenges in scaling generative AI, according to Bain’s survey.
Technology leaders must scale AI with discipline, embedding it across operations, simplifying architecture and using early savings to fund further transformation. By using AI to streamline operations and reduce technology costs, IT can help offset broader AI adoption expenses, creating a flywheel that funds transformation through its own efficiencies.
As AI agents proliferate, strong architecture governance becomes essential. Companies should simplify architecture choices, set clear standards and build in controlled environments. One powerful approach involves a tiered AI model strategy, using smaller, fine-tuned models for high-volume, routine tasks at a fraction of the cost whilst reserving large models for complex, high-impact uses.
A new supply chain is emerging, from optimised chips to multi-model platforms, designed to help enterprises operate on cost-efficient AI architectures. Real AI value appears when the technology is woven into how businesses operate, from delivery to governance. Companies that have shifted to cloud-based infrastructure tend to be more effective at managing AI costs.
Scaling AI across both IT and business operations means rethinking tooling, updating governance and controls, and cultivating an AI-first mindset across teams. Better transparency of technology costs helps executives ensure spending focuses on strategic priorities, whilst disciplined cost management ensures investments deliver expected returns.
