For companies under pressure to improve margins, the most useful AI question is often a simple one: where can it take cost out of an existing business process? The answer varies by sector, but the strongest opportunities tend to sit where labour, errors, delays, fraud or asset downtime already create a measurable financial burden.
Why do most companies ask about AI costs before asking about operational waste?
When executives assess AI adoption, the conversation often starts with the cost of the technology: software licences, AI tools, infrastructure, implementation and ongoing usage. Those figures matter, but they only tell one side of the story.
The larger opportunity may sit in the costs a business is already carrying. A claims team spending thousands of hours reviewing documents, a bank maintaining large compliance operations, a broadcaster manually producing hundreds of versions of an advert, or an energy company sending engineers to inspect equipment on a fixed schedule all have substantial operational costs before an AI system enters the picture.
That changes the way AI investments should be assessed. Rather than asking only how much an AI solution costs, leaders can ask how much of an existing cost base it could remove, reduce or prevent.
The strongest AI business cases usually target one of four areas: labour-intensive processes, avoidable errors, preventable losses and inefficient use of physical assets. These are also areas where efficiency gains can be measured against a baseline.
For CFOs and COOs, this provides a more useful way to think about how to reduce AI costs in your company. The objective is to understand the full economics of the change: the cost of implementing AI alongside the operational costs it can eliminate, defer or control.
The sector analysis below shows where those opportunities are already emerging.
You have the formula. Now you need the inputs.
Most teams stall on the baseline: what a claim, an invoice or an inspection actually costs today, and whether the data behind it will hold up. That is the assessment we run with CFOs and COOs before anyone builds a model.
Insurance: where AI directly attacks loss ratios, fraud, and claims cycle costs
Insurance has several characteristics that make it well suited to AI-driven cost reduction. Claims generate large volumes of documents and customer interactions, fraud creates direct financial losses, and underwriting requires teams to process substantial amounts of structured and unstructured information.
Claims automation
Claims automation can reduce the amount of manual work involved in reviewing submissions, extracting data, assessing straightforward cases and routing more complex claims to specialists.
McKinsey has reported that generative AI could reduce claims resolution times by as much as 75% in some applications, while insurance onboarding costs could fall by 20–40%. These gains can come from automating repetitive tasks and helping employees reach decisions faster.
Fraud detection
Fraud detection provides another route to cost reduction. AI models can examine patterns across claims, policies and customer behaviour to identify combinations of factors that may warrant further investigation.
The financial impact extends beyond the cost of the investigation itself: better detection can reduce the value of fraudulent claims that are ultimately paid.
Underwriting offers
Underwriting offers a third opportunity. AI can help gather and analyse information, summarise submissions and identify relevant risk factors, allowing underwriters to spend more time on cases that require judgement.
For insurers, the business case therefore reaches across the cost portfolio. AI can reduce administrative labour, shorten claims cycles, improve fraud detection and increase underwriting productivity at the same time.
The important distinction is between automating activity and reducing costs. Processing more claims per employee creates an efficiency gain, but the financial case becomes stronger when the insurer can show lower cost per claim, fewer manual hours, faster settlement or reduced fraud losses.
Media: production cost collapse and the content economics AI is rewriting
Few industries illustrate the potential for AI cost reduction as clearly as media. Content businesses face constant pressure to produce more material, for more channels and audiences, while controlling production budgets.
Generative AI is changing that equation. Morgan Stanley estimates that generative AI could reduce costs in television and film production by up to 30%, reflecting opportunities across areas such as content creation, editing, visual effects and other production activities.
The real savings can begin well before content reaches the audience. AI tools can support research, scripting, transcription, editing, translation, subtitling, metadata creation and asset management. Once a piece of content exists, AI can also create multiple versions for different markets, formats and distribution channels without requiring the same level of manual effort.
A useful example comes from UK broadcasting. Runway has reported that a five-person AI team at a UK broadcaster was producing 800–1,000 ads per year, with the organisation estimating savings of around 46,000 hours across its business operations. The significance of the example lies in the operating model: a relatively small specialist team can support production at a scale that would otherwise require considerably more manual work.
Distribution creates another opportunity. AI can personalise recommendations, generate descriptions and metadata, localise content and adapt material for different audiences. Each individual task may produce a modest saving, but the cumulative effect across thousands of assets can be substantial.
For media companies, then, AI adoption can affect the economics of the entire content lifecycle. The question becomes less about the cost of a particular AI model and more about how much production capacity can be created from the same budget.
Finance and banking: the cost reduction hiding inside compliance, fraud, and back-office operations
Financial services have some of the largest pools of structured data and repetitive knowledge work, making them fertile ground for AI cost reduction.
Back-office operations are an obvious starting point. AI can support document processing, data entry, reconciliation, customer onboarding, reporting and internal service functions. In areas where employees spend significant amounts of time moving information between systems or checking routine documentation, automation can reduce processing time and support costs.
Fraud represents a much larger financial lever. Banks already use sophisticated analytical systems to identify suspicious transactions, but newer AI models can analyse more complex patterns and support faster investigation. Juniper Research has estimated that AI could help banks save $447 billion annually, largely through fraud detection and prevention.
Financial planning and analysis provides another opportunity. AI can help finance teams consolidate information, identify variances, generate forecasts and run scenarios. The resulting benefit can come from reducing manual reporting work while giving finance teams more time to analyse the factors affecting business performance.
Credit scoring can also become more efficient when AI systems can process a wider range of information and support consistent risk assessment. The potential value depends heavily on data quality, model governance and regulatory requirements, but even modest improvements in decision speed or accuracy can have a significant operational impact at scale.
There is, however, an important qualification. McKinsey has noted that AI-driven productivity gains may not remain entirely with individual companies as markets adapt. Competition can push businesses to pass some of those savings through to customers in the form of lower prices or better services.
That makes the business case more nuanced. AI can reduce costs, but competitive pressure may determine how much of that reduction ultimately appears as additional margin.
Energy: predictive maintenance, grid optimisation, and the asset cost
For energy companies, some of the largest AI opportunities are tied to physical assets. Equipment failures can mean lost production, emergency repairs, maintenance labour and expensive periods of downtime.
Predictive maintenance addresses this problem by using operational data to identify signs of equipment deterioration before a failure occurs. Instead of maintaining every asset according to a fixed schedule, operators can focus attention where the data suggests intervention is most needed.
Shell provides a concrete example. Its AI system has been used to monitor more than 10,000 pieces of equipment, with the company reporting maintenance cost reductions of around 20% in the relevant applications.
The economics extend beyond maintenance itself. Avoiding an unexpected failure can protect production, reduce emergency call-outs and make better use of specialist engineering resources. For large industrial assets, preventing a single major disruption can justify substantial investment in artificial intelligence systems.
Grid optimisation offers another major opportunity. AI can improve forecasting of electricity demand and generation, helping operators balance supply and demand more efficiently. This becomes increasingly important as renewable generation introduces greater variability into the system.
The International Energy Agency has estimated that AI could generate potential savings of up to USD 110 billion annually by 2035through improved power plant operations and maintenance.
There is also a capital dimension. Better prediction can extend asset life, improve maintenance planning and potentially defer expensive replacement projects. In sectors where individual assets represent millions or billions in capital investment, delaying unnecessary replacement can be as valuable as reducing annual operating expenditure.
For energy businesses, AI cost reduction therefore sits at the intersection of maintenance, production and cost efficiency and capital allocation.
See what this looks like in practice
A power grid operator was scheduling transformer maintenance on the judgement of a handful of senior diagnosticians.
We built explainable ML models for 14 defect types, digitised six years of historical records, and reached over 90% accuracy. Read how AI turned maintenance from a fixed schedule into a cost decision.
Utilities: capital allocation as the real cost problem (and AI technology as the solution)
Utilities face a different cost challenge from energy producers. Their investment decisions operate within regulated markets, long asset lifecycles and close scrutiny over how capital expenditure affects customers and allowed returns. That makes capital allocation particularly important.
AI can help utilities forecast demand, identify emerging infrastructure constraints and assess the condition of assets across large networks. Better forecasts can help determine where investment is genuinely required and where existing systems have sufficient capacity.
Predictive asset management can also support decisions about when equipment should be repaired, upgraded or replaced. Instead of applying the same maintenance strategy across an entire network, utilities can prioritise assets according to condition, failure probability and their importance to the wider system.
Demand forecasting has a similar role. More accurate forecasts can improve resource planning and help utilities anticipate periods of high demand before they create pressure on the network.
The cost savings can therefore appear in several places: fewer unnecessary inspections, better maintenance scheduling, fewer failures, more targeted capital expenditure and improved use of existing infrastructure.
Regulation adds another layer. A utility cannot necessarily treat every efficiency gain as additional profit. Investment decisions are often subject to regulatory approval and rate-recovery mechanisms. That means the value of AI may also show up through better reliability, improved service quality and more efficient use of customer-funded infrastructure.
How to identify where AI will reduce costs most in your organisation?
Start with the cost base, not the technology. Map key business processes and identify high-volume work, repetitive tasks, expensive errors, delays, fraud losses, support costs and assets that require frequent maintenance.
Then assess each opportunity against three questions:
- Where are the costs highest?
A modest reduction in a large cost base can deliver greater savings than a major improvement in a small process.
- Where is the data good enough?AI models depend on data quality. The strongest candidates typically have reliable historical data, clear inputs and measurable outputs. Poor or fragmented data can increase the cost of implementing AI and reduce expected returns.
- Where can the return be measured within a year?
he strongest AI projects have a clear baseline, such as cost per claim, invoice, customer interaction or transaction. This makes it easier to measure efficiency gains and control AI costs.
A simple prioritisation model is:
The full cost of implementing AI also needs to be included: AI tools, data preparation, integration, security, governance, training and ongoing AI costs. A 10% reduction in a large, stable business cost base may deliver more value than a 30% saving in a smaller process.
The priorities will vary by sector: insurance can target claims and fraud; media, production and localisation; banking, compliance and back-office operations; energy, maintenance and asset performance; and utilities, forecasting and capital allocation.
The key is to start with the right business strategy and costs you want to reduce. Then identify where AI solutions can deliver measurable cost reduction, cost efficiency and efficiency gains. This gives CTOs, COOs and CFOs a practical way to guide AI adoption, save money, gain competitive advantage and build a stronger business case for implementing AI.
