How Companies Are Using AI to Reduce Costs and Increase Productivity
For most companies, the appeal of artificial intelligence is no longer about having the newest technology.
It is about getting more done.
Finance departments may use AI for document processing. Customer service departments may use it to reply to the same questions again and again. A developer might even use a AI-powered coding aid to write software more quickly, and a manager may use such a tool to figure out large amounts of business data.
In a sense, these examples are practical uses. They also help us understand why AI for business has been adopted at such a fast pace – from testing to daily work.
In Deloitte’s 2026 State of AI in the Enterprise report, a total of 66% of the organisations studied mentioned they experienced improvements in work productivity and efficiency from AI usage, and 40% of them noted that with AI they managed to cut their expenses.
Yet, there is a point to be made: AI itself cannot cut the costs by merely purchasing AI solutions. The companies really begin to benefit from the new technology when they alter their traditional way of doing things.
How does AI reduce business costs?
The most straightforward opportunity is repetitive work.
Many employees spend part of their day searching for information, entering data, preparing routine reports, answering similar questions or moving information between systems. AI can take over some of those tasks or make them considerably faster.
That can reduce the amount of time required to complete a process without necessarily reducing the number of employees.
For a company, that distinction matters.
The objective is often better use of existing talent rather than simply cutting jobs. An accountant who spends less time checking routine documents, for example, can spend more time investigating unusual transactions or advising the business.
The same principle applies across departments.
How are companies using AI to increase productivity?
Customer service
Customer service is one of the most visible examples.
AI assistants can answer common questions, search knowledge bases, summarise customer conversations and help employees find information quickly.
More advanced AI systems can handle parts of a customer request themselves, while human employees take over when a situation becomes complicated.
That can shorten response times while reducing the administrative workload placed on support teams.
Finance and accounting
Finance departments work with large volumes of invoices, expenses, transactions and documents.
AI can extract information from documents, classify transactions, identify unusual activity and support financial forecasting.
The benefit is not just speed. It can also give finance professionals more time to focus on analysis rather than repetitive processing.
Sales and marketing
Salespeople often spend considerable time preparing for meetings, updating CRM records and searching through customer information.
AI can summarise previous interactions, prepare account briefs, identify potential leads and help keep customer records up to date.
Marketing teams are using similar technology to analyse campaigns, understand audiences and personalise communications.
The value is often found in the small amounts of time saved across hundreds or thousands of activities.
Software development
AI coding tools are changing how development teams work.
They can generate code, explain existing code, write tests and help developers identify potential problems.
The adoption is becoming substantial. McKinsey’s 2026 global AI survey found that about two in ten organisations are scaling agentic coding tools, with adoption reaching 31% among large enterprises.
For companies that depend heavily on software, even modest improvements in development speed can have a meaningful commercial impact.
Operations and supply chains
AI is also being used behind the scenes.
Manufacturers, retailers and logistics companies can use AI to forecast demand, monitor inventory and identify potential disruptions.
Better forecasting can help businesses avoid both excess stock and shortages.
These applications may not attract the same attention as generative AI, but they can have a direct effect on operating costs.
Does AI really save companies money?
It can, but the answer is not as simple as replacing manual work with software.
AI has its own costs.
Companies may need to pay for models, cloud infrastructure, software licences, integration, security, employee training and ongoing governance.
Those expenses can grow quickly as AI moves from a few pilot projects to company-wide use.
McKinsey’s 2026 Enterprise AI FinOps research found that 93% of surveyed organisations had exceeded their AI budgets, while AI spending increased sharply as companies moved beyond experimentation.
That makes AI cost management an increasingly important part of the business case.
A company cannot claim an AI saving simply because an employee completes a task faster. It needs to look at the full cost of the technology and compare it with the value created.
Why are some companies getting more value from AI?
The difference often comes down to what happens after the technology is introduced.
A company can give employees access to an AI assistant and leave everything else unchanged.
Or it can redesign the workflow around AI.
The second approach is harder, but potentially much more valuable.
Deloitte’s 2026 research found that 34% of organisations are using AI to deeply transform their businesses, while another 30% are redesigning key processes around AI. By contrast, 37% are using AI at a more superficial level, with little change to existing processes.
That helps explain why AI adoption figures can look impressive while financial results remain modest.
Using AI more often is not the same as changing the economics of a business.
McKinsey has reached a similar conclusion, arguing that the largest gains increasingly come from redesigning how work and decisions are made rather than simply making individual employees faster.
What are the biggest risks of using AI to cut costs?
Cost reduction can become a poor objective if it is pursued without considering the quality of work.
An AI system may process documents faster, for example, but if employees have to spend additional time correcting its mistakes, the expected saving disappears.
There are also concerns around inaccurate outputs, privacy, cybersecurity and accountability.
Companies need to decide which tasks AI can handle independently and which require human approval.
This becomes even more important as businesses introduce AI agents that can take actions rather than simply generate information.
Deloitte reports that 85% of organisations expect to customise AI agents for their specific business needs, yet only about one in five has a mature governance model for them.
What will AI-driven productivity look like next?
The next stage of AI productivity is likely to involve systems that can complete entire workflows.
Instead of asking an AI tool to summarise a report, an employee might ask an AI agent to collect the relevant information, analyse it, prepare a recommendation and send it to the appropriate person.
That is a different proposition from using AI as a writing or search assistant.
It moves AI closer to the actual operation of the business.
McKinsey’s 2026 research shows that large organisations are increasingly scaling AI agents, although most businesses are still working out how to capture consistent returns from them.
Conclusion
AI can reduce costs and increase productivity, but neither outcome is automatic.
The strongest results come when companies identify a real business problem, redesign the process around the technology and measure the result.
That may mean fewer hours spent on administration, faster customer responses, better forecasting or quicker software development.
The more useful question for business leaders is therefore not “How much work can AI replace?”
It is: “Where can AI help our people do better work, faster, without creating more cost or risk?”
That is a much more practical way to approach AI in 2026, and a better path to turning AI investment into lasting business value.