Understanding AI costs and tips to manage spend

Investment in AI has increased hugely over the last few years, with businesses worldwide spending millions to improve their AI capabilities. Whether it’s single-workflow automations, subscription models or full enterprise transformation programmes, no one wants to be left behind in the AI revolution.

In the UK, companies are spending £15.94 million on AI in 2026, with this figure set to increase by 40% over the next two years.¹

But how do you manage AI costs and avoid overspending? Read on for practical tips to reduce AI spending and maximise business spending, without compromising on quality.

The contents of this article is for informational purposes only and does not constitute legal or tax advice. Decisions related to tax should be made after thorough research, consultation and verification from a qualified financial and legal advisor.

How much does AI cost for businesses?

According to research from Lloyds Bank, around two thirds of UK businesses have invested in AI in some form or other. But how much are they spending?

For the majority of these (33%), spending on enhancing AI capabilities was under £25,000. Just 7% spent £250,000 or more, with the rest falling in between these two benchmark figures.²

AI spending tends to fall into a number of categories, depending on the type and scope of the project:

AI project types and typical costs

While spending on AI projects can vary considerably, it’s useful to know roughly what UK businesses are investing in and their typical budgets for projects at different levels.

Entry level - single workflow automation

The first projects most businesses (particularly SMEs) will embark upon are in this entry level tier. Projects may include the development and implementation of:

• A customer service chatbot
• An automated invoice processing pipeline
• A natural language reporting tool for a finance team

Typical cost: £15,000 to £50,000.³

Mid level - departmental rollout

At this mid-level tier, companies aim to transform the entire workflow of a whole department. Typical areas of focus include marketing, finance, HR or legal. Projects may include:

• AI-driven decision support
• Automated document processing
• Predictive analytics

Typical cost: £50,000 to £150,000.³

High level - large scale enterprise transformation

Unsurprisingly, the most expensive projects involve large-scale AI integration across an entire organisation. This usually involves a full restructuring of business operations, including essential components such as:

• Open-source model adoption and fine-tuning
• Extensive multi-layer security testing
• Remediation of legacy infrastructure
• Implementation of an AI government framework in line with the UK Government’s AI regulatory approach.

Typical cost: £200,000 to £750,000+.³

It’s worth noting that these costs are indicative and often only cover implementation. There are also likely to be additional ongoing costs to maintain, optimise and update AI solutions and processes.

Why it’s important to actively manage AI costs

AI spending is different from traditional software, which tend to have fixed monthly costs and clear pricing models. With AI, costs can be very difficult to predict. This is down to a few factors, but the main one is that AI often involves usage or consumption-based pricing.

Instead of a standard subscription with a fixed monthly cost, AI providers often charge per ‘token’. This means that every search, prompt and automated task incurs a variable cost, and more complex, multi-stage tasks end up costing considerably more. Without the right guardrails in place, these expenses can quickly spiral as the business scales and usage increases.

What’s more, businesses often use a patchwork of different services and tools. This can make it difficult to connect specific costs to tangible business outcomes.

Lastly, there’s experimentation. Many SMEs start by experimenting with AI solutions, to see if they deliver value to the business. The costs of small-scale trials can be easily measured, but these skyrocket when the solution is deployed at scale. This can happen when businesses neglect to factor in ongoing costs such as IT governance, security and data cleaning.

These are all reasons why it’s crucial for businesses to proactively monitor and manage AI costs. It’s especially important if your organisation is growing.

What contributes to AI overspend?

There are many reasons companies overspend on AI - meaning they spend more than they’ve budgeted for, and they aren’t seeing a good enough return on that investment.

Understanding the main causes of overspending can help your business put its own effective budget management controls in place.

Below are some of the common mistakes businesses make when deploying, using and budgeting for AI solutions:

1. Using the most powerful AI model for every task

A common error is assuming that the most powerful or advanced AI model is the best choice for every task. While in some cases, this can deliver impressive results - it also means much higher usage costs.

In reality, most everyday tasks can be easily handled by smaller, more cost-effective models. For example, summarising documents, extracting information from forms or drafting standardised emails.

2. No clear visibility into how AI is being used

Many businesses don’t have a clear picture of how AI is being used across the whole of the organisations. Individual departments (or even individual personnel) may be adopting their own AI tools and creating separate accounts. This makes it very difficult to track both spending and measurable value.

3. Overlap between AI tools

As AI becomes more accessible and available, different teams may start to incorporate and use their own platforms - many of which perform similar functions. This can create unnecessary duplication and overlap, as the business ends up paying for lots of different tools that ultimately do the same thing.

4. Running unnecessary AI requests

One of the dangers of AI is how easy it is for teams and personnel to slip into the habit of using it for every single task - even when it’s unnecessary. This can quickly become expensive, as AI pricing is usage-based, so each request and prompt costs the company money.

5. Inefficient prompt design

Overly long, unfocused or repetitive prompting can increase the number of tokens processed during each request. This inefficient use of AI tools can push up spending considerably.

Common errors include excessively long instructions, including unnecessary background information and passing entire documents when only a small snippet is needed.

6. Leaving experiments running

Most AI projects within organisations start life as pilot schemes. However, once testing is over, some companies forget to shut down all of the supporting infrastructure, cloud resources or API integrations.

These inactive applications and resources can continue generating costs in the background, while providing little to no business value.

7. Scaling before proving value

If a pilot scheme or proof of concept is successful, it’s easy to get carried away and roll out a full-scale solution across a whole department or even the entire organisation. This isn’t necessarily a mistake, but it can be if ROI hasn’t been clearly demonstrated first.

Scaling too fast or too soon can result in expensive issues such as:

• Large increases in API usage
• Additional infrastructure costs
• More support and maintenance requirements
• Licensing fees for users who rarely use the technology.

8. Poor governance and ownership

It’s crucial to have a clear chain of command and oversight when deploying AI solutions, especially when it comes to the budget.

Without clear ownership and transparency, businesses struggle to set spending limits, approve new AI purchases and monitor ongoing usage. They often fail to recognise and retire underperforming projects, and are unable to evaluate ROI.

9. Focusing only on model costs

API and model usage costs aren’t the only expense associated with AI adoption. These are the most visible costs, especially at the initial deployment stage. However, there are many other ongoing costs to consider.

These include costs for:

• Cloud infrastructure
• Data storage and processing
• Development work
• Monitoring and maintenance
• Training staff
• Security and compliance.

Unless these costs are taken into account, businesses face underbudgeting and running out of funds before the value of AI projects can be fully realised.

How to manage AI costs - tips to avoid overspending

To effectively manage AI costs without limiting innovation, businesses need to make sure that every investment delivers value. This means putting spending guardrails in place, alongside processes to measure outcomes and ROI.

Here are some of the best tips for managing AI costs and avoiding overspending:

1. Align AI use cases with business goals

Before investing in new AI tools or projects, aim to pinpoint the business problem you're trying to solve. A good strategy is to prioritise use cases that support strategic business objectives, such as improving productivity or enhancing customer service, for example.

This helps to ensure that budgets are focused on projects that deliver real value. A key part of this is monitoring costs per use cases, and outcomes.

2. Centralise visibility of AI spending

It's difficult to control costs if different teams are purchasing and using AI tools independently of each other.

To solve this problem, use a platform which gives you a central view of AI use and spending across departments, projects and vendors. This can help to identify duplicate purchases and monitor usage trends.

3. Set budgets and spending controls

Robust budget management is key to keeping AI costs under control. Define clear budgets for AI projects and put spending alerts in place to flag up unexpected increases in usage.

4. Standardise AI tools and models across teams

Allowing every team to choose its own AI platform often leads to overlapping subscriptions, which can mean wasteful spending. Develop a strategy which identifies a small number of approved AI tools and models, with clear guidelines for each.

You may also want to look at consolidating vendors to lower licensing, integration and support costs.

5. Develop an AI approval process

Introduce a formal process for evaluating AI tools before they’re officially adopted by the business. Look at:

• Expected business value
• Total cost (including both initial and ongoing costs)
• Security requirements
• Whether an existing solution already meets the need.

This can help to prevent unnecessary purchases and duplication of tools.

6. Review AI investments regularly

It’s crucial to monitor the effectiveness and ROI of AI projects on a regular basis, to ensure they’re still delivering value for the company. Underused or underperforming tools should be retired, and duplicate solutions consolidated.

7. Measure ROI

While it is important to keep track of AI costs, it’s just as crucial to monitor return on investment of AI projects. Look at metrics relating to:

• Cost per task or per project
• Productivity improvements
• Time saved
• Customer satisfaction
• Revenue impact.

This gives you the data to make strategic decisions on which AI projects should be expanded, optimised or discontinued. It can also help you decide whether higher-cost solutions are worth the extra budget, if they deliver significantly better business outcomes.

8. Negotiate enterprise pricing

If your company’s AI usage is growing, it could be worthwhile to contact providers and suppliers about custom enterprise pricing.

This could lead to discounts based on usage commitments or organisation-wide licences, which could be more cost-effective compared to having multiple individual subscriptions.

Save on AI costs with Wise Business

Another key way to manage AI costs is to choose the right solutions when paying for subscriptions.

Many AI platforms are US-based, which often means they bill for subscriptions and tokens in US dollars (USD). This can mean surprise fees and FX costs for UK businesses, when their GBP payments need to be converted to USD.

This is where multi-currency solutions such as Wise Business are invaluable, enabling businesses to pay directly in USD - for low fees and mid-market exchange rates. This could result in savings compared to high bank fees (and poor exchange rates) for international payments.

You can even automate recurring payments using the powerful Wise API, saving your team extra time. And that's not all.

With Wise Business, you can:

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Sources used:

1. SAP - UK business investment in AI to rise by 40% on average over the next two years – but a long term strategy and people focus is needed to make it a success
2. Lloyds Banking Group - UK businesses adopting AI see strong gains in profitability and productivity
3. Primewise - How Much Does AI Integration Cost in the UK? 2026 Pricing Guide

Sources last checked: 29-Jun-2026