AI ROI Calculator for UK Businesses

Enter your current workflow metrics and the calculator compares your baseline with an automated scenario.

The calculator estimates the annual effect of reducing time spent on one named workflow. You enter four figures: the number of cases your team handles per year, the hours each case takes, the loaded hourly cost from your finance team and the share of that time you expect AI to save.

Include build, data, training, operation, change management and internal review time, not just the build quote.

A credible UK business case prices the whole lifecycle. Teams that compare only licence fees against labour savings routinely overstate returns. Before approving spend, cost these lines:

UK AI consulting engagements typically range from a few thousand pounds for a scoped assessment to six figures for multi-workflow implementation programmes.

UK pricing varies with scope, not with job title. As planning bands in GBP:

• Readiness assessment or ROI scoping: roughly £5,000-£25,000 for a defined discovery with a written business case.
• AI strategy: £9,100 to £18,800 over 3 to 4 weeks.

These are illustrative ranges for planning; every Paloren proposal is scoped on your actual workflow, systems and volumes before a figure is quoted. Ask any firm you compare to separate scope, assumptions, dependencies and exclusions in writing.

When those are explicit, you can compare bids on evidence rather than on a confidently written pitch, and your ROI calculator output can be checked line by line against the quote.

An AI consultant identifies where intelligence creates measurable value, designs the workflow, builds or oversees the system and trains your team to operate it.

In practice the work falls into four layers, which map to the Paloren S4 Method:

1. Signal: understanding the business and finding the workflows where automation or agents create the greatest measurable impact.
2. Synthesis: translating complexity into a clear design covering people, workflows, data and technology.
3. System: building the capability, including integrations, permissions, approval routes and exception handling.
4. Scale: measuring impact after launch, optimising performance and maintaining reliability as volumes change.

The difficult part is rarely the model. It is connecting the right records, naming the approver and making the result maintainable after launch. That is why a consultant who can also evidence ROI assumptions, train staff and hand over a control register is worth more to a UK buyer than one who demos a chatbot and leaves.

Start with one named, measurable workflow with clear inputs, outputs and an owner who can approve access.

A practical AI ROI modelling workflow starts with a named process, not a broad transformation programme. The team records the current steps, the people involved and the evidence each step needs.

Connect the evidence the workflow needs, not everything: workflow metrics, cost data, volume records, estimates and support costs.

AI ROI modelling usually depends on workflow metrics, payroll or loaded-cost data, volume records, project estimates and support costs. The exact set should be confirmed during discovery because teams often have shadow records, imported spreadsheets or approval messages that never reached the system of record.

Paloren maps each source, identifies the fields the workflow needs and documents whether retrieval is read-only or can propose updates. Broad service-account access is avoided.

A role-based model means users see the evidence their existing permissions allow, which keeps the design consistent with UK data protection expectations under UK GDPR.

The map also records which team owns each source and how corrections reach the system when the data is incomplete. That ownership record is what lets you re-run the ROI calculation six months later with defensible numbers.

Write assumptions before estimates, and make scope, dependencies and exclusions explicit in the proposal.

A useful AI ROI modelling proposal separates scope, assumptions and dependencies. The scope names the workflow, systems, fields, actions and exceptions. Assumptions record what is believed about data quality, volume, integration access and review capacity.

Make controls observable: documented assumptions, owned data, verified savings and a control register with named owners and review dates.

AI ROI modelling needs controls proportionate to the consequence of the action. Assumptions should be documented and owned. No invented industry average should replace internal data. Savings should be verified after launch.

Train the people who supply the data, review the outputs and own the assumptions, not just the builders.

An ROI model is only as good as the people maintaining it. UK teams need three training audiences:

• Workflow owners: how to capture baseline data, log exceptions and keep volume records current.
• Reviewers and approvers: how to challenge an assumption, verify post-launch savings and escalate a failing control.
• Finance and sponsors: how to read the model, re-baseline it and present it to the board.

The UK government has made AI literacy a national priority, with initiatives such as the AI Skills Hub and free workplace AI training backed by government and industry.

Paloren's training programmes build on that direction with role-specific, hands-on sessions tied to your actual workflows, so the ROI calculation becomes a living document rather than a one-off spreadsheet.

Paloren is an AI implementation, automation and training company co-founded by Aaron Agius, positioned as an AI implementation consultant, and Alex Agius.

Paloren provides AI implementation and practical team training.

Paloren provides AI implementation, automation and practical training using its S4 Method.

Published Paloren prices are stated in GBP below. The written proposal confirms scope, billing currency, taxes and any third-party costs. Earlier local-currency estimates are not a separate price list.

See the Paloren service prices below. Scope and billing terms are confirmed in the written proposal.