AI implementation and business process automation

AI automation for business: what can you improve?

Start with a task where smoother handling would give your team more time for customers and decisions. For example, you could:

• Prepare orders faster. Data from a message or spreadsheet goes into a document for an employee to check and approve.
• Make better use of documents. The system finds customer requirements and the information needed for a reply.
• Prepare quotations more efficiently. Prices, availability and costing data come together in one draft.
• Focus checks on important differences. The system compares documents and highlights lines that need an employee’s judgement.

We select one task and examine what could change. We account for case volume, available data and time spent checking and correcting results. If the starting point is unclear, process analysis helps compare options.

AI and automation in everyday work

These are example applications. Each is adapted to your team’s data, systems and working practices.

If your process is different, describe it during the discussion. We will assess whether and how we can improve it.

AI, automation or integration: which should you choose?

These approaches can work together in one process. AI helps read and interpret content, such as a customer message. Rule-based automation carries out repetitive steps where the procedure can be defined clearly. Integration transfers data between programs.

For example, AI reads order lines, a program checks required fields and an integration sends data to the ERP. An employee approves the result at an agreed point. If data already has a consistent format, AI extraction may be unnecessary.

We first check existing tools and ready-made solutions. If manual data transfer is the main problem, see business system integration. If you need a new feature in your program, see AI in existing software.

What do you receive with implementation?

Before starting, we define what will be delivered and how we will check it meets the requirements. Implementation includes:

• Configuration or development of the solution and the necessary connections to business tools.
• Tests using representative cases, including incomplete data and situations needing employee decisions.
• Result checks and error handling suited to the task.
• Instructions and user preparation for everyday use.
• Outcome assessment against agreed criteria, such as processing time, corrections and running costs.

At handover, we agree who is responsible for maintenance and what documentation they need. You can choose our post-implementation support or arrange it yourself.

How much does AI and automation implementation cost?

The price depends on task scope, data quality, system capabilities and required checks. For agreed scopes, indicative prices are:

• Order or quotation automation without direct ERP integration: £2,900–£4,360 excluding VAT. For example, preparing data in an import file.
• Order or quotation automation with ERP integration: £4,360–£7,260 excluding VAT. The result could be an order or quotation draft in your system.
• Comparing purchase invoices with orders and deliveries: £4,360–£7,260 excluding VAT. This uses ERP data; more complex integration can increase the cost.

These are reference ranges for those applications. We quote assistants using company data, AI in existing applications, private AI and other solutions individually.

Before your decision, we also show expected model, licence, infrastructure and optional support costs.

When assessing value, we include running costs and time needed to check results. Freed-up time may help you handle more work; financial savings depend on how your business uses that change.

A paid analysis is needed when the process or uncertainties require investigation before a quote. Single-process analysis typically costs £730–£1,160 excluding VAT. With a clear task, we can go from the consultation to an implementation proposal. See analysis scope and commercial terms.

How does AI implementation work?

1. Discuss your goal. We cover the task, case volume and tools, and identify what to check before quoting.
2. Agree the scope and criteria. We define deliverables, data needs and how you will assess the result. Where uncertainties remain, we propose a separately priced analysis or test.
3. Build and test. We test cases not used during development as well as development examples. We assess accuracy, checking effort and running costs.
4. Launch for selected cases. We start with a limited scope, such as one document type. Your team checks the solution in daily work.
5. Hand over and assess results. We show your team how to use it and provide instructions. We compare results with the starting point to help you decide on further development.

We need a coordinator, involvement from employees who know the process and agreed data access. Order or quotation automation is usually planned for 4–8 weeks. The proposal specifies the schedule for your project.

We have over 20 years of IT experience in requirements analysis, system design, programming and implementation.

How do you keep control of data and AI results?

Before implementation, we agree who can access data, where it is processed and how long it is retained. We choose cloud processing or private AI based on requirements and task testing. We sign a confidentiality agreement before documents are shared; samples can be anonymised.

We define what the program checks and what an employee approves. Missing data and ambiguous cases are referred for clarification. We prepare and test how work continues when the model or connection is unavailable.

Content updated: 26 October 2026