AI Automation Services

Automation has a bad reputation in a lot of organisations, and usually for good reason: someone bought a tool, automated a broken process, and made the mess faster. AI Applied approaches automation the other way round. We find the work that genuinely does not need a human, remove it properly, and leave your people doing the part of the job that actually requires judgement.

What AI automation can realistically do

The honest version is narrower than the marketing but more valuable than most people expect. AI automation is very good at reading unstructured input and turning it into structured action, at drafting routine output for human approval, at triaging and routing, and at checking work against rules. It is poor at anything requiring accountability without oversight, and it should not be given the final word on decisions that materially affect people.

Common high-value automations

• Inbox and request triage. Classifying inbound email or form submissions, extracting the key fields, and routing to the right queue with a suggested priority.
• Document processing. Reading invoices, purchase orders, application forms, certificates or contracts and writing structured records into your systems, with confidence scores and exception queues.
• Drafting with human approval. Producing first-draft responses, reports, summaries or file notes that a person reviews and sends, cutting the time per item rather than removing the person.
• Data reconciliation. Matching records across systems that were never designed to talk to each other and flagging genuine discrepancies rather than formatting differences.
• Compliance checking. Reviewing submissions against a rule set, evidencing which rule triggered, and escalating the ambiguous cases.
• Meeting and call follow-up. Turning transcripts into structured actions, CRM updates and summaries without anyone retyping them.

How we approach an automation project

Map the process as it really runs

We observe the actual process, including the workarounds, not the version in the process document. This routinely reveals that the expensive step is not the one everyone complains about. The output is a map with volumes, handling times and exception rates attached, which is what makes the business case credible.

Decide what to automate, simplify or delete

A meaningful share of steps in most processes exist because of a system limitation that no longer applies, or a control that duplicates another control. Deleting a step is cheaper and more reliable than automating it. We look for that first, then automate what remains.

Build with exceptions as a first-class concept

Every automation we build has a defined confidence threshold, an exception queue, and a human path. Systems that assume the happy case are the ones that quietly produce wrong results at scale. We design for the awkward 8 percent from the start and instrument it so you can see it.

Measure before and after

We agree the baseline metrics before the build: items processed, handling time, error rate, rework rate, cycle time. After go-live those same measures tell you whether the automation worked, which is a conversation most automation vendors prefer to avoid.

Keeping humans in the right place

We use a simple test. If the outcome affects someone materially, if it commits the organisation to something, or if being wrong is expensive and hard to reverse, a person approves it. Everything else can run automatically with sampling and audit. That line is agreed with you in writing and encoded in the system rather than left to convention.

Integration, not replacement

Automations run against the tools you already have. We build into Microsoft 365, Google Workspace, Salesforce, HubSpot, Dynamics, ServiceNow, Xero, Sage, SharePoint and bespoke internal systems, using supported APIs rather than fragile screen scraping wherever an API exists. Your team keeps working where they already work.

Governance and audit

Every automated action is logged with its inputs, the model or rule version used, the confidence score and the outcome. That log is what lets you answer a regulator, a customer complaint or an internal audit question months later. Our AI governance and compliance work covers the policy layer that sits above it.

Where to start

Pick the process that generates the most complaints from the people who do it. That is almost always where the return is. Book a chat and describe it to us, or read about our wider AI implementation services and data engineering work.

Explore our services

A full index of what we do is on the AI services page. The individual services are:

• AI readiness assessment
• AI implementation services
• Machine learning development
• Data engineering
• AI automation
• AI governance and compliance

Where we work

We deliver across the United Kingdom from our Glasgow studio, with on-site time included: London, Manchester, Birmingham, Edinburgh, Glasgow.