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Case studies

AI automation case studies for small businesses

Ten worked examples of how AI automation can handle leads, customer enquiries, scheduling, documents and internal knowledge. Each one shows the business problem, the step-by-step workflow, the systems involved, where a person stays in control and what we would measure.

The businesses described are fictional and no results are claimed; KPIs show what we would measure in a real project.

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Lead management

  • Construction & Home Services

    AI lead management for an electrical contractor: enquiry triage and quote follow-up

    This illustrative example shows AI lead management for a contractor whose enquiries arrive by web form, email and phone while the team is on site. The proposed workflow structures each enquiry, checks it against the service area and job list, asks for missing details and keeps unanswered quotes in a follow-up queue, while pricing and safety advice stay with the electricians.

    Service: AI Lead Management10 steps · 2 human
  • Real Estate & Property Management

    Real estate lead qualification: portal enquiries matched, routed and booked for viewings

    This illustrative example applies real estate lead qualification automation to an agency receiving enquiries from property portals, its website and email. The proposed workflow matches each enquiry to the correct listing, records what the applicant has actually stated, routes it to the responsible agent and offers viewing slots from authorised calendars, with agents handling negotiations and eligibility decisions.

    Service: AI Lead Management10 steps · 2 human
  • Professional Services

    AI sales assistant for a B2B services company: lead qualification, CRM updates and meeting booking

    This illustrative example describes an AI sales assistant for B2B services that handles inbound leads of varying size and readiness. The proposed workflow extracts each prospect's stated needs, applies transparent qualification rules, drafts a personalised reply, keeps the CRM up to date and offers discovery-call slots, while salespeople own pricing, proposals and commercial decisions.

    Service: AI Lead Management9 steps · 2 human

Customer support

  • Construction & Home Services

    AI receptionist for home services: after-hours call capture and urgent escalation

    This illustrative example describes an AI receptionist for home services that answers calls and web chats outside business hours. It identifies itself as automated, collects contact and job details, triggers the business's approved escalation message for predefined emergency indicators and hands a structured summary to staff, without diagnosing faults or promising arrival times.

    Service: AI Customer Support9 steps · 2 human
  • E-commerce

    E-commerce customer support automation: order status, returns triage and escalation

    This illustrative example shows e-commerce customer support automation for a small online store answering recurring questions about orders, delivery, returns and products. The proposed workflow classifies each ticket, grounds replies in current policies and authorised order data, performs only explicitly permitted actions and routes refunds, disputes and complaints to a person with full context.

    Service: AI Customer Support9 steps · 2 human
  • Tourism & Hospitality

    Multilingual AI customer support for a tour operator: booking enquiries in several languages

    This illustrative example applies multilingual AI customer support to a tour operator receiving booking questions in several languages through its website, email and messaging channels. The proposed workflow detects the language, answers only from approved experience information and live availability, separates confirmed facts from details needing staff confirmation and escalates refunds, accessibility questions and complaints.

    Service: AI Customer Support9 steps · 1 human

Scheduling

Back-office automation

Internal AI assistants

Our approach

How we evaluate an automation opportunity

Every engagement starts with the process, not with a promise that AI will solve everything.
  1. 1

    Map the current workflow

    Inputs, systems, people, exceptions and decision points.

  2. 2

    Establish a baseline

    Current processing time, error rates, delays and relevant outcomes.

  3. 3

    Choose a bounded first use case

    A repeatable workflow with clear rules and a manageable risk profile.

  4. 4

    Define human approval points

    What can be automated, what needs approval, what stays human-led.

  5. 5

    Test against real scenarios

    Incomplete inputs, duplicates, conflicting information and failures.

  6. 6

    Launch with monitoring

    Performance, errors, escalations and user feedback are tracked.

  7. 7

    Compare results fairly

    The same metrics, before and after, over a suitable period.

  8. 8

    Expand only when justified

    More automation only when the evidence shows it is reliable and useful.

Services

The services these workflows are built from

Scope, typical integrations and pricing guidance for each service.

Next step

Have a workflow like one of these?

Tell us how the process works today, which tools your team uses and where work gets stuck. We'll map a practical automation, what should stay under human control, and how success would be measured.

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