AI adoption

AI adoption, from first use case to everyday work

Most companies have tried AI. Far fewer have made it part of how work gets done. This is the path we follow with clients to get there, one proven use case at a time.

Illustration of an operations team assessing a practical AI workflow
The problem

Why AI adoption stalls

The technology is rarely what holds a company back. These four things are.

  • Pilots without an owner

    A demo impresses a meeting, then nobody is responsible for taking it into daily work.

  • Data that is not ready

    The answers live in documents, inboxes and systems the model cannot reach or cannot trust.

  • No measure of success

    Without a baseline and a target, nobody can say whether the pilot worked.

  • People left out

    Teams who were not involved or trained go back to the way they worked before.

The path

The AI adoption path

Four stages. Each one ends with a decision, so you never commit to the next without evidence.

Step 1: Assess

Find where AI is worth it

  • List the work that is repetitive, slow or stuck in documents
  • Score each use case by value, feasibility and risk
  • Check the data: where it lives, who owns it and how clean it is
  • Agree one owner and one measure of success

Use case scoring

12 assessed
Use caseValueFeasibleRiskInvoice readingSupport answersSales research

Owner: Finance ops lead

Measure: hours saved per week

Step 2: Pilot

Prove one use case

  • Build one narrow use case with real users and real data
  • Write evaluation tests before the first prompt
  • Keep a person in the loop for every consequential decision
  • Compare the result with the baseline, then decide to stop or continue

Pilot: invoice reading

Week 4

48/50 evals passed

Time per invoice

Baseline9 min
Pilot2 min

Decision: continue

Reviewed with the owner

Step 3: Integrate

Put it where work happens

  • Connect to the systems people already use, not a separate tool
  • Add access control, logging and data protection
  • Set usage policies and train the teams who will rely on it
  • Monitor quality, cost and latency from day one

Connected systems

  • ERPInvoices posted
  • Email inboxReading attachments
  • Approvals in SlackPerson in the loop
Access controlLoggingData protectionUsage policy
Step 4: Scale

Make it routine

  • Reuse the same platform, patterns and guardrails for the next use case
  • Move from assistants to agents where the evidence supports it
  • Review models and costs as the options change
  • Report outcomes to the business in its own numbers

AI use cases

This quarter
  • Invoice readingLive
  • Support answersLive
  • Sales researchPilot

310 hours saved

Reported to finance, monthly

Use cases

Where companies start

Six places where AI tends to prove itself first.

  • Customer support

    Answer common questions from your own content and hand the rest to people with the context attached.

  • Document processing

    Read invoices, forms, contracts and claims into your systems instead of typing them in.

  • Company knowledge

    One place to ask questions across policies, manuals, tickets and past projects, with sources shown.

  • Sales and marketing

    Research accounts, draft outreach and produce campaign content for a person to edit and approve.

  • Software engineering

    AI-assisted coding, review and testing inside your existing development process.

  • Forecasting and operations

    Predict demand, delays and failures early enough to change the plan.

Illustration of a service specialist reviewing an AI-assisted response
Adoption

What makes it stick

A working model is the smaller half of adoption. The rest is how the company runs it.

  • Governance

    • A short policy on which data and tools are allowed
    • Access control, logging and review for every AI system
  • People

    • Training for the teams who will use it, in their own workflows
    • A named owner in the business for each use case
  • Measurement

    • A baseline before the pilot and the same measure after it
    • Quality, cost and usage tracked for as long as it runs
Industries

Paths by industry

The stages stay the same. The first use cases, the data and the rules change. Choose your industry.

FAQ

Frequently asked

If you have any further questions, send us a message and we will get back to you.

  • AI adoption is the process of making AI a routine part of how a company works: choosing the use cases worth doing, proving them with real users, connecting them to existing systems and running them with the right controls. It is more than buying a tool or running a demo.

Find your first AI use case

Tell us about your business and where the time goes. We will come back with the use cases we would assess first and how we would test them.