Guides

How-to guides for AI and automation

Step-by-step methods, checklists and templates you can use on your own process without talking to us first.

How to launch an AI voice agent in a regulated contact centre: a 10-step checklist

A 10-step checklist that runs governance alongside the build: choosing the call type, setting the baseline, disclosure, handover, identity and payments, testing, controlled release and the evidence you keep each month.

9 min read

AI Cost

How to work out AI cost per task before you build

A step-by-step method for AI cost per task: model calls, tokens, hidden reasoning, price multipliers, checking and volume, with a worked example.

9 min read

AI Governance

EU AI Act Article 50 and 2 December: a checklist for UK and North American firms

What Article 50 of the EU AI Act asks of UK and North American firms, what changes on 2 December 2026, and a checklist to start now.

9 min read

Agentic AI

An AI agent governance framework: a control template for regulated firms

A fill-in template for governing AI agents: register entry, autonomy tiers, approval gates, logging, identity, kill switch and staged rollout.

9 min read

AI Accuracy

How to measure what an AI summary leaves out

Accuracy checks catch what AI adds, not what it drops. A step-by-step method and table template to check an AI summary for missing information.

8 min read

Process Discovery

Process mining, process intelligence or a discovery workshop: which comes first?

Process mining, task mining, process intelligence and discovery workshops compared: what each tells you, what it needs, and which to start with.

9 min read

AI Governance

The AI evidence pack: ten records your regulator or auditor will ask for

What evidence do auditors and regulators need for AI? Ten records, who asks for each, what good looks like, and where firms usually fall short.

10 min read

AI Cost

How to build an AI spend forecast your finance team will sign off

A step-by-step method for turning AI invoices into a forecast by workload: what to collect, how to measure each job, how to price the alternatives and how to present a budget that survives a finance review.

9 min read

AI Accuracy

An accuracy score is not evidence: how to measure AI output so the result stands up

"Our AI is 95% accurate" answers none of the questions an auditor will ask. Measured against what? Counting what? And what about the facts it left out? Here is what the research says a defensible measurement looks like.

8 min read

AI Accuracy

How to check an AI-written document against its sources, claim by claim

A step-by-step method for verifying a summary, report or letter drafted by AI: fixing what you check, breaking it into claims, finding the evidence, giving honest verdicts, looking for omissions and keeping a record that stands up later.

9 min read

AI Strategy

What an AI proof of value should include before it reaches production

A proof of value earns its name by being able to fail. Here is what one has to contain — criteria set before the build, a real workflow, a defined failure path — and what it costs you when any of that is missing.

8 min read

AI Strategy

How to calculate automation value without confusing time released with cash saved

Hours released and money saved are different claims, and presenting one as the other is the fastest way to lose a finance stakeholder. Here is how to calculate both, and how to say which one you have.

8 min read

Data and Analytics

How to evaluate AI retrieval quality and cost on your own documents

Vendor benchmarks tell you how a system performed on someone else's corpus. Here is how to run the test on yours, which measures to keep apart, and the comparison most evaluations quietly avoid.

9 min read

AI Strategy

How to measure the ROI of enterprise AI

The ROI of enterprise AI is measured by operational outcomes — time saved, errors reduced, faster decisions and better service — not by activity or model performance alone. Defining success metrics before deployment is essential.

10 min read

AI Strategy

From pilot to production: how enterprises operationalise AI

Moving AI from pilot to production depends less on the model and more on orchestration, data readiness and governance. A structured approach — orient, prove, govern, scale, compound — is what makes the difference.

7 min read

AI Governance

A practical guide to AI governance for enterprises

AI governance is the set of policies, controls and oversight that keep AI systems safe, compliant and accountable as they scale. Done well, it enables AI adoption rather than blocking it.

8 min read

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