Answer for the business as it works today. Mark the closest response to each question — 0 for no or rarely, 1 for partly or sometimes, 2 for yes or often — and do not score for planned tools, future hires or what the process should be.
Checklist · Readiness
AI Readiness Checklist
A 20-question diagnostic for working out whether your business is ready to turn AI into operating leverage — where revenue is leaking, where admin is slowing the business down, and whether your systems and team are ready for a practical implementation.
LB Labs6 min readUpdated Sep 2026
At a glance
- Twenty questions, each scored 0 to 2, across four sections: revenue, operations, systems and team. About five minutes.
- Two separate totals — opportunity and readiness — because friction and readiness are different signals, and one blended number hides the decision.
- A diagnostic matrix reads the two scores together and names the next move: priority build, selective build, fix the foundation or standardise first.
- A 30 to 60 day priority canvas and ten practical moves turn the diagnosis into a focused business improvement project.
- Score the operation as it works today — not the ambition, and not the tools you plan to buy.
Most AI projects that fail do not fail because the technology was not ready. They fail because the business was not: leads sat unworked, the CRM was half-maintained, nobody owned the project, and the team discovered all of this after go-live instead of before.
This checklist is the assessment we run before recommending any build. It is deliberately vendor-neutral — it does not assume a platform, a model or a budget — and it produces two scores rather than one, because knowing there is a problem worth solving and knowing you can solve it well are different questions.
01
How the checklist works
- 01Read: answer for the operation as it works today.
- 02Mark: choose 0, 1 or 2 for every question.
- 03Total: keep opportunity and readiness as separate totals.
- 04Prioritise: select one workflow with a measurable upside.
02
Opportunity signal: revenue
The first section looks for the places where demand turns into delay, leakage or lost follow-up. A high score here is not bad news — it is the signal that a frequent event with a visible delay and a measurable commercial consequence exists, which is a far stronger automation candidate than a vague desire to use AI. The questions probe:
- Enquiry volume — whether calls, enquiries and web leads arrive at a rate worth systematising.
- Missed calls, after-hours enquiries and slow follow-up.
- Leads sitting unworked for hours before anyone responds.
- Bookings, quotes and callbacks handled manually and inconsistently.
- The repeat questions your team answers every day.
03
Opportunity signal: operations
The second section scores the work your team repeats, remembers and rescues by hand: manual booking and record updates, repetitive admin displacing higher-value work, handovers between admin, sales and service that break down, tasks that run on individual memory rather than on a workflow, and daily or weekly work that could follow the same rules every time.
04
Readiness foundation: systems
AI performs better when the operating data already has a home. This section asks whether you already run a CRM, booking system, helpdesk, ERP or job management platform; whether it is reasonably up to date and actually used; whether it can connect to other tools through APIs, webhooks or native integrations; whether there is one clear source of truth for customer, lead and booking data; and whether the business would benefit from AI working with its current systems rather than replacing everything.
A stack inventory — system, what it owns, what it connects to, data health — sits alongside the questions.
05
Readiness foundation: team
A useful AI system still needs ownership, measures and human boundaries. The final section scores what is truly available now: whether the team would adopt AI that removed obvious low-value work without creating chaos, whether someone in the business can own the project internally, whether outcomes such as response times, bookings, conversions and admin hours can be measured, whether you know which workflows should stay human-led, and whether a 30 to 60 day implementation with clear commercial upside is realistic.
The minimum governance for a focused implementation is small and named: an executive sponsor, an internal project owner, a primary success metric with its baseline, and the decisions that must remain human-only.
06
Reading the two scores together
Opportunity tells you whether the problem is worth solving. Readiness tells you whether the business can implement the solution well. The diagnostic matrix reads them together:
- High opportunity, high readiness — priority build: scope a 30 to 60 day implementation around the highest-value workflow.
- Low opportunity, high readiness — selective build: target one narrow use case with clear evidence of value.
- High opportunity, low readiness — fix the foundation: protect the upside by cleaning data, connecting systems and naming an owner.
- Low opportunity, low readiness — standardise first: clarify processes, ownership and data before adding complexity.
07
Ten practical moves
The checklist closes with ten moves that turn a score into a focused business improvement project. Among them:
- Map where revenue is currently leaking — missed calls, slow response, dropped follow-up, unquoted work, weak reactivation — before choosing any tool.
- Pick one workflow first: the one causing the most lost time or lost revenue, not the most technically interesting.
- Clean up the CRM or main operating system so AI has something reliable to work with.
- Measure a simple baseline before implementing anything.
- Nominate one internal owner — projects stall when nobody is clearly responsible for decisions, access and rollout.
- Keep humans in the loop wherever there is commercial, legal or brand risk. The goal is not to do AI; it is to increase capacity, speed and revenue.