AI Skills Readiness Checklist for Accounting Firms
Purpose: To help firms assess their current state of AI readiness, not just in terms of skills, but mindset, leadership, process, and change management.
đź’ˇ Use this checklist to:
Identify where you are on your AI journey (Awareness → Pilot → Scale → Maturity).
Create an actionable roadmap for your next 90 days.
Engage your team, make AI adoption something everyone helps shape, not something “done to” them.
AI Skills Readiness Checklist
For Modern Accounting Firms – Powered by Bots For That
Category | What “Good” Looks Like | Questions to Ask | Action: Continue/Start/Stop |
1. Vision & Leadership | Clear vision for how AI supports firm goals (efficiency, client value, profitability). Leadership understands why AI matters and communicates it. | • Do we have a defined AI strategy? • Are partners aligned on the “why”? | ✅ Continue communicating progress 🚀 Start defining a shared vision statement ⛔ Stop treating AI as an “IT project” |
2. Culture & Change Management | Open, curious culture where staff experiment safely. Change isn’t feared, it’s managed. | • How does our team react to new tools? • Do we celebrate early adopters? • Are we honest about resistance? | ✅ Continue promoting small wins 🚀 Start a change champions group ⛔ Stop forcing change without communication |
3. Skills & Training | Staff trained in both AI tools andinterpretation (knowing what AI is telling them). Learning embedded in CPD. | • Do our people have time for AI learning? • Is AI literacy part of onboarding? • Do we know what “AI skills” actually mean for each role? | ✅ Continue targeted upskilling 🚀 Start a quarterly “AI in Practice” learning hour ⛔ Stop assuming younger staff “just get it” |
4. Process &  Automation Readiness | Key workflows documented and standardised, ready for automation. Manual chaos minimised. | • Are our core processes mapped and consistent? • Do we have automation priorities ranked by ROI/time saved? | ✅ Continue measuring process health 🚀 Start documenting 3 critical workflows for automation ⛔ Stop automating broken or inconsistent processes |
5. Data Quality & Governance | Data sources are accurate, accessible, and governed responsibly. | • Is our client data centralised and clean? • Do we understand data privacy and AI compliance (GDPR, confidentiality)? | ✅ Continue auditing data quality 🚀 Start defining a “data owner” per system ⛔ Stop feeding dirty data into bots or agents |
6. Tools & Technology Stack | Modern, API-ready systems; experimentation encouraged. | • Are our systems cloud- based and connected? • Do we know where AI can safely plug in? | ✅ Continue exploring integrations 🚀 Start using AI tools for one repetitive task ⛔ Stop relying on outdated on-prem software |
7. Client Communication & Value | Clients informed about how AI benefits them — faster service, fewer errors, more insight. | • Have we shared how AI enhances their experience? • Are we transparent about data use? | ✅ Continue using AI to enhance client delivery 🚀 Start including “AI value” in proposals ⛔ Stop hiding AI behind jargon |
8. Continuous Improvement & Measurement | Success tracked via KPIs: time saved, quality improved, client satisfaction, team satisfaction. | • Do we measure and share outcomes? • Do we review what’s working quarterly? | ✅ Continue using data to improve 🚀 Start benchmarking Time to Value (TTV) per automation ⛔ Stop assuming once implemented = done |
