Sarah M.
CPA Firm Owner
2.7
AIBMM ScoreSarah runs a mid-sized CPA firm with 12 employees serving 150+ small business clients. Her team was drowning in manual work: categorizing transactions, chasing receipts, and closing books. She heard about AI tools but didn't know where to start.
Starting Point: The Struggles
Staff manually categorizing 60% of bank transactions
Spending 2+ hours per client per month chasing receipts via email
Month-end close taking 5+ days for each client
Senior staff pulled into routine work instead of advisory
The 90-Day Journey
Discovery Assessment
Sarah completed the Discovery questionnaire with her operations manager. The assessment revealed they were stuck at Level 2 - using ChatGPT for drafting but not connected to business systems.
Workflow Mapping
Mapped 8 key workflows including transaction categorization, receipt collection, bank reconciliation, and month-end close. Transaction categorization scored highest on value/readiness.
Stage Plan Created
Created Q1 Stage Plan with WIP limit of 3 Lifts. Selected: AI Bank Feed Categorization (L1→L2), Automated Receipt Collection (L1→L2), and Month-End Close Automation (L2→L3).
First Lift: Bank Feed Categorization
Built and piloted AI categorization rules using their accounting software's API. Started with 5 clients, expanded to 20 after 90% accuracy achieved.
Friction Encountered
Integration with one major bank's feed had API rate limits. Logged as blocker, escalated to vendor. Workaround implemented while waiting.
Receipt Collection Launch
Automated email reminders and mobile receipt capture deployed. Reduced receipt chase time from 2 hours to 15 minutes per client.
Metrics Review
Altimeter showed 40+ hours saved per month across the team. Categorization accuracy at 94%. Receipt collection SLA compliance up to 87%.
Stage Review
Graduated 2 Lifts, carried over Month-End Close (still in Pilot). Captured SOPs and prompt templates in Pattern Library for new staff training.
Lifts Executed
AI Bank Feed Categorization
→
94% accuracy, 60% time reduction
Automated Receipt Collection
→
Chase time from 2hr to 15min per client
Month-End Close Automation
→
In pilot with 10 clients
Friction Encountered & Resolved
Issue
Bank API rate limits blocking batch categorization
Resolution
Implemented queuing system and negotiated higher limits with vendor
Issue
Staff resistance to new receipt capture app
Resolution
Created video training and incentive program for adoption
Results: By the Numbers
Transaction Categorization Time
45 min/client
→
8 min/client
Receipt Collection Time
2 hr/client
→
15 min/client
Hours Saved/Month (Team)
0
→
40+
Key Takeaways
Start with high-volume, low-risk workflows like transaction categorization
Pilot with a subset of clients before rolling out to all
Capture SOPs immediately - they become training materials
Bank/vendor integrations often have hidden constraints - budget time for them
What's Next for Sarah?
Complete Month-End Close automation, begin AI-assisted advisory report generation
Start Your Journey
Sarah started exactly where you are. Begin with Discovery to chart your own path.