Incorrect recommendation reports spiked for first-time business owners. Build the RCA plan under scale, incentive, and regulatory constraints

Problem Statement Description

Intuit’s small business cash-flow assistant helps owners interpret business finances and receive recommendations related to cash flow, invoicing, expenses, payroll, taxes, and financial planning. Recently, the rate of “incorrect recommendation” reports has spiked specifically among first-time business owners, a segment that may have limited financial expertise and high reliance on product guidance.

You are asked to build an RCA plan for this issue. The plan should investigate whether the spike reflects a true product-quality problem, a measurement or reporting artifact, a shift in user mix or behavior, or an unintended effect of incentives, onboarding, AI/model changes, data integrations, or compliance-related constraints.

The investigation must account for Intuit’s operating environment: large-scale SMB usage, sensitive financial data, explainability expectations, regulatory risk, trust implications, support and operations load, and the need to avoid overcorrecting in ways that reduce useful guidance for business owners.

The RCA plan should consider:

- How to define the anomaly, including baseline period, denominator, severity levels, and what qualifies as an “incorrect recommendation”

- Segmentation across first-time vs experienced owners, business type, tenure, geography, product surface, recommendation category, data-source coverage, and onboarding path

- Instrumentation checks to validate event logging, report submission flows, duplicate reports, taxonomy changes, and support-channel routing

- Hypotheses across recommendation logic, AI/model behavior, accounting data quality, third-party integrations, tax/payroll rules, user misunderstanding, and incentive-driven reporting behavior

- Evidence needed to distinguish actual recommendation errors from poor explanation, missing context, user expectation mismatch, or regulatory caveats

- Immediate mitigations that protect users and reduce financial harm without unnecessarily disabling valuable recommendations

- Communication, escalation, and review paths involving product, engineering, data science, compliance, support, and customer success

- Prevention mechanisms such as monitoring, alerting, QA coverage, policy review, feedback loops, and launch safeguards for future changes

Your goal is to present a structured RCA approach that would help Intuit quickly understand the root cause, protect customer trust and financial confidence, and make a defensible product decision under scale, incentive, and regulatory constraints.