AI-Assisted Customer Onboarding Platform
87% of customers onboard without any human touch — cutting time from 3 days to 8 hours and achieving 95% satisfaction.
87%
fully autonomous completions
2-8h
onboarding time (was 2-3 days)
95%
customer satisfaction
5k+
onboardings processed autonomously
Role
Lead Technical Business Analyst — AI Implementation
Timeline
Q4 2023–Q1 2024 · 4 months
Delivery context
The Problem
Customer onboarding required multiple manual steps with an average time of 2-3 days. High abandonment rate due to friction. Inconsistent experience across customers. The operations team needed a clear definition of what the AI agent could decide autonomously versus what required human judgment.
My Contribution
I led the requirements analysis and process mapping for this AI-assisted onboarding initiative. I documented every step of the existing human agent workflow using UML process models, identified which steps were strong automation candidates versus edge cases requiring human judgment, and defined the functional specifications for the AI decision boundaries. I designed the UAT shadowing protocol used to calibrate autonomous completion thresholds and ran structured change management sessions with the onboarding operations team to prepare them for the new workflow. I also defined the monitoring requirements and KPI framework for tracking autonomous completion rates post-launch.
Process
Workflow Mapping
Documented every step a human agent performs using UML process models, identifying automation candidates versus judgment-required edge cases.
Requirements
Defined functional specifications for AI decision boundaries — what the system could auto-approve, re-ask for, or escalate to a human agent.
UAT Design
Built a shadowing protocol to run 500 test conversations and calibrate confidence thresholds for autonomous versus escalation paths.
Change Management
Ran structured sessions with the onboarding operations team to prepare them for the new workflow and establish feedback loops for continuous improvement.
The Solution
Requirements-led AI implementation: end-to-end human workflow documentation, automation boundary definition, UAT shadowing design, change management programme, and monitoring KPI framework — ensuring the AI agent was built to the right specifications from the start.
Results
- 2–3× faster onboarding: 2-3 days → 2-8 hours
- 87% of customers complete without human intervention
- Less than 1% escalation rate
- 95% customer satisfaction score
- 5,000+ onboardings processed autonomously
The hardest requirements problem was defining 'confidence' — what threshold of AI certainty justified autonomous action versus human escalation. Getting compliance, operations, and engineering aligned on that definition in a workshop, before any model was trained, was what made the production system trustworthy enough to run at 87% autonomy.
Tech Stack
AI Platforms
Process
Tools
Methodology
Related
How this project connects to the rest of my work.
Services
Work phases this project exemplifies
- 02 · DefineBRDs, user stories, acceptance criteria — translating the problem framing memo into a measurable business case with KPI baselines, target outcomes, and acceptance criteria stakeholders can sign off on
- 03 · DesignArchitecture, data models, API contracts, integration specs
- 04 · DeliverAgile execution, backlog ownership, UAT, defect triage
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