Overview

Product / Initiative: Embedding GenAI into Core Engagement Workflows
Role: Principal Product Manager
Location: Global (Firmwide Initiative)
Scale: 5,000+ partners and engagement managers, 20,000+ engagements annually, multiple functional workflows (Financial Health, Staffing, Risk, Engagement Management)
Outcome: Increased user engagement by 30% by embedding GenAI capabilities directly into critical engagement workflows, enabling real-time insights, proactive risk alerts, and automated actions — reducing operational friction and enhancing decision-making.

Problem / Opportunity

The Firm’s engagement management platform was designed primarily as a system of record, supporting basic administrative tasks. However, user engagement remained transactional and reactive, resulting in lost time, inefficiencies, and delayed decisions.

Key challenges:

🔁 Core workflows like financial tracking, staffing, risk reviews, and engagement creation/updates were siloed and involved multiple tools.
📊 Lack of embedded intelligence — users manually searched and stitched together insights.
⏳ Slow information retrieval and action-taking caused operational delays.
📉 Limited platform stickiness — engagement only when required, not when useful.
🧠 No intelligent orchestration across functions to surface trends or risks proactively.

Opportunity:

• Embed GenAI directly within core workflows to deliver real-time insights, recommendations, and intelligent actions.

• Reduce context-switching and manual work through automation and conversational interaction.

• Proactively flag financial or risk anomalies and recommend actions.

• Transform engagement management from a static workflow to a proactive, intelligent experience.

Goals & Success Metrics

Primary Goal: Increase engagement and decision velocity by embedding GenAI-powered intelligence into day-to-day workflows.

North Star Metrics:

• Active user engagement rate

• Decision/action turnaround time

• AI feature adoption

Supporting Metrics:

✅ +30% increase in platform engagement
⏳ Reduced time to retrieve and act on insights across workflows
🤖 Increased adoption of AI-powered features for financial health, staffing, risk, and engagement management
🧠 Improved decision quality and speed with proactive recommendations

Strategy & Approach

Vision: Transform the Firm’s engagement platform into a GenAI-powered intelligent assistant, seamlessly integrated into user workflows to drive real-time, predictive, and contextual decision-making.

• Embed, Don’t Add: Integrate GenAI directly into existing workflows (rather than creating separate tools).

• End-to-End Coverage: Extend AI support across Financial Health, Staffing, Engagement Creation/Updates, and Risk Management.

• Journey Mapping: Identify friction points and high-value moments for AI augmentation.

• Controlled Experimentation: Validate impact through structured rollouts and measurement.

• Continuous Learning: Use feedback loops to improve accuracy and relevance of recommendations.

Frameworks Used

• North Star Framework to define product vision and measurable outcomes

• Journey Mapping & Jobs-to-be-Done to identify high-impact use cases

• Dual-Track Agile for parallel discovery and delivery

• A/B Testing & Controlled Experiments for feature impact validation

• RICE Prioritization to focus on workflows with highest user and business value

• Telemetry & Observability to measure usage and performance

My Role & Contributions as Senior PM

• Defined the AI integration strategy for core engagement workflows across multiple functions.

• Partnered with engineering, data science, and business teams to embed GenAI capabilities into:

  • Financial Health: Real-time insights, variance alerts, and smart summaries.

  • Staffing: Intelligent staffing recommendations based on roles, skills, and history.

  • Risk Management: Proactive detection of engagement-level risk signals, policy gaps, and escalation triggers.

  • Engagement Creation & Updates: Smart auto-fill, contextual recommendations, and conversational actions.

• Designed controlled experiments to measure the impact of GenAI features on engagement and speed.

• Led adoption strategy, including onboarding, training, and continuous iteration.

• Defined telemetry and success metrics to measure sustained impact and inform roadmap decisions.

Solution & Execution

🤖 GenAI-Embedded Workflows

• Financial Health: Delivered real-time insights, early warning signals, and action recommendations based on live data.

• Staffing: Provided intelligent role matching and predictive availability suggestions.

• Risk Management: Flagged risks based on historical patterns, policy deviations, or unusual engagement signals.

• Engagement Creation/Updates: Automated data population, suggested next steps, and enabled conversational queries.

🧭 Seamless Integration

• GenAI was embedded natively into the platform — no new interface or tool switching required.

• Context-aware prompts and recommendations appeared at relevant moments in the workflow.

• Natural language interaction enabled users to query and act conversationally.

🧪 Experimentation & Rollout

• Launched targeted pilots and A/B tests with select partner cohorts.

• Measured uplift in engagement, task completion time, and feature adoption.

• Iterated rapidly based on telemetry and user feedback.

📊 Telemetry & Insights

• Real-time dashboards tracked GenAI interactions, task completion, and engagement trends.

• Identified opportunities to expand AI coverage to additional functions (e.g., pricing, approvals, performance reporting).

Impact & Results

📈 30% increase in user engagement across core workflows
⏳ Significant reduction in time to retrieve financial, staffing, and risk insights
⚡ Faster decision-making and improved operational velocity
🤖 High adoption of GenAI features, increasing platform stickiness
🧠 Early detection of potential risks, leading to fewer downstream escalations

Retrospective & Learnings

✅ What worked:

• Embedding GenAI into existing workflows drove high engagement without changing user behavior.

• Journey mapping and experimentation allowed precise targeting of friction points.

• Real-time insights for risk and financial health created tangible business value.

• A conversational interface reduced complexity and increased speed.

🛠️ What could be improved:

• Early enablement and training could have accelerated adoption curves further.

• Extending GenAI coverage to adjacent workflows (e.g., pricing committees, investment approvals) could yield more impact.

🧠 Key learning:

The biggest ROI from AI comes not from creating new tools — but from embedding intelligence directly into workflows users already rely on.