Overview

Product / Initiative: Engagement Workflow Modernization with Program-Level Orchestration & AI-Driven Automation
Role: Principal Product Manager – ELM (led cross-functional PM and ops workstreams)
Location: Gurugram, India
Scale: Global, multi-functional operations (Pricing, Risk, Finance, Staffing, Partners)
Outcome: Reduced engagement setup cycle time by 60%, eliminated 100% redundant data entry, and improved partner experience by 25% by embedding program-level orchestration, AI-driven automation, and real-time visibility into the end-to-end workflow.

Problem / Opportunity

The existing engagement workflow was fragmented, manual, and engagement-level focused, creating significant leakage, inefficiency, and risk in handling complex long-term client arrangements.

Key challenges:

🧾 Siloed operations — Pricing, Risk, Finance, and Staffing worked at engagement level with no program-level orchestration, causing breakage and delays.
🔁 Redundant data entry — Partners re-entered the same information for multiple charge codes within one program.
📊 Lack of visibility — Pricing approvals decentralized in email and spreadsheets, preventing tracking or investment analysis.
⏳ Slow cycle time — Engagement setup and approvals took 45+ days due to fragmented reviews and information gaps.
📉 Leakage and inefficiency — Manual processes created working capital accumulation and poor client/partner experience.
🧠 No intelligence layer — Past engagement data was underutilized, with no AI/ML-driven insights to optimize future programs.

Opportunity:

• Introduce program-level orchestration into the engagement workflow.

• Eliminate redundant data entry through automation and cloning.

• Enable AI-powered program generation, recommendations, and predictive insights.

• Digitally link pricing approvals to ELC to enable real-time analytics and reporting.

• Streamline operations across functions to reduce cycle time and improve experience.

Goals & Success Metrics

Primary Goal: Re-architect the engagement workflow to operate at program level, powered by AI-driven intelligence, to improve speed, transparency, and decision-making.

North Star Metrics

• Engagement setup cycle time

• Redundant data entry elimination

• Partner satisfaction score

Supporting Metrics:

✅ 60% reduction in engagement setup time
🔁 100% elimination of redundant data entry for multi-code engagements
📊 100% digital linkage of pricing approvals
🧠 AI-driven recommendations for program design and risk identification
😊 25% increase in partner satisfaction
💰 10% reduction in working capital accumulation

Targets:

• Enable upfront program creation with AI assistance.

• Automate engagement setup through cloning and single-click creation.

• Use ML models to recommend program archetypes and flag risks.

• Establish centralized orchestration and real-time governance dashboards.

Strategy & Approach

Vision: Build a program-first, AI-augmented engagement workflow that unlocks speed, scale, intelligence, and operational resilience.

• Program-Level Orchestration: Introduce programs as a core object, enabling single data entry and automation across engagements.

• Workflow Digitization: Automate engagement creation, approval, and reporting flows.

• AI/ML Integration: Use predictive models to recommend program structures, forecast risks, and drive continuous improvement.

• Governance & Visibility: Establish dashboards and KPIs for real-time orchestration and performance monitoring.

• Cross-Functional Alignment: Integrate Pricing, Risk, Finance, Staffing, and Partners around a unified workflow.

Frameworks Used

• North Star Framework for outcome alignment

• OKRs, RICE & MoSCoW for prioritization and execution

• Dual-Track Agile for parallel discovery and delivery

• AI orchestration & recommendation models for automation and decision support

• User Journey Mapping & Research to identify workflow friction points

• Telemetry & Observability for real-time impact measurement and governance

My Role & Contributions as Senior PM

• Defined product vision and AI-led modernization roadmap for engagement workflows.

• Led PM and ops workstreams across Pricing, Risk, Finance, and Partners.

• Partnered with data science teams to scope AI/ML use cases for program generation, archetype recommendations, and predictive analytics.

• Synthesized partner pain points through research to prioritize automation and intelligence layers.

• Partnered with engineering to design orchestration, dashboards, and integrations.

• Owned success metrics and telemetry frameworks for measuring impact.

• Drove stakeholder alignment and change management across functions.

Solution & Execution

🧭 Program-Level Orchestration

• Upfront program creation during client development, with single data entry feeding downstream engagements.

• Automated propagation of program attributes across multiple engagements.

• Centralized pricing, risk, staffing, and finance orchestration.

🧠 AI & ML Integration

• Automated Program Generation: AI models generate customized program structures based on client requirements, industry best practices, and historical data.

• Program Archetype Recommendation: ML identifies similar past engagements and suggests the most effective archetype for the client segment.

• Predictive Analytics: Models analyze program timelines, feedback, and financial signals to flag risks and opportunities early.

• Continuous Improvement: AI learns from program outcomes to optimize future recommendations and workflows.

🧪 Workflow Automation

• Single-click engagement setup and data cloning for multi-code engagements.

• Automated linkage of pricing approvals for real-time analytics.

• Standardized workflows to eliminate handoffs and delays.

📊 Telemetry & Visibility

• Real-time program-level dashboards for performance, pricing, WIP, and risks.

• Predictive signals for underperforming programs.

• Automated governance cadence with embedded intelligence.

🧰 Technology & Integration Highlights

• Architecture: Program orchestration layer with AI/ML services

• Integrations: Pricing approvals, Risk review, Finance, Staffing, Partner feedback

• Scale: 20,000+ charge codes, 2,500 programs, 8,500+ engagements annually

• Monitoring: Real-time dashboards, predictive alerts, audit trail

Impact & Results

🚀 60% reduction in engagement setup cycle time
🔁 100% elimination of redundant data entry across multiple charge codes
📊 100% digitization of pricing approvals with real-time reporting
🧠 AI-powered recommendations improved program planning and reduced risk exposure
😊 25% increase in partner satisfaction and engagement ease
💰 10% reduction in working capital delay through faster invoicing
🧭 Improved orchestration across Pricing, Risk, Finance, and CST

Retrospective & Learnings

✅ What worked:

• Clear program-first vision combined with AI capabilities created transformational impact.

• Early metric definition enabled transparent tracking and stakeholder confidence.

• AI recommendations improved decision quality and reduced setup effort.

• Centralized workflows improved cross-functional collaboration and visibility.

🛠️ What could be improved:

• Earlier rollout of AI dashboards and self-serve risk insights could have accelerated adoption.

• Additional user training to maximize AI feature utilization.

🧠 Key learning:

True workflow transformation at enterprise scale comes from combining structured orchestration with intelligence-driven automation.
AI/ML capabilities don’t just digitize processes—they elevate decision-making, reduce effort, and unlock scale.