Product Manager - AI Agents

Overview

The Product Manager for AI Agents is responsible for ensuring the AI Agent product achieves product-market fit and can be successfully sold, positioned, and scaled across ibex.

This role serves as the commercial owner of the product, bringing together market intelligence, partner capabilities, client feedback, competitive insights, solution messaging, ROI models, sales enablement, and knowledge management into a single commercialization strategy.

The Product Manager works across Product Partners, Sales, Marketing, Wave iX Solutions, Operations, and Technology to ensure the product evolves in response to both market demand and operational realities. This role is also the primary commercial counterpart to the platform partners’ account teams (Account Executive and Solutions Engineering), responsible for translating the partners’ product roadmaps and proof points into ibex-branded commercial assets, and for identifying co-selling opportunities.

This role does not own engineering, implementation, or delivery. Instead, it supports the SVP, Wave iX Commercial in ensuring that the commercial organization always knows what to sell, why clients buy it, how to differentiate it, and how to position ibex as the enterprise AI operating partner.

Responsibilities

Commercial Strategy - own the commercial strategy for the AI Agent product at ibex.

Responsibilities include:

• Monitor AI Agent market trends
• Identify emerging customer needs
• Evaluate competitive positioning
• Recommend new commercial offerings
• Define target industries and use cases
• Maintain the commercial roadmap for AI Agents

Maintain and expand the vertical playbook (currently five verticals: software/high-tech, healthcare, fintech, retail/e-commerce, telco) in partnership with the platform partner's account team, including new verticals as demand emerges

Market Intelligence - become ibex's internal expert on the AI Agent marketplace.

Includes:

• Competitor analysis
• Platform product comparisons
• Industry trends
• Analyst research
• Client feedback
• Win/loss analysis

Outputs include:

• Quarterly market updates
• Competitive battlecards to educate internal sellers
• Executive briefs
• Opportunity assessments

Use Case Development - develop repeatable AI Agent use cases.

Examples:

• Conversational IVR, replacing press menu IVRs
• Call spikes
• Seasonality
• Low-complexity, high-volume interactions
• Agent-facing copilot for Internal Knowledge lookup
• Multi-channel identity and account unification

Each use case includes:

• Business challenge, AI solution, Client value, ROI, KPIs, Reference architecture, Required integrations, Success stories
• Each use case should reference existing proof points (e.g. cost reduction and resolution-rate benchmarks from live customer deployments) so reps are not building ROI narratives from scratch per deal.

Value Proposition & Messaging - own product messaging.

Develop / maintain:

• Value propositions
• Elevator pitches
• Executive presentations
• Differentiators
• Objection handling
• Industry messaging
• Partner positioning

Ensure consistent messaging across Sales, Marketing, Client Success, and Executive Leadership. Maintain a clear internal delineation between white label-safe messaging (client-facing, no vendor names) and partner-facing technical detail (internal only), ensuring reps never inadvertently disclose the underlying platform partnership in client conversations, proposals, or RFP responses. Own the objection-handling FAQ as a living asset, continuously updated rather than treated as a one-time deliverable.

Sales Enablement - create commercial assets that accelerate revenue.

Examples:

• Sales playbooks
• Discovery guides
• Qualification guides
• ROI calculators
• Proposal content
• Battlecards
• Demo scripts
• Executive presentations
• Customer references

Measure adoption and continuously improve these assets. Own and continuously refine the demo sequencing playbook based on live deal feedback (e.g. leading with lower-risk, non-revenue intents before higher-stakes automation, per lessons learned from active deals). Maintain a standing ROI calculator template (cost-per-ticket, automation rate, resolution fee structure) so reps are not building bespoke models per prospect.

Partner Commercial Management - work with strategic technology partners including AI platform providers.

Responsibilities include:

• Joint GTM planning
• Co-selling programs
• Sales enablement
• Executive relationship support
• Marketing coordination
• Partner business reviews
• Pipeline development – ibex pipeline and hold a standing joint pipeline review with the platform partner's account team, at minimum monthly, covering active deals in the channel, deal stage, and enablement gaps
• Serve as the primary escalation point for deal-specific support requests from partners (e.g. custom demos, ROI model builds), rather than routing these ad hoc through individual account relationships.
• Coordinate closely with internal teams for technical and operational partner management.

Knowledge Management - own the commercial knowledge repository.

Includes:

• Use cases
• Success stories
• Win themes
• ROI benchmarks
• Reference architectures (commercial view)
• Proposal language
• Best practices
• Lessons learned
• Contract templates

Ensure knowledge is searchable, reusable, and continuously updated. Build this repository to extend existing collateral already in place rather than duplicate it in a new system. Refresh ROI benchmarks quarterly directly from the platform partner's evolving proof-point set, since resolution rate, cost reduction, and time-to-production figures update as new customers go live.

Cross-Functional Coordination - serve as the commercial hub across:

• Sales Enablement
• Marketing
• Wave iX Solutions
• Operations
• Technology
• Strategic Partners

Ensure commercial commitments remain aligned with delivery capabilities, including the platform partner's Solutions Engineering team by name, for deal-specific technical scoping (e.g. custom demo builds, POC frameworks)

Qualifications

• 7–10+ years in enterprise software, AI, SaaS, consulting, or CX technology
• Experience with conversational AI, virtual agents, contact center technologies, or customer experience transformation
• Strong understanding of enterprise sales cycles
• Experience creating GTM strategies and sales enablement
• Ability to translate technical capabilities into business outcomes
• Executive presentation and storytelling skills
• Experience working across product, engineering, sales, and operations

Core Competencies

• Commercial strategy
• Product marketing
• AI and CX domain expertise
• Executive communication
• Partner management
• Financial and ROI modeling
• Market research
• Cross-functional leadership
• Knowledge management
• Strategic thinking