As AI agents increasingly mediate how consumers discover, evaluate, and purchase products, the quality and structure of product data has become the single most critical lever in commerce. We help brands transform their product data foundations — from fragmented master data and legacy PIM systems to AI-ready, structured product content that agents can find, trust, and act on across every channel.

WHAT'S IN IT FOR YOU?

• Lead the product data foundation that powers Agentic Commerce transformations for leading brands.
• Work at the intersection of product experience management, AI discoverability, and digital commerce — where data quality directly determines commercial outcomes.
• Define how brands structure, enrich, and syndicate product content so AI agents can find, trust, and transact on it reliably.
• Shape Accenture's product data readiness frameworks, GEO data offerings, and AI-ready PXM accelerators.
• Partner with GEO specialists, commerce architects, and client commercial teams to build the data foundation every agentic capability depends on.

What You Would Do in This Role

As a Product Data & Agentic Commerce Manager, you will lead the product data strategy and implementation workstream within Agentic Commerce transformation engagements. You will be the bridge between what AI agents require to surface and transact on products, and what clients need to do commercially and operationally to get there.

Product Data Strategy & AI Readiness

• Lead product data readiness assessments for clients — diagnosing the gaps between current data states and the structured, machine-readable content that AI agents require to discover, recommend, and transact on products.
• Define and implement product data value chain strategies: from R&D and master data systems (SAP, Salesforce) through PXM enrichment and digital shelf management to AI-ready product feeds consumed by LLMs and agentic commerce protocols.
• Establish unified, structured product data foundations — ensuring attribute completeness, data quality, and content consistency across master data, PXM platforms, and channel-specific feed outputs.
• Translate AI discoverability requirements — LLM citation signals, agentic recommendation ranking factors, protocol data specifications — into concrete product data architectures and prioritized enrichment roadmaps.
• Partner with client Chief Digital Officers, VPs of eCommerce, and category management teams to align product data strategy with commercial priorities and measurable P&L outcomes.

PXM, Product Content & Digital Shelf Excellence

• Lead PXM strategy and platform implementation across Salsify, Akeneo, and Syndigo — guiding clients from PIM-only data management to AI-ready PXM capability built for agentic commerce surfaces.
• Define product attribute schemas and content standards that serve both human-facing commerce surfaces and machine-readable AI requirements — including ingredient claims, usage occasions, consumer benefit attributes, and structured comparison data.
• Design and oversee AI-assisted product content enrichment programs — structuring and scaling enrichment across portfolios of hundreds to thousands of SKUs at commercially relevant pace.
• Architect product feed designs that support multi-channel syndication across retailer portals, marketplace platforms, LLM discovery surfaces, and emerging agentic commerce protocol integrations.
• Establish data completeness and quality measurement frameworks that directly connect product attribute quality to AI discoverability outcomes and share of voice in AI-generated answers.

GEO Data Enablement & Protocol Readiness

• Drive the product data component of Generative Engine Optimization (GEO) programs — ensuring product content is structured, verified, and authoritative enough for LLMs to confidently cite, recommend, and surface brands in AI-generated answers.
• Lead assessments of product feed readiness for emerging agentic commerce protocols (MCP, ACP, UCP) — translating protocol data requirements into actionable specifications that client technology and content teams can execute against.
• Define and implement schema markup strategies — product schema, ingredient and claims schema, review schema — that increase brand visibility and citation confidence across AI-driven discovery surfaces.
• Audit and improve third-party product content ecosystems (retailer portals, syndication networks, review platforms) to strengthen a brand's AI citation footprint beyond its owned channels.
• Advise clients on sequencing product data investment across the Now / Near / Next Agentic Commerce journey — matching data readiness investments to protocol maturity and LLM platform adoption curves.

Data Governance, Quality & Brand Safety

• Define product data governance frameworks that manage attribute completeness, content freshness, and accuracy across all AI-facing channels.
• Establish product data quality KPIs and measurement systems: tracking attribute completeness scores, AI citation rates, share of voice in LLM responses, and product-level recommendation accuracy as direct outcomes of data quality investment.
• Build and govern structured product data pipelines that ensure consistency and traceability from source systems (R&D, master data) through to the agent-facing surface.
• Implement responsible data management practices ensuring accuracy of ingredient claims, allergen data, health and beauty assertions, and regulatory compliance in AI-mediated product recommendations — understanding that an AI agent surfacing incorrect product claims carries both commercial and regulatory risk.

Consulting & Commercial Leadership

• Engage senior client stakeholders — CDOs, VPs of eCommerce, CMOs, and category directors — to articulate why product data is the foundational investment that determines Agentic Commerce success or failure, and translate that into an actionable business case.
• Develop executive presentations, value cases, and roadmaps that connect product data quality gaps to commercial risk, AI visibility loss, and competitive displacement — framing data investment in terms that drive CFO and board-level action.
• Lead the product data workstream within end-to-end Agentic Commerce programs, managing timelines, resources, stakeholder alignment, and client capability building across the engagement lifecycle.
• Mentor consultants and analysts on product data enrichment, PXM platforms, AI discoverability diagnostics, and GEO data practices — building durable team capability in a fast-evolving discipline.
• Contribute to Accenture's Agentic Commerce product data accelerators, GEO frameworks, AI-readiness audit toolkits, and go-to-market offerings.

Basic Qualifications:

• 6+ years of experience in product content management, digital commerce, or customer technology — with deep, hands-on expertise in product data strategy, enrichment, and governance at scale.
• 5+ years of hands-on platform experience with PXM and PIM systems — specifically Salsify, Akeneo, or Syndigo — including data modeling, enrichment workflow design, attribute schema definition, and multi-channel syndication configuration.

Preferred Qualifications:

• Strong working understanding of the end-to-end product data value chain: from R&D formulation and master data systems (SAP, Salesforce) through PXM enrichment and digital shelf management to AI-ready product feeds.
• Substantive industry experience — genuine understanding of category management, SKU portfolio complexity, multi-retailer data requirements, and how product data quality translates into commercial shelf and search outcomes.
• Working familiarity with AI and LLM discoverability requirements: structured product attributes, schema markup standards, machine-readable content formats, and citation signal dynamics — and how these differ from traditional SEO in what they demand from product data.
• Practical knowledge of agentic commerce protocols (MCP, ACP, UCP) from a data readiness perspective — understanding what structured product data must look like to be queryable and transactable by AI agents, without requiring hands-on protocol engineering.
• Experience leading product data quality programs: defining attribute completeness standards, building data quality measurement frameworks, and managing large-scale enrichment programs across complex SKU portfolios.
• Proven ability to translate product data quality gaps into commercial and strategic terms for client leadership — connecting data deficiencies to revenue risk, AI visibility gaps, and competitive displacement.
• Strong consulting skills: executive communication, structured storytelling, value case development, stakeholder management, and end-to-end program leadership across complex multi-stakeholder engagements.
• Experience managing cross-functional delivery teams spanning commerce, marketing, R&D, and technology — with the organizational fluency to align stakeholders who don't naturally share a common data language.
• Comfort operating in a fast-moving, ambiguous space where standards — protocols, measurement frameworks, AI platform requirements — are still forming, and the ability to make sound recommendations while those standards evolve.

Role Location Annual Salary Range
California $94,400 to $293,800
Colorado $94,400 to $253,800
Connecticut $94,400 to $253,800
District of Columbia $100,500 to $270,300
Illinois $87,400 to $253,800
Maine $80,400 to $216,200
Maryland $94,400 to $253,800
Massachusetts $94,400 to $270,300
Minnesota $94,400 to $253,800
New York $87,400 to $293,800
New Jersey $100,500 to $293,800
Ohio $87,400 to $235,000
Virginia $87,400 to $270,300
Washington $100,500 to $270,300