🏢 Agentic Commerce

Strategy, Organization & Business Impact

The leadership perspective on AI-driven retail transformation

📋 Overview 🔧 Technical 🏢 Business

What We'll Cover

📊 Market Context & Competitive Landscape

💡 What Agentic Commerce Actually Means

🎯 Business Value & Success Metrics

🏗️ Organizational Implications

🗺️ Implementation Roadmap

⚠️ Risks & Change Management

📊

Market Context

The competitive landscape is shifting

The AI Adoption Curve

2022

ChatGPT launches
Consumer awareness

2023

Enterprise pilots
Basic chatbots

2024

Agent frameworks
Early adopters

2025-26

Competitive differentiation

We are in the window where early investment creates lasting advantage

What Competitors Are Doing

Company	Initiative	Status
Amazon	Rufus shopping assistant	Live (limited)
Walmart	Conversational commerce pilots	Testing
Instacart	ChatGPT plugin, meal planning	Live
Shopify	Sidekick AI for merchants	Live
Target	Internal AI tools	Building

Nobody has cracked it yet. The field is open.

Consumer Expectations Are Shifting

🤖

AI-Ready

81% believe AI is part of modern service¹

⚡

Instant

51% prefer bots for immediate service¹

🎯

Personal

76% expect personalization¹

60% have purchased from a brand solely based on the service they expect to receive.

¹ Zendesk CX Trends Report 2026

The Strategic Risk

If we don't build agent-ready systems:

Third-party agents become the customer interface
We become a fulfillment backend
Customer relationship shifts to intermediaries
Differentiation erodes

If we lead:

Direct customer relationship
Third-party agents use our APIs
Lock-in through personalization
Competitive moat

💡

What Agentic Commerce Means

Beyond chatbots and search

The Fundamental Shift

Today: User-Driven

👤 → 🖥️

Customer searches
Customer filters
Customer compares
Customer acts

15-30 min per order

Tomorrow: Agent-Driven

👤 → 🤖 → 🖥️

Customer states intent
Agent handles the rest
Customer approves

2-5 min per order

Chatbot vs Agent: Critical Distinction

Chatbot	Agent
Answers questions	Completes tasks
Single turn interactions	Multi-step workflows
Scripted responses	Autonomous reasoning
Reactive only	Proactive suggestions
No memory	Learns preferences

Most "AI" in retail today is chatbots. The opportunity is agents.

The Agent Capability Spectrum

Level 1: Assisted Search — Natural language queries, better recommendations

Level 2: Task Completion — "Add my usual groceries" actually works

Level 3: Proactive Commerce — "You're low on milk, want me to add it?"

Level 4: Delegated Shopping — "Handle groceries this week" (autonomous)

Level 5: Agent-to-Agent — Customer's AI talks to our AI

Each level builds on the previous. Start at 1-2, build toward 3-4.

🎯

Business Value

Metrics that matter

Primary Value Drivers

75%

Will Spend More
with good CX¹

51%

Better Retention
Customer-obsessed orgs²

41%

Faster Revenue Growth
Customer-obsessed orgs²

71%

Improved Loyalty
Personalization leaders³

¹ Zendesk 2026 ² Forrester 2024 ³ Deloitte Digital

Success Metrics Framework

Category	Metric	Industry Benchmark
AI Effectiveness	Bot resolution (simple issues)	80% find helpful¹
AI Effectiveness	AI issue resolution (projected)	80% without human²
Customer Impact	Spend increase (good CX)	75% will spend more¹
Customer Impact	Purchase based on expected service	60% have done this¹
Retention	Churn after bad experience	50%+ switch after 1 bad exp¹
Retention	Loyalty lift (personalization)	71% more likely³

¹ Zendesk 2026 ² Zendesk CX Trendsetters ³ Deloitte Digital

ROI Model Considerations

Costs

LLM inference ($0.01-0.10/interaction)
Engineering investment
Data infrastructure
Training & change management

Returns

75% of customers spend more with good CX¹
41% faster revenue growth²
51% better customer retention²
90% report positive ROI on AI tools³

¹ Zendesk 2026 ² Forrester 2024 ³ Zendesk CX Trendsetters

🏗️

Organizational Implications

What needs to change internally

Team Structure Evolution

Today

Product teams by feature
Search team
Checkout team
Mobile team

Tomorrow (add)

Agent Platform team
Conversation Design team
AI Safety & Quality team
Cross-functional integration

Agents cut across traditional feature boundaries

New Roles & Skills Needed

🧠

Prompt Engineers

Design agent behavior

💬

Conversation Designers

Craft natural dialogues

🔍

AI Quality Analysts

Monitor & improve

🔧

ML Platform Engineers

Build infrastructure

🛡️

AI Safety Specialists

Ensure safe behavior

📊

Agent Analytics

Measure performance

API Strategy Shift

Current: APIs designed for web/mobile UI consumption

Future: APIs designed for agent consumption

UI-First API	Agent-First API
Keyword search	Natural language + context
Returns raw data	Returns data + explanations
Stateless	Session-aware
Rigid parameters	Flexible intent parsing

🗺️

Implementation Roadmap

A phased approach to agentic commerce

Phase 1: Foundation (Q1-Q2)

Deliverables

Agent-ready API layer
Basic conversational search
Customer preference capture
Internal pilot

Success Criteria

API handles 80% of queries
Employee NPS >40
Task completion >70%

Focus: Prove the concept works, learn fast

Phase 2: Limited Launch (Q3)

Deliverables

Public beta (opt-in)
Multi-step task completion
Basic personalization
Feedback capture

Success Criteria

10% try it
Task completion >80%
Basket lift >15%

Focus: Validate value with real customers

Phase 3-4: Scale & Leadership

Phase 3: Scale (Q4)

General availability
Proactive suggestions
Deep personalization
Voice integration

Phase 4: Leadership (Y2+)

Third-party agent API (MCP)
Delegated shopping
Predictive replenishment
Agent-to-agent commerce

⚠️

Risks & Change Management

What could go wrong and how to manage it

Risk Categories

Technical

Hallucinations / incorrect info
Latency / reliability
Integration complexity

Business

Low adoption
Poor experience → brand damage
ROI doesn't materialize

Legal / Compliance

Privacy concerns
Liability for agent actions
Regulatory uncertainty

Organizational

Skills gap
Change resistance
Priority competition

Mitigation Strategies

Guardrails — Limit what agents can do, require confirmation for high-risk actions

Human-in-the-Loop — Graceful escalation to humans when uncertain

Phased Rollout — Start small, learn, expand based on data

Transparency — Clear disclosure that it's AI, easy opt-out

Monitoring — Real-time quality metrics, anomaly detection

Summary

The window is now — 2025-26 is when early movers establish advantage

Agents ≠ Chatbots — This is about task completion, not Q&A

Value is measurable — Basket size, frequency, retention, efficiency

Organization must evolve — New roles, cross-functional coordination

Start now, iterate fast — Phased approach, learn in market

The Choice

Wait and See

Lower near-term investment
Learn from others' mistakes
Risk: Playing catch-up
Risk: Talent goes elsewhere

Lead

Higher near-term investment
Shape the category
Build proprietary data moat
Own customer relationship

🦞

Questions & Discussion

Technical deep-dive: technical.html

High-level overview: overview.html