AI-Driven Commerce: Assisted Commerce and Agentic Commerce

Service Portfolio

AI Commerce Strategy & Roadmap

AI Commerce Strategy & Roadmap

For retailers, marketplaces and D2C manufacturers that want to approach their AI transformation in a structured way. We develop the holistic AI commerce strategy – from the opportunity map and prioritized assisted and agentic commerce levers through to an implementable roadmap with validated business cases. Output: decision-ready strategy document with concrete next steps.

AI Commerce Audit & Readiness Assessment

AI Commerce Audit & Readiness Assessment

Compact entry point for companies that want to assess their current AI status in a commerce context. Structured maturity assessment across five dimensions (AI Search & Discovery, personalization, Conversational Commerce, Agentic Readiness, data & infrastructure), identification of quick wins and strategic gaps. Delivery time: 2 weeks.

Assisted Commerce Design & Implementation

Assisted Commerce Design & Implementation

Concept development and implementation management of AI-supported commerce experiences – from AI Search and recommendation systems through Conversational Commerce to personalized advisory interfaces. We support the process from use case prioritization through to implementation management with external tech partners.

Agentic Commerce Readiness

Agentic Commerce Readiness

Strategic preparation for the era of autonomous AI purchasing agents. We analyze the exposure of your business model to the agentic shift, develop the agent readiness strategy and define concrete measures for the API layer, product data and decision logics.

Business Model Innovation for AI Commerce

Business Model Innovation for AI Commerce

Analysis of the impact of AI-driven commerce on existing revenue models, channel logics and competitive positioning – as well as development of future-proof business models that actively leverage the new rules of AI-supported gatekeeper systems, agentic buying and cross-platform visibility.

Purchase decisions are increasingly no longer made on marketplaces or in the shop – but in AI-supported interfaces. More than one third of consumers already use AI today for product search and selection (Adyen, 2025). Almost three quarters believe they make better purchasing decisions through AI-supported offerings (IFH Köln, 2025). And Amazon Rufus, the AI assistant of the world’s largest retailer, shows: users who use AI-supported advice buy significantly more often. The new competitive logic in retail is clear: Anyone who is not visible in AI interfaces, not describable in structured form and not agent-compatible loses relevance – regardless of brand awareness or traditional SEO strength. At the same time, most companies are not yet sufficiently positioned: Two thirds of retailers rate their data landscape for AI applications as insufficient (EHI Retail Institute, 2025). Only one in six retailers considers AI a strategic core component of their business model. eStrategy Consulting brings more than ten years of commerce expertise for marketplaces, D2C manufacturers and omnichannel retailers – combined with deep AI understanding across all three impact levels (operations, automation, business model innovation). We develop AI commerce strategies that work in live operation – from strategy through to implementation management.

Contact

Cliff Pfefferkorn
Partner & Managing Director

info@estrategy-consulting.de
+49 (0)30 978 936 00

Typical Questions

  • Data & decision logics: Are our product data, availability information and recommendation logics structured in such a way that AI agents and AI interfaces can work with them – or are they insufficiently prepared for machine-based analysis?

  • How is our visibility developing in AI-supported search interfaces (Google AI Overviews, Perplexity, ChatGPT Shopping)? Which categories and products are most exposed – and how do we ensure that we are considered there as a relevant source?

  • Where in our customer journey does an AI-supported advisory interface create the greatest added value – and how do we implement it with which technology and in which prioritization?

  • How exposed is our business model to the agentic shift? In which categories will AI agents first purchase independently – and are we prepared for this?

  • How do we ensure that our offerings work consistently across several AI ecosystems at the same time – instead of being optimized for a single channel?

  • Agent-ready infrastructure: Do we have the technical foundation – Schema.org markup, open APIs, headless architecture – so that external AI systems (ChatGPT, Perplexity, Google AI Overviews) and internal agents can access our data and offerings?

  • Which of our services – advisory, installation, financing, after-sales – are visible to AI systems, described in structured form and digitally bookable? Where are we losing differentiation potential because our services are not machine-readable?

  • Which AI commerce investments pay off in the short term (quick wins), which are medium-term strategic bets – and where should we deliberately not invest yet?

  • Should we build our own AI assistant or conversational commerce interface – and if so, with what scope, which technology and which business case?

  • Which existing revenue sources, channel logics and competitive advantages will come under structural pressure from AI-driven commerce – and how do we adapt our business model in time?