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Pratik Panwar
21 Oct 2025
Insights from this edition to keep you informed and ahead.
Hello — As we step into Q4, a quiet revolution is rolling through digital commerce: not just smarter chatbots, but Agentic Commerce. AI that can act, decide, and transact on behalf of your buyers. At Reveation Labs, we see this as a defining inflection point in B2B eCommerce.
This issue gives you trend data, platform updates, and a practical roadmap to start building your agentic edge this month.
Agentic commerce is rapidly becoming a pillar of next-gen B2B digital strategy.
Conversational AI is driving billions in commerce across enterprise markets, according to Mordor Intelligence.
OpenAI’s Instant Checkout signals the rise of zero-click buying experiences.
Only about 24% of U.S. adults have used ChatGPT, meaning we’re still early.
Featured articles to deepen your digital commerce knowledge
 Blog Article Buy it right here, experience ChatGPT can now recommend and sell products directly within a chat, powered by Stripe’s secure payment infrastructure. |  Blog Article Gen AI solutions for B2B Leaders The question isn't whether to implement generative AI solutions, it's how quickly you can get started and how effectively you can execute. |
Latest trends and tips to boost operational excellence
Here’s what’s fuelling boardroom conversations in Q4: Agentic AI isn’t just about automation — it’s about orchestration.
These agents don’t wait for input; they anticipate demand, run compliance checks, and even initiate RFQs in real time.
According to Forrester’s mid-2025 outlook, over 40% of enterprise commerce leaders now rank conversational/agentic intelligence as a top-five digital investment priority.
The companies seeing the highest ROI from AI agents are those with strong product data taxonomies and open integration ecosystems.
A McKinsey reports that firms with clean, structured product data achieve up to 25% faster deployment of generative AI use cases.
In manufacturing and distribution, early adopters are already embedding AI purchasing assistants directly inside procurement portals, reducing quote-to-cash times by up to 35%. That’s not speculative — it’s operational.
McKinsey’s own “Lilli”: Over 70% of employees use Lilli, an AI agent built atop decades of firm knowledge and data. It handles research, draft creation, internal knowledge queries.
Procurement use case: A case study shows AI‐powered negotiation bots reducing supplier costs by ~40%, using NLP for contract terms and predictive modeling.
Conversational commerce is projected to jump from ~$11.0 billion in 2024 to ~$12.94 billion in 2025 (17.2% CAGR), and long term scale to ~$32.67 billion by 2035 (CAGR ~14.8%).
The broader Conversational AI market (which underpins agentic systems) is expected to surge from ~$11.58 billion in 2024 to ~$41.39 billion by 2030 (CAGR ~23.7%).
Key updates from top B2B eCommerce platforms globally
Shopify & ChatGPT / Instant Checkout — Shopify is preparing integration paths, meaning smaller B2B suppliers may become reachable via AI agents.
Search & Discovery vendors like Constructor are now rated by Forrester for agentic/discovery integration in Q3 2025.
OpenAI APIs and agentic orchestration tools are being embedded into headless stacks — watch platforms (e.g. commercetools, Elastic) for announced support
What this means: The platform layer is shifting. You’ll want your commerce stack ready for conversational + agentic plug-ins.
What you can do right away!
Inventory your product attributes, relational datasets, and APIs. Agentic Commerce flow need clean, comprehensive data to reason about product logic.
Pick a low-risk transaction flow (e.g. reorder, bulk mapping, replenishment) and build a conversational + Agentic AI. Validate with internal users first.
Embed a chat / agent shell, and connect it to your systems (ERP, CRM) so that the AI can “act” vs just “talk.”
Track intent capture, drop-off points in conversational flow, “agent suggestions accepted vs rejected,” and velocity of automated vs manual fallback.
Agentic Commerce doesn’t mean perfection. Ensure your system has safety nets — model-to-human handoffs, audits, feedback loops.
