From AI Pilots to AI Readiness: A Practical Playbook for Commerce Leaders

Artificial intelligence (AI) has moved from experimentation to a baseline expectation. Over the next few years, AI agents are likely to reshape how goods are discovered and purchased online. Preparing for this agentic shift takes a strategy that fits your operational model and includes data readiness, governance and workforce enablement. Many B2C brands are prioritizing hyper-personalized discovery, while B2B organizations are often focused on automating complex quoting and procurement workflows. 

The end goals differ, but the underlying technology requirements stay remarkably similar. AI readiness is not about deploying tools — it is about solving for a series of layered problems where the solution must work together seamlessly to obtain the best result. If your foundational product data lacks structure, nothing above that layer will function correctly.

So, what practical steps should eCommerce executives take now? This blog answers that question, drawing on the recorded panel at the Commerce Live 2026 conference in Chicago — “The AI Readiness Sprint: What B2B and B2C Leaders Must Do Right Now”. The panel brought together EPAM and other industry leaders who shared their expertise on the topic. Based on their insights as well as the industry trends we see, we'll break down the similarities and differences between B2B and B2C AI adoption and walk through a pragmatic, step-by-step sprint plan to get your data, your team and your tech stack ready for the coming wave of digital commerce

How B2B & B2C AI Adoption Strategies Compare

The path to scalable digital transformation looks different depending on your customer. 

B2C retailers tend to prioritize seamless customer engagement and relevant product discovery. For consumer brands, AI readiness is less about fixing what's broken and more about building a modern foundation that supports new customer experiences. That often means using AI to generate rich product attribution, tagging items for specific styles, occasions and constantly changing daily trends, all while maintaining a distinct voice across different labels.

B2B enterprises often face a different reality shaped by legacy systems, account-based pricing models and highly complex purchasing processes. Unlike consumer commerce, where AI is frequently used to improve discovery and engagement, B2B organizations often prioritize operational efficiency across quote-to-cash workflows, procurement processes and customer-specific pricing agreements. AI can help automate quote generation, validate product compatibility, apply negotiated contract terms, route approvals and support distributor or dealer ecosystems. 

At the same time, many B2B buyers now expect self-service experiences comparable to consumer retail while still relying on assisted selling for complex purchases. EPAM advises B2B organizations to focus on targeted, high-value use cases that reduce operational friction and improve transaction velocity rather than attempting to transform the entire customer journey at once. Waiting for perfect data is a trap. Treat existing data as an immediate opportunity for practical wins, and you'll gain momentum far faster than peers pursuing a flawless foundation.

Both sectors share a critical dependency on data quality. At the panel discussion, Ilya Antipin, Principal, Digital Solutions Technology Consulting at EPAM, emphasizes that AI agents consume tokens and data rapidly.  "In most cases, your success of AI implementation would be as good as your data quality," Antipin says.

Key Pitfalls B2B & B2C Brands Must Avoid

Don't assume AI can understand raw enterprise data. Before deploying AI agents, organizations should invest in enriching and structuring their product, customer and operational data so it is agent-consumable. AI performs best when products, services, policies and business processes are described with rich metadata, clear relationships and consistent terminology. 

The goal is not simply clean data — it's making business knowledge discoverable, contextual and actionable for both humans and AI agents. Product attributes, compatibility rules, contract terms, buying guides and operational policies should be organized in a way that allows agents to reason, retrieve and act with confidence.

eCommerce executives driving transformation often stumble over the same roadblocks. Steering clear of these can save your organization time, budget and resources.

How to Build an AI Readiness Sprint Plan

Organizations that want to stay competitive should begin taking practical steps now. Here's a five-step sprint plan to prepare your business for scalable AI integration.

Step 1: Structure Your Core Product Data

AI models need explicit details to function. Choose your top-selling products and convert unstructured marketing descriptions into highly structured data attributes. Structured data is the fuel. Pour in the right fuel, and the engine runs. Pour in sludge, and it stalls.

Step 2: Establish an AI Steering Committee

Don't let individual departments adopt AI tools in silos. Form a dedicated steering committee to govern AI usage, prioritize investment opportunities and manage risk. This committee should also define guardrails, assign owners and measure outcomes, keeping every AI initiative aligned with your broader omnichannel strategy.

Step 3: Layer Brand & Business Context Over Your Data Foundation

Once your product data is clean and structured, apply your unique brand identity. The core data foundation can stay consistent across an enterprise, but the storytelling and visual assets should remain distinct for each individual brand.

Step 4: Enable Your Workforce Immediately

Your employees need hands-on experience to understand what AI can do. At the panel, Antipin encourages teams to start learning through practice right away. "Don't wait for Monday. Open a laptop and build an AI skill," he says. Giving teams exposure to AI and experimentation with it within defined use cases, supported by feedback loops and role clarity, helps them spot practical automation opportunities they might otherwise miss.

Step 5: Select & Trust the Right Technology Partners

Building proprietary AI architecture from scratch is rarely the most efficient path. Prioritize interoperable, API-first solutions that allow your organization to test high-value use cases quickly without deepening technical debt. Just as importantly, choose implementation partners that understand both your business objectives and the operational realities of your industry.

Next Steps for AI Readiness

The most successful AI programs are tied to measurable business outcomes, not technology metrics alone. Before launching an initiative, define the specific outcomes you want to improve and establish a baseline to measure progress. Depending on your business, these metrics might include faster turnaround, higher sales conversion rates, lower customer service costs, better product data quality, accelerated onboarding or improved employee productivity. AI readiness should be evaluated not only by technical capabilities but by its ability to deliver tangible business value.

A strong partner should help identify these opportunities, prioritize high-impact use cases, establish success metrics and continuously measure results. Once organizations select a partner, they should empower this partner to guide the transformation while holding both the technology and the program accountable to clearly defined outcomes.

In the next era of commerce, the long-term advantage will likely belong to those that take pragmatic steps toward AI adoption today. You don't need a perfect tech stack to begin. You need a clear strategy, a structured approach to product data and a willingness to enable your workforce.

For more insights, watch the recording of the panel — The AI Readiness Sprint: What B2B and B2C Leaders Must Do Right Now — and other Commerce Live 2026 talks.