AI is changing how people shop. AI assistants can discover products, compare options, and complete transactions within limits set by the customer. Static journeys and fragmented data cannot keep pace. Concentrix combines real-time context, personalization, connected platforms, and secure transaction controls to create commerce experiences that respond instantly and support the wider lifecycle from discovery to loyalty.
Grow digital revenue
Improve product discovery
Increase conversion performance
Increase customer lifetime value
Prepare for agent-led commerce
01
Define how AI-assisted and agent-led shopping can improve discovery, conversion, retention, and customer value across the commerce lifecycle.
02
Implement and connect the platforms that bring products, transactions, customer data, and digital experiences together for scalable growth.
03
Boost commerce performance across discovery, purchasing, fulfillment, returns, and retention by cutting friction and driving conversion, revenue, and customer value.
04
Enable AI shopping assistants to discover products, compare options, and securely complete transactions on a customer’s behalf within defined permissions and spending limits.
Intelligent commerce is an AI-driven model that personalizes and automates the shopping journey. It uses real-time customer context and behavioral insight to tailor discovery and offers. It also enables AI assistants to search for products, compare options, and securely complete transactions within customer-defined permissions and spending limits. The wider model continues through fulfilment, service, returns, renewal, and expansion.
Ecommerce usually relies on customers browsing sites, comparing products, and completing checkout themselves. Intelligent commerce uses AI to personalize those interactions and automate more of the journey. AI assistants can act on a customer’s behalf, while connected data and platforms support fulfilment, service, returns, subscriptions, and account growth. The result is a more responsive commercial system, not simply a better online storefront.
Revenue can be lost through poor discovery, irrelevant offers, complex checkout, payment failure, limited inventory visibility, inconsistent fulfilment, difficult returns, and disconnected service. Post-purchase friction can also weaken renewal and expansion. We combine journey, behavioral, transaction, and operational data to identify where customers hesitate, abandon, or fail to receive value. This creates a clearer basis for deciding what to improve first.
A platform replacement is not always necessary. Many performance issues come from configuration, integration, content, data, or operating practices rather than the core platform itself. We assess how well the current environment supports the target journeys and business priorities. The answer may be focused optimization, new capabilities around the existing platform, or a broader modernization. Technology change should follow a clear performance need, not become the objective in its own right.
Agentic commerce allows AI assistants to act on a customer’s behalf. They can discover catalogue items, compare products and prices, recommend an option, and initiate or complete checkout. Transactions use defined permissions, tokenized credentials, and spending controls so the customer remains in control. This changes how businesses structure product data, establish identity and trust, and design journeys for both people and AI agents.
Measures should reflect the whole lifecycle rather than conversion alone. Depending on the priority, these can include qualified traffic, product discovery, basket size, checkout completion, payment success, fulfilment performance, returns, repeat purchase, renewal, and customer lifetime value. Operational measures such as cost to serve and release speed also matter. We connect these measures to the journeys and changes that influence them, making improvement easier to prioritize and prove.
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