The effectiveness of Artificial Intelligence (AI)-powered features such as search, navigation, predictive offers, and shopping basket analysis ultimately depends on a high level of discipline about data and architecture. This would ensure that the data on which AI-powered algorithms operate can enable a better, higher-yield e-commerce experience.
To ensure AI tools deliver the e-commerce experience they promise, an organization must concentrate on a few significant areas. An excellent place to start would be to build the right content architecture. It means creating product taxonomies designed specifically to support the tasks often performed by site visitors. The architecture should also support a dynamically generated customer experience and enable cross-selling.
The next step would be to create rigorous rules for supplier and product onboarding. This would help in architecting product attributes in ways that are most useful for site customization and enabling customers to make choices. The subsequent step would be to define a workflow for content ingestion and automated content tagging. An extension of this step would be to architect systems to manage and respect content rights and track promotions lifecycles.
The next significant step would be to manage digital assets to make retrieval and reuse easier through an appropriate content architecture. Refining a personalization strategy, optimizing the omnichannel experience, and embedding analytics to monitor site effectiveness would be the other significant steps.
Progress on these foundational elements, especially the information architecture, will determine the site’s AI readiness. Auditing the progress and putting plans in place to improve will help ensure that the site can effectively use the future developments in AI as it evolves.
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