Why brands should own the AI shopping assistant experience

AI is quickly changing how consumers discover and choose products online. Instead of browsing endless product lists, shoppers are increasingly turning to conversational interfaces to ask a simple question

“Which product should I buy?

This shift is driving the rise of AI shopping assistants—tools designed to guide customers through product decisions in real time.

But as conversational commerce grows, brands face an important strategic decision. Should they rely on third-party AI platforms to guide customers, or should they build their own shopping assistant experience?

In a recent interview with CX Drive, Zoovu CEO Jonathan Taylor shared his perspective on why brands should take ownership of this new interface—and how it can transform product discovery. Taylor has lead Zoovu to be on the cutting edge of gen AI with AI shopping assistants deployed to millions of customers through sites like Microsoft, Bosch, and many others.

The buying journey is moving into conversational commerce

Consumers are increasingly comfortable interacting with brands through conversational interfaces.

Instead of navigating filters, reading dozens of product descriptions, or comparing specifications across multiple tabs, shoppers are asking AI assistants for direct guidance.

This trend is accelerating the growth of conversational commerce, where AI-powered assistants help customers evaluate products, compare options, and make confident purchasing decisions.

According to Taylor, brands have two choices:

1. Own the shopping assistant experience
2. Or allow third-party platforms to mediate product discover

When brands build their own AI shopping assistant, they retain control over the customer experience, product recommendations, and first-party data.

Rather than receiving generic AI answers, customers get accurate, brand-grounded product guidance based on real product data.

This approach can reduce choice overload while improving both conversion rates and average order value.

The benefits of AI shopping assistants

When implemented correctly, AI shopping assistants can significantly improve both customer experience and business outcomes.

A well-designed AI product advisor helps customers move from browsing to confident purchasing decisions by providing personalized product guidance.

Brands often see benefits such as:

• Higher conversion rates
• Fewer abandoned shopping sessions
• Faster purchase decisions
• Reduced product returns
• Fewer customer service requests

Shopping assistants also generate valuable first-party intent data.

By analyzing customer conversations, brands can learn what customers care about, which product attributes matter most, and where shoppers get stuck during the buying journey.

This insight can improve everything from product content to merchandising strategy.

The challenges brands must solve first

Despite the promise of conversational commerce, building a successful AI shopping assistant requires strong foundations.

The most important factor is product data quality.

If product attributes, compatibility information, or policies are incomplete or inconsistent, AI recommendations will quickly become unreliable.

Taylor emphasizes that brands must also address governance and integration challenges, including:

• Ensuring answers are grounded in trusted product data
• Maintaining consistent brand voice and messaging
• Integrating catalog, inventory, and search systems
• Preventing hallucinated responses through guardrail

Without clean product data and clear governance, even advanced AI assistants can undermine customer trust. According to Taylor, it’s the most common reason why AI assistant programs fail. AI needs structured, enriched product data to give helpful, accurate answers to customer questions.

Which brands benefit most from AI product advisors?

AI shopping assistants are particularly effective in industries where customers need help navigating complex product choices.

Brands with large catalogs, many variants, or technical tradeoffs often see the greatest impact.

Examples include:

• Consumer electronics
• Appliances
• Home improvement products
• Beauty and skincare
• Specialty retail
• Industrial manufacturing
• Products with compatibility requirements

In these categories, customers frequently need guidance to understand feature differences, product fit, or compatibility before making a purchase.

However, complexity is not limited to large catalogs.

Even brands with fewer SKUs can benefit when product differences are subtle or when customers need reassurance before committing to a high-consideration purchase.

In these cases, an AI product recommendation assistant can act as a digital expert that helps customers make the right choice.

How AI shopping assistants fit into modern ecommerce architecture

From a technology perspective, a shopping assistant sits within the experience layer of a modern composable commerce architecture.

Rather than functioning as a simple chatbot, it orchestrates information across multiple systems in the ecommerce stack.

These systems typically include:

• Product information management (PIM) platforms
• Search and recommendation engines
• CMS and product content repositories
• CRM or CDP systems
• Analytics and experimentation tools
• Customer service platforms

At the core of this architecture is a grounded AI approach, where answers are generated using trusted product data, manuals, policies, and approved content sources.

This ensures that product recommendations remain accurate, explainable, and aligned with the brand.

Should brands build or buy a shopping assistant?

Many organizations wonder whether they should build their own conversational AI solution or work with a vendor.According to Taylor, brands can—and often should—work with specialized partners to implement AI shopping assistants. His perspective is supported by the data. The 2025 report, MIT GenAI Divide: State of AI in Business, indicates that organizations partnering with specialized AI vendors succeed 67% of the time, while those attempting fully internal builds have a success rate of only 33%.

However, ownership remains critical.

Vendors may provide the technology platform, but brands must maintain control over:

• Product data and policies
• Brand voice and messaging
• Governance and risk decisions
• Performance metrics and KPIs

While outsourcing implementation can accelerate deployment, the brand must ultimately own the knowledge and experience delivered to customers.

Best practices for launching an AI shopping assistant

Brands should approach AI shopping assistants as an evolving product rather than a one-time implementation.

Taylor recommends starting with a focused use case where product decision complexity is high and measurable ROI is clear.

Best practices include:

• Starting with a single category or use case
• Grounding every recommendation in structured product data
• Explaining why products are recommended
• Designing smooth escalation to human agents
• Continuously improving through analytics and feedback

Over time, the shopping assistant can expand to become a core part of the brand’s omnichannel experience.

The future of product discovery

As conversational interfaces become a primary way consumers discover and evaluate products, the brands that succeed will be those that take ownership of the experience.

By grounding AI in trusted product knowledge and focusing on real customer decision-making, brands can transform browsing into confident purchasing.

In the future of ecommerce, product discovery will not just be about search—it will be about guided decisions.

And AI shopping assistants will play a central role in making that possible.

See how Zoovu helps brands deploy AI-powered product advisors that guide customers to the right products.