How to Build an AI-enabled Amazon Strategy

NOTE: This article contains excerpts from our forthcoming whitepaper, The AI Revolution. Check back later on our Whitepapers page for the full piece.

It’s nearly impossible to avoid discussions of artificial intelligence these days. It seems like every time you look at the news or scroll social media, someone is talking about a new AI breakthrough, a new safety or privacy concern, or new AI products or services. Despite AI’s explosive growth, 45 percent of B2B firms said they still do not use AI to support their Amazon sales, according to Enceiba’s 2026 Amazon B2B Industry Pulse Report.

Despite the explosion of AI usage across the internet, many B2B firms are still hesitant to deploy AI in their sales program. And part of that hesitation is a lack of understanding how to to develop an AI-enabled Amazon strategy.

Here at Enceiba, we've been experimenting and implementing AI as part of our goal of improving operations and growing clients' Amazon revenues and profitability. Here's what we find has worked well for our Amazon consulting firm.

How to Build an AI-enabled Amazon Strategy

An AI-enabled Amazon strategy begins with integrating AI into the daily sales processes. That could include (but is not limited to) drafting content, analyzing performance signals, identifying and rectifying catalog issues, and supporting decisions across advertising, pricing, and inventory.

However, it’s not as easy as just giving staff the tools. If you want your employees to accept and use AI to contribute to sales on a daily basis, it’s essential to look at implementation as a change management project. It requires thoughtful and measured training and roll out period, or you risk spending a lot of time and money on tools that employees avoid like the plague. 

What to Build Internally

An effective AI strategy requires a foundation that prevents errors and maintains trust. AI tools can accelerate execution, but they cannot operate without human oversight. B2B sellers must validate outputs, enforce brand and compliance standards, and ensure that AI-enabled automations align with commercial priorities.

Companies can also leverage out-sourced agencies to help accelerate AI-driven processes. For example, at Enceiba, we use AI to accelerate the content creation process, and we have learned that it’s absolutely critical for humans to review the AI’s output. In the case of working with clients, this often involves the client team reviewing the content for accuracy. 

AI becomes most valuable when it augments judgment rather than replaces it. Businesses need to develop AI governance policies and content QA processes to ensure generative tools don’t inadvertently introduce inaccuracies or compliance risks, particularly for products used in B2B applications where accuracy are paramount. 

Businesses also need cross-functional workflows that connect marketing, operations, compliance, and product teams so AI-generated content is accurate and useful. Training teams to use AI responsibly, i.e. knowing when to trust it, when to override it, and how to prompt effectively, will help you create dependable operational AI capabilities.

On the tactical side, sellers need processes that ensure every listing is “AI-ready.” This includes auditing existing content for accuracy, completeness, and structured data quality so Amazon’s models can interpret and recommend products correctly. At Enceiba, for example, we request our client teams to review content prior to posting. 

From there, AI can be embedded into catalog workflows: drafting listings, generating imagery, enriching attributes, and supporting A/B testing, whether you’re using Amazon’s own AI tools or others. The goal is a repeatable system where AI accelerates work, humans refine it, and the catalog steadily improves at scale.

The Role of Specialist Partners

Specialist partners are increasingly important as Amazon’s AI ecosystem grows more complex. B2B sellers can benefit from expert guidance that helps them avoid common pitfalls, implement best practices, and build AI-enabled processes that scale across large, technical catalogs. At Enceiba, we are happy to support our clients by combining Amazon expertise with our own proven AI-driven workflows, helping sellers modernize their catalog operations, strengthen discoverability, and accelerate growth in a marketplace shaped by machine-driven decision-making.

Where to Go from Here

The key to developing an AI-enabled Amazon program is experimentation. Encourage your team to test using the tools (and there are plenty out there, including some that are built right into Amazon). But remember, testing doesn't necessarily mean success. You have to embrace failure.

That's why we recommend starting slow, don't dive headfirst into implementing an AI program without the proper guardrails and procedures. It's important to remember that many of the tools are still new, and as such, unrefined. At Enceiba, we're constantly testing new tools and looking for ways to use them to make our processes more efficient.

It's clear that AI is rapidly transforming how business gets done. If you need help growing your Amazon program or want to learn more about how to use AI to make your program more efficient, Enceiba can help! Contact us to discuss your challenges and how to develop a growth strategy for Amazon.