How and Why to Adopt an AI-First Ecommerce Business Model

As an ecommerce merchant, do you use an AI-powered software solution or have you adopted an AI-first business model? The former doesn’t necessarily equal the latter.

Becoming an AI-first ecommerce business requires a shift in mindset as well as strategy. It means considering AI solutions first to improve operational efficiency, reduce human error, and remove friction from the customer experience.

Adopting an AI-first model can help protect you from the economic headwinds facing merchants today and into the future.

According to CommerceIQ’s 2024 State of Retail Ecommerce report, surveyed merchants reported “a 7.4% decrease in gross margins” in Q1 2024 compared to Q1 2023. The report indicates this decrease was due to “rising Costs of Goods Sold (COGS) and increased discounting, alongside a $0.40 drop in return on advertising spend (ROAS) year-over-year.”

An AI ecommerce business model can offset rising COGS and declining ROAS. Keep reading to learn the building blocks of an AI-first mindset, how it boosts customer satisfaction, and how to start implementing it.

AI-first vs automation first

Automation is a result of artificial intelligence, but not all automation is AI-driven. 

Suppose online shoppers type “file a claim” into a chatbot. The chatbot then links them to the claim-filing web portal. That’s automation. If A, then B.

But say the customer has already filed a claim and received no resolution. Simply flipping the customer to the web portal isn’t a good solution. With AI technology, the chatbot experience could look this way:

• If the customer uses negative language, AI-powered chatbots can recognize this negativity with sentiment analysis. The system knows not to treat this interaction lightly, or the customer might churn.
• If the customer enters their email address at the start of the chat, the chatbot can associate that email with the customer’s purchase history. If the customer has a high lifetime value (LTV), the bot can flip to live chat. A human can likely resolve the issue quickly.
• But what if somebody used that email address recently in a fraudulent transaction? AI fraud detection can block the request or escalate it for manual review.

AI makes automation more intelligent, allowing it to handle complex issues. Over time, AI “gets smarter” as it absorbs new data, giving it a more human-like way of interacting.

Subsets of AI suitable for ecommerce businesses

Artificial intelligence is a blanket term for several subsets of computer-driven decision making. As an ecommerce merchant, two of these subsets will play the most significant roles in your AI-first business model.

Machine learning (ML)

ML looks for patterns in vast warehouses of historical data. These patterns help AI systems understand and predict human behavior. ML helps ecommerce merchants deliver personalized shopping experiences to customers who now expect them.

Natural language processing (NLP)

NLP is an AI subset that seeks to understand the meaning and tone of human language. This subset powers automated chatbots, product searches, and certain SMS and email communications. NLP removes the need for human intervention in many (but not all) customer interactions.

NLP increasingly relies on ML to understand the context and sentiment of words.

Building blocks of an AI-first ecommerce model

AI in ecommerce has moved well beyond personalized product recommendations — as important as these features are. It can now impact practically all business-critical functions, especially the ones below. This understanding is the backbone of an AI-first mindset.

Inventory and supply chain management

AI-driven logistics platforms can make real-time supply-chain adjustments based on forecasted consumer demand. From automatically shifting to a more reliable supplier before a product release to the demand forecasting itself, AI can minimize both excess inventory and stockouts.

Excess inventory equals higher warehousing costs. Stock outs equal disappointed customers who will look to a competitor to find what they need.

Suppose your ecommerce business has scaled to a point where you need to outsource fulfillment. 3PL ShipBob uses an AI-powered Decision Engine trained on demand-forecasting algorithms and past sales of specific SKUs. For example, if the Decision Engine determines demand for your products will spike on the East Coast, ShipBob will shift inventory from a Texas distribution center to one in New Jersey. 

ShipBob client Semaine Health saw transit time drop “by a third.” In terms of efficiency, the company’s co-founder and chief science officer reported “reduced fulfillment costs by over $2 per order compared to our prior fulfillment partner.”

Product development and sourcing

AI can quickly analyze customer conversations and market trends. This feature allows you to get a head start on designing, sourcing, and marketing products to an eager consumer base. This building block is especially critical if you operate in a highly competitive retail segment like fashion or cosmetics. 

Imagine a group of fashion influencers chatting on Instagram about a feature they would love to see on a handbag. Hootsuite’s Talkwalker AI product can surface this conversation and determine whether the influencers are chatting positively or negatively about the handbag feature. You can also learn if your competitors have caught wind of this conversation.

If the sentiment is positive and your competition silent, your brand has an advantage. Talkwalker monitors 30 social media networks and 150 million websites across 239 countries and regions. The system scans blogs, forums, news, and review sites.

Another AI-powered tool, Veridion, can help you find the ideal supplier(s) for this handbag. Veridion’s AI algorithms have been trained on millions of supplier-related data points, including: 

• Supplier location.
• Company size.
• Product offerings.
• Whether the supplier is Environmental, Social, and Governance (ESG) compliant.

Veridion surfaces suppliers based on your inputs, saving you hours of research. This time savings further enhances your ability to gain a competitive edge.

Customer experience and interaction

Customers want everything to happen faster, from finding the perfect product to filing a claim for a lost or stolen package. In a 2024 study, CX expert Shep Hyken learned 60% of surveyed consumers would likely switch to a competitor due to “poor response time.” AI systems help you respond immediately, leading to higher customer loyalty.

AI systems can listen to and learn from customers, leading to more hyper-personalized interactions over time. The greater the personalization and responsiveness, the more reliable the self-service will be.

Once you train your Gorgias-powered AI agent on your brand voice, policies, and products, then add some behavioral guidelines, your customers’ self-service experience can have these features:

• Personalized responses in real time over chat, email, and SMS.
• Instant product recommendations based on customer inputs.
• Repeat customer queries become AI-recommended FAQ entries.
• An automatic flip to a human agent after a certain number of conversation cycles fails to resolve the issue.

Once the self-service experience ends, Gorgias also allows customers to rate its quality. Poorly rated AI-driven interactions get flagged so you can better train the algorithm.

Benefits of an AI-first model

The “smart automation” an AI-first model brings to the building blocks of ecommerce enables other key business benefits, as well.

Hyper-efficiency and scalability

Before artificial intelligence, scaling a business could take years. Owners needed the time to hire and onboard personnel, buy new equipment, and move to or build larger offices. Because AI reduces manual intervention, businesses can scale faster and operate with fewer resources. You get more done for less.

AI systems operate 24/7 and continuously adapt to new situations. These features become especially necessary when rapid growth makes your business a more likely target for fraud. AI can scale fraud prevention as efficiently as it scales customer support and product sourcing.

According to Appriss Retail, an estimated 15% of retail returns in 2024 were fraudulent. That includes common scams like returning empty boxes or boxes filled with bricks, and requesting refunds for products that never get sent back.

To combat this level of retail return fraud, brands are forced to review returns manually, requiring suspected fraudsters to pay for return shipping. 

Sure, this can reduce fraud, but the manual-review process will likely cause personnel costs to skyrocket. And if legitimate customers are asked accidentally to pay for return shipping, those customers are likely to switch brands.

One possible solution? Deploy AI-powered fraud-prevention systems trained to recognize fraudulent online behavior patterns. The software can group similar patterns into “user identities,” then flag returns from shoppers matching those identities. Gone is the need to review as many returns manually, and more legitimate customers are able to start returns without friction.

Unparalleled customer insights

Because AI systems can analyze vast amounts of customer data quickly, you’ll begin to unlock customer insights that humans might miss. The most significant benefit of these insights is the ability to predict customer behavior.

One of these behaviors is churn. Customer acquisition costs (CAC) average $377 for electronics retailers, so reducing churn means a greater return on investment.

Customer automation platform Klaviyo uses machine learning to predict when a customer will likely churn. 

Klaviyo’s predictions rely on three key data points:

• Number of orders
• Time between orders
• The most recent order

Over time, Klaviyo’s AI algorithm adapts to your unique business. At Company A, for example, a customer who goes twenty days without reordering might be a greater churn risk than a customer at Company B who waits fifty days.

For AI-driven churn prevention to work, systems like Klaviyo need a significant enough sample set to learn what separates a regular customer from a soon-to-churn customer. You’ll also need to invest in a customer data platform (CDP) like Emarsys. CDPs centralize and organize data so integrated AI systems can make sense of it.

Global reach with minimal overhead

AI also makes it easier for scaling ecommerce brands to expand into international markets. With the help of AI agents, brands can “localize” visuals, language, and pricing for different regions.

Coca-Cola is working with AI provider NVIDIA and PR firm WPP to personalize advertising images across more than a hundred markets. NVIDIA’s technology works with a Shutterstock library of more than 1 million 3D objects and visual content provided by WPP.

Following written prompts describing the international market, WPP automatically pulls from this visual asset library to create AI-generated images and videos. These new assets are both brand-compliant and suitable for print and digital ads. 

Even if your planned expansion is more modest, like launching a new lipstick brand to complement your skin toner, AI can speed the research and development. Potion uses AI to help cosmetics brands conduct market research and compare chemical formulas for potential products.

How to transition to an AI-first model

While transitioning to an AI-first model is worth it in the long run, it requires planning and possibly some upfront costs. The guide below will help make the transition as smooth as possible.

#1: Address data dependency and privacy concerns.

Because an AI-first ecommerce business model depends on customer data for its predictions and automations, the issue of privacy is inescapable.

Shep Hyken’s 2024 study also found that “75% [of surveyed consumers] are concerned about the privacy and security of their data when interacting with AI-based customer service technologies.”

The best way to offset these concerns and build trust as you move toward an AI-first model is to disclose how your AI systems use customer data.

As far back as 2023, Walmart released a “Responsible AI Pledge.” Among other commitments, Walmart promised to evaluate “AI systems so that the sensitive or confidential information we store is used in ways that protect privacy.” It’s smart to craft a similar statement for your business.

#2: Set a budget for software upgrades.

Many of your existing software providers have likely rolled out AI features. Some charge more for them while others include them at no additional cost. Shopify includes access to its AI “Magic” tools in each plan, while Hootsuite’s AI-powered Talkwater social listening tool is a paid add-on.

More specialized AI tools like BCG X’s Retail AI suite require a demo and consultation before they disclose pricing. 

These specialized AI tools often rely on data from multiple sources. You may also need to invest in a customer data platform that centralizes and stores data in formats AI systems can use.

#3: Hire temporary or permanent AI specialists.

Over time, an AI-first model reduces the need to hire widely as your business scales. But you still may need to hire a consultant to monitor your AI-powered systems. The consultant may need to fine-tune these systems to make sure they continue to deliver the value you expect.

Here are some flaws a consultant should be able to reverse:

• AI outputs display cultural bias.
• CSAT scores connected to AI-backed interactions start to decline.
• The AI systems aren’t incorporating new data into their outputs.

You may even need to hire an IT consultant to ensure that your data warehouse stores data in a way that allows your AI systems to process it.

If your budget is tighter, AI consultants are available on Upwork. Many agencies now offer AI consulting services, as well.

#4: Start small.

Adopting an AI-first model can seem overwhelming, especially since you must balance the transition with the other day-to-day tasks of running an ecommerce business. The solution is to start small.

First, zero in on an area of your business where the customer experience is suffering the most. Are customers waiting too long for agents to resolve support inquiries? Then it’s worth harnessing AI to make your chatbot smarter.

Once the customer-service experience stabilizes, move on to other inefficient business operations.

Begin your adoption of an AI-first ecommerce business model today

Adopting an AI-first model takes time, but consolidating as many software tools as possible is a great way to speed up the process. This way you have fewer software platforms to integrate with specialized tools — if they align with your AI-first strategy. 

Extend’s suite of post-purchase solutions brings together everything you need to optimize operations, cut costs, and enhance the customer experience. From automated claims support and AI-powered ecommerce fraud mitigation to product and shipping protection, Extend consolidates the tools you need to create a productive and cost-effective AI-driven ecommerce business model.

To learn more about the post-purchase experience and zero in on touchpoints where AI-powered tools have the potential to boost revenue, check out this article.