TL;DR:

• Implement quickly and optimize continuously. Cornbread's rollout was three phases: audit knowledge base, launch, then refine. Stacy conducts biweekly audits and provides daily AI feedback to ensure responses are accurate and on-brand.
• Simplify your knowledge base language. Before BFCM, Stacy rephrased all guidance documentation to be concise and straightforward so Shopping Assistant could deliver information quickly without confusion.
• Use proactive suggested questions. Most of Cornbread's Shopping Assistant engagement comes from Suggested Product Questions that anticipate customer needs before they even ask.
• Treat AI as another team member. Make sure the tone and language AI uses match what human agents would say to maintain consistent customer relationships.
• Free up agents for high-value work. With AI handling straightforward inquiries, Cornbread's CX team expanded into social media support, launched a retail pop-up shop, and has more time for relationship-building phone calls.

Customer education has become a critical factor in converting browsers into buyers. For wellness brands like Cornbread Hemp, where customers need to understand ingredients, dosages, and benefits before making a purchase, education has a direct impact on sales. The challenge is scaling personalized education when support teams are stretched thin, especially during peak sales periods.

Katherine Goodman, Senior Director of Customer Experience, and Stacy Williams, Senior Customer Experience Manager, explain how implementing Gorgias's AI Shopping Assistant transformed their customer education strategy into a conversion powerhouse. 

In our second AI in CX episode, we dive into how Cornbread achieved a 30% conversion rate during BFCM, saving their CX team over four days of manual work.

Top learnings from Cornbread's conversational commerce strategy

1. Customer education drives conversions in wellness

Before diving into tactics, understanding why education matters in the wellness space helps contextualize this approach.

Katherine, Senior Director of Customer Experience at Cornbread Hemp, explains:

"Wellness is a very saturated market right now. Getting to the nitty-gritty and getting to the bottom of what our product actually does for people, making sure they're educated on the differences between products to feel comfortable with what they're putting in their body."

The most common pre-purchase questions Cornbread receives center around three areas: ingredients, dosages, and specific benefits. Customers want to know which product will help with their particular symptoms. They need reassurance that they're making the right choice.

What makes this challenging: These questions require nuanced, personalized responses that consider the customer's specific needs and concerns. Traditionally, this meant every customer had to speak with a human agent, creating a bottleneck that slowed conversions and overwhelmed support teams during peak periods.

2. Shopping Assistant provides education that never sleeps

Stacy, Senior Customer Experience Manager at Cornbread, identified the game-changing impact of Shopping Assistant:

"It's had a major impact, especially during non-operating hours. Shopping Assistant is able to answer questions when our CX agents aren't available, so it continues the customer order process."

A customer lands on your site at 11 PM, has questions about dosage or ingredients, and instead of abandoning their cart or waiting until morning for a response, they get immediate, accurate answers that move them toward purchase.

The real impact happens in how the tool anticipates customer needs. Cornbread uses suggested product questions that pop up as customers browse product pages. Stacy notes:

"Most of our Shopping Assistant engagement comes from those suggested product features. It almost anticipates what the customer is asking or needing to know."

Actionable takeaway: Don't wait for customers to ask questions. Surface the most common concerns proactively. When you anticipate hesitation and address it immediately, you remove friction from the buying journey.

3. Implementation follows a clear three-phase approach

One of the biggest myths about AI is that implementation is complicated. Stacy explains how Cornbread’s rollout was a straightforward three-step process: audit your knowledge base, flip the switch, then optimize.

"It was literally the flip of a switch and just making sure that our data and information in Gorgias was up to date and accurate." 

Here's Cornbread’s three-phase approach:

1. Preparation. Before launching, Cornbread conducted a comprehensive audit of their knowledge base to ensure accuracy and completeness. This groundwork is critical because your AI is only as good as the information it has access to.
2. Launch and training. After going live, the team met weekly with their Gorgias representative for three to four weeks. They analyzed engagements, reviewed tickets, and provided extensive AI feedback to teach Shopping Assistant which responses were appropriate and how to pull from the knowledge base effectively.
3. Ongoing optimization. Now, Stacy conducts audits biweekly and continuously updates the knowledge base with new products, promotions, and internal changes. She also provides daily AI feedback, ensuring responses stay accurate and on-brand.

Actionable takeaway: Block out time for that initial knowledge base audit. Then commit to regular check-ins because your business evolves, and your AI should evolve with it.

Read more: AI in CX Webinar Recap: Turning AI Implementation into Team Alignment

4. Simple, concise language converts better

Here's something most brands miss: the way you write your knowledge base articles directly impacts conversion rates.

Before BFCM, Stacy reviewed all of Cornbread's Guidance and rephrased the language to make it easier for AI Agent to understand. 

"The language in the Guidance had to be simple, concise, very straightforward so that Shopping Assistant could deliver that information without being confused or getting too complicated," Stacy explains. When your AI can quickly parse and deliver information, customers get faster, more accurate answers. And faster answers mean more conversions.

Katherine adds another crucial element: tone consistency.

"We treat AI as another team member. Making sure that the tone and the language that AI used were very similar to the tone and the language that our human agents use was crucial in creating and maintaining a customer relationship."

As a result, customers often don't realize they're talking to AI. Some even leave reviews saying they loved chatting with "Ally" (Cornbread's AI agent name), not realizing Ally isn't human.

Actionable takeaway: Review your knowledge base with fresh eyes. Can you simplify without losing meaning? Does it sound like your brand? Would a customer be satisfied with this interaction? If not, time for a rewrite.

Read more: How to Write Guidance with the “When, If, Then” Framework

5. Black Friday results proved the strategy works under pressure

The real test of any CX strategy is how it performs under pressure. For Cornbread, Black Friday Cyber Monday 2025 proved that their conversational commerce strategy wasn't just working, it was thriving.

Over the peak season, Cornbread saw: 

• Shopping Assistant conversion rate jumped from a 20% baseline to 30% during BFCM
• First response time dropped from over two minutes in 2024 to just 21 seconds in 2025
• Attributed revenue grew by 75%
• Tickets doubled, but AI handled 400% more tickets compared to the previous year
• CSAT scores stayed exactly in line with the previous year, despite the massive volume increase

Katherine breaks down what made the difference:

"Shopping Assistant popping up, answering those questions with the correct promo information helps customers get from point A to point B before the deal ends."

During high-stakes sales events, customers are in a hurry. They're comparing options, checking out competitors, and making quick decisions. If you can't answer their questions immediately, they're gone. Shopping Assistant kept customers engaged and moving toward purchase, even when human agents were swamped.

Actionable takeaway: Peak periods require a fail-safe CX strategy. The brands that win are the ones that prepare their AI tools in advance.

6. Strategic work replaces reactive tasks

One of the most transformative impacts of conversational commerce goes beyond conversion rates. What your team can do with their newfound bandwidth matters just as much.

With AI handling straightforward inquiries, Cornbread's CX team has evolved into a strategic problem-solving team. They've expanded into social media support, provided real-time service during a retail pop-up, and have time for the high-value interactions that actually build customer relationships.

Katherine describes phone calls as their highest value touchpoint, where agents can build genuine relationships with customers. “We have an older demographic, especially with CBD. We received a lot of customer calls requesting orders and asking questions. And sometimes we end up just yapping,” Katherine shares. “I was yapping with a customer last week, and we'd been on the call for about 15 minutes. This really helps build those long-term relationships that keep customers coming back."

That's the kind of experience that builds loyalty, and becomes possible only when your team isn't stuck answering repetitive tickets.

Stacy adds that agents now focus on "higher-level tickets or customer issues that they need to resolve. AI handles straightforward things, and our agents now really are more engaged in more complicated, higher-level resolutions."

Actionable takeaway: Stop thinking about AI only as a cost-cutting tool and start seeing it as an impact multiplier. The goal is to free your team to work on conversations that actually move the needle on customer lifetime value.

7. Continuous optimization for January and beyond

Cornbread isn't resting on their BFCM success. They're already optimizing for January, traditionally the biggest month for wellness brands as customers commit to New Year's resolutions.

Their focus areas include optimizing their product quiz to provide better data to both AI and human agents, educating customers on realistic expectations with CBD use, and using Shopping Assistant to spotlight new products launching in Q1.

Build your conversational commerce strategy now

The brands winning at conversational commerce aren't the ones with the biggest budgets or the largest teams. They're the ones who understand that customer education drives conversions, and they've built systems to deliver that education at scale.

Cornbread Hemp's success comes down to three core principles: investing time upfront to train AI properly, maintaining consistent optimization, and treating AI as a team member that deserves the same attention to tone and quality as human agents.

As Katherine puts it:

"The more time that you put into training and optimizing AI, the less time you're going to have to babysit it later. Then, it's actually going to give your customers that really amazing experience."

Watch the replay of the whole conversation with Katherine and Stacy to learn how Gorgias’s Shopping Assistant helps them turn browsers into buyers. 

{{lead-magnet-1}}

TL;DR:

• If your AI is giving wrong answers or getting stuck, it’s likely due to missing or conflicting knowledge. Ensure your AI is trained with up-to-date documents and add guardrails to prevent off-topic replies.
• Loops and escalations usually mean your escalation rules aren’t specific enough. Define when AI should step in, when it should hand over, and create “escape phrases” that trigger human takeover.
• Customers still want human help. Always offer a path to a real person and make sure your agents get full conversation context when a handoff happens.
• Inconsistent tone between AI and agents can make disjointed experiences. Align your brand voice across all support channels and choose tools that let you customize AI tone.
• AI works best when its role is clearly defined. Decide which topics it can handle, train it using real conversations, and review performance regularly to fine-tune your setup.

You’ve chosen your AI tool and turned it on, hoping you won’t have to answer another WISMO question. But now you’re here. Why is AI going in circles? Why isn’t it answering simple questions? Why does it hand off every conversation to a human agent?

Conversational AI and chatbots thrive on proper training and data. Like any other team member on your customer support team, AI needs guidance. This includes knowledge documents, policies, brand voice guidelines, and escalation rules. So, if your AI has gone rogue, you may have skipped a step.

In this article, we’ll show you the top seven AI issues, why they happen, how to fix them, and the best practices for AI setup. 

{{lead-magnet-1}}

1. AI sends the wrong answer — with confidence

AI can only be as accurate as the information you feed it. If your AI is confidently giving customers incorrect answers, it likely has a gap in its knowledge or a lack of guardrails.

Insufficient knowledge can cause AI to pull context from similar topics to create an answer, while the lack of guardrails gives it the green light to compose an answer, correct or not.

How to fix it: 

• Update the AI knowledge base. Create a new document that covers the affected topic in its entirety. To ensure AI follows every step, write your instructions in a when/if/then format.
• Define topics that AI should not handle. As a preventive measure, specify the topics the AI should skip and hand over to a human agent. For example, add words such as ‘disappointed’, ‘bad’, and ‘unacceptable’ to your AI off-limit list, so that human agents automatically handle negative-intent tickets.

2. Customer is stuck in an AI loop 

This is one of the most frustrating customer service issues out there. Left unfixed, you risk losing 29% of customers. 

If your AI is putting customers through a never-ending loop, it’s time to review your knowledge docs and escalation rules.

How to fix it:

• Double-check for conflicts in knowledge. You may have provided multiple resolutions for the same issue across different knowledge sources, such as uploaded documents, website pages, and in-app instructions.
• Add “escape routes”. Choose a set of phrases that automatically escalate conversations from AI to your support team. For example, “it’s not working” or “I already tried that”.
• Set a max number of failed interactions before escalation. Opt for a one-fail-and-escalate approach for every conversation, or specify the number of failed interactions for certain topics.

3. AI escalates too quickly, even for easy questions

It can be frustrating when AI can’t do the bare minimum, like automate WISMO tickets. This issue is likely due to missing knowledge or overly broad escalation rules.

How to fix it:

• Train AI on your FAQs and common issues. Which customer questions do you repeatedly receive? Create a document that lists out every question and its answer.
• Update vague escalation rules. AI works best with specificity. For example, if you told it to escalate conversations about “returns,” it may even escalate frequently asked questions about return eligibility.

4. Customers can’t find a way to reach a human

One in two customers still prefer talking to a human to an AI, according to Katana. Limiting them to AI-only support could risk a sale or their relationship. 

The top live chat apps clearly display options to speak with AI or a human agent. If your tool doesn’t have this, refine your AI-to-human escalation rules.

How to fix it:

• Set phrases to trigger escalation. In your knowledge docs, define which phrases should tell AI to hand a conversation over to your support team. For example, “I want to talk to someone” or “Can I talk to a human?”
• Add a visible option to connect with a human. This can be a button in your chat widget, a note in your contact page, or even a link in your website footer. At minimum, give customers an easy-to-find way to reach a real person.

5. Handoff happens — but the agent gets no context

If your agents are asking customers to repeat themselves, you’ve already lost momentum. One of the fastest ways to break trust is by making someone explain their issue twice. This happens when AI escalates without passing the conversation history, customer profile, or even a summary of what’s already been attempted.

How to fix it:

• Use rules to auto-tag conversations based on AI activity. Set up logic to tag tickets when certain conditions are met — like when AI attempted a specific action, couldn't resolve the issue, or triggered escalation.
• Audit your escalated tickets. Look for patterns where context is missing, and adjust the AI-to-human transition logic accordingly.
• Use an AI platform that provides automated ticket summaries. Choose a tool like Gorgias that provides a quick overview of every ticket.

6. The tone between AI and agent is jarring

Sure, conversational AI has near-perfect grammar, but if its tone is entirely different from your agents’, customers can be put off.

This mismatch usually comes from not settling on an official customer support tone of voice. AI might be pulling from marketing copy. Agents might be winging it. Either way, inconsistency breaks the flow.

How to fix it:

• Create shared brand voice guidelines. Align tone, formality, and language rules across both AI scripts and agent responses.
• Define emojis and punctuation use. A consistent visual style helps conversations feel smoother and more human.
• Use AI tools that allow tone control. Choose platforms that let you customize the voice and personality of your AI to match your brand.
• Train your agents with examples of ideal tone. Give your team brand voice examples of how conversations should continue when handed off.

7. You haven’t defined what AI should actually handle

When AI is underperforming, the problem isn’t always the tool. Many teams launch AI without ever mapping out what it's actually supposed to do. So it tries to do everything (and fails), or it does nothing at all.

It’s important to remember that support automation isn’t “set it and forget it.” It needs to know its playing field and boundaries.

How to fix it:

How to set up AI that actually works

Once you’ve addressed the obvious issues, it’s important to build a setup that works reliably. These best practices will help your AI deliver consistently helpful support.

1. Define clear AI boundaries

Start by deciding what AI should and shouldn’t handle. Let it take care of repetitive tasks like order tracking, return policies, and product questions. Anything complex or emotionally sensitive should go straight to your team.

2. Train it using real customer conversations

Use examples from actual tickets and messages your team handles every day. Help center articles are a good start, but real interactions are what help AI learn how customers actually ask questions.

3. Set up fallback triggers

Create rules that tell your AI when to escalate. These might include customer frustration, low confidence in the answer, or specific phrases like “talk to a person.” The goal is to avoid infinite loops and to hand things off before the experience breaks down.

4. Make sure agents receive full context

When a handoff happens, your agents should see everything the AI did. That includes the full conversation, relevant customer data, and any actions it has already attempted. This helps your team respond quickly and avoid repeating what the customer just went through. 

An easy way to keep order history, customer data, and conversation history in one place is by using a conversational commerce tool like Gorgias.

5. Keep tone and voice consistent

A jarring shift in tone between AI and agent makes the experience feel disconnected. Align aspects such as formality, punctuation, and language style so the transition from AI to human feels natural.

6. Review handoffs regularly

Look at recent escalations each week. Identify where the AI struggled or handed off too early or too late. Use those insights to improve training, adjust boundaries, and strengthen your automation flows.

If your AI chatbot isn’t working the way you expected, it’s probably not because the technology is broken. It’s because it hasn’t been given the right rules.

AI that works your way and knows when to escalate

When you set AI up with clear responsibilities, it becomes a powerful extension of your team.

Want to see what it looks like when AI is set up the right way?

Try Gorgias AI Agent. It’s conversational AI built with smart automation, clean escalations, and ecommerce data in its core — so your customers get faster answers and your agents stay focused.

Get started with Gorgias AI Agent →

TL;DR:

• Position Shopping Assistant as a revenue-driving tool. It boosts AOV, GMV, and chat conversion rates, with some brands seeing up to 97% higher AOV and 13x ROI.
• Highlight its role as a proactive sales agent, not just a support bot. It recommends products, applies discounts, and guides shoppers to checkout in real time.
• Use cross-industry case studies to make your case. Show leadership success stories from brands like Arc’teryx, bareMinerals, and TUSHY to prove impact.
• Focus on the KPIs it improves. Track AOV, GMV, chat conversion, CSAT, and resolution rate to demonstrate clear ROI.

Rising customer expectations, shoppers willing to pay a premium for convenience, and a growing lack of trust in social media channels to make purchase decisions are making it more challenging to turn a profit.  

In this emerging era, AI’s role is becoming not only more pronounced, but a necessity for brands who want to stay ahead. Tools like Gorgias Shopping Assistant can help drive measurable revenue while reducing support costs. 

For example, a brand that specializes in premium outdoor apparel implemented Shopping Assistant and saw a 2.25% uplift in GMV and 29% uplift in average order volume (AOV).

But how, among competing priorities and expenses, do you convince leadership to implement it? We’ll show you.

Why conversational AI matters for modern ecommerce

1) Meet high consumer expectations

Shoppers want on-demand help in real time that’s personalized across devices. 

Shopping Assistant recalls a shopper’s browsing history, like what they have clicked, viewed, and added to their cart. This allows it to make more relevant suggestions that feel personal to each customer. 

2) Keep up with market momentum

The AI ecommerce tools market was valued at $7.25 billion in 2024 and is expected to reach $21.55 billion by 2030. 

Your competitors are using conversational AI to support, sell, and retain. Shopping Assistant satisfies that need, providing upsells and recommendations rooted in real shopper behavior. 

3) Raise AOV and GMV

Conversational AI has real revenue implications, impacting customer retention, average order value (AOV), conversion rates, and gross market value (GMV). 

For example, a leading nutrition brand saw a GMV uplift of over 1%, an increase in AOV of over 16%, and a chat conversion rate of over 15% after implementing Shopping Assistant.

Overall, Shopping Assistant drives higher engagement and more revenue per visitor, sometimes surpassing 50% and 20%, respectively.

How to show the business impact & ROI of Shopping Assistant

1) Pitch its core capabilities

Shopping Assistant engages, personalizes, recommends, and converts. It provides proactive recommendations, smart upsells, dynamic discounts, and is highly personalized, all helping to guide shoppers to checkout. 

Success spotlight

After implementing Shopping Assistant, leading ecommerce brands saw real results:

2) Position it as a revenue driver

Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.  

Success spotlight

“It’s been awesome to see Shopping Assistant guide customers through our technical product range without any human input. It’s a much smoother journey for the shopper,” says Nathan Larner, Customer Experience Advisor for Arc’teryx. 

For Arc’teryx, that smoother customer journey translated into sales. The brand saw a 75% increase in conversion rate (from 4% to 7%) and 3.7% of overall revenue influenced by Shopping Assistant. 

3) Show its efficiency and cost savings

Because it follows shoppers’ live journey during each session on your website, Shopping Assistant catches shoppers in the moment. It answers questions or concerns that might normally halt a purchase, gets strategic with discounting (based on rules you set), and upsells. 

The overall ROI can be significant. For example, bareMinerals saw an 8.83x return on investment.  

Success spotlight

"The real-time Shopify integration was essential as we needed to ensure that product recommendations were relevant and displayed accurate inventory,” says Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations, UK at bareMinerals. 

“Avoiding customer frustration from out-of-stock recommendations was non-negotiable, especially in beauty, where shade availability is crucial to customer trust and satisfaction. This approach has led to increased CSAT on AI converted tickets."

4) Present the metrics it can impact

Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.  

Success spotlight

For Caitlyn Minimalist, those metrics were an 11.3% uplift in AOV, an 18% click through rate for product recommendations, and a 50% sales lift versus human-only chats. 

"Shopping Assistant has become an intuitive extension of our team, offering product guidance that feels personal and intentional,” says Anthony Ponce, its Head of Customer Experience.

5) Highlight its helpfulness as a sales agent 

Support agents have limited time to assist customers as it is, so taking advantage of sales opportunities can be difficult. Shopping Assistant takes over that role, removing obstacles for purchase or clearing up the right choice among a stacked product catalog.

Success spotlight

With a product that’s not yet mainstream in the US, TUSHY leverages Shopping Assistant for product education and clarification. 

"Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” says Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY. 

“Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”

The bidet brand saw 13x return on investment after implementation, a 15% increase in chat conversion rate, and a 2x higher conversion rate for AI conversations versus human ones. 

6) Provide the KPIs you’ll track 

Customer support metrics include: 

• Resolution rate 
• CSAT score 

Revenue metrics to track include: 

• Average order value (AOV) 
• Gross market value (GMV) 
• Chat conversion rate 

Shopping Assistant: AI that understands your brand 

Shopping Assistant connects to your ecommerce platform (like Shopify), and streamlines information between your helpdesk and order data. It’s also trained on your catalog and support history. 

Allow your agents to focus on support and sell more by tackling questions that are getting in the way of sales. 

{{lead-magnet-2}}