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. 

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TL;DR:

• Train your AI on your brand voice. A clear voice guide that covers tone, style, and formality helps your AI sound more natural and aligned with your brand.
• Add short delays before AI responds. A one- or two-second pause can make AI responses seem more thoughtful.
• Avoid generic phrases. Swap out formal responses for on-brand language that sounds like a real person on your team.
• Mention customer context in replies. Referencing order history or previous conversations makes AI sound more human and builds trust.
• Balance automation with human support. Let customers know when they are speaking to AI and escalate to a human when needed to avoid frustration.

Your AI sounds like a robot, and your customers can tell.

Sure, the answer is right, but something feels off. The tone of voice is stiff. The phrases are predictable and generic. At most, it sounds copy-pasted. This may not be a big deal from your side of support. In reality, it’s costing you more than you think.

Recent data shows that 45% of U.S. adults find customer service chatbots unfavorable, up from 43% in 2022. As awareness of chatbots has increased, so have negative opinions of them. Only 19% of people say chatbots are helpful or beneficial in addressing their queries. The gap isn't just about capability. It's about trust. When AI sounds impersonal, customers disengage or leave frustrated.

Luckily, you don't need to choose between automation and the human touch. 

In this guide, we'll show you six practical ways to train your AI to sound natural, build trust, and deliver the kind of support your customers actually like.

1. Train your AI on your brand voice

The fastest way to make your AI sound more human is to teach it to sound like you. AI is only as good as the input you give it, so the more detailed your brand voice training, the more natural and on-brand your responses will be.

Start by building a brand voice guide. It doesn't need to be complicated, but it should clearly define how your brand communicates with customers. At minimum, include:

• Tone: Is your brand warm and empathetic? Confident and cheeky? Straightforward and helpful?‍
• Style: How does your brand write? What is your personality? Short or long sentences, contractions or not, punctuation choices, and overall rhythm.‍
• Formality: Do you use slang? Emojis? Address customers as “you,” “y’all,” or something else?‍
• Friendliness: How personable should your AI sound? Is it playful, or should responses stay neutral and professional?

Think of your AI as a character. Samantha Gagliardi, Associate Director of Customer Experience at Rhoback, described their approach as building an AI persona:

"I kind of treat it like breaking down an actor. I used to sing and perform for a living — how would I break down the character of Rhoback? How does Rhoback speak? What age are they? What makes the most sense?" 

Next step

✅ Create a brand voice guide with tone, style, formality, and example phrases.

2. Delay responses to mimic human behavior

Humans associate short pauses with thinking, so when your AI responds too quickly, it instantly feels unnatural.

Adding small delays helps your AI feel more like a real teammate.

Where to add response delays:

• Before sharing info that would realistically take a moment to look up, e.g., order history
• Before confirming an action like issuing a refund or applying a discount
• Transitioning or escalating between steps or agents
• Emotional messages, like customer complaints and product quality issues

Even a one- to two-second pause can make a big difference in a robotic or human-sounding AI.

Next step

✅ Add instructions in your AI’s knowledge base to include short response delays during key moments.

3. Avoid generic phrasing and canned language

Generic phrases make your AI sound like... well, AI. Customers can spot a copy-pasted response immediately — especially when it's overly formal.

That doesn't mean you need to be extremely casual. It means being true to your brand. Whether your voice is professional or conversational, the goal is the same: sound like a real person on your team.

Here's how to replace robotic phrasing with more brand-aligned responses:

Next step

✅ Identify your five most common inquiries and give your AI a rewritten example response for each.

4. Use context to inform answers

One of the biggest tells that a response is AI-generated? It ignores what's already happened.

When your AI doesn't reference order history or past conversations, customers are forced to repeat themselves. Repetition can lead to frustration and can quickly turn a good customer experience into a bad one.

Great AI uses context to craft replies that feel personalized and genuinely helpful.

Here's what good context looks like in AI responses:

• Order awareness: The AI knows the customer placed an order yesterday and provides an accurate delivery estimate without asking for the order number again.
• ‍Conversation continuity: If the customer reached out earlier that week from a different support channel, the AI references that interaction or picks up where things left off.‍
• Customer type: First-time shopper? VIP? The AI adjusts tone and detail level accordingly.

Tools like Gorgias AI Agent automatically pull in customer and order data, so replies feel human and contextual without sacrificing speed.

Next step

✅ Add instructions that prompt your AI to reference order details and/or past conversations in its replies, so customers feel acknowledged.

5. Balance automation with human handoff

Customers just want help. They don't care whether it comes from a human or AI, as long as it's the right help. But if you try to trick them, it backfires fast. AI that pretend to be human often give customers the runaround, especially when the issue is complex or emotional.

A better approach is to be transparent. Solve what you can, and hand off anything else to an agent as needed.

When to disclose that the customer is talking to AI:

• You can disclose it at the start of the conversation, or include a disclaimer in your chat widget, contact page, or help center to let customers know AI may assist
• When the customer asks to speak to a human or expresses frustration
• If the AI cannot fulfill the request and needs to escalate
• Anytime the AI is making decisions, like issuing refunds or processing cancellations
• When transitioning from AI to a human agent

For more on this topic, check out our article: Should You Tell Customers They're Talking to AI?

Next step

✅ Set clear rules for when your AI should escalate to a human and include handoff messaging that sets expectations and preserves context.

6. Add intentional imperfections to sound human

We're giving you permission to break the rules a little bit. The most human-sounding AI doesn't follow perfect grammar or structure. It reflects the messiness of real dialogue.

People don't speak in flawless sentences every time. We pause, rephrase, cut ourselves off, and throw in the occasional emoji or "uh." When AI has an unpredictable cadence, it feels more relatable and, in turn, more human.

What an imperfect AI could look like: 

• Vary sentence length and structure. Some short and choppy, others long. 
• Add subtle grammatical “mistakes” like sentence fragments or informal punctuation. 
• Mix in casual phrasing or idioms where appropriate. 
• Avoid mechanical-sounding transitions. 
• Occasionally use filler phrases like "kinda," "just checking," or "I think."

These imperfections give your AI a more believable voice.

Next step

✅ Add instructions for your AI that permit variation in grammar, tone, and sentence structure to mimic real human speech.

Natural-sounding AI is easier to set up than you think

Human-sounding AI doesn’t require complex prompts or endless fine-tuning. With the right voice guidelines, small tone adjustments, and a few smart instructions, your AI can sound like a real part of your team.

Book a demo of Gorgias AI Agent and see for yourself.

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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. 

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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 →