TL;DR:

• Most chargebacks occur due to poor merchant communication rather than fraud. Customers choose this path when they feel ignored or frustrated.
• 80% of customers report never being contacted by merchants after filing a chargeback. 23% file immediately after an issue and 38% file within 1-3 days if unresolved.
• The most common chargeback reason is "product not received" (35%). 79% of all chargebacks are actually "friendly fraud" filed for invalid claims.‍
• Prevention requires fast customer support and automated chargeback management. Combining Gorgias for AI-powered support with Chargeflow for automated dispute management provides a comprehensive solution with faster resolutions and higher win rates.

Chargebacks are more than a thorn in a merchant’s side — they’re a growing financial and operational threat. According to Ethoca, chargebacks are projected to more than double, from $7.2 billion in 2019 to $15.3 billion by 2026 in the U.S. alone. And while fraud plays a role, the primary reason customers file chargebacks is simpler: they feel ignored. 

At Chargeflow, we recently published a comprehensive report analyzing why customers dispute chargebacks. The findings were eye-opening. While it’s true that fraud is a real concern, most chargebacks happen for a different reason: a lack of communication between merchants and customers.  

Top stats from Chargeflow’s report:

• 23% of customers file a chargeback immediately after an issue.
• 38% file a chargeback within 1-3 days if unresolved.
• 80% report never being contacted by the merchant.
• 52% are likely to dispute if the response is too slow.

When customers feel ignored or frustrated, they often turn to their bank for a solution instead of reaching out to the merchant first. Understanding these behaviors is key to preventing disputes before they escalate and cause chaos. 

So, what actually drives customers to dispute charges? Here’s what the data says.

Why customers file chargebacks

While chargebacks are often the cost of doing business, the truth is that many disputes are preventable — but only if merchants understand the root causes. We identified five key drivers behind chargebacks.

1. Customers take immediate action

According to our research, most customers file a dispute right away after encountering an issue, leaving no opportunity to resolve the problem. Another 38% file within one to three days if they don’t receive a timely response. 

Why? Customers assume the fastest way to get their money back is by filing a chargeback, especially if they receive no response from the merchant.

2. Lack of communication leads to disputes

We found that 80% of customers never receive a follow-up after filing a chargeback. Additionally, 64% of customers state immediate communication is crucial, yet many businesses fail to reach out.

• 90% of customers tried to reach out to the merchant first.
• If they don’t receive a response, they quickly file a dispute. 

Why? Customers expect businesses to be proactive. When they don’t hear back quickly, they assume the merchant won’t help, making a chargeback seem like the best option.

3. Chargebacks are too easy for customers

98% of customers report a neutral to highly satisfactory experience when filing chargebacks, and only 12% are denied. 

Why? Many customers believe chargebacks are faster and easier than dealing with merchants directly, especially if return policies are unclear. 

4. Transaction issues drive chargebacks

The most common reason for filing a chargeback is “product not received” (35% of the cases). Other common reasons included:

• Fraudulent transaction claims - 16%
• Product significantly not as described - 15%
• Unauthorized transaction - 15%

Why? When customers don’t receive clear shipping updates or experience delivery delays, they assume their order won’t arrive and file a chargeback rather than waiting.

5. Friendly fraud is a major problem

Friendly fraud occurs when a cardholder makes a legitimate purchase but later disputes the charge as fraudulent or unauthorized, leading their card issuer to reverse the payment. 

Our research found that:

• 21% of customers admitted to not fully understanding the chargeback process. 
• Another 20% aren’t even aware of what a chargeback is. 
• 97% of consumers believe they’ve never filed a chargeback incorrectly, while only 3% admit they have.

According to our State of Chargebacks report, 79% of chargebacks are actually friendly fraud, meaning they were filed for invalid reasons.

Why? Many customers mistakenly believe that a chargeback is just another way to request a refund, rather than a process intended for fraud or merchant failure. 

📌 The takeaway: Most chargebacks aren’t actual fraud, but rather a result of customer confusion, impatience, or poor communication from merchants.

The solution: how to stop chargebacks before they happen

Merchants who want to stop chargebacks before they happen need a two-part strategy:

• Fast, customer-focused support to resolve issues before customers dispute charges. 
• Automated chargeback management to detect and fight disputes efficiently, so merchants don’t lose revenue to invalid claims.

Chargebacks result from slow response times, poor communication, and unresolved issues, not fraud. Adopting AI-driven customer support and chargeback automation allows businesses to significantly reduce disputes and retain more revenue. 

How AI-powered support & chargeback automation work together

Instant responses prevent frustration-driven chargebacks

Many chargebacks happen because customers don’t receive a fast enough response. In fact, 52% say they will dispute a charge if the response time is too slow. AI-powered chatbots provide real-time support, resolving issues before they escalate. 

Proactive communication reduces uncertainty

Customers expect updates regarding orders and refunds, but often don’t receive them. 80% of customers report never hearing from a merchant after filing a chargeback. 

Automated order updates, refund confirmations, and proactive notifications keep customers informed, reducing unnecessary disputes.

24/7 availability ensures no issues go unanswered

Customers expect round-the-clock support, but most businesses can’t provide live assistance. AI-powered ticketing and automation ensure every customer receives help, regardless of the time zone or urgency.

The result? Fewer chargebacks, faster resolutions, and increased customer satisfaction.

Actionable strategies for improving response times

Prioritize long-term clients

It’s impossible to please every customer. On average, chargebacks take 50 days to resolve successfully. Focus your energy on retaining high-value, long-term customers.

Prioritize high-risk inquiries

Lost inquiries take on average 15 days to resolve, and lost chargebacks take 38 days. Prioritize cases based on impact. 

Build efficient escalation systems

Advanced automated ticketing systems can route inquiries and prioritize urgent cases.

Use pre-approved resolution templates

Ensure customer service teams have quick-response templates to speed their resolutions.

Work closely with shipping carriers

“Product not received” was the most cited reason for delivery-related chargebacks. Work closely with carriers and third-party suppliers to improve fulfillment and reduce disputes.

Leverage chargeback management tools

Use automated tools for real-time analytics, enhanced communication, and proactive alerts, which will reduce response times. 

Gorgias & Chargeflow: A fully automated chargeback prevention system

Successfully tackling chargebacks requires both proactive customer support and automated dispute management. That’s why Gorgias and Chargeflow work so well together to give merchants a comprehensive defense against disputes.

Post-purchase automation isn’t just about reducing customer support workload or quick replies. It's about finding the most effective ways to increase customer loyalty and prevent disputes.

Learn more about how AI-driven automation enhances post-purchase experiences here.

How Gorgias prevents chargebacks with conversational AI

• Automated real-time responses engage customers before they decide to dispute charges.
• Proactive customer communication ensures customers receive updates on their orders, refunds, and transactions.
• 24/7 availability ensures customers receive the support they need without increasing overhead. 

How Chargeflow automates chargeback prevention & recovery

• Pre-dispute alerts notify merchants before a chargeback is finalized and provide proactive intervention.
• AI-powered chargeback responses to automate evidence collection and improve win rates. 
• Smart analytics to help merchants understand why disputes happen and how they can prevent them. 

Final thoughts: Stop chargebacks before they start

As you know, chargebacks are costly, frustrating, but most importantly, preventable. Our research shows that most chargebacks don’t stem from fraud, but from poor communication, slow response times, and customer uncertainty.

By prioritizing fast, AI-driven customer support and automated chargeback management, merchants can resolve issues before they escalate, improve customer experience, and protect their revenue. 

With Gorgias handling proactive customer support and Chargeflow managing chargeback disputes, merchants get a powerful, end-to-end prevention system that ensures fewer chargebacks, higher dispute win rates, and, at the end of the day, happier customers. 

Don’t let chargebacks drain your revenue. Take control today with faster, smarter automation.

Download Chargeflow’s full Psychology of Chargebacks Report to dive deeper into the data and start preventing disputes before they happen.

TL;DR:

• Use AI across both support and sales. Ecommerce brands are using AI to drive revenue and efficiency by combining automation in chat, email, and customer data with personalized product guidance and upsells.
• Analyze post-purchase surveys with AI to uncover customer insights. AI quickly identifies themes, sentiment, and trends from open-ended feedback to inform product, shipping, and support decisions.
• Predict customer intent with AI before they take action. By analyzing behavior like cart activity or page views, AI can engage high-intent shoppers with personalized nudges in real time.
• Automate QA and proactive support with AI. AI reviews 100% of conversations, flags quality issues, and triggers outreach for known problems — all before customers even ask.

Shoppers aren’t just open to AI — they’re starting to expect it.

According to IBM, 3 in 5 consumers want to use AI as they shop. And a McKinsey study found that 71% expect personalized experiences from the brands they buy from. When they don’t get that? Two-thirds say they’re frustrated.

But while most brands associate AI with support automation, its real power lies in something bigger: scaling personalization across the entire customer journey. 

We’ll show you how to do that in this article.

AI for customer data 

Before AI can personalize emails, recommend products, or answer support tickets, it needs one thing: good data.

That’s why one of the best places to start using AI isn’t in sales or support — but in enriching your customer data. With a deeper understanding of who your customers are, what they want, and how they behave, AI becomes a personalization engine across your entire business.

Enriching surveys with AI

Post-purchase surveys are gold mines for understanding customers — but digging through the data manually? Not so fun.

AI can help by analyzing survey responses at scale, identifying trends, and categorizing open-ended customer feedback into clear, actionable insights. Instead of skimming thousands of answers to spot what customers are saying about your shipping times, AI can surface those insights instantly — along with sentiment and behavior signals you might’ve missed.

Try this prompt when doing this: "Analyze 500 open-ended post-purchase survey responses. Identify the top 5 recurring themes, categorize customer sentiment (positive, neutral, negative), and surface any trends related to product quality, delivery experience, or customer support."

Predicting customer intent before they even say a word

One of AI’s biggest strengths? Spotting intent.

By analyzing things like page views, cart activity, scroll behavior, and previous purchases, AI can identify which shoppers are ready to buy, which ones are likely to churn, and which just need a little nudge to move forward.

This doesn’t just apply to email and retargeting. It also works on live chat, in real time.

Take TUSHY, for example.

To eliminate friction in the buying journey, TUSHY introduced AI Agent for Sales — a virtual assistant designed to guide shoppers toward the right product before they drop off. 

Instead of letting potential customers bounce with unanswered questions, the AI Agent steps in to offer:

• Personalized product recommendations based on shopper questions
• Compatibility guidance (especially for customers unsure which bidet works with their toilet)
• Real-time installation tips and links to helpful how-to articles

With a growing product catalog, TUSHY realized first-time buyers were overwhelmed with options — and needed help choosing what would work best for their home and hygiene preferences.

“What amazed us most is that the AI Agent doesn’t just help customers choose the perfect bidet for their booty — it also provides measurement and fit guidance, high-level installation support, and even recommends all the necessary spare parts for skirted toilet installations. It’s ushering in a new era of customer service — one that’s immediate, informative, and confidence-boosting as people rethink their bathroom habits.”

—Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY

Forecasting revenue by segment

AI also helps you see the road ahead.

Instead of looking at retention and loyalty metrics in isolation, AI can help you forecast what’s likely to happen next and where to focus your attention.

By segmenting customers based on behaviors like average order value, order frequency, and churn risk, AI can identify revenue opportunities and weak spots before they impact your bottom line.

All you need is the right prompt. Here’s an example you can run using your own data in any AI tool:

Prompt: “Analyze my customer data to forecast revenue by segment. Break customers into at least three groups based on behavior patterns like average order value, purchase frequency, and churn risk. 

For each segment, provide:

1. A projected revenue trend for the next quarter
2. A key insight about their behavior
3. One actionable recommendation to either grow or retain revenue from that segment.”

Here’s what a result might look like:

• VIPs (Top 5% by LTV): Predicted 15% growth next quarter based on repeat behavior
• One-time Buyers: 70% churn risk flagged—time to trigger a win-back campaign
• Discount-Only Shoppers: Revenue likely to dip unless incentive strategy changes

Instead of flying blind, you’re making decisions with clarity — and backing them with data that scales.

AI for sales 

When used strategically, AI becomes a proactive sales agent that can identify opportunities in real-time: recommending the right product to the right shopper at the right moment.

Here’s how ecommerce brands are using AI to drive revenue across every part of the funnel.

Dynamic pricing that responds to the market (and the shopper)

Your prices shouldn’t be static — especially when your competitors, inventory, and customer behavior are anything but.

AI-powered pricing tools like AI Agent for Sales help brands automatically adjust pricing based on shopper behavior. The goal is to make the right offer to the right customer.

For example:

• Show a discount to a price-sensitive shopper who’s hesitating at checkout
• Recommend premium add-ons to high-LTV customers who are more likely to spend

With dynamic pricing, you can protect your margins and boost conversions — without relying on blanket sales.

Turning chat into a personal shopper (that never sleeps)

AI-powered chat is no longer just a glorified FAQ. Today, it can act as a real-time shopping assistant — guiding customers, boosting conversions, and helping your team reclaim time.

That’s exactly what Pepper did with “Penelope,” their AI Agent built on Gorgias.

With a rapidly growing product catalog (22 new SKUs in 2024 alone), Pepper knew shoppers needed help discovering the right products. Customers often had questions about styles, materials, or sizing, and if they didn’t get answers right away, they’d abandon carts and move on.

Instead of hiring more agents to keep up, Pepper deployed Penelope to live chat and email.

Her job?

• Instantly answer questions about fit, fabric, or product differences
• Guide shoppers toward the best option for their needs
• Recommend complementary products (like matching panties or bottoms)
• Free up agents to focus on higher-value 1:1 moments, like virtual fit sessions

“With AI Agent, we’re not just putting information in our customer’s hands; we’re putting bras in their hands... We’re turning customer support from a cost center to a revenue generator.”
—Gabrielle McWhirter, CX Operations Lead at Pepper

Let’s look at how Penelope performs on the floor:

Real-time recommendations

A shopper asked about the difference between two wire-free bras. Penelope broke down the styles, support level, and fabric in plain language — then followed up with personalized suggestions based on the shopper’s preferences.

Proactive engagement

Using Gorgias Convert chat campaigns, Pepper triggers targeted messages to shoppers based on behavior. If someone is browsing white bras? Penelope jumps in and offers assistance, often leading to faster decisions and fewer abandoned carts.

Intelligent upsells

If a customer adds a swimsuit top to their cart, Penelope suggests matching bottoms. No full-screen popups, no awkward sales scripts — just thoughtful, helpful guidance.

Support and sales in one

Penelope also handles WISMO tickets and return inquiries. If a shopper is dealing with a sizing issue, Penelope walks them through the return process and links to Pepper’s Fit Guide to make sure the next purchase is spot on.

By implementing AI into chat, Pepper saw a 19% conversion rate from AI-assisted chats, an 18% uplift in AOV, and a 92.1% decrease in resolution time.

With Penelope handling repetitive and revenue-driving tasks, Pepper’s team now has more time to offer truly personalized touches — like virtual fit sessions that have turned refunds into exchanges and even upsells.

Curating bundles with AI-powered sales data

Bundling is a proven tactic for increasing AOV — but most brands still rely on subjective judgment calls or static reports to decide which products to group.

AI can take this a step further.

Instead of just looking at what’s bought together in the same cart, AI can analyze purchase sequences. For example, what people tend to buy as a follow-up 30 days after their first order. This gives you powerful clues into natural buying behavior and bundling opportunities you might’ve missed.

If you’re looking to explore this at scale, you can use anonymized sales data and feed it into AI tools to surface patterns in:

• Frequently bundled items
• Follow-up purchases within a set time frame
• High-value product pairings with repeat potential

Try this prompt:

 "Analyze this spreadsheet of order data and identify product bundle opportunities. Look for: (1) products frequently purchased together in the same order, (2) items commonly bought as a second purchase within 30 days of the first, and (3) patterns in high-value or high-frequency product pairings. Provide insights on the most promising bundles and why they might work well together."

Just make sure you’re keeping customer data anonymous — and always double-check the insights with your team.

Related: Ecommerce product categorization: How to organize your products

AI for support

AI isn’t just here to deflect tickets. From quality assurance to proactive outreach, AI can elevate the entire support experience — on both sides of the conversation.

Quality checks powered by AI

Manual QA is slow, selective, and often feels like it’s chasing the wrong tickets.

That’s where Auto QA comes in. Instead of reviewing just a handful of conversations each week, Auto QA evaluates 100% of private messages, whether they’re handled by a human or an AI agent.

Every message is scored on key metrics like:

• Resolution completeness
• Brand voice
• Empathy and tone
• Accuracy

It gives support leaders a full picture of how their team is performing, so they can coach with clarity, not just gut feeling.

Here’s what brands can do with automated QA:

• Save time by focusing only on the conversations that need attention
• Ensure consistency across agents and AI with a single scoring standard
• Improve agent performance with targeted coaching and feedback
• Deliver higher-quality support that customers actually notice

Let’s walk through a real example.

Customer: “Hi, my device broke, and I bought it less than a month ago.”

Agent: “Hi Kelly, please send us a photo or a video so we can determine the issue with your device.”

Auto QA flags this interaction with:

• Communication Score: 3/5 — The agent was clear, but could have shown more empathy in tone.
• Resolution Score: Complete — The issue was addressed effectively.

Proactive support that reaches out first

Reactive support is table stakes. AI takes it a step further by anticipating issues before they happen — and proactively helping customers.

Let’s say login errors spike after a product update. AI detects the surge and automatically triggers an email to affected customers with a simple fix. No need for them to dig through help docs or wait on chat — support meets them right where they are.

Proactive AI can also be used for:

• Order delay notifications with live tracking updates
• Subscription renewal reminders
• Back-in-stock alerts with support follow-up for next steps

This saves the time of your agents because the AI will spot problems before they turn into tickets.

Understanding sentiment at scale

Your customers are telling you what they think. AI just helps you hear it more clearly.

By analyzing reviews, support tickets, post-purchase surveys, and social comments, AI can spot sentiment trends that might otherwise fly under the radar.

For example:

• Multiple reviews mention “runs small”? AI flags it, so your team can update the product description or add a sizing chart.
• A sudden rise in “frustrated” language in support tickets? Time to check if something’s off with your shipping or product quality.

Related: 12 ways to upgrade your data and trend analysis with Ticket Fields 

Personalization at scale starts with the right AI stack

Whether you’re enriching customer data, making smarter product recommendations, triggering dynamic pricing, or proactively resolving support issues, AI gives your team the power to scale personalization without sacrificing quality.

With Gorgias, you can bring many of these use cases to life — from AI-powered chat that drives conversions to automated support that still feels human. 

And with our app store, you can tap into additional AI tools for data enrichment, direct mail, bundling insights, and more.

Personalized ecommerce doesn’t have to mean more work. With the right AI tools in your corner, it means smarter work — and better results.

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TL;DR:

AI is everywhere in customer service—powering live chats, drafting responses, and handling inquiries faster than ever. 

But as AI takes on more of the customer experience, one question keeps coming up: Should brands tell customers when they’re talking to AI?

Legally, the answer depends on where you operate. Ethically? That’s where things get interesting. Some argue that transparency builds trust. Others worry it might undermine confidence in support interactions. 

So, what’s the right move?

This guide breaks down the debate and gives CX leaders a framework to decide when (and how) to disclose AI—so you can strike the right balance between innovation and trust.

The legal landscape: What are the disclosure requirements?

Depending on where your business operates, disclosure laws may be strict, vague, or nonexistent. Some laws, such as the California Bolstering Online Transparency Act, prohibit misleading consumers about the use of automated artificial identities.

For maximum legal protection, it’s best to proactively disclose AI use—even when not explicitly required. 

A simple disclaimer can go a long way in avoiding legal headaches down the line. Here’s how to disclose AI use in customer interactions:

• In email: Use your email signature to indicate that AI has assisted in generating the response.
• In chat: Update your Privacy Policy to clarify when AI is involved in customer interactions.

Truthfully, AI laws are evolving fast. That’s why we recommend consulting legal counsel to ensure your disclosure practices align with the latest requirements in your region.

But beyond avoiding legal trouble, transparency around AI usage can reinforce customer trust. If customers feel deceived, they may question the reliability of your brand, even if the AI delivers great service.

Related reading: How AI Agent works & gathers data

How does disclosure impact trust and satisfaction?

Research shows that 85% of consumers want companies to share AI assurance practices before bringing AI-driven products and experiences to market.

But what does “transparency” actually mean in this context? An article in Forbes broke it down, explaining that customers expect three key things:

1. Clear disclosure: They want to know when AI is (and isn’t) used in customer interactions.
2. Simple, non-technical language: AI disclosures shouldn’t feel like reading a terms-of-service agreement. Keep it digestible.
3. Easy-to-find information: AI disclosures should be visible—not buried in fine print. A chatbot notification, a banner on your site, or a brief message before an AI-powered chat begins can make a big difference.

How you disclose AI matters just as much as whether you disclose it. At the end of the day, AI isn’t inherently good or bad—it’s all about how it’s implemented and trained. 

The business perspective: Risks and benefits of AI transparency

The way a brand approaches AI disclosure can impact trust, satisfaction, and even conversion rates—making it a decision that goes beyond simple legal requirements.

While some customers appreciate honesty, others may hesitate if they prefer human support. Brands must weigh the pros and cons to determine the best approach for their audience.

Risks of disclosure

Let’s be honest: AI in customer service still carries baggage. While some consumers embrace AI-driven support, others hear "AI" and immediately picture frustrating, robotic chatbots that can’t understand their questions.

This is one of the biggest risks of transparency: customers who’ve had bad AI experiences in the past may assume the worst and disengage the moment they realize they’re not speaking to a human.

For brands that thrive on personal connection and high-touch service, openly stating that AI is involved could create skepticism or drop-off rates before customers even give it a chance.

Another challenge? The perception gap. 

Even if AI is handling inquiries smoothly, some customers may assume it lacks the empathy, nuance, or problem-solving skills of a live agent. Certain industries may find that transparency about AI use leads to more escalations, not fewer, simply because customers expect a human touch.

Benefits of disclosure

Despite the risks, transparency about AI can actually be a trust-building strategy when handled correctly.

Customers who value openness and ethical business practices tend to appreciate brands that don’t try to disguise AI as a human. 

Being upfront also manages expectations. If a customer knows they’re speaking to AI, they’re less likely to feel misled or frustrated if they encounter a limitation. Instead of feeling like they were "tricked" into thinking they were talking to a human, they enter the conversation with the right mindset—often leading to higher satisfaction rates.

And then there’s the long-term brand impact. 

If customers eventually realize (through phrasing, tone, or inconsistencies) that they weren’t speaking with a human when they thought they were, it can erode trust. 

Deception—whether intentional or not—can backfire. Proactively disclosing AI use prevents backlash and reinforces credibility, especially as AI becomes a bigger part of the customer experience.

Example: How Arcade Belts used AI transparency without losing the human touch

Arcade Belts, known for its high-quality belts, wanted to improve efficiency without compromising customer experience. By implementing Gorgias Automate, they reduced their reliance on manual support, creating self-service flows to handle common inquiries.

Initially, automation helped manage routine questions, such as product recommendations and shipping policies. But when they integrated AI Agent, they cut their ticket volume in half. 

The transition was so seamless that customers often couldn’t tell they were interacting with AI. “Getting tickets down to just a handful a day has been awesome,” shares Grant, Ecommerce Coordinator at Arcade Belts. ”A lot of times, I'll receive the response, ‘Wow, I didn't know that was AI.”

You can read more about how they’re using AI Agent here.

Decision-making framework: Should you disclose AI?

We mentioned it earlier, but deciding whether or not to disclose your use of AI in customer support depends on compliance, customer expectations, and business goals. That said, this four-part framework helps CX leaders evaluate the right approach for their brand:

Step 1: Assess legal requirements

Before making any decisions, ensure your brand is compliant with AI transparency regulations.

• Research regional laws governing AI disclosure, as requirements vary by jurisdiction.
• Consult legal counsel to confirm whether your AI usage falls under any mandated disclosure policies.
• Stay informed on evolving AI governance frameworks that could introduce new compliance obligations.

Step 2: Review customer expectations and brand positioning

AI transparency should align with your brand’s values and customer experience strategy.

• Consider whether transparency supports your brand’s messaging—does your audience expect openness, or do they prioritize seamless interactions?
• Analyze customer sentiment through surveys and engagement data to determine if they prefer knowing when they’re speaking with AI.
• Review past AI interactions to identify patterns in customer reactions and adjust your approach accordingly.

Step 3: Test both approaches and measure the impact on CSAT

Rather than making assumptions, run controlled tests to see how AI disclosure affects customer satisfaction.

• Conduct A/B tests comparing interactions with and without AI disclosure.
• Track key support metrics like response time, CSAT scores, and AI resolution rates to measure effectiveness.
• Experiment with different positioning strategies—does framing AI as a helpful assistant improve customer perception?

Step 4: Adjust based on customer feedback and industry trends

AI strategies shouldn’t be static. As customer preferences and AI capabilities evolve, brands should refine their approach accordingly.

• Regularly collect customer feedback to understand how AI disclosure impacts their experience.
• Monitor industry trends to see how competitors and market leaders are handling AI transparency.
• Stay flexible—if sentiment shifts, be ready to adjust your disclosure strategy to maintain trust and efficiency.

Best practices for AI disclosure (if you choose to disclose)

If you decide to be transparent about AI in customer interactions, how you communicate it is just as important as the disclosure itself. Let’s talk about how to get it right and make AI work with your customer experience, not against it.

First, make AI part of your brand voice

AI doesn’t have to sound like a corporate FAQ page. Giving it a personality that aligns with your brand makes interactions feel natural and engaging. Whether it’s playful, professional, or ultra-efficient, the way AI speaks should feel like a natural extension of your team, not an out-of-place add-on.

Instead of:
"I am an automated assistant. How may I assist you?"

Try something on-brand:
"Hey there! I’m your AI assistant, here to help—ask me anything!"

A small tweak in tone can make AI feel more human while still keeping transparency front and center.

Read more: AI tone of voice: Tips for on-brand customer communication

Clarify the AI’s role

One of the biggest mistakes brands make? Leaving customers guessing whether they’re speaking to AI or a human. That uncertainty leads to frustration and distrust.

Instead, be clear about what AI can and can’t do. If it’s handling routine questions, product recommendations, or order tracking, say so. If complex issues will be escalated to a human agent, let customers know upfront.

Framing matters. Instead of making AI sound like a replacement, position it as a helpful extension of your support team—one that speeds up resolutions, but hands off conversations when needed.

Blend human and AI seamlessly

Even the best AI has limits—and customers know it. Nothing is more frustrating than a bot endlessly looping through scripted responses when a customer just needs a real person to step in.

AI should be the first line of defense, but human agents should always be an option, especially for high-stakes or emotionally charged interactions.

A smooth handoff can sound like:
"Looks like this one needs a human touch! Connecting you with a support expert now."

Frame AI messaging positively

AI disclosure doesn’t have to feel like an apology. Instead of focusing on limitations, highlight the benefits AI brings to the experience:

• Faster responses
• 24/7 availability
• Instant answers to common questions

It’s the difference between:

"This is an AI agent. A human will follow up later."

vs.

"I’m your AI assistant! I can answer most questions instantly—but if you need extra help, I’ll connect you with a team member ASAP."

The right framing makes AI feel like an advantage, not a compromise.

Monitor customer feedback and adjust messaging

AI perception isn’t static. Regularly analyzing sentiment data and customer feedback can help refine AI messaging over time—whether that means adjusting tone, improving explanations, or updating how AI is introduced.

When you follow these best practices, AI can be a real gamechanger for your customer support. Just take it from Jonas Paul… 

When AI is done right: Jonas Paul’s success story

Jonas Paul Eyewear, a direct-to-consumer brand specializing in kids' eyewear, needed a way to manage high volumes of tickets during the back-to-school season without overwhelming their customer care team. 

To streamline these conversations, Jonas Paul implemented AI Agent to provide instant responses to FAQs. This allowed human agents to focus on more complex cases that required personalized attention.

“Being able to automate responses for things like prescription details and return policies has allowed us to focus more on the nuanced questions that require more time and care. It’s been a game changer for our team,” said Lynsay Schrader, Lab and Customer Service Senior Manager and Jonas Paul.

Jonas Paul saw a 96% decrease in First Response Time and a 2x ROI on Gorgias’s AI Agent with influenced revenue. You can dive in more here.

Make AI transparency work for you with AI Agent

Whether or not your brand chooses to disclose AI in customer interactions, the key is to ensure AI enhances the customer experience without compromising transparency, accuracy, or brand identity.

So how can you get started? Gorgias AI Agent was built with both effectiveness and transparency in mind. 

For every interaction, AI Agent provides an internal note detailing:

• The Guidance, Articles, or Macros it referenced
• The source of any account information it used
• A prompt for your feedback to continually refine and improve responses

Excited to see how AI Agent can transform your brand? Book a demo.

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