Top AI Mistakes Companies Make in Customer Experience

Learn from your mistakes, but it's wiser to learn from others'. Explore AI customer experience mistakes and solutions in this guide.

Top AI Mistakes Companies Make in Customer Experience


A world of AI lies in front of us. No matter how it evolves, there’s no denying it’s here to stay. As CS professionals, the question of how we adapt our customer experience to make efficient use of AI writer and email client capabilities seems increasingly relevant.

Today, we’ll break down some commonplace mistakes CX teams typically make with AI and automation, as well as discuss ways to avoid them.

How Is AI Typically Used in CX Today?

Most of us have already had some form of experience with either a chatbot, a procedural language model, or both. However, CX has been using some form of AI or predictive automated system for nearly a decade. Adding more recent tactics, the list of ways CX can leverage AI becomes impressive:

  • Personalized recommendations 
  • Chatbots
  • Automation flows
  • Auto-purchases
  • Helping customers at any time
  • Automatic upsells
  • AI-assisted meetings
  • Voice chatbots and AI agents
  • AI-assisted reactive and proactive customer engagement
  • AI-powered writing
  • AI email client

However, the advance of AI has not been without its challenges. For example, chatbot implementation faces significant hurdles, with 70% of customers still preferring to speak with live agents.

Still, the fact that 87% of global customers still find chatbots effective is a good sign that we’re on the right track. All we need now is to apply the lessons learned along the way to a new generation of advanced language models. So let’s review some lessons on what not to do when it comes to AI:

Top AI Mistakes in Customer Experience

1. Using AI and Automation without personalization

One of the biggest issues with AI and automation is the lack of integration with customer data. If you use basic, out-of-the-box AI to interact with your customer base, it’s going to raise a number of issues, including:

  • The AI will feel robotic and rigid.
  • The lack of necessary customer information to complete customer interactions
  • Difficulty in developing its learning algorithm
  • Limited  assistance for your team members

To address this common mistake, opt for an AI-powered email client like CanaryMail that helps you save time and effort when writing emails. Because of its AI capabilities, CanaryMail can create emails that are tailored to every receiver. 

Canary Mail's AI Copilot

H3. 2. Not Being Upfront about Your Use of AI

Customers today prefer when chatbots are upfront about being artificial – either through a fake name or by simply stating it. If you set up AI customer interactions to seem as human as possible, there’s always the risk of entering the uncanny valley. Once that happens, it’s difficult to erase a negative customer experience with AI, and your entire initiative is at risk from a simple strategic miscalculation.

To avoid this common pitfall, simply remember that AI is at its best when you’re honest and upfront about using it, just like how an email client works best when its functionality is transparent.

3. Not thinking of customer expectations post AI-adoption

Once you tell customers you’re using AI, however, their expectations go through the roof: faster resolution times, frictionless processes, and conversational AI that can actually hold a conversation.

According to research by Adobe, customers want businesses to use generative AI (like ChatGPT) responsibly, meaning:

  • Having strong protections to “ensure it is used ethically”
  • Using AI to make better products
  • Using AI to decrease the workload on your employees
  • Making customer experiences awesome

To meet, deliver, and exceed these expectations, select an efficient AI-powered email client such as CanaryMail, and find a financially sound procedure for efficient setup and use.

3. Using bad AI tools

As with any new technology, the digital world abounds with knockoffs and cheap tools that are AI in name only in an attempt to capitalize on the trend.

Using suboptimal AI tools will naturally result in:

  • Poor overall customer experience
  • Specific, memorable negative experiences that’ll stick around long term
  • Unnecessary friction for customers in interacting with your brand
  • Elevated customer churn rates
  • Higher time to value for customers
  • Increased number of unsolved support requests

Remember that once customers start having negative experiences with your brand interactions, it will be hard to wash those away.

Suboptimal AI examples

4. Improvising an AI strategy because everyone’s doing it

You shouldn’t start doing AI haphazardly just because everyone’s doing it. Sit down, pick the right tool for the job, and write down a strategy.

Once you have that sorted, remember four concepts:

  • Human-led. Actual humans need to oversee setup, maintenance, and upgrades on any AI tech you have, and must be ready to step in whenever called upon.
  • AI-assisted. Consequently, your AI engine has to be able to talk to customers 
  • Data Science. This one splits in two:
    • Data Governance. Essentially make sure you have all your tools integrated, all your data neatly gathered in one dashboard, and all your AI systems correctly set up to take advantage of that data.
    • Data Hygiene. You also need to ensure your data is correct. For proper data hygiene, go through a data validation process to effectively test the accuracy and integrity of your datasets.
  • Automation. Once you’re sure about all of the items above, it’s time to scale through the use of automation flows – through specific automation tools or customer success automation tools (for even better CX and proactive engagement).

Sure, there’s a chance your AI approach will work because Dave from accounting had a brilliant idea. However, a data science approach is mandatory; otherwise, you’ll be making very costly guesses, particularly if you’re dealing with a very large user base.

5. Not Thinking about AI Exits

AI fatigue is very real. Once customers go through several unsuccessful exchanges with AI, it starts to get frustrating. To effectively combat this, think about AI exits.

That means that once a conversation with an AI has gone on longer than an X number of minutes or questions (depending on the business, product, audience, or other account specifics), the AI should self-evaluate and pass on the discussion to a human, just like an efficient email client would route an email to a human when needed.

Example of a Chatbot with no exit algorithm that simply repeats the same issue over and over again

6. No Testing and Fixing for AI / Automation Bugs

Any automated process always has a chance to fail. We’ve seen automation flows randomly stop because somewhere ten steps ago, someone picked the wrong item from a drop-down menu, breaking the entire flow. 

The same can be true for AI if you don’t implement a QA system of checks that ensures the setup is correct, the customers are receiving the correct communications, and any part of the process that fails can be passed over to a human agent.

Example of an AI chatbot that can’t complete a basic function after telling the user that it can - clearly something broke inside the AI setup and no one bothered to check.

Summing Up

The same Adobe report cited above shows 72% of global consumers say generative AI will improve their customer experiences, while on the other side of the debate, 89% of CX professionals say it will help them better personalize customer experiences and reach the right customers.

So, to sum up, both customers and customer service professionals believe AI will be a force for good. The challenge that lies before us is meeting and exceeding those expectations.

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