Gartner forecasts that worldwide artificial intelligence software revenue will grow to $62.5 billion this year, up 21.3% from 2021.

“The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity,” said Alys Woodward, Gartner senior research director, in a prepared statement.

The forecast encompasses applications with AI embedded in them, such as computer vision software, as well as software used to build AI systems. Gartner’s AI software prediction is based on use cases, measuring the amount of potential business value, timing of business value and risk to project how use cases will grow.

But just because companies are adding AI doesn’t mean they are using it in a way to derive the greatest benefits. Below are five of the biggest mistakes companies make when deploying AI:

1. Failing to Mitigate for Bias

“The problem is that algorithms can absorb and perpetuate racial, gender, ethnic and other social inequalities and deploy them at scale — especially in customer experience and sales environments where AI usage is really taking off,” said Sharad Varshney, OvalEdge CEO.

CX managers recognize this tremendous problem as a customer service pitfall, Varshney added. AI bias is simply a data quality problem, and AI systems should be subject to the same level of process control as an automobile rolling off an assembly line. Companies can solve AI bias with robust automated processes around their AI systems to make them more accountable to stakeholders.

Varshney recommended that developers look for AI bias as part of their pre-deployment testing: “Quality test suites will enforce 'equity,' like any other performance metric. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate in customer experience environments.”

Related Article: 4 Tips for Taming the Bias in Artificial Intelligence

2. Ignoring Real-Time Signals

Customer support routines can become too rigid when AI is at the helm, ignoring customer sentiment and causing firms to miss opportunities to turn losses into wins, according to Matthew Paxton, Hypernia founder and owner. “Not all AI customer service apps are tuned in to real-time signals that can help businesses respond effectively, quickly and successfully."

Paxton cited a CompleteCSM poll, showing that 82% of firms effectively used AI to analyze real-time signals to improve customer satisfaction and correct bad interactions. The other 18% are missing those signals.

3. Forgetting Personalization

Many businesses adopt a less-personalized approach when using AI to automate CX,” said Beerud Sheth, Gupshup CEO. “Businesses need to connect one-on-one with every consumer.

"The good thing is that many brands acknowledge this," he added, "and understand the need for personalization at scale, especially since the onset of the pandemic.”

Learning Opportunities

4. Restraining AI Opportunities 

CX begins with the very first engagement a customer makes with your brand, often through advertisement, said Yaron Morgenstern, Glassbox CEO. “AI is increasingly used in digital ad buying, with great success. One of the biggest mistakes we see is a tendency to constrain the AI by over-targeting a campaign, therefore, relying too much on human logic to ensure digital media buying is optimized. This leads to underspending and a diminished value of return on ad spend.”

Media campaign KPIs are typically determined using dozens of individual line items and strategies, Morgenstern added. But KPIs never tell the full story and can be misleading.

Optimal budget allocations constantly evolve, following complex market dynamics. AI adds value here by scoring impressions and selecting only those predicted to deliver the required budget with the best possible results. Increasing the available volume of impression opportunities — not restricting them, as a human would be inclined to do — allows the AI to be more selective in its choices.

"AI performs best when it can take all data points into account at one time, something impossible for the human brain to do,” Morgenstern said. “This dichotomy often leads to manual overrides, with humans restricting AI in an effort to optimize results when the best outcome is to give AI all possible information and let it do its work. If CX begins at first impressions, making sure your ads are seen at the optimal times is the best place to start.”

5. Failing to Prepare Employees

For many companies, the main purpose of AI in customer service is to automate simple tasks that are also considered tedious, said Nikita Chen, LegitGrails founder and CEO. Deploying AI often comes from the desire to free up employees and focus on more complex tasks.

However, when using AI to automate repetitive tasks, the customer support team will be forced to handle a heavier load of demanding tasks, Chen added. “In such situations, you should support your employees with proper training that will prepare them for tougher challenges to maintain their enthusiasm and energy levels. As the CEO or manager of the company, you can also ensure effective communication in this case so that your employees know you have their back.”

Final Thoughts

Many brands already use AI as part of their customer experience and support strategies. However, these efforts, when not deployed correctly, can fall short of meeting goals. 

If you're committing one of the AI faux pas on the list above, consider how you can make insightful changes that will lead to results.