Avoid these 3 generative AI pitfalls—and what to do instead, according to IT leaders

In the year since generative AI became widely available, the world has changed enormously. As of February, ChatGPT set the record for the fastest-growing user base. Once a novelty, generative AI is now part of the baseline of different technologies. Even beyond technology, generative AI is asserting itself in finance, pharmaceuticals, and art.

With generative AI, there’s a strong sense of uncharted territory for how it can be used. It’s a truly exciting (and confusing) era, as technologies and processes we once felt to be evident are changing before our eyes.

IT leaders are looking for ways to incorporate generative AI into their own products. While some applications are obvious—customer service chatbots, for example—other use cases are more creative or abstract. In the 2023 Zendesk report for IT leaders, we found that 80 percent of IT leaders plan to increase budgets to accommodate generative AI over the next year, with 83 percent agreeing that generative AI “will unlock large operational efficiencies within the next two years.”

In 2023, these brands embraced the possibilities of generative AI

Companies have had a year to experiment with generative AI, with many finding ways to simplify their customer journeys and personalize the customer experience. With all the excitement, it can feel like businesses that are not investing in some sort of AI-powered experience are falling behind. But as brands innovate, there is a strong sense that nearly anything is possible.

• TripAdvisor launched Trips, an itinerary generator powered by AI to help travelers get instant personalized recommendations.
• Wendy’s piloted generative AI voice controls in certain drive-thrus in an attempt to automate and simplify the ordering process.
• Google is testing virtual try-ons for select brands in Search, allowing shoppers to visualize how clothing fits a variety of models with different body types and skin tones.
• Etcembly, a biotech firm, used a home-grown large language model (LLM) to create an immunotherapy drug.
• Spotify’s DJ provides music recommendations interspersed with a realistic AI-generated voice for a personalized listening experience.

While some brands can leverage data to create personalized, AI-augmented experiences or products, many businesses are behind the curve. In one case study of a business that was “blindsided” by the rapid spread of generative AI, Wired reported that it was because “consumers [are embracing] experimental but capable tools such as ChatGPT,” which caused the business to experience an unprecedented amount of churn in a very short amount of time.

They’re not alone. Generative AI is a disruptive force, from technology to art, and companies need to brace for its impact.

3 pitfalls to avoid in the generative AI space race, and what to look for instead

In the 2023 Zendesk report titled “IT leaders tackle new challenges with security, AI, and CX,” we identified three common pitfalls IT leaders should avoid in order to remain competitive in a quickly changing landscape.

1. Lack of direction and prioritization

Over half of the IT leaders surveyed in the report expressed concern that the AI landscape is changing quickly, affecting their ability to keep up with the competition.

A chief digital and information technology officer at an enterprise company said, “I’m interested in applying technology, but I’m uncertain about how to use generative AI in my industry.”

While the speed of change is overwhelming, what is certain is that most IT leaders see the potential for generative AI in CX: 83 percent of surveyed leaders believe that using generative AI across the customer journey will be more important over the next year.

Read more: Here’s how customer service teams are actually using AI

2. Poor data quality and underprepared tech stacks

IT leaders are worried about the quality of their data, with 60 percent of surveyed leaders reporting that their organization struggles to collect and label sufficient, high-quality data to train AI models effectively for automation. Just under half of survey respondents report concerns with implementing generative AI in their tech stack.

While some stand-out brands may be able to leverage their own data to create personalized customer experiences, the reality for most companies is that doing so might pose a privacy or security risk. By partnering with trusted vendors, businesses can implement generative AI into their customer service applications without risking violations of data privacy.

Read the report: Balancing data privacy and personalization in customer experience

3. Skill gaps in IT teams

A common sentiment found in the report is that some IT teams feel unprepared for the generative AI wave due to skill gaps in their teams.

“We’re not a huge company,” one chief information technology officer said. “I don’t have the resources on staff with data scientist skills. We want to know how we can scale with the staff we have.”

With 57 percent of IT leaders reporting skill gaps relating to emerging AI technologies, businesses with an edge on the competition are working with trusted, strategic partners to realize the potential of generative AI in their fields.

While generative AI may ordinarily require long lead times to set up, many solutions using generative AI can be ready to go out of the box. For example, companies can deploy generative AI bots trained on existing knowledge base material to deflect customer conversations from support agents.

Read more: The capabilities and limitations of ChatGPT for customer service

The rundown on AI for customer service

Today, the market is flooded with AI products and promises. But some processes are best left to the experts. Zendesk AI is built on billions of real customer service interactions, with tangible applications to improve the customer experience right in front of the customer and backstage, behind the curtain.

To learn more about generative AI in customer service, check out all of our offerings here.