MarTech Interview with Eric Williamson, CMO at CallMiner

Eric, please outline how CallMiner views the current state of AI adoption, particularly in the realm of Customer Experience (CX)?

Since ChatGPT was introduced a year ago, generative AI has been the topic of conversation – and for good reason. With the potential to up-end entire industries and how we work, it’s one of the most transformative innovations of our time. But the rise of generative AI also sparked a ‘fear of missing out’ frenzy for organizations, with many adopting or introducing generative AI features into their products without strategic consideration.

However, starting in the last few months of 2023, we saw more business leaders working to balance agility and responsibility when it comes to generative AI implementation, focusing more on driving business value and outcomes as opposed to adopting AI for the sake of AI. This includes thinking about potential risks, particularly for customer service and customer experience (CX) applications. According to CallMiner’s 2023 CX Landscape Report, 45% of CX and contact center leaders are concerned about AI exposing their company to security risks, 43% are worried about the technology spreading misinformation, and 41% are fearful of AI giving biased or inappropriate responses to customers.

These fears are important and valid. While I expect AI adoption to only accelerate in 2024, when it comes to CX, I expect more leaders to weigh the fears, pitfalls and shortcomings of AI in combination with strategic business applications. The outcome will be more responsible and impactful applications of AI that truly deliver value for organizations and their customers.

What are the driving factors in CX that will continue to accelerate AI adoption and how can organizations stay competitive in this rapidly evolving AI landscape?

As AI gets pushed closer to the consumer, many are more aware and accepting of the fact that they’ll likely engage with AI at some point in their buying or customer service journey. For example, more customers are using self-service options, such as AI chatbots, to attempt to handle their issues before reaching a human. AI is driving this transformation.

That said, no matter how many use cases AI and automation can solve for, human interaction in customer service is still critical. This is because complex CX issues are not disappearing, and most customers still want humans involved in the process.

To stay competitive in this space, organizations will need to embrace AI and automation for certain use cases. But they can’t do this at the disregard of customer preferences when it comes to how and when they do want to talk to a human. Not only does there need to be a balance, I predict the most successful organizations will be the ones that can use AI to make every customer interaction more efficient, whether that’s via chatbot or in support of humans in the contact center.

Generative AI dominated 2023. What specific aspects of generative AI contributed to its dominance, and how do you see its role evolving in 2024?

AI is not new, forms of it have been around for decades. But the launch of OpenAI’s ChatGPT in November 2022 brought AI (generative AI) into the hands of consumers in a way that it had never been before. Regardless of demographic, generation or technical capability, everyone was asking ChatGPT questions and receiving answers in seconds. And some people quickly found ways to use ChatGPT to enhance their personal and professional lives, from writing work emails to planning trip itineraries – and this type of exposure is what really contributed to its dominance in 2023.

Beyond individuals, organizations also quickly realized how generative AI could enhance their existing solutions. That said, I think we’ll see more business leaders come around to the idea that generative AI is not a silver bullet – and it is most powerful when used for specific business use cases. In fact, there are other types of AI that can perform some use cases better than generative AI. I predict that the organizations who ‘get it right’ will be the ones that effectively balance AI velocity and agility with responsibility and security. Those that do will find themselves in the position to deliver the most value to their customers and improve the bottom line.

Could you share some successful use cases where CallMiner has applied generative AI? How does CallMiner integrate generative AI with other techniques to meet specific business needs?

The CallMiner platform leverages generative AI for contact summarization, automatically condensing voice and text-based customer interactions into short, digestible summaries. This is valuable for consistent analytics (i.e. every interaction summarized with the same requirements and guidelines) and reducing after-call work, meaning contact center or customer service agents can spend more time focusing on high-value tasks and supporting customers.

Also, CallMiner’s internal helpbot, CallMiner GPT, enables users of our platform to ask questions while working in the product, getting quick answers about functionality and other key topics to help them do their jobs better in real-time.

With the increasing adoption of AI, what common challenges do organizations face, and how can they overcome these challenges, especially in customer experience and contact centers?

One common obstacle we see with AI is ensuring agents are comfortable embracing and using the new technology. It’s true that CX, marketing, and contact center departments are accustomed to new technology implementations, but that doesn’t mean all are well received or run smoothly. Among customer service agents, for example, there’s a perception that AI is only intended to catch them when they do something wrong.

This has been accelerated by the introduction of generative AI, and the many conversations about the jobs it could take over entirely.

Particularly in CX and customer service, agents should have an active role in choosing and implementing solutions, and business leaders should clearly articulate the goals of any new technology. In the case of AI, as more agents accept that the technology is intended to improve their job performance, satisfaction and engagement – not just catch their mistakes – they become more invested in the success of these tools.

Looking ahead, what emerging trends do you foresee in AI, specifically in customer experience, and how is CallMiner positioning itself to stay ahead of these trends?

Now that more organizations are balancing responsibility and agility when it comes to AI adoption, I expect there will be more conversations about how to navigate the potential pitfalls of AI, like if and when it negatively impacts customer interactions (ex: spreading misinformation or providing discriminatory responses). There are plenty of stories of this already happening. Just last month a regional car dealership saw its GPT chatbot agree to sell a car for $1 (not legally binding, of course) and engage in other non-car related conversations. While this example is fairly light-hearted, it’s not hard to imagine how wrong these customer-facing interactions could go.

Responsible AI has always been at the core of what we do at CallMiner. We aim to deliver AI-powered solutions that drive both business value and security. To do this, we’re constantly auditing our models, whether those are built in-house or via partners, to identify issues like drift, hallucinations, and other issues before they impact customer interactions.

As a final note, what advice would you give to marketing and CX leaders navigating AI adoption?

AI shouldn’t happen in a vacuum. Adopting unproven solutions to solve single, one-off tasks will never drive business value. To experience ROI on these investments, AI initiatives need to be considered enterprise-wide, with support and communication across business units. This includes building a culture of education around AI, ensuring that employees – regardless of level or position – understand the foundations of the technology and how it’s impacting your business and by extension, your customers. When most of the business world remains uneducated on AI, it’s impossible to make the right technology investments.

I also believe, for most organizations, building homegrown AI solutions or relying solely on public large language models isn’t going to deliver long-term value. While these paths might be a temporary solution for minimal tasks, they don’t scale, and they introduce even more risk and privacy concerns – for example, how sensitive data is being used to train public models.

Partnering with established, enterprise vendors that have extensive experience researching, developing and investing in AI-powered solutions is the best way to embrace emerging technology, while also ensuring short-term and long-term value. At the end of the day, AI applications must serve your business goals, and working with the right partners and vendors can make achieving this easier and more effective.