Qualtrics, a supplier of cloud-based experience management software, is on mission to shape the way businesses interact with their customers and employees – by gleaning data on human sentiments and emotions and providing recommendations on the best course of action.
It recently announced that it would invest $500m over the next four years to build more artificial intelligence (AI) capabilities into its platform and use the billions of experience profiles it has amassed to train its large language model that can be used to coach customer facing employees.
Qualtrics was acquired by German software giant SAP in 2018, only to be spun off through an initial public offering (IPO) less than two years later. In March 2023, the company was taken private through an acquisition by private equity firm Silver Lake, in partnership with the Canada Pension Plan Investment Board.
In a wide-ranging interview with Computer Weekly, Qualtrics’ president for products, user experience and engineering, Brad Anderson, and its Singapore-based head of Southeast Asia, Mao Gen Foo, outlined the company’s product directions, including its work in AI, its traction in the region and the future of its platform.
Could you tell us more about the work that Qualtrics is doing in AI?
Anderson: We’ve participated in a number of what I would call big platform transformations over the past 30 to 40 years. Over that time, the internet has completely transformed how products are built, and what’s happening in AI right now is going to be bigger than what we’ve seen in the past. It’s going to impact every function, and every company is going to benefit from it.
The potential of AI to enhance humanity and advance the human experience is quite remarkable. But it’s important to understand that AI is only as good as the data that you have to train the models. And so, organisations that have the most unique datasets are going to be those that will deliver the most value.
We’ve been in business for over 20 years, and we have the largest collection of human sentiment and human emotion data on the planet. It’s organised based on what we call experience identities. Every time a customer or an employee of one of our customers interacts with a company, we store that experience in a unique profile. Today, we have more than 12 billion profiles which are growing at 60% year-over0year. By the end of 2023, we would have more than 16 billion profiles.
What that allows us to do use all of that human experience and sentiment data to train our models, giving us a very unique large language model that we can then deliver value from. Now over the next four years, we’re going to invest over $500m in R&D specifically related to AI. We believe AI can make businesses more human because it can be used to coach frontline workers on the best way to serve a customer and deliver the best experience.
Since we’ve been taken private, we’ve had $1.7bn in annual revenue and 20,000 customers that are using our technology in a category that’s still in its infancy. The upside potential and opportunity for Qualtrics to be the next great enterprise tech company is one of the things that attracted me from Microsoft Brad Anderson, Qualtrics
The business impact of that is huge. It’s well understood that happy customers spend more with an organisation, but Harvard University recently conducted some interesting research which found that customers who are emotionally connected to a brand are 52% more valuable to a company than customers who are highly satisfied. And so, one of the things that we see in AI is the ability to help organisations deeply connect with their customers by delivering very personalised experiences in a very human way.
What sorts of capabilities are you looking to build with the $500m investment? Also, is the large language model proprietary to Qualtrics and what kind of guardrails are you putting in place to tame the AI beast?
Anderson: Let’s start with the latter questions. What we’ve built for our engineering team is an AI bench that has a set of third-party large language models as well as our own first-party models. That allows us to use the best model that’s fit for purpose. It also enables us to manage costs because large language models can get very expensive to build.
As to how we do the safeguarding, it’s aligned with what the industry is doing to check for biases and hallucinations. You have a stage gate where what gets exposed to the public is different from what your team is working on. We’ve been doing AI for five to six years, and we’ve learned from the industry the best way to do that. Our engineering team is made up predominantly of people whom we’ve recruited from Microsoft and…
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