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How Qualtrics is driving experience management with AI

During the Sydney leg of its X4 conference, Qualtrics’ top executives dived deeper into its efforts to help organisations improve customer and employee experiences with AI

Qualtrics is baking artificial intelligence (AI) capabilities across its platform to help enterprises improve digital experiences for employees and consumers.

Speaking at the X4 event in Sydney this week, Qualtrics CEO Zig Serafin said the company’s AI capabilities are aimed at helping enterprises understand what actions they can take to improve experiences, what’s most important and the impact of those actions on their business.

“For example, you can automate certain actions to speed up processes, or suggest quick improvements for employees on how to better manage their teams,” Serafin explained. “For store managers, we can offer recommendations on how to enhance store performance compared to other locations. This is how AI brings experience management to the masses.”

In July 2023, Qualtrics announced a $500m investment over the next four years to develop its AI capabilities and leverage the vast trove of experience profiles it has amassed to train its AI models.

Underpinning Qualtrics’ AI strategy is Qualtrics AI, which uses more than 100 AI models, including private models and those from the likes of OpenAI, to provide recommendations and insights across various areas, including customer sentiment, intent, churn, segmentation, and employee attrition.

“The questions you ask about customer experience and churn, for example, are answered by an AI specifically tuned and optimised for the human experience,” Serafin noted.

Besides providing insights, Qualtrics AI can also predict business outcomes and the impact of implementing its recommendations, said Brad Anderson, president for products, user experience and engineering at Qualtrics.

For example, through an employee attrition analytics dashboard that analyses both employee experience and operational data, organisations can identify risk factors contributing to attrition and understand the impact of corrective actions on attrition rates, recruitment costs, and training expenses.

“This is the power of specialised AI fine-tuned on the Qualtrics platform to understand the employee experience,” Anderson said. “It significantly levels up skills, as most managers are not experts in employee experience or data scientists.”

At the event, which drew some 2,000 customers and partners, Qualtrics executives also addressed data privacy and security concerns associated with the use of AI. Serafin assured attendees that the company’s platform adheres to enterprise-grade security and compliance standards, preventing data from being used to train global AI models or leaked to the public domain.

Anderson further emphasised that the billions of experience data points used to train Qualtrics AI are anonymised and aggregated. “Only you can access the data that is proprietary to your organisation – your competitive advantage will always remain confidential and only be used for your benefit,” he added.

Qualtrics customers can access the platform’s AI capabilities through the Qualtrics Assist AI assistant. Anderson described the tool as specialised and trained to support customer service teams, frontline teams in industries like retail and hospitality, as well as digital and consumer research teams.

During a demonstration of the tool, Anderson showcased how a contact centre manager can use Qualtrics Assist to receive suggestions on coaching and performance improvement for agents, drawing on best practices developed by industrial and organisational psychologists.

Recognising the importance of prompt quality in AI assistant interactions, Anderson said Qualtrics employs prompt engineering to enhance prompts. “If a manager asks, ‘What action should I take?’, we can improve it with something like, ‘What actions can this manager of seven people in this industry take?’”

Qualtrics has also implemented a set of rules to ensure its AI assistant responds appropriately and avoids offensive language. Additionally, a dedicated machine learning operations team reviews the assistant’s outputs daily to identify potential biases, hallucinations, and ensure responses are equitable and accurate.

“We look for consistency with the dashboards – if the generative AI solution comes back with numbers that are different from what’s on the dashboard, the user is going to lose all confidence,” Anderson said.

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