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AI Becoming More Prevalent in CX Apps and Platforms
Artificial intelligence (AI) is becoming increasingly prevalent in customer experience (CX) applications, according to a new report from Dash Research. The report shows AI is nearly ubiquitous in CX use cases as well as throughout a variety of other industries, and is being used across customer-facing functions as well as back-office systems and processes.
The "Artificial Intelligence for CX Applications" report finds AI functionality is increasingly being integrated into both CX platforms and applications. It is typically used with low or no-code interfaces to cater to CX, sales and marketing professionals who have little coding and data science experience. This enables workers to manipulate data and algorithms to support a variety of functions focused on improving overall CX and customer interactions.
“Each step in the AI/CX continuum represents a progression of AI maturity and sophistication,” said Keith Kirkpatrick, principal analyst for Dash Research. “As AI maturity increases, so does the required depth of integration of data sources within an organization, which can encompass customer and account data, product and service data, billing and fulfillment data, and service interaction data.”
Kirkpatrick said integration of AI within the CX industry generally falls under five categories. These include intelligent insights, predictions, preferences, recommendations and automation. There are also four market drivers that are accelerating the adoption of AI use within CX initiatives. The first is an increased demand for customer-facing automation and assistants. A higher demand for back-end automation and intelligent analysis is also driving adoption, along with the growing need for data-led insights and providing a holistic customer journey. Finally, businesses are understanding they can derive more value from deeper customer engagement and that is also driving adoption.
Dash also found there are several technical and operational barriers holding back full market adoption of AI within CX platforms. These include limited scope and quality of data along with regulatory issues. Other barriers include a lack of alignment between CX challenges and AI solutions as well as limited data governance policies and privacy concerns.
Edited by Luke Bellos