Dash Research Finds AI Essential for CX

Artificial intelligence (AI) has become nearly ubiquitous across a range of industries and use cases, and customer experience is no different, according to a new report from Dash Research.

The research found that AI is being used across both customer-facing functions and in back-office systems and processes. This AI functionality is being integrated or incorporated into CX platforms and applications, with low- or no-code interfaces that allow CX, marketing, and sales professionals with little data science or computer coding experience to manipulate data and tune algorithms to support several different functions, it found.

Additionally, many organizations have already seen the benefit of deploying AI across customer-facing and back-office functions, according to the research. These include the generation of intelligent insights, predictions, customer preferences, next-best-action recommendations, and the support of higher levels of automation.

AI, Dash Research said, heavily relies on the capture, organization, and activation of customer data, processing the data, and capturing various aspects of interactions with customers. As more data is captured and processed, more complex algorithms or combinations of algorithms can be deployed, resulting in greater value and a greater return on investment, the firm concluded.

"Each step in the AI/CX continuum represents a progression of AI maturity and sophistication," according to Dash Research Principal Analyst Keith Kirkpatrick. "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."

Dash Research uncovered the following four key market drivers spurring the adoption of AI within CX:

• Increasing demand for customer-facing automation and assistants;
• Higher demand for back-end automation and intelligent analysis;
• Growing appetite for data-led insights and customer journeys; and
• More value seen with deeper customer engagement.

However, while AI is becoming part of the very fabric of CX platforms, like any technology or approach, there are technical and operational barriers to complete market adoption, according to the research. These barriers include the following:

• Limited scope or quality of data;
• Lack of alignment between CX challenges and AI solutions;
• Limited data governance policies and privacy concerns; and
• Regulatory issues.