Dash Research’s report, “Artificial Intelligence for CX Applications”, focuses on the market drivers and barriers surrounding the adoption and use of AI in CX platforms, applications, and programs, the general use case categories for AI, and several representative case studies detailing the use of AI to improve CX. The report also details current AI regulations, which generally focus on the proper collection and use of personal information. An Executive Summary of the report is available for free download on the firm’s website.

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

According to Dash Research principal analyst Keith Kirkpatrick, the use of AI within CX can be classified under five broad categories: intelligent insights, predictions, preferences, recommendations, and automation. “Each step in the AI/CX continuum represents a progression of AI maturity and sophistication,” says 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 analysis indicates that there are four key market drivers spurring the adoption and use of AI within CX initiatives:

Increasing demand for customer-facing automation and assistants
Higher demand for backend automation and intelligent analysis
Growing appetite for data-led insights and customer journeys
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. Dash Research finds that the key remaining market development barriers include:

Limited scope or quality of data
Lack of alignment between CX challenges and AI solutions
Limited data governance policies and privacy concerns

[Regulatory issues]

Dash Research’s report, “Artificial Intelligence for CX Applications”, focuses on the market drivers and barriers surrounding the adoption and use of AI in CX platforms, applications, and programs, the general use case categories for AI, and several representative case studies detailing the use of AI to improve CX. The report also details current AI regulations, which generally focus on the proper collection and use of personal information. An Executive Summary of the report is available for free download on the https://www.dashnetwork.com/research/artificial-intelligence-for-cx-applications

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