Editor’s note: The following is a guest article from Don Scheibenreif, distinguished VP analyst with Gartner’s customer experience research group.
Generative AI is not a technology that warrants a “wait and see” approach; the time to act is now — not just for customer experience but for the organization as a whole.
Generative AI is already being incorporated into many CX technologies — from Voice-of-Customer and customer service platforms to CRMs — offering an immediate opportunity to enrich customer understanding.
As deployment accelerates, generative AI will transform the three pillars of CX management: understanding customers, setting CX strategy and coordinating CX across the enterprise. CX leaders must identify use cases for generative AI that create the most value for customers and the organization, while mitigating risks.
Understanding customers
To understand customers — a key pillar to providing good CX — businesses must research customer needs, listen to customers and garner insights. To do so, CX programs rely heavily on technology for VoC data collection and insights.
Before deploying new generative AI tools, CX leaders should assess the current adoption of existing AI functionality in the organization’s VoC platform.
Many traditional AI capabilities, such as machine learning algorithms and natural language technology, have long been embedded in VoC solutions. This functionality is already available in VoC features like speech and text analytics to derive insights from customer conversations, email and chat interactions.
But VoC vendors are now introducing new features that embed generative AI. That includes deploying generative AI to query large customer research datasets, encompassing everything from vendor-led studies, organizations’ studies, surveys and customer feedback across all channels and modalities.
These aggregated datasets are now searchable by applying filters and asking natural language questions such as, “What are the top feedback themes for customers in our manufacturing segment?”
CX leaders should be exploring VoC and CX applications to leverage what is available in production environments and understand what is on the roadmap for the solutions they already use.
In the near term, CX leaders should work with CX business partners to plan and deploy small pilots of new AI capabilities. From there, they can identify the benefits from the pilot results and budget for and launch larger-scale incorporation of generative AI into VoC programs.
Setting CX strategy
The advance of generative AI should not change an organization’s CX vision or associated metrics. It can, however, accelerate and scale them.
AI will impact how organizations use technology and change the way business leaders approach technology investments as a part of their CX strategy. CX leaders are responsible for helping the organization navigate the opportunities and risks of AI technology in how it can service the broader CX strategy.
Generative AI can further organizations’ CX strategy across three dimensions:
Acceleration:
Generative AI can access and synthesize large datasets across many modalities, including images, audio, video and text, faster than a human.
Right now, the acceleration is in the retrieval and summarization of VoC data. In the near future, generative AI will also enable accelerated production of key CX insights, including customer persona creation and the mapping of customer journeys.
Creativity:
Generative AI offers an exciting possibility for CX to generate designs of entirely new experiences by creating novel combinations that use high volumes of experience data.
That process involves gathering VoC feedback, mapping the current-state journey with that feedback and then brainstorming ideas to innovate new customer interactions.
Augmentation:
Another opportunity to employ generative AI is to augment human judgment — specifically for helping with the prioritization of CX initiatives.
Prioritizing CX investments, especially technology investments, can be a complicated task, depending on the constraints within the business and how organizations measure CX progress. AI can augment decision-making by recommending the best priority to invest in.
CX leaders can use generative AI to develop recommendations by feeding it prompts, such as resource or budget constraints.
Businesses should assess the potential benefits of generative AI along these dimensions and evaluate which will drive the most benefit at a use-case level and program level, and which will directly support delivery of CX goals.
Coordinating CX across the enterprise: governing the opportunity and risk
As CX leaders coordinate CX strategy across an organization, they need to account for the potential risks that come with deploying generative AI.
Generative AI is only as good as the data provided. Building and fine-tuning your company’s foundational model is crucial because poor data will generate incorrect outputs. Leaders must also test, curate and supervise AI interactions — not doing so could do harm.
In the bigger picture, there is always the risk that keeping up with the rest of the market on generative AI technology does not create any differentiation whatsoever in the near term. Organizations’ governance structure must enable long-term investment in generative AI to yield a sustainable advantage.
CX leaders should determine which generative use cases will deliver the most value. They must assess how the technology could help deepen the organization's understanding of customers and speed up delivery of insights. They should investigate vendors’ roadmaps of VoC and other platforms. And lastly, they ought to limit the risks associated with generative AI and capitalize on its opportunities.
