2024 has seen AI adoption spiking across all industries and services. “65 percent of CX leaders see AI as a strategic necessity that has made previous CX operations obsolete and dated”, according to the Zendesk Customer Experience (CX) Trends Report 2024.
Gen AI has been leading the productivity frontier and generating value, per a recent McKinsey report. In customer experience support services, AI, machine learning (ML), deep learning (DL), natural language processing (NLP), and cognitive computing have delivered immediate value in many CX areas, such as:
As the leader in customer experience support, the ApexCX teams of consultants, implementers, and our partners in AI experience design (AIXD) are your collaborators in enabling cutting edge, responsible, and trustworthy AI technology to advance both the quality and quantity of customer interaction and acquisition.
In this Part One of a two-part series, we will first focus on AI adoption strategies and assessment; in Part Two, we will continue with design, implementation, and the future of CX AI.
AI should be approached without fear or hype, as a tool for advancing humanity for the better, hopefully.
Instead of wielding an AI hammer and seeing everything as a nail for AI, “companies should always start with the business problem you want to solve”, said Eric Lamarre in a McKinsey podcast.
Not every business problem is best solved by AI. Leadership needs to first go through a diagnostic process about the types of ailments, and then prescribe the best remedies, AI or not.
For CX support, the bottom line should always be measured with customer-centric metrics for customer satisfaction, customer acquisition, and customer loyalty. If AI makes the system or process seem to be more “efficient”, but makes customers more frustrated and unsatisfied, the end result will be self-defeating, a wasteful use of resources and time. Always first evaluate the reasons for solving business problems, large or small, to ensure that AI-facilitated operational efficiency is not at the expense of customer satisfaction.
If solving the identified problems indeed leads to improved customer experience, and also aligns with an organization’s values, mission, and goals, then we move on to assessing CX AI feasibility.
Customer-driven opportunities: First and foremost, identify customer-driven opportunities on a customer journey, since what creates competitive differentiation is customer experience, everything else being equal.
Before deciding what AI tools to use, we need to do a thorough analysis of challenges, opportunities, resources, and capabilities.
Challenges and opportunities: Through interviews, investigations, research, analyses, and collaboration with your organization, the ApexCX experts provide invaluable Insights about challenges and opportunities for adopting the right CX AI tools for specific use cases, in order to achieve targeted results to enhance your customer experience and satisfaction.
High quality data: Once you are clear about what key points in your customer journey can be improved and amplified with ML, DL, NLP, and cognitive computing, ask yourself if you have high quality data – valid, valuable, timely, and relevant – to train an AI system. Since the quality of AI depends on the quality of data, the ever-green rule is “garbage in, garbage out“. “Your data and data strategy is what keeps the AI flywheel in motion … that then delivers predictions. These predictions positively impact business outcomes that in turn lead to more or deeper customer relationships, sparking the creation of more or higher quality data (network and flywheel effect)”, per an AWS white paper.
Foundational capabilities: “Your foundational capabilities that, above all else, drive success or failure when adopting AI”, per the same AWS white paper. Foundational capabilities for AI and Gen AI usually refer to operations, data security, platform, governance, people, and business.
Among these, people are the most critical part, not technology, according to the “70/20/10 Rule” explained on this Harvard Business Review article: “Seventy percent of the effort of changing an organization—its processes, ways of working, key performance indicators, and incentives—involves people. Twenty percent entails getting the data right. The remaining 10% is about the technology foundation.”
Next, identify and prioritize the use cases based on potential impact, readiness, and feasibility.
Use cases: To zoom in on specific use cases where CX AI will make the most impact requires domain expertise, years of experience in the customer experience support services, and in depth knowledge of the ecosystem as well as all intricate business processes. Knowing how each contact center or a business model is uniquely positioned to specific customer needs is a prerequisite to adopting CX AI.
This is where ApexCX shines, in guiding customer-facing companies to get the most bang for your buck, if we together reach the decision that there is a great fit between CX AI and the use cases. Whether the use cases are to improve customer onboarding, service issue identification, claim processing, and other assisted and self-service engagements, we will help you reach your goals with human-centered AI strategies and implementation.
Generic or domain-specific AI tools: In adopting any of the available AI tools such as ML, Gen AI, NLP, cognitive computing, and AI agents in the future, let the customer-driven opportunities determine what types of AI tools are the best fit for your business’ specific needs, whether it will be generic, “off the shelf”,“out-of-the-box” AI models, or domain-specific, bespoke models designated for verticals where small language models (SLM) that are fed with proprietary, exclusive data might be a better fit.
©Jerry Briggs all rights reserved.
We at ApexCX look forward to working with you to take your CX AI to the next level, and deliver the ultimate consumer experience, outpace your competition, strengthen brand value, and increase workflow efficiency. Together, we identify key areas and use cases, develop operational processes for collecting quality data and maintaining data security, integrating and applying CX AI to customer-driven opportunities.
Please contact us for more information.
To be continued in Pt 2: “Design, Implementation, and the Future of CX AI.” Please stay tuned, thank you!
