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Enhancing CX with Generative AI solutions

Posted: Oct 17, 2023
Read time: 7 minutes
#CX Strategy #AI
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The future of AI in Customer Experience (CX)

What is the current status of humans and artificial intelligence?

Humans created machines to help them supplement, enhance, and replace various daily activities. This has evolved from simple machinery to sophisticated artificial intelligence engines that are developing to dominate the world of tomorrow. Still to date there are two fundamental elements that differentiate humans from machines, which are the capacity to imagine and the ability to feel. This differentiation is what makes humans essential in the process of invention of new product/service offers and in designing customer experience journeys.

“How do we want our customers to feel?” is not a question a machine can answer without input, it requires a human capacity to understand behavior, how relationships are managed and what triggers emotion. It could be that in the future machines will be able to imitate these skills, but currently we are still a must-have in any CX equation.

In this article I would like to describe the way I see the journey management space evolving in the future, harnessing the power of artificial intelligence in the service of better designed customer experiences.

The challenges of the customer journey management approach

Volatile markets require brands to quickly adapt to fluctuations with little time to prepare, as was formulated a couple of years ago by the VUCA concept (a framework for understanding VUCA). One of the proven techniques to maintain a flexible backbone, open to rapid transformation, is to adopt the journey management model. This model is defined as the process of designing, implementing and optimizing the interactions that customers have with a brand along the customer journey as they are perceived and understood by the customer. 

CX program framework

The implementation of this type of approach encompasses many benefits for the organization, such as improved quality of service, better alignment with customer needs and expectations, increased profitability and more personalized product and service offers.

While the practice has many outstanding benefits and is a must for any organization that wants to put its customer at the heart of the business activity, it still relies on significant human intervention along the process in the form of data entry, data analysis, training, governance, timely execution of initiatives and more. This fact could be a barrier to many organizations that struggle with time allocation, resources availability and budget constraints, resulting in restrained customer experience programs that are not delivering the expected outcomes, thus generating frustration, which finally leads to their abandonment.

This is where the integration of artificial intelligence in the journey management space can be of value; saving time and energy for CX teams in various tasks such as: building persona profiles, designing customer journeys, detecting customer pain points and extracting insights that would contribute to the improvement of customer experience as well as the achievement of the business objectives.

AI and CX in the Generative Journey model: How Artificial Intelligence can help Customer Experience management

The answer to the current journey management practice challenges is the establishment of a model, that I would like to call Generative Journeys, which will make use of the various forms of artificial intelligence (cognitive, analytical and generative), capable of not just collecting data and extracting analytics, but also of building the journey automatically from the data and taking actions by creating novel and realistic content such as text, images, audio and video. AI will become the automation engine of many of the CX Program tasks, that are conducted today by CX professionals and other functions in the organization, freeing their time to focus on generating business value.

So how will the model work?

Experiences are triggered when customers interact with the organization through the communication channels across the journey stages. This layer is the place where the actions the organization is taking to improve CX. During the interaction process a significant amount of data is collected through the touchpoints and stored in different information systems, such as CRM, ERP, BI and other business applications. This data lake is the source upon which AI mechanisms can nourish to produce their insights and drive action.

The generative journey model will make use of all the data collected and will add on top of it the human inputs regarding the organizational brand values, the customer experience strategy, the business goals and the market trends, to decide which type of approach to apply to each customer segment on every touchpoint. The result will be an autonomous system that can collect customer feedback, analyzing and acting upon it automatically by producing personalized content or taking an action that will fix an issue a customer is facing, in real time.

Generative journey model

6 examples of Generative AI for Customer Experience  in practice

AI possesses the capacity to profoundly enhance customer experience in various domains, leading to valuable outcomes, beyond just revenue generation and cost reduction. Generative AI technologies can be applied across the customer lifecycle to enhance CX by delivering personalized content that creates engaging and memorable experiences.
Here are a few practical examples:

  1. Persona profiling: Personas are fictional characters that represent a segment of customers with similar characteristics. Building a persona profile is a time-consuming task that requires research, quantitative and qualitative data, the ability to analyze past transactions and more. Artificial intelligence could assist in collecting data from internal and external sources, analyze large volumes of information to detect recurring patterns and propose a profile that contains demographic, behavioral and psychological attributes. The generative part can produce call scripts that will suit the different persona profiles, planting the right key words and tone of voice instructions. The mechanism could also automatically associate customers from CRM/ERP systems to the right persona segment, which will trigger sales & marketing automations. AI as a tool to make life easier for Marketing teams.

  2. Customer Journey Mapping: mapping a customer journey is a task that requires gathering various stakeholders and brainstorming around the relationship cycle between the customer and the brand. The technique brings valuable gains in sharing experiences across teams, uncovering customer pain points and detecting opportunities, but the creation and maintenance of the map over time requires an effort which could be significant. AI could assist here by extracting transactional, sentimental and operational data from the touchpoints and building the framework of the journey in an automatic manner, leaving the team the work to complete it based on their knowledge. The human element here is not replaceable but could use the support of an AI engine that consolidates relevant data elements to nourish the journey with facts that support better decision making. The data processed is directly injected to the map to form a data-infused map that is a living and breathing journey (solving some of the difficulty in keeping the journeys up to date). AI as a tool to make life easier for Service designers.

  3. Personalized content: Generative AI can produce tailored and engaging content for each customer based on their preferences, behavior, transactional context and feedback. For example, generative AI can write personalized emails, generate product recommendations, create dynamic landing pages and design customized ads. AI as a tool to make life easier for Product Marketing.

  4. Customer feedback: Generative AI can analyze customer feedback from various sources such as surveys, reviews, social media and chatbots resulting in a set of insights and suggestions on how to improve customer satisfaction, loyalty and retention. Consequently, the engine could also take real-time actions to survey unhappy customers, solve issues automatically, consolidate ideas for improvement, identify churn risks, detect upsell opportunities and more. AI as a tool to make life easier for Insights teams.

  5. Process automation: Generative AI can automate various tasks such as answering FAQs, resolving issues, booking appointments and providing guidance. It can also generate natural and empathetic responses that match the customer's tone, mood and personality. For example, generative AI can power conversational agents that can handle complex and diverse customer queries. AI as a tool to make life easier for Customer Success teams.

  6. Extracting Insights: Once you have mapped various journeys, opportunities for CX improvement start to pile up. The challenge comes in classifying these opportunities and choosing the ones to prioritize for execution. That’s not an easy task, which relies on many parameters such as benefits for the customer, benefits for the business, budget and resource constraints, market tendencies and more. AI could assist by identifying recurring opportunities, consolidating them into common themes, benchmarking the potential actions versus industry standards, simulating the gains expected to the company and estimate the effort needed for execution. AI as a tool to make life easier for Decision makers.

Where will conversational AI take Customer Experience in the future?

The technologies capable of executing the various CX tasks are already available, they are just not fully orchestrated around a journey management methodology. The evolution towards this model will be gradual until a certain point in time where all the operational work will be managed by artificial intelligence engines. CX professionals will concentrate their efforts on defining the right CX strategies, developing the customer experience culture in the organization, surfacing the ROI of CX initiatives and connecting them to business goals. This is how the joint effort of AI and CX professionals will make the right use of the competitive advantage each one has in the benefit of an optimized operating model that truly seeks to continuously improve customer experience over time.

From a journey management technology perspective, the approach will expand to connect journey mapping tools and CX orchestration/insights platforms to create a holistic model of collection, analysis and visualization of customer feedback data. AI for CX will participate in the analysis of the data but will also add generative capabilities that trigger automated actions to address customer issues, needs and expectations, completing the missing ACTIONABLE piece where many companies struggle.

Why it’s time to act now to embrace AI in CX

Like many big bang tech trends, AI only works when it applies to a real and practical context, otherwise it can be lost in theory and never add real value.
This is true for the Journey Management space as well, which remains a domain that requires much human involvement and manual work.

Ai innovation cx

The future of journey management, as explained in this article, is a combination of powerful generative artificial intelligence engines together with human emotional intelligence (known as “Human-AI teaming”) that will harness the best of breed of both worlds in the service of better customer experience. Journey maps will evolve from manually maintained maps to dynamically orchestrated data infused journeys that autocorrect themselves and provoke actions that will continuously improve customer experience.

My personal estimation is that 2024 will be transformative for the journey management domain; companies, software vendors and consultants will need to shift their way of doing things to accommodate the new trends that will dominate the future.

The revolution has started and now we need, as CX professionals, to apply it correctly to make use of its power for the benefit of customer experience improvement. I am jumping on this train with much enthusiasm to discover how to best transform the theory into practice and will publish my next articles around the way this should be implemented progressively.

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