A couple of months back, I was on my way to a medical appointment and the service loaner vehicle that I was driving suffered significant tire damage due to an unforeseen road hazard condition. The car detected “the collision”, and the vehicle immediately offered the option to connect with an Emergency Response Specialist.
The agent confirmed that I was safe and dispatched a tow service for the vehicle to be towed back to the dealership. Unfortunately, this was about the only high point of the experience! My requests for a replacement vehicle were met with rude and indifferent responses from the emergency agent as well as customer service personnel at my dealership. I was forced to stay with my vehicle until the tow service arrived despite mentioning that it was crucial for me to make my doctor’s appointment. No alternative transportation options were offered, and I had to wait for someone from my family to finally drop me off at the doctor’s.
This is a classic, and unfortunately, all too common example of an experiential disconnect. Where the brand failed on customer experience (CX) was not being able to connect the different pieces of my loaner vehicle journey to my expected outcome. Past insights such as my tenure as a customer, service/sales referrals, past service records, amount spent at the dealership, long standing relationship with my service advisor etc., felt neglected.
Intelligent orchestration allows enterprises to bring forward relevant insights from customer, organization, and other stakeholder relationships like the insurance company, tow service, or rideshare companies and apply these insights to the current interaction. Making this complex connection across numerous different contexts within a single customer interaction and correlating it to understand and meet a customer outcome, is where generative AI (GenAI) shines.
GenAI enables deeper and more accurate contextual awareness in customer engagements and more accurate recognition of customer intent. As a result, experiences are closer to customer desired outcomes.
With its ability to apply generative foundation models that can be trained on extraordinarily large amounts of data, GenAI is positioned well to strengthen foundational capabilities that power intelligent experience orchestration. These include:
Capturing richer customer context: Context defines a customer’s immediate need(s), and refers to the customers’ updated preferences, prior and current actions, behavior, sentiment, intent, location, goal/purpose, and circumstances.
Customer conversations are built on semantics, structures of concepts and observations that need to be inferred – an area where GenAI vastly outpaces predictive and interpretive AI.
GenAI was born to find patterns and correlations in unstructured data (e.g., sentiment, intent, emotion etc.), driven by trained sets of vast collections of textual data organized by discovered proximities of usage.
Improved context continuity: Context continuity means to be able to bring forward relevant insights from past customer, organization, and ecosystem, data, actions/inactions, and outcomes and apply these insights as relevant to the current engagement scenario.
The 2023 Future of Customer Experience survey found that just about a fifth of enterprises globally have the capability to maintain context continuity for all customers across all their brands. Other factors, which will continue to impact enterprises’ ability to manage context continuity, will be the massive proliferation of channels as well as the rise of connected customer data and insights. With GenAI, customer journeys can be infused with insights across multiple modes/channels.
Anchor customer journeys on outcomes (vs. outputs): In the earlier service incident example, the brand’s loaner vehicle journey only focused on making sure that the vehicle could be returned to the dealership – i.e., the output, and inward looking, organizational output at that. While required, this action begs the question if customer outcomes were even considered as part of the design?
GenAI inherently makes use of a declarative approach where a goal is the starting point. Combined with foundational models, trained on a vast knowledge base of richer contextual customer data, GenAI can even assist enterprises to begin journey design with specific customer outcomes. In addition, GenAI’s active learning capability can adjust customer interactions and journeys to meet these outcomes by actively accounting for changes in real time in customer needs, emotions, and intent in each interaction.
Improved system of connected insights: Optimizing orchestration depends on industrializing a system of connected insights – i.e., consuming a continuous stream of accurate and authentic customer intelligence.
The ability to consume vast amounts of unstructured data across channels/modalities offers enterprises a low-cost way to make collection of insights a byproduct of their customer engagements and not a separate process. Industrializing insights also includes wiring a deep understanding of customers into the company’s day-to-day actions – essentially, customer insights driving the business model.
Real-time journey automation and optimization: A key part of delivering intelligent orchestration is the fundamental automation capability required to connect data, tasks, and outcomes together. At its core, automation comprises of data and process connectivity and correlating insights to determine and execute actions.
With customer journeys becoming more non-linear, GenAI can increase the adaptability of customer journeys to be more dynamic. The model can evolve new responses based on changing customer/business events, while keeping the customer outcome constant. For instance, the exception noted in the earlier service loaner vehicle example. GenAI’s LLMs can suggest alternative journey steps or even navigational pathways (multiple journey steps), essentially redesigning journeys dynamically.
While GenAI excels at connecting and orchestrating insights at scale, it will not solve for the most crucial gap plaguing customer experience transformations – delivering value parity. Parity in the value exchange with customers means that the customer and the organization, equally, get something meaningful out of the exchange. Customer value parity is crucial since an imbalance can lead to loss of customer trust, and often, ends in customer attrition.
The age of AI Everywhere promises to offer enterprises significant experience-based market differentiation. However, to grow profitably in a highly competitive digital economy, enterprises must capitalize on intelligent orchestration, anchor on customer desired outcomes, and aim to achieve value parity between customers and brands.
Visit our Future of Customer Experience website to learn more about achieving customer empathy at scale and get more insights on our thought leadership for how intelligent customer experiences can drive profitable growth.
