This is excerpted from our Ebook, “Unleash the Power of Generative AI for Customer Journey Orchestration.”
Artificial Intelligence (AI) is enabling powerful advances across every industry and helping to solve many complex challenges and driving improved business results. In banking, for example, financial institutions are using AI to strengthen predictive analytics, automate repetitive tasks, improve voice recognition and combat fraudulent transactions. The healthcare industry is using AI to fastrack disease detection, provide personalized treatment plans and automate diagnostics, and retail organizations are using AI to improve and automate inventory management, among a host of other applications.
At the core of all these applications is the growing importance of AI in supporting the customer experience. With AI, businesses can provide personalized, efficient and frictionless interactions with customers at scale - a requirement for organizational success in a world where consumers are accustomed to real-time service and support across their channels of choice. However, the successful implementation of AI as a value driver requires careful thought and consideration of customer needs and expectations.
In the lightning-fast business world of today, customer experience (CX) is a make-or-break factor for success. Businesses, regardless of size, need to understand their customers' journeys, scale them up, automate tasks, empower employees to respond in real-time, and connect partners to these journeys to drive customers towards completion.
AI is revolutionizing the way organizations approach CX management, providing them with the tools and insights they need to deliver personalized and connected experiences to customers.
Before we dive into the value of AI in CX, it's important to understand what AI is and how it works.
The first thing most people think about when they hear “AI” is sentient robots ready to take over the world. The reality, however, is far less eventful.
AI refers to computational technologies that can perform tasks that typically require human input by mimicking aspects of human intelligence, including learning, reasoning and self-correction. AI includes many subfields, each focused on solving specific problems. Companies leverage AI to automate tedious tasks at scale so that employees can focus their time on innovation and problem solving.
Narrow AI is focused on addressing very specific tasks based on “common knowledge” and limited to the tasks they are designed for. Examples of narrow AI include voice assistants like Alexa or Siri.
Broad AI refers to AI systems that can understand, learn and perform a wide range of tasks similar to what a human being can do. Examples include systems within a bank that can analyze the balance sheets of corporate customers to recommend optimal hedging strategies.
General AI goes beyond narrow AI and broad AI by achieving a level of intelligence that is comparable or surpasses human capabilities. It remains an aspirational goal for AI research and is far from reaching its potential.
For the enterprise, a fine balance between Narrow and Broad AI is optimal to support the vast majority of their use cases. Specifically, there are three core areas of AI within these domains that are critical to creating connected customer experiences:
According to PwC, AI is set to be the key source of transformation, disruption and competitive advantage in today’s fast changing economy, with the potential to contribute up to $15.7 trillion to the global economy in 2030. Similarly, Global Market Intelligence firm IDC predicts companies will use AI interactions and analytics to help automate customer engagement, eliminating over 40 percent of human touchpoints in marketing and sales.
Generative AI is a subset of both Machine Learning and Natural Language Processing that focuses on generating new content or outputs based on patterns from a given dataset. Generative AI models are trained using large datasets to capture underlying structures in the data, often using deep learning techniques to capture complex patterns and generate high-quality data that resembles the training data. They are also trained to iteratively adjust to minimize the difference between generated and desired outputs.
With the release of ChatGPT in November 2022, the AI game changed entirely. Organizations are now looking at generative AI as a cost-efficiency engine across their business. Although there is a long way to go in this regard, especially when it comes to brand control, accountability and quality, the reality of today’s business landscape is simple: Organizations that fail to capitalize on the potential of generative AI risk falling far behind their competitors.
Today’s consumer has an abundance of options for almost every product imaginable, making it easy to switch brands with a few clicks of a button if their expectations are not met.
According to PwC’s Customer Loyalty Survey 2022, more than 25% of respondents had stopped using or buying from a business in the past year, largely because of poor experiences and subpar customer service. Similar numbers are echoed across other recent customer loyalty and satisfaction surveys, signaling an important reality: in a world where product differentiation is diminishing across competitors, CX is the ultimate differentiator when it comes to customer satisfaction, loyalty and business success.
However, providing exceptional CX is not without its challenges. Some of today’s top CX challenges are:
When properly implemented, generative AI expands the scope of what can be automated across several fields. From this foundation, it can support businesses in overcoming the hurdles that prevent them from delivering outstanding CX:
Read the next article: “Six Key Generative AI Use Cases for Journey Orchestration”