From manufacturing to sales, and customer service to marketing, artificial intelligence (AI) use cases span every business department across all industries. Conversational AI is a common use case for enhancing customer experience, and one that is familiar to many consumers. Live chatbots are ubiquitous for helping customers find a product, for example, to handle simple queries, or to simply ease the workload of customer service agents.

Yet as AI use cases ramp up, businesses need to consider a use case not only from the perspective of enhancing one aspect of a customer’s experience, but its contribution to the overall customer journey. The real question to ask when deploying AI or machine learning, in other words, is whether the application or model will produce revenue. Instead, what often happens today is businesses will deploy AI on one channel – a live chatbot, for instance – that is not integrated with every other channel. A customer may have a pleasant exchange with a chatbot, and it may even materially improve the CX, but how often is a record of that conversation included in a unique customer profile, or shared instantly with every part of the business that might need it (such as an actual customer service agent)?

Using AI to offer convenience to a customer is a good start, but any benefit will quickly erode if the customer has to ask the same question elsewhere, or re-start a process. A chatbot that does not know anything about the customer beyond the issue at hand, or beyond the data that resides in that one channel, will cease to provide value to the customer the moment the interaction ends.

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Move at the Cadence of the Customer

For AI and machine learning to move from offering incremental gains to becoming true revenue-generating engines, advanced analytic models must be embedded across the complete customer lifecycle, spanning every channel or touchpoint. That is the difference between a standalone use case that may offer basic personalization vs. a differentiated customer experience that is highly personalized in the cadence of a unique customer journey, based on an individual customer’s updated behaviors, interests and intent, and delivered in real time on any channel.

Embedded intelligence with code-free, self-training machine learning models provides a differentiated CX because the models are based on a single customer view, a persistently updated, real time unified customer profile or golden record that tells a brand everything there is to know about a customer. A golden record combines every unique customer ID with a long-tail transactional record that includes all behaviors and preferences.

Intelligent, lights-out models run 24/7 looking for opportunities in the golden record. Simulation engines constantly iterate models, and will move new models into production that predict better outcomes based on testing results. To be able to move at the cadence of a customer, one key facet of embedded intelligence is that models do not have to be built offline by data scientists based on predictive rules. The problem with models based on a set of predictive rules is that the models go stale quickly, such as when a golden record is updated, or a business changes its desired outcome.

Alternatively, in-line models tuned to exploit revenue opportunities in a dynamic golden record never go stale because they’re optimized for the dynamically updated single customer view; brands can run hundreds or thousands of models concurrently, all with a single-minded purpose of delivering a next-best action unbound by channels or data siloes that is perfectly synchronized to the precise moment of interaction.

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Capitalize on the Moment of Interaction

With the chatbot example, we’ve seen how the power of AI is muted when there is no synergy between channels, and thus no contextual awareness of a unique customer journey. With embedded intelligence, by contrast, marketers and other end users of the data will know in real time everything there is to know about a customer. A call center agent or chatbot will know, for instance, if a customer who receives a specific offer then went to the website anonymously before engaging with the agent or chatbot. Within milliseconds, a machine learning model will produce real-time information about the customer, and it will produce a next-best action based on the consistently updated single customer view.

Used to its full potential, AI does what humans cannot, which is to account for thousands of permutations along a path to purchase for millions of customers and generate a highly personalized, relevant next-best action – at scale – in milliseconds. Embedded intelligence accounts for up-to-the-second change, knowing that the context that produces one result one minute – a discount offer, an email, a notification, etc. – may produce an entirely different result the next minute or the next seconds depending on the customer’s next action, new data, or an updated prediction of the customer’s intent. That is the true power of AI at work in the context of delivering a superlative customer experience that drives revenue.