There are few things in life more satisfying than feeling like you’re getting a personalized service – something created for you, centered around you and which provides exactly what you’re looking for at that moment. As consumers, we are becoming increasingly demanding thanks to our on demand lifestyles; no longer are we willing to accept a “one size fits all” approach from our banks, supermarkets, even our employers.

Take shopping for example, if you walk into a department store you are presented with endless choices but no guidance. The sprawling rails offer you a huge number of possibilities in a range of sizes, colors and price points – most of which are distracting you from what you are really looking for.

Contrast this with going into your favorite store where you regularly shop. You are greeted upon arrival by a personal shopper and immediately engaged in conversation. What’s more, they have selected several items in your size that they believe will suit you. This is because they have the context from previous times you have been there to know what you like, your personal style and what you might be looking for. The service is phenomenal but it has come at a cost.

It is that level of personalization that is essential as we replace current customer service roles with AI tools, such as Amelia, if we want to build increased customer satisfaction. Unlike personal shoppers, it needs to be within reach of the masses.

The relevance, understanding and ability to provide us with the right information at the right time are how we build trust, which leads us to repeat visits and a higher level of satisfaction.

Amelia understands the importance of providing “just in time” knowledge. Although she has absorbed a vast amount of information on all relevant sectors, it’s her ability to draw out a customer’s intent and decipher from her internal library what information is most relevant that provides added value.

Put simply, if we don’t know the intent, how could we ever be able to provide a response that meets the situational parameters of the conversation? In essence, learning is the byproduct of interaction and with Amelia, we are capturing the audience’s intent and context – driving the creation of knowledge in the vocabulary of the user. This means that this knowledge is based on what is actually being asked for and not what we imagine to be the case. This concept is called the “outside-in model” or “just-in-time learning.” With Amelia, all user conversations result in new learnings gained, with the subject matter expert deciding if it should be stored for reuse or not.

Her ability to pass over questions she cannot answer to a human customer services representative, listen in on that conversation and absorb these responses allows her knowledge bank to increase at a phenomenal rate. This scale of learning is something that just cannot be replicated within a human team – we lose vital information every time an employee leaves and even the smartest human has limits on their ability to retain knowledge.

The difference between providing information based on assumptions rather than what the customer actually wants is where technology, such as AI, risks falling down. We need to be creating content specifically for the audience in question if we want to provide an elevated level of customer service, similar to a visit to their favorite store.

It is not enough to recite facts, figures and reams of information in a scattergun approach. Savvy customers want information that is relevant, targeted and which provides them with just enough without overwhelming them. If we want to create trust in knowledge workers as they become increasingly integrated in our lives, we need them to be able to offer the same insight as humans, but with the added value of accuracy and increased efficiency: just-in-time, just enough and just for you.