Part 2 in the 3-part “Driving Innovation with Conversational AI” series
In my recent blog series UX Design for CIOs, I analyzed trends fueling the rise of conversational AI. Now, let’s look at how conversational AI can be used to innovate your business model. To do so, I will use one of the most successful business model representations currently: the Business Model Canvas (Osterwalder and Pigneur, 2010), which has been widely taken up by practitioners. Since I am a big fan of the business model canvas, I will use this framework as a guiding light, analyzing how the injection of conversational AI propagates through the canvas, thus transforming the business model. The focus will be on the following building blocks:
- Value proposition
- Customer relationships
Please note that the business model canvas has nine building blocks; I am concentrating on those where the impact and propagation effects on other blocks are greatest. In my approach, I will loosely follow the digital business modelling methodology described by my colleagues Uwe Riss and Marco Cigaina in their excellent white paper.
Let’s consider conversational AI as a digital capability. Other examples of digital capabilities are the Internet of Things, Big Data, blockchain, machine learning, and so on. Introducing a digital capability will affect each of the building blocks of the business model canvas. A company’s business model describes how the organization creates, delivers, and captures value. Therefore, I am interested in how the addition of a digital capability like conversational AI to an existing business model will generate value, and how it will affect the other business model components.
So, enough with theory – let’s put it into action!
Innovate your value proposition
The heart of the business model canvas is the value proposition: the value delivered to specific customer segments, which customer problems are being solved and which needs are being satisfied.
In the picture below, you can find a representation of the business model canvas. Different blocks and colors show the digital value drivers, and explain the impact of injecting conversational AI into the value proposition. The arrows indicate the relationship between the different components, and nicely show the logic behind the business model.
First, let’s have a look at the blue boxes and arrows. Conversational AI has yielded completely new products and services that did not exist before (I am ignoring sci-fi movies here). Examples are Amazon Echo or Google Home, which are both smart speakers meant to be intelligent personal assistants at home. The Amazon Echo, introduced in 2015, marked the start of a whole new product category. This new product category of smart speakers appeals to new customer segments in the market, segments that Amazon or Google, or any other manufacturer, did not address before. In this way, smart speakers create new revenue streams when customers are buying these new devices.
A slightly different impact of conversational AI is depicted with the green boxes and arrows. Here, conversational AI is used to enrich existing products and services – that is, it provides extra functionality on top of an existing product. A good example is Apple Siri: it was introduced in 2011 as an integral part of the iPhone 4S, augmenting its value proposition. At that time (and probably still today), nobody bought an iPhone exclusively because of Siri, but having the assistant available at one’s fingertips (literally!) certainly contributed to the overall value perception of the device. The same goes for Google Assistant.
Another example, but in a business context, is SAP CoPilot, SAP’s digital assistant for the enterprise. SAP CoPilot offers new ways of interacting with SAP software, with features like collaboration and context awareness – and soon, also natural language interaction.
Adding new functionality to existing products
For all these examples, conversational AI has added new functionality to existing products and services to address a wider range of customers. The new functionality will appeal to a wider audience so more people will be converted into customers, leading to incremental revenues. Incremental revenues will also be generated when new functionality leads to increased usage of your product or service.
A special group worth mentioning in this context are users with special needs, such as blind or visually-impaired users, or users with physical disabilities who cannot easily operate graphical user interfaces.
The yellow boxes and arrows show some “spinoff effects,” which create value and indirect revenues. One of the effects of conversational AI is that it lowers the barrier to interact with a product or service. Combined with its ubiquitous access, the effect will be that you can more easily lock customers into your ecosystem. For example, it’s likely that a user of Amazon Echo also uses other Amazon services such as Amazon Prime.
Another effect is the collection of customer data through millions of conversations with users. This data is a true goldmine. Not only can it be used to train and improve the machine learning models powering conversational AI; it will also allow for more personalized conversations and advertisements. It’s a closed loop: the more conversation you have with the system, the more the system gets to know you and learns your patters and preferences, and the better it will be at predicting your needs and making suggestions.
In my next blog, we’ll explore how conversational AI can change the way you interact with your customers.