What it will take to go beyond “personalization” with AI
Organizations are racing to apply the latest AI breakthroughs to their perceived needs. Many are chasing the promise of personalization at scale in areas like product discovery, marketing content and customer service interactions.
And they’re right to. Improvements in personalization thanks to AI could add billions of dollars in value to the economy and particularly to the benefit of specific companies.
But chasing this kind of personalization is thinking too small.
How personalization lost its power
“Personalization” is at risk of becoming a dirty word in the minds of a lot of customers. The once-powerful concept has been watered down by overuse in one particular area — targeted marketing.
Too often, when businesses tout their personalization efforts, it simply means they’re using aggregate data to target digital ads at specific customer demographics. Or they’re using data about an individual’s buying behavior to tweak their email campaigns in the hope of increased promotional conversions.
These practices have some merit, and if they’re done carefully they can add measurable value and efficiency to a business’s marketing efforts. But they don’t actually add much real value for the customer. “Personalization” increasingly means something done to us, by companies for their sole benefit.
True personalization should be something done for us as customers. Or better yet, with us. I prefer the term “individualization.”
Individualized products and services are not just variations on a standard offering that people “like you” have preferred in the past. They are offerings that take your intentions and motivations into account. It’s really a process of co-creation between organization and customer.
A new kind of individualized relationship with customers
In a recent piece for The Conversation, my friend and Inrupt’s Chief of Security Architecture, Bruce Schneier, does a great job of explaining how AI enables this totally new type of relationship between organizations and customers:
Imagine your next sit-down dinner and being able to have a long conversation with a chef about your meal. You could end up with a bespoke dinner based on your desires, the chef’s abilities and the available ingredients. This is possible if you are cooking at home or hosted by accommodating friends.
But it is infeasible at your average restaurant: The limitations of the kitchen, the way supplies have to be ordered and the realities of restaurant cooking make this kind of rich interaction between diner and chef impossible. You get a menu of a few dozen standardized options, with the possibility of some modifications around the edges.
That’s a lossy bottleneck. Your wants and desires are rich and multifaceted. The array of culinary outcomes are equally rich and multifaceted. But there’s no scalable way to connect the two. People are forced to use multiple-choice systems like menus to simplify decision-making, and they lose so much information in the process.
Artificial intelligence has the potential to overcome this limitation. By storing rich representations of people’s preferences and histories on the demand side, along with equally rich representations of capabilities, costs and creative possibilities on the supply side, AI systems enable complex customization at scale and low cost. Imagine walking into a restaurant and knowing that the kitchen has already started work on a meal optimized for your tastes, or being presented with a personalized list of choices.
It’s a simple example, but a profound concept. Apply this same dynamic at a broader scale within industries like retail, media or financial services (which Bruce does in the rest of this article), and it becomes clear that he’s really talking about customer-driven product creation and business growth. For example, imagine the increased customer loyalty and business efficiencies that a bank could drive by quickly creating a completely new bundle of loans, insurance coverage, and investments based on your unique needs and budget. That’s much more powerful for both businesses and customers than the narrow kind of personalization so many are chasing today.
The missing piece
But read Bruce’s words closely and you might notice a big question left open. He writes:
By storing rich representations of people’s preferences and histories on the demand side, along with equally rich representations of capabilities, costs and creative possibilities on the supply side, AI systems enable complex customization at scale and low cost.
Storing data on preferences and past behaviors “on the demand side” means not just storing data about customers but actually storing it with customers. This is absolutely crucial to the relationship because customers are the only party that can actually have access to all the information about their preferences, histories and intentions. No matter how good organizations are at collecting and analyzing data, no single business can ever have a full view of us as individual customers, unless we’re willing to help them out.
But almost none of this data is stored “on the demand side” today. All customer data is instead stored on the supply side, tightly coupled to a business’s data about costs, capabilities and system performance.
The tight coupling of two very different types of data saps all the power and potential out of the AI-enabled system that Bruce describes.
What he describes is, after all, a two-sided relationship — an effective system for matching organizational capabilities with customer needs. That’s impossible if the customer has no tools to describe their needs.
In order to enable AI-driven individualization of their products and services, a business needs to first find a way to store customer data in such a way that the customer can curate, update and augment that information.
Of course, that’s exactly what a Solid Pod does.
Pods are purpose-built to enable rich, trusted information exchange between organizations, apps and users. As Bruce describes, the addition of AI turbocharges the value of Pods by making it easier for all three parties to use that rich data in mutually beneficial ways.
As usual, my co-founder Sir Tim Berners-Lee foresaw this end state years ago when he coined the name “Charlie” — a truly personal AI assistant made possible by its access to Pod data. Now it’s just a question of which organizations first capture the opportunity to take us all beyond “personalization” and into a world of individualized products and services.