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Illustration Credit: Edoardo Tresoldi
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Mercury, a former BCG client, is not in the business of building technology, so its leaders decided to begin with open-source AI. Given that most such tools charge according to usage, the upfront fixed costs would be low. Mercury concentrated on how to integrate available AI solutions with its content-management, fraud, and eligibility systems, and many other front- and back-end systems. The company then automated its marketing processes, again drawing on available AI tools but using its own code for all the tests needed to learn what worked for whom and for tracking past results. The system it built focused on managing hundreds of variables for targeting purposes and creating content in a microgranular way. Within six months the pilot had generated a 10% improvement in actions taken as a result of the fintech’s messages. Mercury knew it was on to something big.
AI is required to achieve precision and scale in personalization. It can gather, analyze, and use enormous volumes of individual customer data and tailor the customer journey at every touch point. Mercury’s experience, and the experience of CVS and Starbucks (which we’ll explore in detail), debunks the prevailing notion that extracting value from AI solutions is a complicated technology-building exercise. That thinking keeps companies from capturing the power of AI. They needn’t build it; they just have to properly integrate it into a particular business context.
When you recognize the value of focusing your resources on integration and process change, it sharpens what you look for in an AI system. You begin to understand the importance of seeing your data and the design of your tech architecture as competitive assets. And you push the rest of your organization to drive more testing that can feed the intelligence of your AI system.
But AI is probably only about 10% of the secret sauce. The other 90% lies in the combination of data, experimentation, and talent that constantly activate and inform the intelligence behind the system. Personalization is the goal; it’s what constitutes a company’s strategic brawn. The technology is merely the tool for reaching it. In this article we’ll present what it means to integrate AI tools and what it takes to continually experiment, constantly generate learning, and import fresh data to improve and refine customer journeys.
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