Here is an excerpt from an article written by Silvio Palumb and David Edelman for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.
Credit: Edoardo Tresoldi
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Talent and data are just as important as technology.
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.
Rethink How You Acquire Technology
In more conversations than we can count, we find ourselves disabusing executives of their notions about what creates an AI advantage. A company needn’t aspire to be another AWS, Microsoft, Google, or Adobe—all builders of core AI tools that are, after all, in the business of selling them. Familiar masters of AI, such as Uber, Netflix, and Spotify, may research and design new solutions, but generally they do so to extend applications that accommodate their uniquely huge scale or to perform specific functions not otherwise available (such as movie-frame analysis in the case of Netflix’s recommendation algorithm). But few companies outside the tech world are monetizing their own digital innovation. For them, innovation involves offering a fresh solution atop a base of digital capabilities. Competitors all have access to the same AI, yet business outcomes vary profoundly. One critical difference is the data a company feeds it. Competitive advantage hinges on unrelenting data collection, curated transformation or enrichment, and feeding the AI libraries that inform next-best-action capabilities. The marketer’s job is to creatively apply those AI-driven recommendations to marketing campaigns and iteratively learn from them
A wealth of open-source technology exists today, including most AI tools—broad ones, such as GPT-4 from OpenAI, and full-fledged libraries (applications written in open-source languages that are packaged for a specific use, such as XGBoost, for training a specific type of machine-learning model). Big tech makes many of its libraries or task-specific tools available—Meta (a BCG client), for instance, with its Prophet library for forecasting, and Airbnb with Airflow, a workflow management platform for data engineering pipelines. AI capabilities are embedded in many common customer-experience tools, such as the “experience clouds” of Salesforce and Adobe (both companies are BCG partners). Those are constantly improving too: Thanks to application programming interfaces (APIs) and the architecture of modern tech systems, it has become easier to get systems to talk to one another, as we’ll explore later.
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Here is a direct link to the complete article.