Weekly Business Insights from Top Ten Business Magazines | Week 308 | Strategy & Business Model Section | 1

Extractive summaries and key takeaways from the articles curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Since September 2017 | Week 308 | August 4 – 10, 2023

What Smart Companies Know About Integrating AI

By Silvio Palumbo and David Edelman | Harvard Business Review Magazine | July–August 2023 Issue

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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. The experience of the companies such as Starbucks, 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.

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.  The four keys to smart integration are:  

1. Clarity and alignment of goals. AI-based marketing requires clear optimization objectives for every use case, and those goals need to be reasonably narrow. Broad general objectives, such as “accelerate sales growth,” make it impossible to know how to attribute results. A more appropriate objective for AI might be “minimize wait time,” “lower the incentive cost per sale,” or “make a suggestion the customer will accept.”
2. Sound data instrumentation. The mechanisms that record, organize, and share data on customer interactions, the company’s associated actions, and outcomes across touchpoints are the nuts and bolts of a company’s AI personalization program. This data instrumentation includes everything from call center logs and data sourced from second and third-party relationships (such as channel partners, media companies, and data brokers) to automation software that generates and tracks digital communications (such as that from Salesforce, HubSpot, and Illumin).
3. A loosely connected tech architecture. The customer-experience technology stack consists of a prediction engine, a sequencing (or experience management) engine, a content engine, channel delivery engines, and an experimentation and analysis engine. In addition, the AI draws from five or more systems to stitch together a customer journey: marketing, customer service, product usage, billing, online channels, and sometimes a retail store. Given the likelihood that new capabilities will be added and that several AI engines may need to be plugged in, it is best to design the stack in a modular way.
4. An experimental culture. AI stokes creativity by allowing a company to test ideas rapidly and to do more at scale. Furthermore, it learns from the past, across millions of data points, unlocking innovation quicker than a human could. But AI does not invent; it just predicts, on the basis of past patterns. Marketers invent, and the AI learns what works, for whom, when, and how. Invention requires a culture that values experimentation and risk-taking.

3 key takeaways from the article are:

1. 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. The experience of the companies such as Starbucks, debunks the prevailing notion that extracting value from AI solutions is a complicated technology-building exercise.
2. 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.
3. The four keys to smart integration are: clarity and alignment of goals, sound data instrumentation, a loosely connected tech architecture, and an experimental culture. 

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Topics:  Strategy, Technology, Artificial Intelligence