Board Member Advice & Prediction of Positive Lifestyle Changes
Two timely articles in the Harvard Business Review (HBR) explore how AI will impact both the boardroom and daily life for the average person, providing a peek into how everyone will either need to adapt to the change or expect that technology will learn to adapt to them.
An article by Douglas Merrill, CEO and founder of ZestFinance, gives a series of tips on how board members will need to get up to speed on AI technologies to keep their companies from being left in the dust. Quoting a 2019 Gartner CIO survey that says half of those queried plan to implement AI systems by the end of 2020, he points out that just 14% use AI in some capacity today⏤comprising a giant leap into the future.
Citing another study by NewVantage Partners, 75% of companies gave the top reason for investing in AI as FOMO⏤Fear of Missing Out on business if competitors who benefit from data eat their lunch; or more drily stated as “disruption.”
“The questions boards are going to have to ask themselves are similar to those they would ask in the face of any large opportunity investment: Why are we spending all this money? What’s the economic benefit? How does it impact our people and our long-term competitiveness?” Merrill writes.
He proposes following these guidelines before blindly investing in what could become incredibly expensive and sometimes problematic technologies that are still being refined:
- It’s math, not magic. Boards should realize that any machine-learning or AI purchase should be an outgrowth of their business model. They don’t need to be experts, but they do need to be well versed in the technology important to their particular business segment.
- Well-run AI projects should be easily understood. “The best-run AI projects should be explainable in plain English. It should be clear how real groups of people, whether employees, customers or management, will be affected.”
- You don’t have to get creepy to get value out of data. No need to find out what customers eat for breakfast. It’s more important to gather relevant data. Besides, Merrill asserts, “Our work developing machine learning-based credit underwriting models with banks and lenders has shown that social media data doesn’t provide such strong signals, anyway.” Machine learning programs can mine the important information that helps tailor the company’s offerings to its customers.
AI in Daily Life
Theodora Lau, founder of Unconventional Ventures and co-host of Rhetoriq, a tech-related podcast, imagines in her HBR essay what life will be like when we have AI at our disposal. She envisions a world where robots and AI make everything easier, faster and better, but with some caveats.
“Beyond making reservations and conducting simple dialogues, virtual assistants will need to become far more useful and further integrated into the fabric of our everyday lives. Not only will they need to anticipate what we need before we ask, they also need to understand the context of our conversations and react accordingly. Imagine a snow day where school is cancelled for the kids. Knowing that you must now stay home with your children, your phone would prompt you, asking if you’d like your meetings moved to the following day; your entertainment console would automatically suggest movies to watch and e-books to read. Best of all, your smart speaker would recommend meal options for lunch while you are out shoveling snow.”
A business traveler’s AI will automatically arrange for a ride based on their travel itinerary and ask questions to make additional decisions. Banking will more or less “disappear” as AI takes care of all transactions seamlessly.
The only drawback could be potential complications.
“Imagine that one day this ambient technology knows us so well that it can act as our personal CFO and continuously help us get to the best financial outcomes over time, based on its knowledge of our household, our life choices, our health, and our longevity. Will we trust it enough to make decisions for us automatically? A large part of that will be driven by the society’s perception and acceptance of machines.”
Already Japanese consumers accept robots who work in hospitals and nursing homes to assist them, children learn from educational robots who teach them better English skills and even find companionship through holographic AI characters through Gatebox.
Through gathering data about each person, an AI will be able to “learn, process, and augment creates a symbiotic relationship between humans and machines.” Amazon, Lau points out, is already working on enabling Alexa to have conversations with its customers in an empathetic way and suggest products to help them.