How Does AI Get to the Mass Market?
In our August newsletter we looked at the mass market for AI technology. We focused on the question, “Can we identify a mass market application or business model to justify the huge valuations of AI companies, and companies that provide the tools and hardware that AI systems require (like Nvidia’s chips).” We used the scenario planning approach to conduct a thought experiment looking at four different possible futures for the AI market. [In case you missed it, you can read that newsletter here.]
The newsletter received a lot of attention, and one of the many responses we received was from Fred Brown, who is indeed using AI in many advanced applications to accomplish work that humans cannot do. So I asked Fred it he would allow me to interview him, to which he graciously agreed.
The full video of our 26 minute conversation can be found [here], on a new YouTube channel we have set up called “AI Strategy.” We have lined up quite a few additional interviews and will be adding them to the channel on a regular basis.
Here are some of the super interesting highlights of the conversation with Fred, in which he highlighted two powerful use cases that are already providing huge value and showing exactly how AI can impact science, business, and society.
AI Accelerates Drug Development and Testing
Fred describes a detailed use case about a company he supports called GATC. Over many years, GATC developed and tested an AI model using the data from millions of past clinical trials for new drugs, and now their AI system looks at tens of thousands of factors simultaneously to predict the likelihood of success of a clinical trial. The underlying functional category here is “pattern recognition,” and it’s on a massive scale that is far beyond what any human could possibly do. Thus, AI is advancing human knowledge in a way that only it can.
This capability enables GATC to simulate a clinical trial to assess its risk, which turns out to be an incredibly useful tool for the scientists, of course, and also companies like Lloyd’s of London, which insures clinical trials, and banks, which lend to pay for clinical trials, and investors, who buy the stocks of pharma companies. In other words, AI is fundamentally enhancing the entire scientific-business ecosystem around new drug development.
There are currently around 490,000 clinical trials under way globally, an astonishing number which suggests how large GATC’s market is, and gives us an idea of how enormous its impact could be.
Thus, it seems likely that one day a drug that your doctor may prescribe for you may have been developed and funded with the help of GATC’s technology, which will have probably compressed the long process required to bring you a therapy or treatment, making it available much faster than was ever possible before.
GATC’s web site promotes its capability this way: “We have successfully changed the way drugs are discovered, disease is predicted and human health is improved through AI”
AI Improves Health Care Access and Equity
Fred also shared a mass market example. ArtiMed. a company where he is CEO, uses AI to assess up to 12,000 variables to help patients, doctors, hospitals, insurers, charity and services organizations determine which members and patients are eligible for what health benefits.
The eligibility criteria for disability insurance, Medicaid, and community support and charity organizations that help with medical and social service assistance are so complex and constantly changing that social workers and healthcare professionals simply cannot keep up with the opportunities that their patients may be eligible for. ArtiMed's AI-ML based algorithms, proprietary data sets of tens of millions of patients (hence, mass market), expert systems, and large language model provides a turnkey solution that patients, health care providers, and social workers can use to determine and enroll patients to obtain the benefits they are entitled to.
This problem of eligibility complexity makes the situation of medical debt much worse for millions of people, and also worsens that problems of access and equity that many patients are experiencing in the U.S. today. With this solution in hand, ArtiMed is working with the Mayo Clinic to integrate this new capability into Epic, the leading electronic health records system in the US market, with more than 300 million records.
Upon completion of the installation, expected by the end of this year, ArtiMed will thus become immediately available to thousands of hospitals, tens of thousands of social and community workers and over 100 million patients. The new access to benefits that ArtiMed offers will provide those in greatest need with new sources of income, insurance coverage, and services to help them and entire communities improve their health care and their lives.
The Business Model: Embedded Enabler
These two examples help us to more fully understand one of the most impactful business model innovation opportunities that will ultimately impact literally everyone. This certainly meets the definition of a “major mass market,” but they also reveal the use of AI not on the front end, consumer-facing side of products and services, but rather embedded on the back end to analyze, sort, risk-assess, predict, and thus advise companies and consumers on complex choices that they must make.
While AI systems like Siri and Google Search are already sorting knowledge and providing an agent-assisted interface for consumers, Fred’s two examples show how AI will also impact the market in equally significant ways through new business models of which AI is the “embedded enabler.”
We can thus expect that entrepreneurs and developers will find (are finding) AI applications wherever there is a lot of data and difficult choices to be made. And where consumers must interact with a complex system, such as in health care, finance, real estate, and government. To which idea Fred commented, “Oh, one hundred percent. AI will be your agent.”
For AI systems to be effective, he added, scale is mandatory. “If we tried to do GATC with a data set of ten million, we would fail. We’re doing pattern recognition that requires data sets of hundreds of millions.” Which, again, humans cannot do, but AI systems can. Hence, a unique sweet spot for a transformative technology.
In our discussion Fred and I also talked about the critical issues of bias in AI systems, privacy, security, and trust. You can watch the full, and fascinating, 26 minute-long interview here.
Thanks, again, to Fred for taking the time to educate us!
As noted above, given its huge impact and importance, we’re lining up additional interviews with leaders in AI from across the full spectrum of economic activity and use cases. We’ll bring these to you in the coming months. If you know of someone (such as yourself) who we should interview, please let us know by contacting lmorris@innovationlabs.com.
Early Warning
Systems Modeling
In a recent talk, Michelin’s brilliant Chief Information Officer Yves Caseau provides a fascinating look at the practice of systems modeling, and explains how he is using very advanced techniques to help Michelin’s top leaders make critical business decisions. This is essential viewing (a bit on the nerdy side) for systems thinkers and futurists.
Yves also has developed a fine web site for his systems modeling work, which you can find here: https://sites.google.com/view/modelccem/home
We plan to interview Yves in the coming weeks, which is sure to be a fascinating conversation. We will share the video on our channel.
The Chief Innovation Officer
Are you a Chief Innovation Officer, or do you aspire to become one? Here’s a great discussion led by Scott Kirsner of Innovation Leader, in which a group of really knowledgeable people discuss the past, present, and future of this vital role in the corporation.
Thank you, as always, for reading our newsletter.
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To learn more about InnovationLabs and Langdon Morris, go to www.langdonmorris.com