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Three Ways to Lose with AI in Equipment Finance

Date: Jan 03, 2024 @ 11:00 AM
Filed Under: Technology

Every new technology presents risk. If we jump in as early adopters, will we lose our investment and time we could have spent on something better? If we wait, will we lose market share to the early adopters? The key to managing risk is avoiding undesirable outcomes; so if a business can identify ways to lose with AI, the odds of gaining competitive advantage using it go up.

Such is the lesson in a recent article titled “How to lose at generative AI” by MIT professor Sanjeet Paul Choudary, author of Platform Thinking, whom I found several years ago while researching the keys to building internet-connected business platforms, two-sided marketplaces, such as YouTube, Etsy, Airbnb and in the equipment finance space, Syndifi. Looking at a problem from the opposite direction is often illuminating and I immediately found his analysis provided insights into how others will win and lose with AI.

Following Choudary’s lead, here are three quick ways to lose with AI in equipment finance.

Fight the Inevitable

Evolution trained us to either stand-and-fight or run-to-safety when we encounter unknown entities. AI has definitely emerged as such this past year - an unknown entity – and many responses have been true to our evolutionary training. The fighters are trying to stop AI’s development and application with everything from federal regulations to tech-celebrity agreements hoping to slow or stop the application of AI as an existential threat. On the other side the run-to-safety groups are adding ethical threats and effective altruism as a reason to not use or engage with AI. The most recent example of the latter strategy was the brief ousting of founder and CEO Sam Altman at OpenAI, and the former was used by the Writers’ Guild of America (WGA) in their negotiations with studio and streaming producers. WGA used AI as a disembodied “bad guy” threatening the writers’ livelihoods (existential threat) during the financial negotiations precipitated by technology innovations that changed the business model - streaming, Tik-Tok and YouTube.

In the end, the WGA struck new financial arrangements with Hollywood producers and the new generation of streaming studios that better accommodated the way technology has changed the entertainment industry. But they also came around to understanding the futility of fight-or-flight with AI. The WGA gave up on trying to get AI out of the writing process, rather it wants only WGA members to use it in the production of new content. In the end they accepted the value of AI as part of a creative process and are hoping to keep it within the constraints of union rules. Since neither Tik-Tok nor YouTube influencers are governed by WGA agreements, AI is likely to flourish as a tool for new and engaging entertainment irrespective of the WGA’s designs.
Such is the view of professed techno-optimist Marc Andreessen. Recently, he published a treatise on Why AI will Save the World that is required reading for everyone interested in AI. While some of his examples or ideas are far reaching and futuristic, the takeaway message is that people like Andreessen – and Tik-Tok creators - will engage AI and become significantly more productive and competitive. AI can enhance or strengthen every competitive strategy because it naturally accelerates decision making while constantly searching for and learning from new and better solutioning experiments, i.e., innovating.
Equipment finance companies that are engaging AI are those that are naturally curious and open to learning how AI can solve old problems faster and cheaper while enabling the application of scare resources, e.g., their workforce, to new and more important problems. Just as the WGA leaders came to realize, equipment finance teams who engage AI will be more productive, learn faster, and compete harder with better, faster decisions.

Don’t Leverage Your Data

Data is not the new oil. The metaphor is wanting because the supply of oil is finite, and oil is basically inorganic. The supply of data in a digital enterprise is infinite and the very nature of the data expands and adapts as technology advances. Data is the life force of enterprises using AI. Thus, another quick way to lose in an AI-enabled contest is to not leverage your data.

Think of AI as “just another technology”

If “the curious will win with AI” perhaps the quickest way to lose is to treat AI as “just another technology.” This was how Blockbuster treated subscriptions and streaming for entertainment. This was how Kodak treated digital imaging in a chemical supply chain. This was how Motorola treated the iPhone when flip-phones were the new norm of telecommunication. True innovators see the upside with new technology. They are curious as to what problems the new technology can solve and who cares about those problems. Innovators see making the technology invisible to users and customers as the problem. They see changing customer behavior to the benefit of both the customer with technology as the problem.
Equipment finance companies use technology, they do not produce it. Those leaders and businesses who have developed a design thinking culture of curiosity and empathy will naturally engage AI wondering, “What can this do for our customers?” “What can it do for us?” Those who have already matured in their AI-progression will ask: “What do we need to predict to win and be more productive?”

Business strategist Michael Porter said that the key to any strategy is “knowing what we are not doing.” In this case, winning with AI will come by “knowing what not to do.” Don’t run or fight. Don’t procrastinate, put your data to work. And don’t think of AI as just another technology. Avoid these three things and get ready for a new level of business performance and competitive advantage using AI.

To follow are Three Steps for Using your Data

  1. Make sure the business is capturing, structuring, and storing all its data in one place. Yes, this requires some infrastructure, standardization of definitions, and collaboration between functions, but the process is known and well-traveled.

  2. Make sure that all enterprise data sources and data streams are robustly connected. This means all data fields, both inputs and outcomes, are associated and connected by unique identifiers. Unique IDs enable everything from simple reporting to sophisticated business intelligence analysis to AI predictions of desired and undesired outcomes. The challenge in equipment finance is not just choosing the identifier, it’s also deciding which of the disparate systems is the system-of-record with authorization to generate and govern the IDs. Does the CRM create opportunity numbers? Does the Lease Origination System (LOS) create application numbers? Does the CMS create contract or lease numbers? What about the GL and invoice numbers? The right answer is that the business platform will have multiple IDs, but they must be unambiguously connected throughout the workflow to enable end-to-end analysis of operations.

  3. Make sure that the data streams of the organization are comprehensive in gathering both the inputs that influence decisions and the outcomes that matter to the organization. This is where the depth of an organization’s curiosity comes into play. What are the outcomes that measure productivity? What are the outcomes that matter to the business? What outcomes matter to customers? How do different customer segments perform differently within the business model and with the different offerings of the business? What are all the parameters that define customer segments? What are all the variables that affect each outcome that matters to the business? The point is obvious: data has value only to those who have questions. The curious will win with AI.

Scott Nelson
President and Chief Digital Officer | Tamarack Technology
Scott Nelson is the President & Chief Digital Officer of Tamarack Technology. He is an expert in technology strategy and development including AI and automation as well as an industry expert in equipment finance. Nelson leads the company’s efforts to expand its impact on the industry through innovation using new technologies and digital transformation strategies. In his dual role at Tamarack, Nelson is responsible for the company’s vision and strategic planning as well as business operations across professional services and Tamarack’s suite of AI products. He has more than 30 years of strategic technology development, deployment, and design thinking experience working with both entrepreneurs and Fortune 500 companies.
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