As AI transforms the equipment finance industry, Paul Bent argues that the next generation of leaders must double down on the one advantage machines can’t replicate — human intuition, imagination and connection.
There is a lot of talk today about human jobs being taken over by non-humans — robots or, in the case of equipment financing, the white-collar version of robots, AI. Nearly everyone has ventured into this exciting and yet possibly terrifying new world, whether through experimentation using ChatGPT, more advanced document analysis with Claude, image and video generation using Adobe Firefly, writing with the support of Microsoft CoPilot, general-purpose research using Google AI Mode or many other widely available, increasingly specialized and ever-evolving AI tools.
But as interesting (and sometimes fun and sometimes frightening) as these new software tools may be, might they also be sowing the seeds of long-term seismic shifts in the nature of work itself, and in the positioning of humans in the natural order of things? And if so, how will such a shift affect the equipment finance industry?
In a recent poll conducted by the Society for Human Resource Management (SHRM), the world’s largest professional HR association, 78% of hiring managers predicted that the use of AI will lead to layoffs of recent graduates at their organizations, and 70% opined that AI can already do the jobs of interns. While the rapidly increasing use of AI, whether generative or rules-based, can be found in virtually every facet of life and is being harnessed successfully in a wide variety of applications, the U.S. Bureau of Labor Statistics (BLS) projects that by 2029 the U.S. will lose 1 million jobs in office and administrative support occupations because AI has the potential to substitute for or replace these functions.
Our industry is already familiar with the beginnings of an “AI revolution,” whether in the use of AI methods in well-known applications such as credit scoring, unsupervised handling of back-office administration or even customer-facing issues (sometimes with scary accuracy and competence), and it is also moving into less obvious spaces such as creating and negotiating legal documents, determining long-term business plans and models and even supporting front-line sales of leasing and finance products. All of these fast-moving developments are understandably leading to anxieties about job security, having to learn new methods and practices, and somehow coming to grips with new realities in the workplace.
Looking beyond the immediate anxiety, however, and assessing how the equipment finance workplace actually functions, it can be said with some certainty that there will always be a need and an opportunity for human skills and knowledge. This article will address these needs and opportunities in equipment leasing and finance.
THE IMPORTANCE OF THE HUMAN FACTOR
It is a truism that computers can never make mistakes. If they are programmed to add up a column of numbers, they will do so accurately and repeatedly (as long as they are plugged in), and extremely rapidly — carrying out enormous and endless iterations of calculations with no slowing down and no errors. Mathematical algorithms and processes, no matter how complex, can likewise be performed literally in the blink of an eye. (We have all looked up a location on Apple Maps or Google Maps and seen a pinpoint accurate rendering appear before we could even finish typing in the street address.)
The same may be said about AI — producing a thorough analysis of a medical procedure or a construction plan or a biographical novel in seconds and with a breathtaking degree of accuracy. But is that all there is to AI? It’s not surprising that AI can carry out remarkable feats of information recall and analysis at blinding speeds — after all, it is powered by massive computers. But can it “think” in any meaningful way? Can it figure out for itself the best path forward when confronted with a leadership or operational dilemma, especially if no one has faced exactly the same problem in the past?
It is not the operation of computers (or of software) alone that is required for making sound business decisions and for providing the appropriate solutions to business problems, no matter how seemingly small they may be. It is the participation of human beings, with their vast collective store of knowledge, experience, intuition and creativity, which is necessary — with the support of AI technology — for the advancement and management of the world around us.
This is not a new issue. In the early days of digital computing, the great U.S. philosopher Mortimer Adler opined that computers would not be capable of achieving what humans had achieved for one somewhat surprising reason: They cannot make mistakes. They cannot learn from mistakes, novel applications and false starts. They are not capable of intuition, hunches and gut instinct, from which many of the most important achievements in human history have sprung.
Much of the talk about AI centers on whether it can produce the equivalent of human thought, but a human adding machine is not really a human. It is incapable (no matter how complex its algorithms and “machine learning” methods) of applying human values, intuition, creativity and instinct. These are the human attributes that will, together with the speed and accuracy of AI methods, provide for the effective management and operation of business methods and objectives into the future.
HUMAN SKILLS THAT AI CANNOT REPLICATE (YET?)
To explore this premise further, and with a focus directly on our industry, let us consider the specific and indubitably human attributes that will enhance rather than compete with the use of AI in the EF workplace.
Sales And Business Development
No AI “bot” has ever looked a prospective customer in the eye and determined on the spot whether that customer was truthful or trustworthy, as an experienced and skilled salesperson can. Although a “well-trained” rules-based AI engine may glean very useful and important details about a potential customer and may even, over time, provide glimmers of insight into a customer’s particular thinking or objectives, it cannot develop a genuinely human connection — a sense of common understanding, purpose or trustworthiness that propels a human relationship to a mutually satisfying conclusion.
Likewise, absent a great deal of data analysis (and a lot of underlying data) regarding a specific customer, an AI-powered algorithm cannot immediately relate to a customer’s personal situation, motivation or points of pain or pleasure. It cannot learn simply from observation or infer from a casual comment what a customer’s background, needs or interests may be or intuitively share any common ideas, objectives or past connections.
Importantly, front-line sales people of the future will learn how to harness the strengths of AI (immediate data recall, massive statistical resources, unwavering efforts to make a first contact or establish an initial connection, instant and infallible recall of prior events, facts and circumstances) together with applications of time-tested skills in human behavior, active listening, human insight, trust and human intuition. Acting in concert, this combination of enormous data resources in tandem with actual human experience, interaction and behavior will allow equipment finance professionals of the future to achieve faster and better results in sales and customer relationship building — and still allow for the fully human feelings of satisfaction and pride in a job well done.
Customer Service And Support
In the same way that the application of human customer-facing skills and experience is essential to effective sales and marketing, the integration of genuine human factors with the total recall and accuracy of rules-based AI and large language models will likely remain important in addressing real-life customer service and support issues. Simply teaching human-sounding AI bots what to say in every predictable situation, even with their access to massive volumes of data and information, may not be sufficient in dealing with emotional, high-pressure, time-sensitive or unique issues.
As many of us have learned from firsthand experience, even the most carefully “taught” and well-trained AI systems may be incapable of understanding or dealing with human input and may, in fact, exacerbate issues that could have been addressed more fully with the intervention of people explicitly experienced in conflict resolution techniques, active listening and problem solving.
The future of customer service and support, just as with sales, is likely to be in integrating the first-line interface and access to vast amounts of historical detail provided by AI systems with the insight and intuitive skills of experienced human problem solvers who may provide novel or unusual solutions and, in many cases, simply a better understanding of the problem. Although AI and large language models may be trained to react in many situations based upon the combined experience of others, human professionals will continue to play an important role in taking over from automated front-end algorithms and applying their uniquely human skills to solving unique customer-facing problems.
Credit And Underwriting
The use of computer-driven algorithms and techniques has already become commonplace in our industry in this critical area. Many equipment finance companies already apply the methodology of rules-based AI in carrying out extremely fast and generally very accurate underwriting of credits, particularly in specialized areas of focus for specific lenders and lessors. Once a scoring model is developed that suits the analytical and underwriting needs for a company’s chosen markets it may be used repeatedly with good (and typically predictable) results.
But what happens in the case of credits that don’t fit the specific parameters designed into the scoring algorithm? What happens if, even in smaller ticket transactions, there are gray areas and nuances that cannot be sussed out by a pre-determined scoring model or a rules-based AI program? What about balance sheet items that somehow just don’t look right for the potential customer’s industry segment or the borrower’s chosen collateral asset classification or that in some way “tickle” the concerns of a seasoned credit underwriter or manager?
In these gray areas, there will very likely be a continuing need for the application of human knowledge and insight that can only be derived from experience and uniquely personal intuition. Seeing subtle nuances and addressing those itchy feelings are skills unique to humans with extensive experience and insight. Even with the help of customized AI models, there will continue to be a vital role for human oversight — addressing the exceptions while AI takes care of the vast majority of credit decisions based upon its own training.
Future credit analysts and officers will likely employ increasingly sophisticated scoring models and methods, but the integration of these methods with human understanding based upon years of experience (and applying that experience to honing the AI models to a finer edge of decision-making) will allow the use of AI in credit and underwriting to reach its full potential.
Risk Management
As a close cousin to credit analysis, risk management has always played a very important role in determining pricing, terms and details of the safest deals when contrasted with the riskiest ones. And imagination and foresight, as essential components of intuition, predominate in assessing risk in the equipment finance world.
Perhaps the most essential attribute of a skillful and successful risk manager is curiosity — what can possibly go wrong? What are the factors we’re missing? Are there unknown or unobvious details, facts, participants, issues that should be uncovered and addressed? And, as a very important and potentially nefarious second cousin, is there fraud lurking in this deal?
The most advanced AI models are being trained to anticipate risks arising from these questions and factors, but their strength is in synthesizing and learning from past experiences and results, and even then they are constrained by rules-based algorithms, which largely lack the capability of assessing new and truly unforeseen circumstances. Once again, it is the imagination and intuition of actual human actors that will be relied upon, at least into the foreseeable future, for the final determination of types and degrees of risk.
In equipment finance, risk may take many forms — from credit risk to asset risk to fraud risk and beyond — and the oversight or integration of human resources with the underlying research and processing power of AI systems is likely to provide the most effective risk assessment capability. The AI-powered analysis and learning from facts and details may be enhanced by human-directed questioning and scoping. Here again, the inquisitiveness and imagination of a human with actual experience in fraud detection and methods will be supported by AI rules-based analysis and, conversely, AI models may be made much more effective, particularly in novel and unique situations, through human interaction and direction.
With its ability to anticipate real-life things that might go wrong, imagination will become a super-skill in the new AI-driven world of equipment finance.
Asset Management
The basics of asset management, appraisals and equipment usage are widely known in equipment finance and are largely amenable to implementation through rules-based AI algorithms and methods ready access to large databases of asset values over time, trend data, demand and utilization details. But the real skills of experts in this field lie in anticipating change, determining valuation based not just on utilization forecasts but on understanding the specific uses of all types of assets in particular industry sectors and in unusual or poorly understood applications.
These skills are honed and perfected through years of practice, personal inspections and knowledge and, once again, an experience-driven intuitive grasp of how and when specific assets or asset classes will or will not be available, how much they will or will not be in demand, how new regulations and industry requirements will affect valuations, and whether or not they will have long-term residual value. Although AI models may provide historical and even current details regarding asset values and availability, that information may be greatly enhanced or compounded through the integration of experienced and intuitive human participants in the process.
There is no AI-driven substitute for “kicking the tires” — engaging in real-world inspections and reviews, interviewing target industry participants and relying on experience and insight to fine-tune the basic information available through a computer algorithm.
ANTICIPATING THE FUTURE
AI and automated large language models and methods are here to stay. At the same time, in many areas of our industry, they are not a replacement for but rather a tool that can help enhance human intuition, judgment and imagination.
Many equipment finance professionals of the future are already skilled in the technical aspects of AI usage — devising meaningful queries, interpreting results, targeting specific areas of interest or inquiry, honing responses to seek out greater details — but none of these skills alone will prepare up-and-coming equipment finance leaders for the complex world that awaits them. They will do very well to address real-world requirements too — requirements that may only be developed through hands-on experience — and then to integrate those experiences with the analytical power available through AI platforms.
The future of equipment finance will be in the hands of thoughtful, imaginative and intuitive professionals, not robots.
Paul Bent is a senior managing director at The Alta Group and the leader of Alta’s Legal Services practice. He offers clients the benefit of several decades of direct management and practical experience in virtually every critical aspect of equipment leasing management, transactions, operations and legal affairs.
