Equipment finance organizations have many new opportunities thanks to the highly developed and evolving artificial intelligence (AI)-driven tools and resources, including those that use of natural language processing like ChatGPT and Google's brand-new AI product Bard. Though equipment finance professionals are aware of these tools, it remains largely unknown the full extent to which these advanced resources will be used in financing assets and asset management.

Like many other leading-edge technology resources today, these advanced AI tools possess incredible promise. Still, equipment finance executives need to understand the pros and cons of how far they can rely on such tools in their day-to-day decisions, as well as for their clients.

What Is ChatGPT?
These tools are increasingly in the news because the equipment finance industry has been buzzing about new-age resources like Open AI ChatGPT since it was initially released in November 2022. Although the Chat Generative Pre-Trained Transformer (ChatGPT) is still new, executives are interested in the technology due to its capacity to produce quick and thorough responses. What’s more, because the tools are driven by AI, they leverage human-like language during interaction.

ChatGPT promises to dramatically increase organizational visibility, build a baseline for strategies and plans, streamline internal and external communication, and effectively optimize end-to-end operations.

While many organizations are looking to leverage AI and ChatGPT in their own unique ways, equipment finance professionals are highly interested in the role it may play in equipment financing and asset management strategies.

As an example, when you ask ChatGPT why asset management is important for equipment finance, it offers the following excerpts:

“Asset management helps to maximize the value of equipment over its lifespan by ensuring that it is properly maintained and used efficiently. This can lead to reduced downtime, increased productivity, and extended equipment life, ultimately increasing the equipment's overall value. Asset management enables finance companies to effectively plan for equipment replacement or upgrades, ensuring that they can provide clients with the most up-to-date and efficient equipment possible. This can also help finance companies to manage cash flow and budget more effectively.”

While this provides a mostly compelling overview of the role of asset management, there are inconsistencies that are inaccurate. For example, traditional finance companies and banks play no role in the planning of equipment replacement or upgrades.

As an example, if you ask ChatGPT to build an asset management plan for a Class 8 heavy-duty truck fleet, it will generate a high-level overview covering topics such as equipment inventory, preventative maintenance, telematics and IoT solutions, driver training and safety programs, replacement and upgrade planning, budget and cash flow management, and reporting. These are all baseline topics that need to be considered for asset management.

But it’s important to keep in mind that ChatGPT will not answer financial/mathematical questions, but it will defer to pros and cons of a business transaction like buying or leasing. It also tends to give wrong answers because it pings millions of web references to what you ask and then tries to compile the most frequently mentioned text. Without the proper knowledge and expertise, you could make wrong decisions that could negatively impact your operation.

Therefore, it is crucial to understand that any inaccuracies produced by an AI tool may have consequences in the areas of financial loss outcomes, legal or even defamation toward an organization. This also includes defining the true source of who actually produces any material developed entirely or in part by an AI tool such as ChatGPT. While there currently are no laws that determine the responsibility of any inaccuracies, it is widely speculated that regulation could soon be forthcoming as a set of guidelines.

The Dangers Posed to Equipment Finance Professionals
However, ChatGPT won’t serve as a solution to help build a custom financing and asset management strategy. In fact, aside from the general considerations, ChatGPT is not built to analyze asset operating data such as miles driven per year by a transportation fleet to build an effective procurement strategy based on a life cycle that best suits that organization. And while it’s hard to fathom organizations will actually rely on ChatGPT to create their entire strategy, some may confuse it with analytic tools in the market. For example, depending too much on a standardized approach during the procurement planning phase without incorporating actual vehicle operating data can be problematic.

When building a proper procurement plan for vehicle asset management, it is critical to arrive at a strategy that produces optimum flexibility and agility within the financial and operational business model. While ChatGPT on its own can’t produce a plan with this level of detail, organizations can utilize existing analytic tools that are providing AI-driven analytics to help closely monitor key metrics such as:

• Lease versus purchase analysis
• Life cycle cost analysis
• Sales Tax analysis
• Unbundled vs. Full-Service Lease Analysis
• Comparative Cost Analysis to determine the optimal time to upgrade equipment, etc.
• Per unit P&L
• Predictive Life Cycle Modeling 

Where AI Is Proving to Be Advantageous
Aside from finance, asset management and procurement, AI tools are impacting operations for organizations in various industries.

Several recent studies illustrate the significant benefits that AI tools have over traditional spreadsheet analytics. A recent study by McKinsey & Company estimated that AI-powered technologies can reduce errors between 20 percent to 50 percent for organizations focused on supply chain management.

Furthermore, the Boston Consulting Group (BCG) offers a report that shows how AI may help organizations achieve $1.5 trillion in additional value from increased productivity and reduced downtime in the global industrial sector by 2030. For M&R operations within the manufacturing sector, a separate McKinsey report found that AI-enhanced predictive maintenance of industrial equipment will generate a 10 percent reduction in annual maintenance costs, up to a 20 percent downtime reduction and 25 percent reduction in inspection costs.

Total Cost of Ownership (TCO) analytic tools that provide life cycle cost management with billions of miles of data and understand the full scope of TCO are continuously monitoring economic factors, used truck values, depreciation, emissions, performance data, and equipment costs to determine the optimum procurement and asset management strategy.

This is important because TCO analytic tools that leverage predictive modeling allow equipment finance professionals to help organizations with transportation fleets create future business insights with a significant degree of accuracy. These companies can use historical and current operating data to accurately forecast budget trends in milliseconds, days, or years into the future with the aid of sophisticated data analytic tools and modeling. As more AI-powered resources are made available, it is crucial for companies to work closely with their finance and asset management partners to ensure these tools are being used effectively to boost operations and their bottom lines. Understanding the company’s specific challenges, finance and asset management partners can also help identify which AI-powered tools are best suited to address those challenges and how to properly integrate them into their operations.