(This Recent Opinion by Scott Nelson originally appeared on Monitor in March 2023 )
Scott Nelson of Tamarack Technology delves into what drives AI learning, how to achieve that same level of curiosity and ways to use AI to embrace failure in pursuit of success.
Innovation is an organizational muscle that can be trained and strengthened
World-class athletes put in long hours working out to strengthen their muscles and hone their skills in order to compete at the highest levels.
Organizations, like athletes, need to develop the strength and skills necessary to execute a business plan or “game plan.” For example, implementing a new competitive strategy in the marketplace. The most successful businesses – think Ford, Apple, McDonalds, Amazon, Netflix, Samsung – include innovation as part of their organizational training and go-to-market game plans. They have built innovation muscles and put those muscles to work launching new products, attracting new customers and capturing new market share.
Innovation, as defined by Peter Drucker, “Is an effect in economy and society, a change in the behavior of customers, of teachers, of farmers, of doctors, of people in general.”
All organizations built on innovation understand this definition – even if they do not attribute it to Drucker – and exemplify it because it guides their primary efforts: change the behavior of customers to buy and use their products or services. When we consider the great innovations from the companies above, we reflect on how they have changed the behavior of society and consumers.
Drucker also famously said that “Business has only two functions: marketing and innovation.” Finance leaders understand that marketing is a function of driving sales and business growth. Most finance organizations have marketing leaders and departments who work hard to deliver results. But how many finance companies do the same with innovation?
Organizations talk about innovation, but only a few view innovation as a muscle that they can develop. However, the fundamentals of innovation are just that – fundamental. Innovation comes from organizations that understand the objective – to change customer behavior. And also, to diligently practice the fundamentals of innovation: empathy, curiosity, and embracing risks that can come with failure. Companies that use these fundamentals learn faster than their competition and acquire customers with better offerings.
AI can also help an organization strengthen innovation muscle faster and in new ways. Organizations that embrace AI will find that it is both a tool to strength innovation muscles and to build a learning, innovative culture.
Empathy has long been recognized as the foundation of design thinking which, in turn, is the foundation of the innovation process. Microsoft CEO Satya Nadella famously said, “Empathy makes you a better innovator.” Leaders like Nadella facilitate empathy in their organizations because he knows it will foster a culture of innovation. Ignoring empathy could create risks that lead to customer apathy or business failure. Product designers will tell you that a lack of empathy creates assumptions that identify problems that don’t matter and leads to solutions that nobody buys.
Traditionally, empathy has been viewed as a “soft skill” attributed to product designers and managers within an organization and, as such, can often go missing from service organizations focused on execution. But data and AI can provide new tools that naturally engage an operational team as well as the product development team. Data aggregates and documents the behaviors of both the organization and its customers. Business leaders can train their teams to “listen to customers” through data and modify their actions to better serve customers.
We have all experienced how eCommerce companies leverage AI as a tool to deliver product offerings. They encourage customers to buy by predicting what the customer wants and providing the best options to that customer based on past purchases or search results. Conversely, the best way to change behavior for the better is to predict the undesired outcomes/behaviors and avoid them.
Empathy is one of the keys to innovative cultures and AI can help focus an organization on listening to and engaging with customers through the data their activities generate and the behaviors that the enterprise wishes to avoid or encourage. When applied to customer engagement, AI is intrinsically empathetic.
Curiosity is a trait that always pays forward. Curiosity is a driving force of learning, of design thinking, and of innovation. The curious ask “why do we always do it this way” and then craft alternatives to answer the question. The Diverge | Converge method used in design thinking and brainstorming processes, is a great visualization of curiosity. An individual or group approaches a problem first by thinking of all the ways one might address the situation. Curiosity generates choices that diverge from the solution set – “What about this? Why not try this? Why not change this?” Then analysis and experimentation reverse the process and converge on the best choice.
AI is not curious, but AI-based solutions are built to work the same way – they continuously try new ways to best solve a problem and learn from the outcome of each effort. AI strengthens the curiosity skills of an organization in two primary ways. First, curiosity is a required trait to make data useful. I firmly believe that “Big data serves the curious.” Big data (large amounts of data from both inside and outside an organization), becomes dark data unless some curious analyst asks it a question. If an organization does not use and strengthen its curiosity, its investments in data and data analytics will fail because no questions are asked of the data, and nothing is learned.
The second way AI creates curiosity is through challenges to traditional analytics. AI agents are built from machine learning models built on historical data. But ML models are much deeper than human analytics and often create predictions that challenge the analyst’s experience. “Do I agree with that prediction? Why isn’t that customer a better choice? Why is that market segment predicted to be better than this one?” AI can create challenges to business as usual and will increase the pace of learning when a curious organization engages AI to learn systematically and faster.
To deploy AI, the organization must first pick the outcomes which it needs to learn, and the outcomes it wants to change. AI agents use machine learning models built on past outcome data to predict pending or future outcomes. The organization can then act on those predictions to either pursue or change the outcomes to improve performance. When an organization becomes confident in how the AI predicts the outcomes desired, it can automate using those predictions to increase productivity and learn even faster from automated trial and error. An organization that turns to AI to address curiosity in this fashion accelerates its learning while improving the experiences of their customers. They are able to innovate faster.
Embrace the risk of failure.
Failure is an undesired outcome. No business can survive by failing more than it succeeds. But an organization that is afraid of failure, that will not take the risk of failing, will not learn fast enough, and stay competitive. Anyone familiar with basic economics knows that higher returns come from taking more risk. Yet many finance companies spend all their effort avoiding risk. Innovators are comfortable with failure – Eric Reis taught them to “fail fast, fail early.” They know they can learn faster through small failures, measured failures, and by taking measured risks.
AI, like most humans, learns from trial and error. But the errors have to be acceptable. Consider how AI leaders like Amazon and Netflix use predictors to constantly and continuously find new ways to get users to buy goods or watch specific selections. But failures in these efforts are inconsequential to the organization unless they fail so often and egregiously that a user never comes back. The guard rails to avoid failure are easy.
The key to the Amazon and Netflix models is a culture that does not fear failure. These companies use failure to learn faster and converge on desired outcomes. AI enables them to do this because they have implemented it to take acceptable risks and learn from the results.
Figure 1 (a) Predicted deal performance and (b) Portfolio performance after more precise underwriting using AI.
Figure 2 A “precision C-credit portfolio outperforms by using AI to remove the undesired outcomes.
Look at an equipment finance example: a lender embracing failure by engaging the C-credit market. Many do this, but defaults are real and a few of the wrong sized defaults can ruin portfolio performance. However, if the organization uses its past data and builds an AI predictor that calculates the probabilities of failure across deals (Figure 1a and 1b), the portfolio can be assembled with a more precise distribution that performs better than an A-credit strategy (Figure 2). AI can empower an organization to embrace failure but enable it to avoid suffering the consequences of the risk.
It takes years of training to become a world-class athlete. Similarly, innovation isn’t something that just occurs; it is an organizational muscle that must be identified, strengthened, and trained. Innovation has long standing fundamentals and objectives, but the accelerated digital transformation of the leasing workflow over the past two decades has created an opportunity for equipment finance companies to deploy their data using the learnings of the past. AI is the tool that will strengthen innovation fundamentals and help businesses innovate more and faster.
About the Author: 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.