Does your service business need AI? Here are 4 rules to help you decide
Artificial intelligence is the big thing right now, with retail industries from finance to health care scrambling to adopt AI or risk being left behind. But speaking as professors of business, we think some companies might be jumping the gun.
Our recent research suggests that service providers shouldn’t automatically jump on the AI bandwagon. Instead, they should make a choice informed by their strategy. In short, when it comes to AI and service firms, more isn’t necessarily better.
Why service providers face a different calculation
Are you a manufacturer? Then if AI reduces your costs without lowering quality and gives you the return on investment you need, go ahead and try it.
But service businesses – firms that do things for customers, rather than making physical products – are different. Unlike manufactured items, services are “co-produced” by the customer. Customers can complicate something as simple as ordering pie.
Dealing with customers introduces what academics call customer interaction uncertainty. That uncertainty comes from two sources: the extent of interaction with customers, and – because customers may want a lot of different things – a potentially wide range of offerings.
As an example, consider a restaurant. A customer orders what they want, combines different dishes as they see fit, and then eats the food when it comes out. The customer might make bad decisions, but the restaurant is stuck with them.
If you let the customer interact with a server – or, even worse, the cook – they may ask for substitutes, question the ingredients or try to convince you to make something special. None of that will happen if you confine them to choosing from a set menu via a tablet. To continue the analogy, a restaurant can offer a small number of standard dishes, or it can offer many dishes the customer can customize.
If you run a service business, you’ve already made any number of choices based on your customer interaction strategy. Imagine, for example, you run a financial services firm. Are your offices comfortable and convenient for your customers, designed for long meetings to go over their needs? Or do you restrict your time with your customers and work with them over the phone or even an app?
Similarly, do you limit your offerings so that you know pretty much what you’ll be doing for each customer? Or do your services vary widely depending on the customer’s needs and the choices they make? Think, for example, of CPAs versus tax preparation apps.
Doing business in an uncertain world
How much uncertainty do you allow your customer to introduce into your production process? This should be one of the main things guiding whether your service business adopts AI.
To understand why, let’s take a detour into what academics call information processing theory. According to this body of work, organizations cope with uncertainty by using knowledge to reduce risk. The core challenge for service firms is deploying knowledge in service production.
Individual knowledge – also known as human capital – reduces uncertainty in service production as human workers solve problems and meet customer needs. But human capital has its problems: It belongs to the employee and not to the firm, and it’s not scalable. On the plus side, customers still value human interaction.
The other form of knowledge is known as “organizational capital”: codified knowledge that the firm itself owns. Organizational capital has inherent advantages: It belongs to the firm, and it scales. AI, a form of organizational capital, clearly has these advantages.
Information processing theory gives us three techniques for organizing knowledge to deal with uncertainty.
The first is having rules and programs – a form of organizational capital. The second is having hierarchical structures. Here, front-line workers escalate intricate matters to more knowledgeable managers. The third is goal-oriented coordination: Businesses can deal with uncertainty by empowering lower-tier employees with decision-making autonomy, guided by overarching organizational objectives. These last two rely on knowledgeable, experienced workers – human capital.
Here’s how that fits with service strategy. Mostly, firms with fewer options for consumers and limited customer interaction use organizational capital. Nowadays, that typically means tech solutions on top of rules and programs. Firms with a wide range of offerings but limited customer interaction use a hierarchy, where challenges get passed up the chain. And firms with both a wide range of offerings and high customer interaction use front-line knowledge workers coordinated by targets or goals.
Tech may augment the latter two modes, but the cost of offering a wider range of services or greater customer choice is that the firm becomes more dependent on human knowledge workers.
The strategic use of AI
AI, a sophisticated form of organizational capital, can reduce customer interaction uncertainty. The firm owns it and can scale it. Yet it is still bound by its rules and dataset, and there are areas of uncertainty where human capital still offers advantages: finding creative solutions, linking disparate concepts and understanding the nuances of human interaction, to name a few.
The challenge is to strategically navigate all of this, combining customer strategy and human and organizational capital in a cohesive way. We came up with four rules that should help:
By following these guidelines, service firms can navigate the complexities of integrating AI into their operations, leveraging the best of all worlds to meet their clients’ needs effectively and sustainably.
Authors: David Cohen Associate Professor of Management and Business, Skidmore College / Christopher Meyer Lecturer, Zicklin College of Business; Advisor, Lawrence N. Field Center for Entrepreneurship, Baruch College, CUNY / Sudhir Nair Associate Professor of Business, University of Victoria
As published in the Conversation
|
|
|
|
