Embracing AI is wonderful. From a practical business perspective, though, there are limits. This issue is broader than AI. However, I’ll constrain the conversation to that for now, given the attention AI is getting these days.

Yes, some processes are undoubtedly good candidates for automation, but avoiding “technocentrism” is critical to the success of the business and the technology.

Among many characteristics, technocentrism views technological efficiency and quantitative metrics as the main measures of success. As businesses rush to embrace AI, it’s important to remember that people and profitability are even more important. Two of my recent travel experiences were eye-opening and highlighted technocentric pitfalls worth avoiding.

In London, the journey began with me checking into a hotel. Many hotels have gyms on-site, but not this one. I like to work out even while traveling, so I got a suggestion from the concierge for a nearby facility that required online registration. Okay, no big deal; this is the digital age, right? What seemed simple resulted in no registration after an hour spent creating an account, performing multifactor authentication and downloading an app. Not to mention, I paid money for the experience and had no means of human communication for real-time assistance.

Determined, I arrived at the gym early the next morning to be greeted with automated entry using a combination of security pods and QR codes, which made me feel like I was in a scene from Black Mirror. Fortunately, I was able to intercom for help, but the worker lacked the authority to help in a meaningful way, leaving me to wait for a call from a manager later that day. Workout postponed. Brand experience: craptastic!

Back at an airport in the US, another instance of technocentrism surfaced. An employee closed the point-of-sale register while there was a long line to order food, frustrating customers, including myself. The alternative offered to us was to order using a kiosk. As a first-time customer, I was looking forward to asking questions to someone about the menu, recommendations and possible substitutions.

Problematic timing aside, the employee’s choice to divert everyone to a kiosk eliminated the opportunity for personal, empathetic interactions that might have brought a host of hungry customers back next time. The quantitative output may have been the same, but the process eroded the brand, so I’m likely never to return.

AI substitution versus augmentation

These travel experiences illustrate something we’re all experiencing in a myriad of ways in today’s global economy. There are tradeoffs between deploying AI and automation, often for legitimate reasons, as opposed to employing people for those same tasks.

While cost efficiencies, repeatability and sustainable productivity are profitable business drivers, if they are counterbalanced with brand erosion, dissatisfied employees and inequitable distribution of goods and services, the perceived gains are short-term at best and threaten an organization's resilience.

These experiences can distort a brand’s image and diminish customer satisfaction, even though the logic for automation may save costs and have other beneficial factors. Such situations can alienate customers, especially those who are not digitally adept or have specific accessibility needs. Therefore, we’re tasked with balancing the impact of automation and interpersonal customer experiences.

Left unchecked, technocentrism gives way to human substitution bias: the assumption that whatever the task, technology is as good or better than a human. Of course, this is not universally true; however, intelligent methods for augmenting with AI such that we benefit while providing fulfilling work for employees and attracting profitable customers can be true. These tenets are not opposed to one another.

For instance, according to Statista, first-quarter youth unemployment in London is well above the UK average. It seems the gym I visited could have kept the online registration, empowered a teenager to work the front desk to resolve guest questions, and maybe even cleaned the place for less than the annual cost of installing and maintaining a security pod (and certainly for less than the cost of losing a customer). Such a choice would provide fulfilling work, help the community and preserve the brand.

Harmonizing human expertise and automation

A small, albeit relatable example is the increasing use of QR codes as restaurant menus following the COVID-19 pandemic. Instead of the time-consuming process of printing and managing physical menus, digital versions are now widely accessible. This change simplifies the workload for servers. Of course, a few hard copies for the visually impaired or those without QR reading devices is a best practice.

Harmonization, as opposed to technocentrism, creates an environment where human ability, AI and automation work together to deliver an exceptional, personalized experience. While this is a simple example limited to the services industry, organizations are charged with creating such a balance. When done well, the balance combines social good with long-term profitability.

Responsible innovation begins with responsible innovators, and as business leaders, we must all see ourselves as innovators. Achieving the goal of responsible innovation requires vigilance, adaptability and organizational commitment to a future where AI and humans work in harmony.

Check out this ebook and get steps to a comprehensive approach to trustworthy AI governance.

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About Author

Reggie Townsend

Vice President, SAS Data Ethics Practice (DEP)

Reggie Townsend is the VP of the SAS Data Ethics Practice (DEP). As the guiding hand for the company’s responsible innovation efforts, the DEP empowers employees and customers to deploy data-driven systems that promote human well-being, agency and equity to meet new and existing regulations and policies. Townsend serves on national committees and boards promoting trustworthy and responsible AI, combining his passion and knowledge with SAS’ more than four decades of AI and analytics expertise.

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