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• As AI takes over more business tasks, future business leaders will need to keep human actors at the center of business systems and processes.
• If leaders focus too narrowly on maximizing efficiency and minimizing cost, they will design processes that do not serve the needs of their internal or external stakeholders.
• Business students must learn to design systems that do not simply save time and money, but that also adhere to ethical standards and improve human lives.

 
Business systems designers have long placed the human actor at the center of the processes they create—whether they are developing new systems and technologies or improving existing ones. As a management consultant who has worked with companies worldwide, I count myself among this group.

Lately, however, my research on business services design has led me to this conclusion: Too many businesses are abandoning a human-centered philosophy of service design. Instead, as we enter the Age of Artificial Intelligence (AI), businesses are intentionally separating humans from making decisions, processing transactions, and designing systems meant to enrich our lives—a phenomenon I call the “human divide.”

Make no mistake, when it comes to the ways we use technologies and deliver services, addressing the human divide will be our next great ethical dilemma. As we continue to adopt AI tools in our professional and personal lives, how will we use AI in ways that keep humanity at the forefront? How will we ensure that newly minted graduates are aware of AI’s potential for bias and ethical breaches as they use the technology to conduct transactions, remove bottlenecks, and design new products and services?

While the threat of a human divide looms, the danger is not inevitable. The sky is not yet falling. But business schools can work to avert the crisis by changing their educational model.

I propose a roadmap for incorporating human-centric process design into business curricula. For this, business faculty must expose students to various business services and build their problem-solving skills. Through experiential learning and case study analysis, faculty can put students to work evaluating and improving real-world business processes—not just in standalone summer internships, but throughout the curriculum.

It’s critical that educators understand how automation will impact businesses, so that they can design curricula that prepare students for an automated future. We must teach students how to keep business processes and services as “human” as possible—even in the Age of AI.

What Is Human-Centered Design?

Designers espoused a human-centered approach as far back as the 1960s, holding that business systems, processes, and services should be purpose-built for how humans think and operate.

Simple in theory, this approach requires designers who are willing to consider human abilities, recognize human limitations, and take the additional steps required to encourage humans’ acceptance of change.

With this philosophy, any process design or redesign should be undertaken not merely to save time or money, but to improve human lives. That said, incorporating all three principles can be difficult and often expensive.

But the effort is worth it. Just consider the example of manufacturing, where this approach to design can benefit human workers. To apply the first principle, process engineers first observe the body and eye movements of the humans who are executing tasks on a production line.

To apply the second principle, the engineers create a prototype of a task sequence intended to reduce the time and effort required for workers to complete the task, as well as decrease their exposure to work-related injuries. To honor the third principle, the engineers run the idea by focus groups of production staff, who can share their experiences and feedback to refine the new way of working.

That last step is especially important. By involving the production line workers in the design process, engineers make it more likely that workers will adopt the change and champion the new method to others.

The Danger of Standardization

Unfortunately, senior-level leaders too often focus solely on maximizing efficiency and preserving resources. They standardize business processes in ways that limit human choice rather than incorporating it, and they make standalone changes with little thought about how those changes will affect the larger process ecosystem. In other words, they focus on the individual trees in their organizations but fail to see the entire forest.

For human-centered designers, however, the forest is the focus. Before making any change, they consider the complex interactions among all the business services that a company provides, as well as how these services affect internal and external stakeholders.

Although human-centric designers will be the stewards of business processes, in the future they will not be the essential actors executing the delivery of many business services.

Consider a company’s finance and accounting function. This function has dozens of business services organized in six areas of capability: financial accounting, management accounting, treasury, customer credit, business intelligence and reporting, and corporate governance. Each service is supported by a business process, data, application, and technology infrastructure architecture. Think of all the financial decisions human actors must make to offer, sell, produce, and deliver products to external customers, as well as receive payments for those products.

Business leaders will always look for ways to standardize as many of these decisions as possible, in the name of efficiency. The key question for the future is which of these decisions we will leave to humans, which we will delegate to AI—and how the two will interact.

The Measures of Maturity

In business, the leaders and teams who are responsible for seeing a process through from start to finish are called , or GPOs. How well GPOs help their companies deliver day-to-day services, internally and externally, depends on each GPO’s maturity level. In general, there are three levels of maturity:

Level 1—At this level, GPOs lead the design process, directing resources to drive essential services in ways that eliminate processing bottlenecks, improve process efficiencies, and rationalize operating costs. Level 1 GPOs might direct the day-to-day activities of the management teams in their areas of responsibility, or they might simply provide those teams with standard policies, system designs, and performance level targets. 

Level 2—Here, GPOs set the same priorities as those at Level 1. The difference is that Level 2 GPOs act as conductors that empower local teams to find the most efficient paths for service delivery. These GPOs also set standards for common systems and processes to allow for centralization and outsourcing of service delivery. At both Levels 1 and 2, human process managers act as “ringmasters” who largely control process design.

Level 3—The best Level 3 GPOs focus on the needs of consumers. They also realize that they and their local management teams are the internal consumers of the systems and services they create. At a time when some bold organizations are placing AI at the center of their processes, Level 3 GPOs focus less on the paths their organizations take to deliver their services, and more on the quality of AI’s outputs, including the tasks it executes, the decisions it supports, and the information and recommendations it generates.

At every maturity level, GPOs and their subject matter expert teams are the human stewards of businesses processes. However, in the future, they will not be the essential actors executing the delivery of many business services. Their biggest challenge, then, will be this: As each AI-driven improvement reduces the need for human interaction, how will GPOs keep members of their workforces motivated to learn, to commit to a given career path, and to act in the best interests of stakeholders?

The contrarian in me might have once characterized the move from Level 2 to Level 3 as demoting humans from “ringmasters” to “sideshow performers.” But I now believe that it is more accurate to describe this leveling up as one in which managers move from acting as “ringmasters” to being “AI champions.”

Driven by human-centered design, the best AI champions achieve three primary goals in their process design:

They deliver more value across the enterprise. AI champions use AI tools to gather unique data sets, identify trends, and predict outcomes of various decision paths, all to better serve stakeholders throughout their organizations.

They expand their roles to achieve new heights of service. Supported by AI, champions are no longer engaged in putting out fires in day-to-day processes. This gives them time to expand their skill sets, as well as to better understand the end-to-end cycles of the services their organizations offer. With this knowledge, they can take on expanded roles and create more nuanced solutions to problems.

They act as trainers and mentors who help their teams bridge the human divide. The more organizations automate business transactions and decision-making, the more they eliminate opportunities for human workers to learn by doing and by watching others tackle challenging issues. At the same time, as process leaders increasingly use AI to streamline their tasks, they can devote more time to training, mentoring, and preparing members of the management team to be the next group of leaders.

Creating a Human-Centric, AI-Enabled Future

So, with these realities in mind, how can business school administrators and faculty prepare students for AI-enabled careers? I recommend they follow a roadmap that prioritizes four actions in the design and delivery of their courses:

1. Teach students how business process cycles work and what it takes to deliver them. Develop course content that dives deeply into best practices in process design and into the technologies that can enable these practices. Ensure that undergraduate students, especially, cultivate a thorough understanding of end-to-end business process cycles. Finally, teach future service designers to become data analysts who can benchmark the performance of their processes against those of leading companies.
2. Develop service-based problem-solving skills. Use case studies and simulations that require students to work in teams to analyze business processes and identify ways to advance the maturity of those processes to Level 3.
3. Embrace the use of AI in process design. Train students to solve process-based problems. Make students aware that they can use AI technology for more than writing essays or answering standalone questions. Provide students with detailed examples of how businesses are using AI tools to make processing decisions and how the quality and effectiveness of those decisions are improved over time.
4. Champion the use of AI to improve human lives. Striking this delicate yet essential balance requires a three-part strategy:

• Integrate AI tools into the curriculum, so that students become accustomed to using AI efficiently to complete their tasks and class assignments.
• Encourage faculty and students to expand their research into the ethical deployment of AI technologies in the workplace.
• Assign students teams to evaluate the effectiveness and accuracy of AI’s informational and analytical output. As more decisions relating to core business processes are relegated to AI, the ability to critically evaluate how automation will function in society will become an essential business skill for human actors.

Preparing for New Ways to Work

We must prepare students for big changes in how future businesses operate. Otherwise, they will get rolled over by the bots—and so will the stakeholders they serve.

And just as businesses must emphasize human-centric approaches in the way they design their processes and apply AI technologies, business schools must do the same in the way they design and deliver their business courses and programs. After all, they are educating humans, not AI algorithms.

The ethical dilemma we face in the Age of AI involves how we can use new technologies to reduce human input, while continuing to value human contributions and experience.

How well we address this topic in our classrooms will become especially important as AI supplants many opportunities for our graduates to learn “on the job,” argues Matthew Beane, an assistant professor at the University of California, Santa Barbara. In his  in The Wall Street Journal, Beane warns that the use of AI to achieve greater process efficiencies “comes at a cost.”

The new technology, he writes, “allows experts to do more, independently, so they don’t need younger, less-experienced workers to help them out anymore—so those novices are left without mentors to teach them the skills they need to do their job.”

If Beane is correct, then managers who use AI to its best advantage will most likely eliminate the opportunities for training that human workers typically receive midway through their careers. In this case, how will future leaders obtain the experience they need to advance their knowledge and continue to refine AI-enabled services? How do we educate students in this new way of working?

Such questions highlight the ethical dilemma that we face in the Age of AI—how we can use technologies to reduce the need for human input, while continuing to value human contributions and experience. I believe the answers to these questions rely, in part, on how well business schools train future leaders to appreciate and enhance the human experience, seek out new knowledge, and mentor the next generation to navigate an increasingly automated world of work.