Automation: Focus on Augmentation, Not Just EliminationAutomation: Focus on Augmentation, Not Just Elimination The threat of automation to labor is not new, but its current impact is significantly different due to AI’s self-learning capabilities. While automation can bring financial benefits, it also poses ethical and equity risks. Risks of Automation * Job fragmentation: AI primarily targets routine clerical and bureaucratic jobs, leading to lower-paying jobs for displaced workers. * Job polarization: Automation eliminates low-paying manual jobs and high-paying creative jobs, leaving a middle segment of jobs vulnerable to automation. * Socioeconomic inequality: Job fragmentation exacerbates inequality, leading to potential unrest and volatility. Augmentation over Automation To avoid these risks, organizations should focus on augmentation, where AI collaborates with humans to enhance job content and foster creativity. This requires: * Investment in Human Roles: Allocate a significant portion of AI budgets to creating new jobs that require enriched cognitive responsibilities. * Employee Reskilling: Enable employees to adapt to working alongside AI by providing necessary skills and training. Key Decisions for True Augmentation * Shift Budget from Technology to Job Enrichment: Dedicate more resources to training and development to support new human-centric roles. * Invest in People from the Start: Build a culture that values human creativity and partnerships with AI from the outset of AI adoption projects. Benefits of Augmentation * Improved Job Satisfaction: Enhanced job roles provide increased meaning and growth opportunities for employees. * Increased Productivity: Collaborating with AI enables employees to focus on higher-level thinking and problem-solving. * Reduced Inequality: Augmentation helps maintain a balanced job market and workforce. By prioritizing augmentation, organizations can harness the benefits of AI while minimizing the potential negative consequences. This approach fosters a more humane and sustainable workplace that promotes human innovation and growth.
The threat of automation to labor is nothing new. Humans have a long history of new technologies disrupting our workflows by automating tasks, from the printing press in 1439 to public access to the internet in 1993. Today, we have intelligent technology at our disposal that can reduce both the cost of labor and the threat of irrational (human) work behavior that deviates from streamlined operational processes. This time around, the threat of automation is significantly different, as AI’s self-learning capabilities allow it to mimic and sometimes even eclipse human cognitive abilities…
For many of my executive students, the developments in AI are bewildering and they seem to struggle to come up with arguments for why they should be cautious about adopting this tool. They focus entirely on the financial benefits that AI brings, ignoring the importance—and perhaps even the long-term existence—of their own humanity. They often note that given AI’s potential to create value for businesses and contribute more to economic growth than any other employee could possibly do, they as business leaders have no choice but to pursue automation strategies unilaterally. From a short-term perspective, this reasoning makes sense. Businesses that prioritize automation will indeed report some performance improvements. But the bottom line is that automation’s contribution to an organization’s business growth will ultimately slow it down and sometimes even work against it. Automation is at best a short-term fix that will bog your organization down in the long run…
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The reality of automation strategies is that human jobs are becoming fragmented…. Job fragmentation pushes workers into lower-paying jobs. This trend stems in part from the well-researched phenomenon of job polarization. Due to the inherent limitations of developing and deploying AI, it is typically too expensive and difficult to automate low-paying manual work (for example, it is often cheaper and more efficient to hire a cleaner than to use a robot cleaner). On the other hand, it is too expensive and difficult to automate high-paying creative and strategic work. This leaves a middle segment of jobs—routine clerical, bureaucratic office work—as the primary target for automation. And when such workers are displaced, they cannot immediately upgrade their skills to take on higher-paying jobs. As a result, they are trapped in low-paying, low-value jobs, positions that further exacerbate inequality. …
So, as an AI-savvy leader, you need to be acutely aware that AI efforts that focus solely on automation quickly introduce ethical and equity risks. Your automation efforts could exacerbate job polarization and increase socioeconomic inequality, which could trigger unrest, instability, and other volatility, even violence. In turn, these responses to inequality will threaten your organization, which has become less human and therefore less able to adapt to volatility.
You can combat the negative effects of too much automation by investing in building out the human roles within your organization over the long term.
Commitment to augmentation requires serious investment, as new jobs require enriched job content and added cognitive responsibilities for employees to learn and grow to become a better version of themselves. In these new jobs, where AI and humans work together, your employees need to acquire the necessary skills to get used to having a smart machine as a colleague.
What you really want is for your employees to become better at a key skill that people develop early in life and that benefits companies the most: creativity.
In the creative process, the human identifies a problem, which will serve as input for the generation process driven by AI, and the generated output is then interpreted, corrected, and used by the human. So, a human is needed to initiate and complete the creative process, while AI drives the generation process, which involves the hard work of bringing all the information together. To get the most out of AI, you need to avoid making automation your priority and instead focus on true augmentation. To achieve that outcome, you need to make the following two key decisions.
Reduce the technology investment and increase the job enrichment investment in your AI budget: During a lunch meeting to meet with the CEO of a company I worked with, he told me that while he wasn’t a tech expert, he felt like something was off with the company’s recent AI adoption effort. Why? I asked. “Well,” he said, “I see all these fancy algorithms being developed and deployed, but when I look at the numbers, I don’t see much improvement in the performance of our teams.”
I asked how much of the budget was still available after the technology had been invested. He could, I suggested, use that portion of the budget to fund training sessions for the new jobs he was likely to create. He looked at me with a somewhat guilty face and said, “What new jobs are you talking about exactly? And no, most of our budget has already been spent.”
It’s a typical response I’ve heard when discussing corporate AI adoption projects. As many change consultants will tell you, when organizations start their AI adoption project, they typically spend up to 90 percent of their budget on the technology itself. The result is that there’s little money left to put AI to work in partnership with the workforce.
This outcome is both unfortunate and counterproductive. It will undermine the chances of success of your AI adoption project. If you’ve gained a good business sense of AI by now, you know that it’s your duty to invest more – more time, more money – in creating better, more human-centric jobs for your employees, both because it’s the right thing to do and because the long-term costs of not doing so are significant. The most successful companies are the ones that invest heavily in their people when the AI adoption project begins.
Reprinted with permission from Harvard Business Review Press. Excerpted from The AI-Savvy Leader: 9 Ways to Take Back Control and Make AI Work by David De Cremer. ©2024 Harvard Business School Publishing Corporation. All rights reserved.
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