We are entering uncharted territory. In the coming years, CEOs will be challenged like never before.
Bill Gates has described the AI revolution as “fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone.” That’s a big deal. But, it’s also something we can wrap our heads around. After all, we just spent the last twenty-plus years making sense of digital transformation.
At the same time, more than 1,000 AI researchers, academics, and tech leaders just signed an open letter urging a moratorium on the development of even more powerful AI, warning that it presents “profound risks to society and humanity.”
Maybe, the analogy we have in our heads about what AI means isn’t sufficient.
The coming disruption will remake our organization… The scale and speed of change may well overwhelm our ability to make good choices.
One thing is certain. The coming disruption will remake our organization. There’s a good chance we will get it wrong. The scale and speed of change may well overwhelm our ability to make good choices. This is all the more reason to establish some firm ground so we understand how — and more importantly why we should — use AI to transform our organization to meet the coming challenges.
Given that the novel target of AI’s disruptive potential is white collar work, we need first to define the purpose of this work. Roger Martin offers among the most clear-eyed definitions, as outlined in his HBR article “Rethinking the Decision Factory.” He notes that any large company has a product or service factory at its heart. GM or P&G have factories that create products in the form of cars or personal goods. Bank of America or Verizon have factories that produce services in the form of bank branches or digital experiences.
But, the white collar workforce in headquarters are removed from these production activities and misconstrue the function of management as coordination and control. They think the job of management is to ensure the factories are directed well so they do what they’re supposed to.
However, Martin argues that “a more useful conceptualization is that they work in factories too, but their output is decisions. The entire white-collar workforce of the modern company works in decision factories where they pump out decisions such as what and how much to produce, how to market and sell what is produced, what new things to consider producing and selling, where to sell,” etc. In this view of the large, modern company, the correct way to organize is around decisions: measure output in terms of decisions; measure and work on enhancing the productivity of producing decisions; and so on.
Of course, few companies organize their management operations around decisions. Instead, most businesses borrow their organizational model from their product and services factories and organize around flat jobs, jobs where the person comes to work everyday and performs the same tasks. But, decisions are lumpy, so Martin argues the decision factory needs to be organized around projects, not flat jobs.
This is the crux of the problem with innovation in established companies.
For a long time now, organizations have struggled to find the right innovation model and to balance core business operations with exploration and experimentation. One key blocker has been the flat jobs powering the coordination and control managerial structure. They consume a lot of oxygen on repetitive tasks that keep everyone busy. There’s never sufficient time or space for innovation in the core business.
The good news is that AI may change that. Noah Smith likens their impact to machine tools: “Currently, a lot of our time and effort is spent producing this stuff [PowerPoint presentations, business emails, memos, grant applications, advertisements, instruction manuals, corporate paperwork…]; LLMs could cut that time and effort down by a lot, just like machine tools made it easier to shape metal and plastic in a factory.” If these tasks could be automated away, energy could be reallocated to adaptation.
This shines a light on the need not just for a new approach to management, but also for more expertise in adaptation (i.e., design thinking, call it what you will) throughout the entire organization. This must be a strategic imperative.
For the first time, we will be able to automate a new class of white collar tasks. The temptation will be to streamline functions as they exist today, to cut jobs and let the machines do it. But, this would be a mistake. Because everyone has access to these same tools, that’s the new competitive floor. We should be thinking about how we can reallocate capacity away from mundane tasks and toward innovation and adaptation. The notion of the decision factory offers a framework not just to change at the level of the task or job, but at the level of the organization. And that’s what’s demanded by the scale of disruption we’re facing.
Sincerely,
Shaun Gummere
Shaun Gummere is the Chief Experience Officer at Cantina, an innovation consultancy headquartered in Boston. For more than 15 years, Cantina has partnered with visionary leaders to challenge the status quo, reimagine products & services for a customer-centric age, and drive new growth and efficiencies for their client’s organizations. Shaun is responsible for shaping Cantina for continued success through strategic planning; storytelling, branding, and market positioning; the design of product and service offerings; and defining the overall client and employee experience. Over the course of his career, he’s worked with start-ups and Fortune 100s to design not just better products and services but also the organizations and systems that create them.
References:
https://www.gatesnotes.com/The-Age-of-AI-Has-Begun
https://hbr.org/2013/10/rethinking-the-decision-factory
https://rogermartin.medium.com/unclogging-your-decision-factory-bc68bece831b
https://noahpinion.substack.com/p/why-americans-fear-the-ai-future
