It might surprise some people to learn that formal futures studies has a lot of history in it. When it comes to the topic of economic life and new technologies, one historical pattern we see is periods of experimentation when new technologies are introduced. Lots of trial and error by a lot of different people during these periods. At the beginning, no one really knows what will work or, more importantly, what will end up sticking.
Today, with “artificial intelligence” (and right now, with generative AI most intensely) we are seeing the same thing. Right now, it seems like everyone is playing with these new machines. For most, the experimentation will occur at the level of their daily work and roles. How can they use these emerging tools in their everyday effort to get their things done? For some, however, the experimentation will reach down to the level of their very business model. Some people will be fundamentally rethinking what they could do and how they might do it moving forward.
Given where AI seems to be today and considering how past waves of key technologies have washed through society, this seems like the time for organizations to start having more explicit and forward-looking discussions about how these machines could or should be incorporated into the organization – and what it might mean for their business model.
How Do You Think About AI?
So, to start off, we will take another inspiration from history. One way to think about technologies that mechanize or automate is to organize them according to whether or not they replace labor or if they enhance labor. Will they allow the same thing to be done, just without the traditional labor? Or will they now enable that labor to do new things? Do the new machines replace or augment human labor?
For example, automated telephone switches replaced human telephone operators, just as the standardized shipping container lead quickly to huge losses in longshoreman jobs. In contrast, the computer (combined with spreadsheets) enhanced accountants rather than replacing them.
When you look at the emerging technologies (AI) that you and your team might now be discussing, do you see them as labor replacing or labor enhancing? Are you thinking about them more in terms of automating out human activities or more as tools that will empower or even redefine your staff’s capabilities? And in case you’re worried, there’s no right answer here.
Looking at Different Paths Forward
How you see these new tools is one important component. Another is more about vision or values. Are you interested in fundamentally rethinking your organization? Or are you committed to retaining the structure and model you have today? How you answer the replacing/enhancing question and how you answer vision/values one probably leads you down different paths for adopting AI in your organization.
Based on these choices, and considering how organization have responded to new technologies in the past, we might expect four different paths that leaders take when it comes to these new machines:
“Bolt on” new automation tools to the existing structure and processes (Bolt On)
Pursue new efficiencies through reducing headcount (Efficiencies)
Redesigning processes with the idea of “human-machine teaming” (Teaming)
Reimagining the organization as built on machines (AI.biz)
Obviously, these are very broad paths, and the point is that people will be following some version of these paths as they experiment with these new tools. With all of the uncertainty, excitement, and anxiety now swirling around AI, it might be incredibly useful to make these choices explicit.
To simplify, we could use the following matrix to frame a discussion about which basic adoption path is more appealing or appropriate for your organization.
In future posts we will introduce additional tools to help frame these discussions. In the meantime, reach out to us to start mapping more of the AI-related threats and opportunities emerging on the horizon.