Here’s a thought…
how far are we away from kids at Harvard coming up with a fully automated business for a class project?
I said it in that context for a couple:
- I suspect fully automated businesses already exist in the fringe but I’m talking about something more widely applicable
- By the time the kids at Harvard are doing it, it’ll be big news and government regulators will have to start shifting around this potential
Imagine a company called Widgets.com. Widgets.com is a market place for widgets where manufacturers of widgets can list their products. Buyers of Widgets can come to the site, pick the widget they want and place an order for delivery. When a customer places an order, the order is relayed through to the manufacturer and the manufacturer will ship the widget directly through to the buyer.
Widgets.com outsources it’s live chat and call center. And their web design. And their IT. And their Legal. And their Accounting. And their digital marketing.
Widgets.com would also have extensive data analytics that would help track key information for making strategic decisions. These data points would include customer feedback and reviews, website activity, error tracking, legal reporting, financial reporting, and social media stats. And anything else you wanted to include.
The Widgets.com algorithm would be capable of making executive decisions, but would aim to outsource nuanced details. For example, if pink widgets were trending on social media, a note would go out to the digital marketing team and manufacturers of pink widgets, while a request would go to the web designer to feature pink widgets. If the situation was more nuanced, say with a zero star review, the algorithm would track that info, forward it to a capable customer service rep and have them work to resolve the issue.
It’s actually a fun exercise because you can do this with just about any decision being made within a company. I’m pretty sure these are the steps to building this decision engine:
- Identify the cues to look for when identifying a problem
- Use additional cues to verify the problem
- Review past solutions to the problem or similar problems
- If a past solution has worked, use it again
- If a past solution works again, make a note
- If a past solution doesn’t work, go back to step 2
- If a past solution didn’t work, look to variations of solutions to similar problems.
I know that’s a bit of an oversimplification but what I’m getting at is that with enough time and insight, a top CEO could effectively upload his decision engine into a neural net. Perhaps a decision engine wouldn’t make the best CEO for a complex company that operates in a rapidly changing environment with an actively engaged customer base… but maybe at that point, a human CEO isn’t cutting it either.
That’s where I see this going, especially because it’s already happening. Big data analytics is an early stage version of human/digital hybrid CEO. Right now, we’re mostly using data analytics to provide the human CEO with more information. If the human CEO sees that everything X happens, Y needs to happen, he can automate it. Once it’s automated, that’s the responsibility of the digital CEO. As more information starts to get tracked, more patterns will emerge, and more automation will occur. As that process progresses further and further, the human side is needed less and less.
I’m not sure how this will play out, but I do know that today’s pundits are suggesting that the CEO’s role will be among the last to be taken out by automation. That before the CEO role goes digital, manufacturing will be replaced by 3D printing, warehouse workers will be replaced by robots, delivery drivers will be replaced by automated trucks and drones, and even computer programmers will be replaced by computers who programmers have taught to program. Considering that the new Atlas looks like its about to try out for Cirque, who knows.