4 Key Lessons on Investing in the Future of Work from AI at Work

Posted by Alyssa Verzino on Feb 15, 2019 1:19:37 PM

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When you’re investing, knowing the forecast of future trends is essential. On Episode 28 of AI at Work, James Cham of Bloomberg Beta joins us to share four top insights on investing in the future of work.

We’ll cover how the tech business model landscape may soon be shifting in the wake of AI, strategies for adopting AI and emerging as an AI leader, and a glimpse into the paradox of why the real experts on automation may not be willing to share their secrets, yet.

1. The dominant business model for AI will be one we haven’t seen yet

Building machine learning models is very different from normal software development. With the latter, you can almost always build what you set out to build. With machine learning models, that’s simply not true. “There's plenty of times that there's not enough juice in the data. The reality is, you need to recast the question or just give up,” James explains.

The specific characteristics of software and online software development led to the rise of license software and subscription software as business models which made sense. James explains that in part because the model building is different enough, he’s pretty convinced that there will be a dominant business model that we really haven’t seen yet.

“We're two blocks from Salesforce Tower right now. And, the guy or gal who figures out that dominant business model is going to be successful enough that they might have two towers. That's mostly what I'm looking for,” says James.

2. The companies who are smart about discerning when to automate and when to not will move ahead as AI leaders 

A question that’s often asked is “will falling behind in adopting AI preclude my company from catching up to those who adopted earlier?” Yes and no, says James. He elaborates that there’s a “realization that the people who figure out how to apply machine learning in the right ways, and sooner, are going to be the ones that win.” 

The crucial elements lie in understanding when to trust the machine and when not to, when to automate and when not to automate, when to bump issues up to people and when not to. James says, “My guess is that the companies that are most aggressive and smart about that are going to be the ones that actually win.” 

These companies, he explains, will move swiftly ahead “not because they have better data, but because they understand where their models are good enough that they can trust them. As a result of trusting them, they can automate in different ways. As a result of that, they can fundamentally change their economics. I think that's the actual sort of big opportunity. 

3. One major challenge to overcome

“In most big companies it is the people at the ground who actually understand the opportunity for automation,” explains James and yet, “The way that we're set up, they have no incentive to tell anyone. If there's someone who really knows they could write a little script that would replace 60% of their job, why would they tell anyone?” 

There’s a disincentivization to share that valuable information, because you potentially risk getting fired or having your role restructured in ways you might not want. The problem isn’t rooted in company culture. Simply put, it is a straightforward economic question. James says, “What incentive do you have for telling everyone else that actually, you know what, part of my job really should be replaced by a computer. I've not seen anyone solve that.” Yet this challenge will be one that’s essential for companies to address in the future. 

4. Getting started in AI doesn’t involve going after the latest research trend - It’s about trial and error with applying AI, starting with small business processes

For executives who are thinking about getting started in AI, James recommends identifying small business processes that can be replaced with models and experimenting with what effect that has, as opposed to going after the latest research trends, which are really important, but just not relevant to most companies.

“Anything from some small churn prediction piece just to start seeing whether we can build a model that does a better job of predicting churn or some piece around predicting some logistics question,” he explains as an example, “Starting small but model- and data-centric creates the muscle memory needed for executives to figure out how to have good intuition.” 

***

Drawing upon James’ expertise, we’ve seen an interesting forecast for the future of work. The companies who will emerge as AI leaders will be focused more on understanding when to trust their models and when to automate rather than simply rushing to be the first ones to adopt advances in AI. Getting started, the key is to take small steps, testing out models on concrete business processes in order to build intuition as an executive bringing AI to your company.

Whoever will solve the two business puzzles of incentivizing on-the-ground workers to share opportunities for automation in a way that won’t pose a risk to their own job security and innovating an optimal business model that best fits AI, it seems, will be the one to take the lead of the market. It’s an exciting time, the forecast predicts - many changes are yet to come.

To learn more, tune in to this episode of AI at Work And, be sure to subscribe on iTunes, Spotify, or Google Play and share with your friends. 

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Topics: AI at Work