When smart people and good tools trump AI experts and 10x engineers

The conventional wisdom on hiring for tech positions is that more experience is better, and that it’s more difficult to find people with the right experience as new technologies catch hold. This was the mindset with cloud computing and data science over the past decade, and it’s the mindset now with artificial intelligence and distributed systems (aka Kubernetes). However, it’s probably based more in theory, and even self-imposed restrictions, than in reality.

Bloomberg published an interesting story this week that speaks to one aspect of this situation: the increasingly common requirement of a Ph.D. for certain positions, despite the questionable benefits a terminal degree actually provides outside of academia. It’s the kind of requirement that makes a lot of sense for the relatively small number of industry research labs, but might make significantly less sense for work that requires putting knowledge into practice either building products or working with customer problems.

There’s also another practice that can happen in an attempt to find the “right” employees, which is the kind of age discrimination of which IBM is being accused. According to a lawsuit, the company was laying off older workers while hiring and glorifying younger workers, which it believes are “generally much more innovative and receptive to technology than baby boomers.”

The problem with both approaches—overvaluing academic credentials and undervaluing industry experience—is that they ignore the ground truth of where technology is actually headed and what tends to work in practice. For example, as software development and infrastructure operations become more abstracted, you should need fewer “10x engineers” and fewer operations staff making sure the systems stay up. Rather, you might want more employees who write code reasonably well; understand your business reasonably well; and can use creativity to solve problems or drive new initiatives.

And instead of trying to hire artificial intelligence experts, it might be more useful in many circumstances to train existing employeesboth business and technical—on what AI really is and how they can work together to maximize the company’s AI efforts. Even today, there is no shortage of tools that simplify the process of deploying AI models and building AI applications, especially for techniques that are actually viable in production at reasonable scale.

This isn’t so much a commentary on the generalist-versus-specialist debate (although, if you’re interested, this recent interview with Cloudera’s Hilary Mason provides some good insight into how that applies to the integration of data scientists and application teams) as much as it’s a celebration of business sense and customer empathy. Both of those are more important than ever in the era of digital transformation, when everybody is focused on building software-powered user experiences. What sets one application apart from another is how much customers like it, trust it’s safe and can rely on it.

Also whether they trust that the company is committed to keeping it around. Every time a company like Google kills a product, its reputation among consumers takes a hit. Most companies can’t out-engineer Google, but they can commit to building high-quality applications that are backed by deep industry knowledge and an emphasis on customer experience.  


The CIO's guide to CI/CD

Automakers level up on automation

Volkswagen and Amazon team up to create an industrial cloud (Fortune): Another case of a company, VW, attempting to share its work with others in its industry. Although you have to assume much of the value is at the layer above the cloud platform.

Microsoft and BMW add industrial IoT platform to their long-running cloud partnership (GeekWire): See above, but replace “VW” with “BMW,” and “Amazon” with “Microsoft.”

Daimler Trucks buys a majority stake in self-driving tech company Torc Robotics (TechCrunch): This type of investment—and sometimes outright acquisition—is becoming the norm in the auto industry. Automakers realize that the world is changing, and it makes sense to invest, buy and partner rather than waste time trying to build wholly new capabilities on their own.

The cloud could open up in China

China weights lettings US cloud providers operate their own data centers (Data Center Knowledge): This could be a significant change, although the details (and there are many of them) are still unclear. But cloud providers and customers alike might prefer getting rid of the local telco middle-man in these engagements. 

AI brings opportunity and misunderstanding

JPMorgan harnesses AI, aiming to boost client services (Wall Street Journal): The real gist here is that the bank is trying to get its data situation in order so that it can actually pull off its AI plans. For anything not net-new, this is the critical first step.

Food Lion, other grocers will use AI for food suppliers (Wall Street Journal): Yes, AI can power cashierless grocery stores and the like, but these less-sexy back-office applications are where the money is today.

Walmart partners with Google on voice-enabled grocery shopping (TechCrunch): Regardless how this particular integration works out, it’s yet another example of Walmart (among other retailers) stepping up its efforts to reach consumers where they are in the name of competing in the world that Amazon has created.

A picture’s worth 1,000 words: Why retailers are going after visual search (CB Insights): This is a technology whose time has come. It’s easy enough to imagine applications across industries, and the techniques for doing it are mature.

Enterprise AI in 2019: What you need to know (ZDNet): Lots of good info and use cases here, but the main takeaway might be to think about AI as a way to improve efficiency and customer experience rather than to save money.

Survey: Tech leaders cautiously approach artificial intelligence and machine learning projects (ZDNet): This isn’t too surprising, and it might be a matter of not totally understanding what’s feasible today. Relatively simple applications, like visual search, can provide value without huge investments in people and technology.


Now Tech: Application modernization and migration services, Q1 2019 (Forrester; subscription required)

The cloud strategy cookbook, 2019 (Gartner; subscription required)

2019 CIO agenda: A U.S. perspective (Gartner; subscription required)

Why you need to rethink your data and analytics roles now (Gartner; subscription required)

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