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Uber shows how AI is more about automation than revolution 

Uber's recent S-1 filing sheds light on some of its artificial intelligence efforts, which, while intimidating on the surface, should probably give traditional enterprises the confidence that they can do AI, too. Uber's entire business might seem intimidating because of just how differently companies can operate when they're built around data, algorithms and mobile apps from the start. But, in the end, Uber is using AI to power automation in ways that any large organization with a solid understanding of the underlying technologies can likely pull off itself.

We'll start with the intimidating-dare we say "revolutionary"-part, which can be summed up in this industry-analyst quote from a Wall Street Journal article about Uber's AI efforts: "Its drivers don't have human bosses. They literally answer to algorithms." Uber hasn't created a super-intelligent system that's going to pass any sort of Turing test or threaten humanity, but the company does trust its data and its algorithms enough to let them run the show when it comes to scheduling rides. That's a level of trust in algorithms that many companies might only ever aspire to.

And getting there took Uber collecting, and then analyzing, quantities and varieties of data in a manner that's a little easier for companies born during the "big data" era-if only because the concepts (e.g., data science)  and systems (e.g., Hadoop) already existed. There was no "What's our big data strategy?" discussion. Big data was the strategy.

When you peel back the layers of the onion and get to the core, though, you see that Uber's AI strategy is relatively simple-automate tasks that can be done faster by machines than by people. This definitely applies to the complex logistics of ride-scheduling and dynamic pricing, but also to other areas where Uber uses AI. For things like building conversational interfaces; scanning drivers' licenses and restaurant menus; and, eventually, operating self-driving cars.

However, Uber still employs more than 20,000 people (not including drivers) to carry out the parts of the business that can't be easily automated and that will always require a human touch. These include things like software development, business strategy, marketing and government relations You don't need to accept Uber's myriad cultural shortcoming to appreciate its approach to AI as a force multiplier for its core business of getting people (and things) from Point A to Point B.

The driving question for any company seriously looking to adopt AI, or even to modernize how it does IT overall, should be: "Where are we expending too many resources on undifferentiated tasks, and are there technologies and/or techniques to automate those tasks?" The answer to the second question is probably yes, and the solutions will probably require a much smaller engineering investment than Uber is willing to eat because it like to build everything itself.

The important thing is to first identify the problems or opportunities, rather than locking into a solution ("We are going to remake ourselves with AI!") and then looking for places to apply it. Because the ideal solution might look different for everyone and every application depending on business requirements, legacy systems, and what kind of talent you have in place to carry it out.

For more on the topic of automating monotonous tasks and even bridging the AI gap by including humans in the training process, check out this trio of recent posts:

Blending artificial intelligence and human creativity (CIO)
Using AI to make knowledge workers more effective (Harvard Business Review)
 Microsoft: AI's next frontier is experts teaching machines (VentureBeat) 

The cloud still isn't free

Apple spends more than $30 million on Amazon's cloud every month (CNBC): While lots of companies should not spend too much time on DIY infrastructure, Apple probably should. Giving hundreds of millions to its rivals is not an ideal situation.

How multiyear contracts with big business are changing the economics of the cloud (GeekWire): More on the contract mentioned above, as well as AWS deals with companies such as Lyft and Pinterest. It's proof that cloud computing is now part and parcel with enterprise IT, although it's up for debate whether these huge contracts are wise for the buyers.

The management-engineering nexus

Embracing papercuts (Dropbox): Some good advice on how to manage engineers by giving them freedom to make mistakes, without forgetting that the buck stops with you.

Good engineers can become good leaders (VentureBeat): More advice in the same vein as the piece above. But CIOs and other tech execs also need to remember that some folks will benefit from promotions that don't involve management.

Digital transformation done right ... and wrong

T-Mobile Money goes national: 'Un-carrier' doubles down on banking as next target for disruption (GeekWire): It's always smart to figure out who your customers are, which parts of their lives you touch on a daily basis, and how you can use that to help them (and yourself) in new ways. 

Safeway stores offering health care powered by artificial intelligence (AZ Central): A smart idea in an era where people are under-insured and over-worked. Combining shopping, checkups, and medicine in a single place is time- and cost-efficient.

Pentagon's designer of $10 billion JEDI cloud is stepping down (Bloomberg): A profile of the man behind the contract that has cloud providers duking it out, and also behind the Defense Department's "tours of duty" for technologists to help modernize its processes. 

Mid-size IT firms need business acumen to deliver digital outcomes (Gartner Blog Network): Sounds about right. If you have limited headcount, everyone needs to understand why they're doing what they're doing.

Accenture sued over website redesign so bad it Hertz: Car hire biz demands $32m+ for 'defective' cyber-revamp (The Register): There's really no excuse anymore for outsourcing such a critical part of your business.

Going cloud, slowly

IT spending in 2019 revised down among data center woes (ZDNet): Data center spending is down-but certainly not gone-while enterprise software spending is up. 

Why workload migration metrics get it wrong (InfoWorld): This is probably truer than not much of the time. There's the time/money to do something in an ideal world, and then there's the facts you're actually working with.

5-2x.png Containers, etc.

Tinder's move to Kubernetes (Tinder Engineering): Another reminder that building out a Kubernetes infrastructure on your own is not for the faint of heart or engineering talent.

What is Docker: Docker containers explained (InfoWorld): Just what it sounds like, which actually is more than meets the eye.

Key differences in security management for serverless vs. containers (The New Stack): Very similar, yet very different sets of concerns-at least for now. That may change as the two architectures merge.


Adaption, Not Adoption, Is the Key to Digital Transformation (Cloud Foundry)

From Grease To Code: Industrial Giants Must Bet Their Futures On Software (Forrester; subscription required)

Show, Don't Tell, Your Developers How to Write Secure Code (Forrester; subscription required)

The Future of Enterprise Data Centers-What's Next (Gartner; subscription required)

Reduce Cloud Spend and Risk by Identifying I&O's Role in Cloud Governance (Gartner; subscription required)

2019 CEO Survey: CEOs Are Divided on the Impact of Digital Giants - Now Is the Time for CIOs to Act (Gartner; subscription required)

How Application Leaders Use Storytelling to Win Support for Digital Business (Gartner; subscription required)

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