AI, startup hacks, and engineering miracles from your friends at Faraday

Where do you stand in the current AI landscape?

Perry McDermott on

Artificial intelligence technologies

You've probably noticed the growing buzz around AI and ML. You've likely done some research into the field, read some AI success stories, and believe that artificial intelligence is more than just a buzzword.

Sound like you? Good, you're totally on the right path!

So is all the hype true? Is AI really as promising as it seems? Have most companies already adopted AI? Or even worse, are you too far behind to be competitive with AI?

Luckily, you're not too late.

Hype versus reality

In a survey of over 3,000 senior executives, McKinsey found that only 10% of respondents said they have actually operationalized AI at scale.

That said, another 10% claimed to have operationalized AI in at least one core business function, and about 40% are experimenting with some form of AI.

Couple those numbers with the massive increase in AI-related investments over the last few years, and we can see the time to get serious about AI is undoubtedly now.

Where's the interest, and how much is there?

Over the last five years, investments in AI have more than tripled. In 2016 alone, total investments (internal and external) ranged from $26 billion to $39 billion, with over 75% of that coming from tech giants like Amazon, Apple, Google and Baidu.

These cash-rich giants have mostly invested internally — on R&D and deployment — but they’ve also invested significantly in hard-to-find talent, primarily through major acquisitions.

Source: MGI Artificial Intelligence Discussion paper.

How can I possibly catch up and get ahead?

These massive investments can feel deflating, and rightly so if your plan is to go head-to-head with the giants by building up your own AI tools in-house. The good news is that you don't have to, and often shouldn't develop AI from scratch.

A wealth of AI startups have caught some serious traction by developing fit-for-purpose solutions that address focused sets of business challenges.

Whether those challenges rest in IT and security, finance and accounting, or revenue growth and marketing, organizations can easily adopt and operationalize these “off-the-shelf” solutions in a fraction of the time, and at a fraction of the cost of alternative “one-size-fits-all” solutions like IBM’s Watson.

The key is to automate tasks that have an immediate impact on your top and bottom lines. AI technologies can't replace your entire marketing team, but they can arm that team with powerful customer insights and predictions, simply unattainable without powerful machine learning algorithms.

Some use cases

For more on this, and to read some specific use cases for off-the-shelf AI solutions, download our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business