“Without data, you’re just another person with an opinion.” – W. Edwards Deming

The game has changed.

Artificial Intelligence (AI) is powering a paradigm shift in business that promises to realize the potential of the 4th Industrial Revolution.

To that end, I attended "The AI Summit" over 2 days here in NYC.

Unlike the more academic and research oriented AI conferences, this one is described as "The world's number one AI event for business" and it most definitely delivered.

The corporate interest and investment in Artificial Intelligence was on full display.

More intriguing though were the concrete examples of results and leading indicators of returns expected over the next 12-24 months by progressive companies across a diverse set of verticals.

My hope with this post is to summarize some of what I learned and experienced as well as to make my enthusiasm for what is going to unfold over the next 12-24 months as contagious as possible!

Bear with me as it's a little bit longer than I hoped but it's a complex topic... ;)

What's at Stake?

An "AI Summit" survey of C-suite executives in Fortune 500 companies conducted between January-September of this year had some very compelling insights:

  • Over 95% recognize AI as a pivotal issue, they agree it will transform their industry
  • 98% perceive it as essential for their organization
  • More than 80% compare the impact of AI to that of the Internet, with less than 10% still believing that the impact of AI is more hype than essence
  • 77% expect to see a reduction in overall costs while 66% also anticipate enhanced accuracy in their operations
  • 80% expect a major change to business structures, roles and hierarchies due to AI development/implementation
  • Over the next 10 years, enterprise spending on AI technology will increase from $200M to over $50B

AI is confusing, right?

It doesn't take a rocket scientist to figure out that AI isn't the easiest subject to wrap your head around as it's a bit "abstract" but the following stat from the survey was especially telling:

  • The single biggest obstacle to AI adoption highlighted by 67% of respondents is the lack of understanding about AI's capabilities or limitations; despite the hype, knowledge on the opportunity around AI is still rather limited

What is vs what isn't AI?

There are 2 main definitions of AI: "General AI" and "Narrow AI".

"General AI" relates to a machine's capability to perform any task that a human being can which is where a lot of the confusion and concern kicks in (for obvious reasons). This is also where the fear and hype intended to increase the fear sits...

"Narrow AI" is narrow in application where AI is applied to a specific task or solving a very specific problem. This is where immediate results are being achieved by automating dimensions/tasks of knowledge work that unlock productivity, growth etc.

AI is an umbrella term for a bunch of different component technologies but it might be helpful to define a couple of the more significant ones:

"Natural Language Processing" or "NLP" allows computers to understand human language as it's written or spoken and to produce humanlike speech/writing.

"Machine Learning" or "ML" is a set of techniques that empowers computers to find patterns in data without using rules prescribed by humans to effectively learn and get better and better at uncovering actionable insights to improve the probability of the “next best action” achieving the desired outcome.

"Deep Learning" is a form of "ML" uses the model of human neural nets (i.e. your brain) to make predictions about new data sets in a hierarchical process. This stuff is heavy duty and a bit further out but eventually this will be where massive data sets will successfully be analyzed, creating super valuable predictive intelligence for us to act on at scale.

"Image Recognition" is the ability for a machine to identify images or objects, and process information based on an analysis of pattern identification. Remember the first time Facebook prompted you to confirm the name of the person in the picture you just uploaded?

Now that we have the above taken care of, let's get into the application of AI aka value creation.

Commercial application of AI?

While we are far away from "General AI", "Narrow AI" is here and is where a lot of the commercial application opportunities exist.

Narrow AI drives the automation of knowledge work.

Now, before we jump to any conclusions about "automating jobs", this was one of the most important insights from the conference.

There will definitely be jobs that will be automated as machines can do them more effectively.

An example would be those in machine maintenance who would no longer need to "physically check-in" on the machines as IoT sensors would report back in real-time the status.

But the real area of opportunity is to deconstruct jobs into their component tasks and determine where automation can be introduced to improve decision making and productivity.

People augmented by machines so they can make better decisions and be more productive. Not humans vs machines but humans + machines.

Nestle: Tangible Example of Humans + Machines

An intriguing example of this would be Sid Raisoni, Head of Analytics at Nestle who has implemented a system that helps Nestle identify customers projected to end their relationship with the company within 90 days.

The system has an 85%+ confidence rating and enables them to address these “at risk customers” 90 days before they leave so they can allocate loyalty techniques to retain them by improving the situation. In doing so, humans are empowered to then reduce customer churn which is good for everyone. 

On the other end of the spectrum, the same system is providing automated insights about their most loyal customers that enables Nestle to introduce super interesting insights into the Corporate Strategy / Innovation realm which then enables them to create new offerings to expand the customer relationship including concierge services. Net new revenue results and of course, everybody likes that too.

The third category of job will be those resulting from the innovation and ingenuity opportunities presented by enabling new opportunities to create value for customers. Tough to project what those look like until we create them but using Sid’s example above, one could tie them back to the results of the Corporate Strategy / Innovation group’s efforts.

"At the heart of this culture is an understanding that an organization's ability to learn and translate that learning into action rapidly, is the ultimate competitive advantage." - Jack Welch

The more things change, the more they stay the same…and Jack Welch’s quote about “good business” is as true as ever today, especially when it comes to the implications of embracing AI.

13 key takeaways from presenters, conversations, literature etc.:

  1. Just like each industrial revolution over the last 200 years, we have a moral obligation to use anything we have that’s this powerful the right way
  2. AI is viewed as a tech capability but in reality, AI is a business capability
  3. How well data is used in your company is directly correlated to customer happiness and therefore business success
  4. AI must be embraced by organizations with the right mindset going in
  5. A lot of progress with the science but not yet as much in the application...it hasn’t crossed the chasm just yet, we’re effectively in the late 50’s, early 60’s
  6. Domain expertise is indispensable
  7. Returns on solving the right problem(s) are exponential
  8. Use AI to make sure every customer interaction is customer relevant
  9. Because we’re early, the push isn’t coming from the business aka what’s our AI solution for this problem? It’s coming from the IT/tech teams.
  10. Landscape is changing so fast, do we have the right individuals plugged into our swat teams to evaluate technology and also, more importantly implement?
  11. Huge shortage in talent that has the ability to frame/contextualize the problem
  12. Why aren’t more politicians embracing AI, talking about how they can help folks update their skills, evolve with the times?
  13. Regarding the potential for a 3rd Winter for AI, incumbent on us to be prudent with our storytelling and commercial with our application.

The AI train has left the station. Are you on it?

E: Alec.coughlin@sapientrazorfish.com; T/IG: @Alec_Coughlin