Remembering Black Monday (30th anniversary)

I remember Black Monday (Monday, Oct 19, 1987) like it was yesterday.  I was a teenager, but completely enamoured with the capital markets as I am today.  My stepdad and I were down at the local Dominion Securities office watching the prices on the Telerate terminal.

It is hard to comprehend the carnage that was actually happening on trading floors and to people’s lives while creating a launchpad for some of the biggest names in the investment management business today. Ultimately, it delayed my entry into the investment business too.

I enjoyed reading this Bloomberg article from some of the biggest names in the investing world.

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Disrupted by AI?

While CEOs will provide stakeholders and more specifically shareholders with an assurance of continuity, the opposite is true.  The average lifespan of a company listed in the S&P 500 index of leading US companies has decreased by more than 50 years in the last century, from 67 years in the 1920s to just 15 years in 2012, according to Professor Richard Foster from Yale University.

From my conversations with industry, I only see the mortality rate of businesses accelerating. There are 8 companies (5 largest market cap – Alphabet, Amazon, et al and the BAT from Asia) that have AI at the core of their business and a number of startups (notice I didn’t say large number of startups as many companies claim to be AI companies, but in many cases it is marketing bravado). Most companies in between are either searching for an AI strategy and not knowing where to start or not believing AI applies to their business (yes, this happens).

Companies need to make big bets, because investing in AI is expensive and good people are hard to come by.  Additionally, if the work and research is really cutting edge, timelines can be uncertain.  Where does the responsibility for AI investment need to reside?  With the CEO.  If not, your AI team will be at risk of being evaluated by the level of expense, and not goals, putting it at risk before benefits are realized.

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What are the opportunities to apply AI?

Everywhere you turn there is something about AI and the impending revolution.  Lots of companies want to participate, but one of the most common things I hear is they aren’t sure how it applies.  That said, there is no magic AI fairy dust to sprinkle on your problems.  The very first step is having digital data.  No data, no AI.  So what are the actual opportunities for AI? The following list is how I think about opportunities to apply AI:

• What processes are automated?
• Where do we use data to make decisions?
• Where and or how do you currently use prediction? (i.e. equipment maintenance, credit default, life time value of a customer, etc.)
• Where do you have zero sum or adversarial business transactions?

The list can be longer but this is where it starts.

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What is an AI Strategy?

I was fortunate to be invited for a fireside chat with Frank Chen of Andreessen Horowitz at the 2016 a16z Summit (podcast) and I stated companies need an AI strategy.  Listeners have subsequently asked what is an AI strategy?  So this is my brief follow-on.

I divide creating an AI strategy into 3 parts (as I would evaluate any investment decision):

  1. How can you impact revenues? Using a pie analogy you can create a pie, make it pie larger or take someone’s pie. So by utilizing/deploying AI which one is it?  Can you derive insights that capture a greater share of wallet (more revenue from the same customer), personalize the customer experience to increase the lifetime value of the customer relationship, etc.?
  2. How can you maintain or grow profit margins? It is easy to think about patents as a natural barrier to competitors, but what else? Can you be more efficient (reduce costs), increase efficiency, reduce timelines, etc.
  3. How are you executing? You need to measure performance. Do you see AI as the ultimate hammer looking for just the right nail or are you applying it appropriately?  Are you getting the results you are seeking?  What is the ROI?

This framework is just a starting point for thinking about developing a strategy.  You can take an exploratory approach to data, but as a starting point, I think applied AI requires rigueur in terms of defining what problem(s) you are trying to solve.

Next post will identify what is needed as a foundation for applying AI.

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