Summary:  COVID-19 and the changes it creates in the business environment for the next 12 to 24 months means our current AI strategies need to thoroughly reviewed and probably retargeted.

Whether you are a Zoom-connected CXX level living out your WFH isolation or one the ‘fortunates’ in essential industries…

Summary:  Can all AI strategies be defined by a few common needs or are the different AI strategy models sufficiently unique that they need to be considered as separate approaches.

There’s an article in the current Harvard Business Review titled “…

Summary:  Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive.  It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful.

As AI/ML becomes increasingly…

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.  Here are the rest.

McKinsey says platform companies will represent 30% of global…

Summary:  McKinsey says platform companies will represent 30% of global business revenue by next year (2020).  Here are some lessons and examples to help mature companies evaluate where they can create AI/ML-enabled platforms to remain competitive.  This is a long topic so this will be Part 1 of 2.

As data…

Summary:  A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success.  Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.

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Summary:  Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

There are many articles written from a tools…

Summary:  A major pain point is standing in the way of many companies’ ability to maximize the value of their ML/AI initiatives.  The competing goals of data flexibility versus single version of the truth can only be solved with an effective data governance strategy.

There are many reasons why companies…

Summary:  Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations.  We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.

If you’re a CEO or any other flavor of top executive leading a…

Summary:  Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company.  Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.

Hope you’ve been following our latest series of articles describing and…

Summary: Now that we’ve detailed the four main AI-first strategies:  Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick.  Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.

In our last several articles we’ve taken a tour of the four major…

Summary:  Getting an AI startup to scale for an IPO is currently elusive.  Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy.

Looks like there’s a problem brewing in AI startup land.  While AI is most certainly…

Summary:  A defensible data strategy increasingly defines those AI businesses that will be successful.  VCs know this and are steering the funding to this strategy.  Read here about what a defensible data strategy is and how to identify your next AI opportunity using this technique.

The most recent issue of MIT Technology…

Summary:  Before starting to develop an AI strategy, make sure your team understands the limits of what is reasonable today, as well as incremental improvements that might be overlooked.  Focus should be on your LOB leaders who understand the business.  Make sure they are also able to recognize AI opportunities.

I…

Summary:  There are an increasing number of larger companies that have truly embraced advanced analytics and deploy fairly large numbers of data scientists.  Many of these same companies are the one’s beginning to ask about using AI.  Here are some observations and tips on the problems and opportunities associated with managing a larger data science function.

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