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.
Added by William Vorhies on February 3, 2020 at 11:15am — No Comments
Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner.
Added by William Vorhies on January 7, 2020 at 8:30am — No Comments
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.
Added by William Vorhies on April 29, 2019 at 9:35am — No Comments
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…Continue
Added by William Vorhies on April 15, 2019 at 7:43am — No Comments
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.
Added by William Vorhies on April 8, 2019 at 9:29am — No Comments
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.
Added by William Vorhies on April 1, 2019 at 9:29am — No Comments
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.
Added by William Vorhies on March 25, 2019 at 8:30am — No Comments
If there was an AI winter, we are clearly in the peak of its summer. I do not know if we will ever build something as Skynet, but we are going to build much simpler things that will change the course of our lives. This shows a new application of analytics in the field of finance. A radical new approach that let flourish a new face of finance never seen…Continue
Added by Ramon Serrallonga on February 15, 2019 at 6:06am — No Comments
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.
Added by William Vorhies on February 11, 2019 at 10:15am — No Comments
Organizations, often in their me-too hurry to adopt a new technology, just pour their old-wine (data) into a new bottle. What was originally called ‘sales-information-system’ in the good-old-days underwent many avatars before it became BI (business intelligence) and then off-late, it is time to switch again. The latest avatar is called Data-Visualization-Tool, and every CIO…Continue
Added by Krishna Pera on October 17, 2018 at 10:00am — No Comments
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…Continue
Yes, I know, this has been tried a few times and no one listens.... At least not yet. Despite several studies showing otherwise, teams still punt more than they should. Admittedly, some of these studies have been less than rigorous, and often times, assumptions are made that warrant scrutiny (assuming a 50% success rate on all 4th down attempts for example). But I don't think it is the lack of scientific rigor that keeps change at bay. I think the failure to adopt a novel strategy has a lot…Continue
Added by Ray Hall on August 30, 2018 at 9:30am — No Comments
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.
Added by Matthew Gierc on August 4, 2018 at 10:30am — No Comments
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.
Added by William Vorhies on July 31, 2018 at 8:20am — No Comments
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.
Added by William Vorhies on July 17, 2018 at 7:00am — No Comments
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.
Added by William Vorhies on July 10, 2018 at 7:00am — No Comments
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.
Added by William Vorhies on May 8, 2018 at 9:30am — No Comments
In this article I explore some of the key concepts of data quality management and how to build a strategy for continuous improvement. I won’t be covering every possible scenario, process, method or problem; only those that are common across most industries and those that have proved useful on my own personal journey.
Hopefully, we already agree that good data quality is an essential part of business intelligence and a foundation on which you build your systems, processes and…Continue
Added by Richard Cook on February 21, 2018 at 5:00am — No Comments
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.