Great video that got me to buy and read the book. Well worth the time for anyone in management, considering for right or wrong everyone is expected to consider AI in their roadmap today.
As a reader who had read her book, I would suggest if you're working on AI in tech company, skip the book because there's not much relevant info. If you're working in traditional business like retailer, marketing, e-commerce etc, or you worked in Tech but will join one of those traditional industries to work on any format of so-called "AI", her book might give some prospects you may run into. Her most prominent achievement was with IBM Watson back in early 2010s, which was called "AI" back then, but we all know thats whole lof different. I don't even think any of that experience would help any guy in Tech industry now. She's smart, candid, tenacious person though, and had a proven record on corporate ladder.
I agree this book isn't very useful to someone developing an AI product at a tech company. But it didn't appear to be targeting that audience. The book seems geared towards directors and VPs who are leading AI initiatives at non-tech companies. (for context, I'm an IT director in the healthcare industry) I found it quite useful in helping me collect my thoughts, highlighting things I wasn't considering, and advising me on how to make a business case and implementing a successful initiative. I'd say 80% of the wisdom also applies to a non-AI program, but it's important to understand that 20% which applies uniquely to AI projects. I read it too recently to know how much impact it will make on my success, but I was quite happy with the time investment I made in reading it.
Enterprise AI adoption not happening due to: (1) Decisionmakers/Executives have absolutely zero knowledge & wisdom on how/when to move forward on AI (2) Most old-age IT guys getting territorial, clinging to comfort zone traditional tech, didn't even learn/adopt basic ML /predictive AI are stubbornly resisting Gen AI (3) Very few who really understand AI in enterprise are not heard or valued
I'd add too many companies don't have the data management foundation to build AI-enabled capabilities at scale. It's not more critical than anything on your list, but I feel it belongs on the list.
Sounds like she needs a lot more knowledge and experience in AI, as she pretty much just lumps ML, with DL as AI, which will bite most companies and CXO in the… in the next couple years, and destroy competitive advantages 9g the companies they work for.
ML is a subset of AI, and DL is a subset of ML. Both are part of the AI ecosystem, so I don't understand your criticism here. Perhaps I misunderstood your comment, though.
Great video that got me to buy and read the book. Well worth the time for anyone in management, considering for right or wrong everyone is expected to consider AI in their roadmap today.
As a reader who had read her book, I would suggest if you're working on AI in tech company, skip the book because there's not much relevant info. If you're working in traditional business like retailer, marketing, e-commerce etc, or you worked in Tech but will join one of those traditional industries to work on any format of so-called "AI", her book might give some prospects you may run into. Her most prominent achievement was with IBM Watson back in early 2010s, which was called "AI" back then, but we all know thats whole lof different. I don't even think any of that experience would help any guy in Tech industry now. She's smart, candid, tenacious person though, and had a proven record on corporate ladder.
I agree this book isn't very useful to someone developing an AI product at a tech company. But it didn't appear to be targeting that audience.
The book seems geared towards directors and VPs who are leading AI initiatives at non-tech companies. (for context, I'm an IT director in the healthcare industry) I found it quite useful in helping me collect my thoughts, highlighting things I wasn't considering, and advising me on how to make a business case and implementing a successful initiative. I'd say 80% of the wisdom also applies to a non-AI program, but it's important to understand that 20% which applies uniquely to AI projects.
I read it too recently to know how much impact it will make on my success, but I was quite happy with the time investment I made in reading it.
Enterprise AI adoption not happening due to:
(1) Decisionmakers/Executives have absolutely zero knowledge & wisdom on how/when to move forward on AI
(2) Most old-age IT guys getting territorial, clinging to comfort zone traditional tech, didn't even learn/adopt basic ML /predictive AI are stubbornly resisting Gen AI
(3) Very few who really understand AI in enterprise are not heard or valued
I'd add too many companies don't have the data management foundation to build AI-enabled capabilities at scale. It's not more critical than anything on your list, but I feel it belongs on the list.
impressive, ordering the book right now
Absolutely spot on.
Immensely insightful talk. Sol is definitely one thought leader on enterprise AI that I'm definitely following from here on out.
Superb 🙌🏻
Thank you! Cheers!
I hear a well rehearsed and skilled orator not saying much apart from some skilled management talk.
Sounds like she needs a lot more knowledge and experience in AI, as she pretty much just lumps ML, with DL as AI, which will bite most companies and CXO in the… in the next couple years, and destroy competitive advantages 9g the companies they work for.
ML is a subset of AI, and DL is a subset of ML. Both are part of the AI ecosystem, so I don't understand your criticism here. Perhaps I misunderstood your comment, though.