Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments

by Sol Rashidi, Edelyn Okano (reader)

Practical and proven AI deployment strategies for non-technical business leaders

In Your AI Survival Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments, business executive and technologist Sol Rashidi delivers an insightful and practical discussion of how to deploy artificial intelligence in your company. Having helped IBM launch Watson in 2011, Sol has first-hand knowledge of the ups, downs, and change management intricacies that can help you with a successful deployment beyond all the AI hype. She walks you through various frameworks for how to establish your AI strategy, pick your use cases, prepare your non-technology teams, and overcome the most common obstacles standing in the way of successfully implementing AI in your business, based on her many years of deploying AI projects in businesses, which few can claim. Sol demystifies the topic of artificial intelligence in a way that business leaders and business owners—and those who want to be more business minded—can easily understand. The book also

Real-world use cases from ten different industries, including retail, healthcare, energy, insurance, agriculture, and more; ten different functions, including supply chain, manufacturing, procurement, legal, and more; and personal stories, anecdotes, and insights gained from implementations Techniques for facilitating executive-level buy-in for your most ambitious and promising AI strategies Jargon-free and accessible language that simplifies a seemingly complicated topic And practical advice that’s not based on AI hype Perfect for executives, managers, directors, founders, entrepreneurs, practitioners and other non-technical business leaders, Your AI Survival Guide is the ideal guide to help you deploy artificial intelligence in your business and increase your chances of success whether your business goal is top-line growth, increased productivity, or efficiency gains without having to add headcount as the go-to answer.

Genres: Business, Artificial Intelligence

1 pages, Audio CD

Published November 12, 2024

Reviews

[Pablo María Fernández · Rating 4 out of 5]

I consider Sol Rashidi one of the leading figures in Data and Analytics. I have worked for more than 15 years in this field, read tons of books, articles and papers, watched hundreds of videos and attended several events and training sessions. Many times I found pseudo-experts that talked about theory but have no real experience in the corporate world. The author is the opposite: she was the Chief Data Officer (CDO) of many Forbes 500 companies and has a very hands-on approach. I had also watched some of her interviews and her storytelling skills (speaking in "plan English" as she likes to say) are fascinating.

The book consists of nine chapters. The best ones for me are 3, 4 and 5 in which Rashidi provides her framework, the five pillars, the six phases and how to deal with key people in your team. Chapter 6 is about AI ethics and responsibility and in the final three chapters the book becomes more a generic listing use cases from different industries that you can find in many other sources. The first two chapters are more personal and I skimmed through them because I am usually not interested in that stuff. Maybe some of the tools she presents here are not brand new (like a prioritization matrix very similar to the ICE methodology) but her tips, guidance and warnings are solid and useful because they come from years of experience in different career challenges. The stories like the one on which her team resist collaborating with her are eye opening, especially for newer data leaders facing this type of challenge at traditional companies or industries.

In summary, this is a great book both for experienced and novice data leaders. Both of them will find actionable advice and even emotional connection and empathy with someone who faced the same challenges and now shares her learnings.

Most important concepts:
1) The five key principles: ask “why?”, develop your AI strategy, think big, start small, scale quickly and choose your technology partner.

2) Five strategies you want to aim for: efficiency, effectiveness, productivity, expert and growth. What’s important to you and what you can realistically deploy, successfully, based on your company’s current maturity.

3) Multiplier effect:
1. Estimate the timeline of your project.
2. Understand the culture and pace of your organization.
3. Understand your own personal place.

4) Framework for AI initiatives. Six-phases:
1. Conducting an AI readiness assessment
2. Selecting an AI strategy.
3. Creating and selecting use cases
4. Preparing and designing
5. Creating solutions
6. Deploying and going live.

The extent of work you need to do for each phase is dependent on your personal style and your company’s culture.

5) Use case selection:
1. Criticality (does it, or can it, impact sales and growth, operations, company culture and public perception?)
2. Complexity (does it impact bandwidth for other projects , what degree of change management is needed?, do we have clear ownership of this use case?)

6) Blueprint:
- The vision: the ‘why’, business value, end-state vision, defined KPIs, defined scope,
- The impacts: ethical controversy, possibilities of Bad PR, safeguards on Data, cross-functional impact, partnerships.
- The support: go-live support & maintenance, enterprise integration, security measures, data sources, systems and quality, risk mitigation process.
- The approach: project management approach, change management approach, talent training, ownership & accountability, stakeholder identification, communications, staffing.
- The process: MVP/POC/functioning prototype, performance expectations, ownership of code, budget, handling unintended consequences, workflows & ‘Day in the life’ and procurement & acquisition.

7) Ideal team: grit, ambition, resilience. The 10 AI archetypes: the naysayer, the evangelist, the doer, the discerner, the blind, the curmudgeon, the saint, the optimist, the data scientist, the know-it-all.

8) Top 10 list of essentials for AI projects: manifesto, leadership alignment, transition, communication, involvement, small victories, monitoring, retrospectives and changes.

Some quotes I liked:
“(...) realized my role was to push boundaries and people so popularity wasn’t my priority, my job was my priority.”
“I had to exchange popularity for progress.”
“‘Doing AI’ is not a technology problem: it’s a people problem and a mindset problem. It creates obstacles only a rogue leader can overcome.”
“I learned my success depended less on my intelligent quotient (IQ) and more on my emotional quotient (EQ), business quotient (BQ), and social quotient (SQ). “
“Only 30% of the workforce does most of the work.”
“When deploying AI projects, you’ll face resistance because of the fears people have about losing their jobs.”
“Bend it but never break it.”
“To bend the rules is to understand them deeply and to see the spaces between the lines where there’s room for improvement and innovation.”
“The real project killers are your people.”
“The team had purposely not documented anything and relied solely on their tribal and institutional knowledge as a form of job security.”

[Kevin Troy · Rating 2 out of 5]

There are some interesting lessons here, and some good food for thought on what use cases might be most promising for AI over the next few years. Unfortunately, a great deal of the book is padding and throat-clearing around those ideas, with the author constantly interrupting herself to remind us of her impressive resume and self-image as a “rogue executive,” or saying that topic XYZ may sound confusing, but it’s really just a matter of X followed by Y and then Z.

The book is also riddled with mistakes, ranging from simple typos to outright howlers. (My favorite is in the forward by Richard Greco, where he refers to himself as a “NASDAQ space CEO,” which is clearly a case of nobody checking the output of the speech-to-text software he used to dictate the content.) The editing and production team at John Wiley & Sons seems to have phoned this one in.

Best sections: chapter 7 on AI use cases, chapter 6 on the importance of keeping a “human in the loop.” (Though note that Rashidi’s anecdote that brings that point to life is somewhat self-aggrandizing.) Chapter 8, on AI technology, is incoherent. Chapters 3 through 5, about how to manage AI projects, are OK, but not very AI-specific — the lessons really apply to any major IT investment. (And Rashidi’s approach to staffing projects by just shutting out “the wrong people” seems kinda impractical to me, but what do I know?)

Finally, for all the repeated promises of “bruised elbows and lessons learned,” there are maybe three real-life “case studies” in the whole book. Could have used more of that sort of thing.

[Bobsie67]

This is more a memoir and general "how to do big strategy" book than anything to do with AI. Indeed, you can use this book to map out how to roll out any new technological development or even a new product. You could probably condense the ideas within this nook into a 1-hour PowerPoint presentation. AI could probably help you do that.

[Linda Hertz · Rating 3 out of 5]

I found this book a great introductory to AI disruption in the career marketplace, as well as industrial, educational and manufacturing segments (frankly almost all aspects of life). A little less author self commentary would have been appreciated in early chapters, I began skimming to the meatier later chapters. Good introductory AI book.