Wednesday, January 26, 2022

g-f(2)859 THE NEW WORLD (1/26/2022), MIT SMR + BCG, Transforming a Technology Organization for the Future: Starbucks’s Gerri Martin-Flickinger

ULTRA-condensed knowledge

"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageThe New World, Artificial Intelligence, Transforming a Technology Organization for the Future (1/26/2022)  g-f(2)426 

Opportunity, MIT SMR + BCG  

EXCEPTIONAL “Full Pack Golden Knowledge Container”

Starbucks’s former chief technology officer explains how to lead a successful digital transformation.

Some Golden Knowledge (GK) Juice

  • Why does how you describe your team — down to its name — matter? Gerri Martin-Flickinger, former executive vice president CTO at Starbucks, joins the Me, Myself, and AI podcast to describe some of the technology initiatives the coffeehouse chain has been able to pursue since rebranding its technology team and articulating its mission.
  • Gerri Martin-Flickinger
    • It’s been really exciting in the last five or six years to see that we’re finally at a place where technology, both compute and storage — and data — have gotten to the point where we can actually start to achieve some of those visions we had way back then.
    • There’s no such thing as a generalized AI model that will solve anything or everything. That just isn’t possible. If you think about where people are having the most success right now with AI at scale, a lot of it is tightly coupled to statistical analysis, frankly, and is a lot about taking very large learning sets and building very sophisticated models that can be predictive in nature. And that’s awesome, and what that takes is a lot of data to make it accurate.
    • For me, transformation comes down to people. And to do any transformation with tech has nothing to do with the tech as much as it has to do with the people who are making it happen. They have to feel inspired, they have to feel what they’re doing is important, and they have to feel like they have room to be part of the invention of the future. And I think that’s all we have to do as leaders, is make room for that.

Genioux knowledge fact condensed as an image



Sam Ransbotham (@ransbotham) is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Review’s Artificial Intelligence and Business Strategy Big Ideas initiative. Shervin Khodabandeh is a senior partner and managing director at BCG and the coleader of BCG GAMMA (BCG’s AI practice) in North America. He can be contacted at

Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.

Gerri Martin-Flickinger, Starbucks

As executive vice president and CTO at Starbucks, Gerri Martin-Flickinger led the Starbucks Technology team through a transformation into a best-in-class retail technology organization. She was the passionate leader behind the technology strategy that plays a critical role in propelling the Starbucks mission — “empowering partners and delighting customers, globally.”

Before joining Starbucks in 2015, Martin-Flickinger was senior vice president and CIO at Adobe, where she led portions of its technology transformation to a cloud-based subscription services business. Previously, she served as CIO at Verisign, McAfee, and Network Associates and held numerous senior leadership roles at Chevron, where she began her career.

Martin-Flickinger currently sits on Charles Schwab’s board of directors and serves as a member of Arizona State University’s Fulton School of Engineering Advisory Board, Sierra Ventures’ CIO Advisory Board, and The Wall Street Journal CIO Network.

Extra-condensed knowledge

Lessons learned, MIT SMR + BCG  

Some GK Juice

  • Gerri Martin-Flickinger
    • I think one of the misconceptions that I’ve certainly talked to a lot of executives about in the last few years is that in order to do AI in a really meaningful way, you’ve got to get your data in order. It isn’t as simple as just saying, “Hey, we have loads of data! We should be able to have amazing AI models.” You’ve got to have a little bit of structure to that data. You have to have a little bit of thought about where that data sits, even something as simple as, what does your data lake look like? Where are you putting that data? What is the currency of that data? Do you need the models to retrain in real time? Do you need to build them once and then retrain them once a quarter? And all of that starts to get really wrapped up into your data architecture. So I think the one thing that I always ask people when they want to talk about AI is, “Tell me about your data. Do you have data? Where is your data? Do you own it? Can you use it? Do you have the right to use it?”
    • This is the other thing about consumer data that’s so very important: You have to make sure you’re not doing things that you shouldn’t be doing with that data. So I really think that one of the misconceptions out there is this idea that there’s this thing you buy called AI, and you plug it in and it all works. That is almost the end of a long series of things you need to do and architect.

Condensed knowledge

Lessons learned, MIT SMR + BCG  

Some GK Juice

  • Gerri Martin-Flickinger
    • My point being that as tools become more available to more people, more people are able to explore more ideas. How many times do all of us open spreadsheets all week long to do everything from help our kids with homework to track our personal finances, or just keep a list? The best list thing I have is a spreadsheet.
    • Now, do you think a store manager knows or cares that when they build a labor schedule for their store, there’s actually a reasonably sophisticated ML model behind the scenes doing that? No. So I think the amount of embedded AI and ML that we all … We all probably have some in our cars right now; a lot, probably. Your power companies, your phone companies … It’s embedded everywhere. Your credit card companies have had it probably longer than you even realize, for fraud detection, so it’s already pretty pervasive. And I think the question is, how much more accessible could it be as people become more sophisticated? I don’t know.
    • I can certainly highlight a couple of examples. We have a moniker, Deep Brew, which stands for a broad section of AI projects underway across the company. The ones that folks are most familiar with are personalization models.
    • Whether it’s personalization on the mobile app or personalization when you pull into a drive-through where they have a digital display, those experiences are being driven from models that are based on lots of different inputs, some of which are very personal, like maybe your own buying patterns. Some of them are regional, like, “What’s going on with buying patterns in this region?” They could be environmental factors, like, “What’s the weather today?” It could have to do with supply chain loads, like, “What do we actually have in stock that we need to sell?”
    • Getting really clear on that is part of what gets all the constituencies to go at loggerheads — somebody carries the hat of revenue, and somebody else carries the hat of margin, and somebody else carries customer experience. You’ve got to get clear on what you’re solving for. And you can’t solve for all of it at the same time. Now, you can benefit it all, but you have to get really clear on “What are we going after?” So that’s my first advice: [to] be really clear on what problem you’re trying to solve.
    • Words matter. Words matter. They really do. 
    • When I say technology, people say, “The future. Innovation. New things.” Right? So if you’re in a business, and someone introduces someone who’s in the IT department, they have one reaction. If I introduce someone to you and I say, “This is” — in the case of Starbucks — “Starbucks Technology,” which one sounds and feels more future-leaning?
    • We were talking about transformation and how do you transform a technology organization to the future. And so, one thing that’s one of these tried-and-true things is, did you name your organization in a way that makes the organization proud, so that every single person in the organization sits up a little straighter and maybe works a little harder? Have you named the organization in a way that really represents what you want it to become? And have you named it in a way that everyone else in the business looks at it and goes, “Oh, that’s something a little different”?
    • The next thing that I think can make a difference is, you need a tagline. I hate to say it, but everybody in business, everyone who’s a CEO, knows it. You’ve got to tell your story, and you don’t get five hours to tell your story. You get six to 10 words, and you’d better get people curious to ask more. And so, put a tagline in place. Super simple: “Talented technologists delivering today, leading into the future. Starbucks Technology.” That’s it.
    • And that simple phrase, which is used today still, after six years, just continues to reinforce the value of the organization, the value of the people, the importance of getting the work done — as well as continuing to build for the future. And so, for me, transformation comes down to people. And to do any transformation with tech has nothing to do with the tech as much as it has to do with the people who are making it happen. They have to feel inspired, they have to feel what they’re doing is important, and they have to feel like they have room to be part of the invention of the future. And I think that’s all we have to do as leaders, is make room for that.

Some relevant characteristics of this "genioux fact"

  • Category 2: The Big Picture of the Digital Age
  • [genioux fact deduced or extracted from  MIT SMR + BCG]
  • This is a “genioux fact fast solution.”
  • Tag Opportunities those travelling at high speed on GKPath
  • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
  • Type of validity of the "genioux fact". 

    • Inherited from sources + Supported by the knowledge of one or more experts.


“genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)859, Fernando Machuca, January 26, 2022, Corporation.


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