How To Create (or Destroy) Value with Generative AI

Also “Why Data Quality is YOUR MOAT”, “How the Modern Data Stack could be simplified”…and more!

In this week’s CarCast, we bring a very special guest to discuss if “Data Leaders Set Up To Fail” and if they are truly taking full advantage of the Gen AI wave.

We also cover the latest in Gen AI research from the Boston Consulting Group. Check out the below blog for references and details and follow the channel to catch the video premiere on Monday AM.

1. How People Can Create and Destroy Value with GenerativeAI. According to Boston Consulting’s latest research, 90% of participants improved their performance when using generative AI for creative product innovation and in fact converge on a level of performance that was 40% higher than that of those working on the same task without Gen AI. HOWEVER, when participants used the technology for business problem solving, they performed 23% worse than those doing the task without GPT-4. Even participants who were warned about the possibility of wrong answers from the tool did not challenge its output. Bottom Line: Gen AI is powerful LEVELER of performance BUT seems that people might mistrust the technology in areas where it can contribute massive value and to trust it too much in areas where the technology isn’t competent.
2. The Modern Data Stack needs SIMPLIFICATION…and a little more sanity. A semantic layer to help govern and “understand a company’s knowledge” could help. More here.
3. Why Data Quality is YOUR MOAT on CXO Talks’ Generative AI Strategy in the Enterprise with Michael Krigsman. More here.

And some EXTRAS:

1. Netflix Marc Randolph: Stop telling employees that “your company is like a family”. IMHO, it’s more like a sports team.
2. The latest Cybersecurity MAP.
3. The future of Generative AI. In 15 charts.
4. Are Data Leaders Set Up To Fail?!
5. And a special thanks to April Dunford, the world authority on positioning.

Generative AI Strategy in the Enterprise

What a fun time to talk with CXO Talk’s Michael Krigsman! Drawing from examples of great organizations (Wendy’s, Mayo Family Foundation, Walmart, Wayfair, Bloomberg…etc) and research from BCG, McKinsey and more, I unveil my “MT-CAR” acronym to select the right use-cases for Enterprise Gen AI applications. We also discussed:

1. Why Data Quality is YOUR MOAT: Data Trustability is THE key ingredient of any Enterprise Gen AI application. Prioritize provenance, governance, security, attribution…before activation.
2. Gen AI FOMO and FOMU: FOMO is exacerbated by news and analyst coverage (Hype Cycle!) but FOMU is what ultimately can slow down adoption if your data strategy is poor.
3. Focus, Focus, Focus. There might be 60+ use-cases for Gen AI but 75% of the value comes from 4 areas: 1) Customer operations, 2) Marketing and Sales, 3) Software engineering, and 4) R&D.

Engage here. Link to session here and below.

Michael Krigsman on LinkedIn: Generative AI Strategy in the Enterprise

The Future of “Data Apps”

I am NOT a big fan of Breaking Analysis’ “Uber Analogy” BUT I am a fan of the “uber message” in this Breaking Analysis: The modern datastack needs a semantic layer to help govern and “understand a company’s knowledge”.
I would add a couple of thoughts:

1. The modern data stack needs massive simplification. Distributed data, distributed users and distributed use-cases are here to stay. HOWEVER, it doesn’t mean that our stacks have to be complicated. Every CXO I’ve worked with needs more SIMPLICITY.
2. Over the next 5 years, the companies that will win are those who design their architecture AND teams to build “intelligent data products”.
3. We have to stop saying “embedding” or “infusing” AI in apps. Next-gen applications have to be built from “AI up” IMHO.

Great job to Bob Mugliaa for setting up the conversation and to Dave Vellante and George Gilbert for prompting the session.

Video below, engage here.

Are Data Leaders Set Up To Fail?!

So….just a few months ago, in a @Harvard Business Review article, Randy Beann reported a decline in data driven cultures…are we losing focus?! Here are the alarming specifics:

1. 39.5% of executives reported that their companies were managing data as a business asset, a DECLINE from 46.9% four years ago.
2. Just 23.9% — under one quarter — of executives reported that their companies have created a data-driven organization, DOWN from 31% four years ago.
3. A meager 20.6% of executives — barely one in five — reported that a data culture had been established within their companies, a 27% decrease from the 28.3% of companies reporting having established a data culture back in 2019.* Regression, not progress.

After MAD, the Cybersecurity MAP!

Remember MAD?! (The Machine Learning, Artificial Intelligence & Data Landscape)….well there is now a NEW type…the Cyber Security Ecosystem! If you’re interesting in learning more about this space, Francis Odum is the person to follow!

His Beginner’s Guide to Cybersecurity is terrific and he’s put a lot of work into trying to simplify the space for all of us. Check out the resources and links to below!

Beginner’s Guide to Cybersecurity @ https://lnkd.in/gbC4RyNu

More here

The future of Generative AI. In 15 charts.

Via McKinsey & Company. My top 3 featured in graphs. Which is your favorite?!

Engage here (list below the image)

1) Gen AI timeline from November 2022 to today
2) The road to human-level performance
3) How Knowledge work will benefit from Gen AI
4) Use Cases…a non-exhaustive list
5) High tech & banking to see more impact potential to accelerate software development.
6) Assessing impact by industry
7) 60% said their organizations rarely or never use GenAI or ML for commercial activities.
8) Marketing & sales leaders are most enthusiastic about lead identification, optimization, and personalized outreach.
9) Gen AI can increase developer speed…but less so for complex tasks.
10) Gen AI assistance could make for happier developers.
11) Momentum among workers for using gen AI tools is building
12) Organizations still need more gen AI–literate employees
13) Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used.
14) Gen AI could ultimately boost global GDP
15) Gen AI represents just a small piece of the value potential from AI

More here!

How People Can Create and Destroy Value with Generative AI.

Great piece of research by Boston Consulting Group (BCG). Highlights below, engage here.

1. Gen AI is POWERFUL LEVELER OF PERFORMANCE: When using generative AI for creative product innovation, around 90% of participants improved their performance. Event more, level of performance that was 40% higher than that of those working on the same task without Gen AI.
2. BUT, when participants used the technology for business problem solving, they performed 23% worse than those doing the task without GPT-4. Even participants who were warned about the possibility of wrong answers from the tool did not challenge its output.
3. It seems that people might mistrust the technology in areas where it can contribute massive value and to trust it too much in areas where the technology isn’t competent.

EXTRAS

April Dunford on Positioning and Sales Pitch!

Thanks for stopping by the office this week AND making a special delivery of your book, April! I’m honored to be in your network! For those in the community who don’t know April yet (I’m not sure they are many!), check out her latest book on positioning at the below link!

Pre-order @ https://lnkd.in/ghw_Yn33

Love this #MarcRandolphism…(I’ve been following Marc Randolph for a while so I figured I’d put a name on his nuggets of knowledge). One question for you my lovely community…if a company is NOT like a family, what is it then?! I say: a Sports Team. Thoughts?!

Engage here.

Nail it…then Scale it.

IHMO, this is not just about GTM. It can be applied to so much more. It’s about system thinking, first principles or purely just winning. Another winner from Dave Kellogg. My favorite sections:

1. Design good experiments: Be sure that it works before scaling it.
2. Model-driven scaling: Break down the problem into a series of models, each of which is defined by goals, roles, and ratios.
3. Data, Data, Data: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine” (Jim Barksdale’s special).

The below is one of my favorite slides….check out the full blog and deck though!