Agentic AI is here, but what is it? What are the differences between the traditional LLMs and this new agentic AI we're hearing about? With AI systems making autonomous decisions, driving analytics, and reshaping data strategies, what does this mean for analysts? We're joined by Vin Vashishta, CEO at V Squared and an expert in AI strategy and data science. Vin's book, From Data to Profit, lays out a roadmap for turning AI and analytics into real business value.  AI isn't just a tool anymore; it's becoming a collaborator. How should we think about adapting? Don't miss his insights in this show! What You'll Learn: How Agentic AI will redefine the role of analysts in analytics. What makes an AI 'agent' different from a traditional LLM? Why knowledge graphs are the key to AI's next leap forward. How to future-proof your career in analytics.   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Vin Vashishta and I chat about the current state of AI in business, the challenges of implementation, the pervasive hype surrounding AI technologies, influencers, and more.

Panel: How AI Is Shifting Data Infrastructure Left | Joe Reis, Vin Vashishta, Carly Taylor, Chad Sanderson | Shift Left Data Conference 2025

The rapid rise of AI has dramatically elevated the value and strategic importance of data, transforming how upstream software engineers perceive and interact with data workflows. In this expert-led panel, industry leaders will share their experiences and insights into effectively bridging the gap between data teams and software engineers. They will discuss practical strategies for proactively managing data infrastructure, enhancing collaboration, and ensuring high-quality data to support advanced AI-driven development initiatives.

Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash? Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning. Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program. In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, examples of high data maturity businesses, data security and compliance, quick wins in data transformation and infrastructure, why long-term vision and strategy matter, and much more. Links Mentioned in the Show: Connect with Tiffany on LinkedinConnect with Vin on LinkedinVin’s Website[Course] Data Governance Concepts Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Vin and I chat about the challenges of writing books, how companies can mature with data science, why data scientists need to learn strategy, and much more.

(We experienced a slight internet delay around the 15:30 mark, otherwise great)

LinkedIn: https://www.linkedin.com/in/vineetvashishta/

Book: https://www.amazon.com/Data-Profit-Businesses-Leverage-Bottom/dp/1394196210

Site: https://www.datascience.vin/

If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

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Today I’m chatting with Vin Vashishta, Founder of V Squared. Vin believes that with methodical strategic planning, companies can prepare for continuous transformation by removing the silos that exist between leadership, data, AI, and product teams. How can these barriers be overcome, and what is the impact of doing so? Vin answers those questions and more, explaining why process disruption is necessary for long-term success and gives real-world examples of companies who are adopting these strategies.

Highlights/ Skip to:

What the AI ‘Last Mile’ Problem is (03:09) Why Vin sees so many businesses are reevaluating their offerings and realigning with their core business model (09:01) Why every company today is struggling to figure out how to bridge the gap between data, product, and business value (14:25) How the skillsets needed for success are evolving for data, product, and business leaders (14:40) Vin’s process when he’s helping a team with a data strategy, and what the end result looks like (21:53) Why digital transformation is dead, and how to reframe what business transformation means in today’s day and age (25:03) How Airbnb used data to inform their overall strategy to survive during a time of massive industry disruption, and how those strategies can be used by others as a preventative measure (29:03) Unpacking how a data strategy leader can work backward from a high-level business strategy to determining actionable steps and use cases for ML and analytics (32:52) Who (what roles) are ultimately responsible in an ideal strategy planning session? (34:41) How the C-Suite can bridge business & data strategy and the impact the world’s largest companies are seeing as a result (36:01)

Quotes from Today’s Episode “And when you have that [core business & technology strategy] disconnect, technology goes in one direction, what the business needs and what customers need sort of lives outside of the silo.” – Vin Vashishta (06:06)

“Why are we doing data and not just traditional software development? Why are we doing data science and not analytics? There has to be a justification because each one of these is more expensive than the last, each one is, you know, less certain.” – Vin Vashishta (10:36)

“[The right people to train] are smart about the technology, but have also lived with the users, have some domain expertise, and the interest in making a bigger impact. Let’s put them in strategy roles.” – Vin Vashishta (18:58) “You know, this is never going to end. Transformation is continuous. I don’t call it digital transformation anymore because that’s making you think that this thing is somehow a once-in-a-generation change. It’s not. It’s once every five years now.” – Vin Vashishta (25:03) “When do you want to have those [business] opportunities done by? When do you want to have those objectives completed by? Well, then that tells you how fast you have to transform if you want to use each one of these different technologies.” – Vin Vashishta (25:37) “You’ve got to disrupt the process. Strategy planning is not the same anymore. Look at how Amazon does it. ... They are destroying their competitors because their strategy planning process is both expert and data model-driven.” – Vin Vashishta (33:44) “And one of the critical things for CDOs to do is tell stories with data to the board. When they sit in and talk to the board. They need to tell those stories about how one data point hit this one use case and the company made $4 million.” – Vin Vashishta (39:33)

Links HumblePod: https://humblepod.com V Squared: https://datascience.vin LinkedIn: https://www.linkedin.com/in/vineetvashishta/ Twitter: https://twitter.com/v_vashishta YouTube channel: https://www.youtube.com/c/TheHighROIDataScientist Substack: https://vinvashishta.substack.com/

We discussed monetization roles and the capabilities people need to move into those roles.

The key roles are ML Researcher, ML Architect, and ML Product Manager.

We talked about:

Vin's career journey

What does it mean to "monetize machine learning" Important monetization metrics Who should we have on the team to make a project successful Machine Learning Researcher (applied and scientist) - background, responsibilities, and needed skills Developing new categories  The best recipe for a startup: angry users + data scientists What research actually is ML Product Manager - background, responsibilities, and needed skills How product managers can actually manage all their responsibilities (and they have a lot of them!) ML Architect - background, responsibilities, and needed skills Path to becoming an architect  How should we change education to make it more effective  Important product metrics

And more! 

Links:

https://twitter.com/v_vashishta​ https://linkedin.com/in/vineetvashishta​ https://databyvsquared.com/​

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