#133 Cutting Through AI Hype: Building Effective Governance, Strategic Tech Ecosystems, and AI-Driven Decision-Making

Hey Listener!

How does an AI-powered business make money? What do they do differently?

In this episode, Elizabeth Samara-Rubio from SiMa.ai, gives us a practical guide to the “Business of AI” and the implications of integrating AI into real-world processes like manufacturing and agriculture.

Her experience spans significant roles, including leading over 20 AI services at AWS and pioneering efforts in generative AI. Elizabeth's career trajectory also includes serving as Managing Director at Accenture, where she spearheaded growth initiatives in cutting-edge technology sectors—experiences that established her deep-seated expertise in bringing technological advancements to traditional business sectors.I was blown away by how deep Elizabeth was willing to go in this interview. I learned so much about the practical logistics of implementing AI into a business, and you won't hear this level of practicality in most AI interviews.

Actionable Takeaways

Table of Contents

1. Understand AI's Role in Business: “How do you make money?”

2. Empower Your Ecosystem: “Intro to Edge Machine Learning Systems”

3. Embrace the Maturity Curve: “The Required Time Horizon to Implement AI”

4. Responsibility in AI Implementations: “Are you willing to invest and nurture this for at least 10 years?”

Understand AI's Role in Business

"AI is not just a shortcut. It takes time and maturity to understand where it adds value." Elizabeth Samara-Rubio

By viewing AI as a long-term investment rather than an instant solution, businesses can gradually integrate AI to improve operations.

Takeaway: Consider AI a marriage; you're in it for the long haul. Evaluate where it can add value to transform business processes over time.

Empower Your Ecosystem

"We don't just say, 'Come back when you're ready.' We're here to help build your ecosystem, You can do the most great things in the world, but if you don't have a data pipeline, you don't have a governance structure, you don't have the skill set…

You can talk about it.  You can proof of concept it over and over again, but you're not going… to scale."

Elizabeth Samara-Rubio

SiMa.ai develops AI computing hardware to be embedded into their client's systems. So that traditional businesses can start building their AI capabilities in-house.

Most traditional businesses, like manufacturers and agriculture, don't have the talent or capabilities to develop their own AI from scratch. More and more, you have to consider partnering with companies that can grow with you and give you the base capabilities to develop AI in business.

Takeaway: Integrating AI into a traditional business is as much a hardware problem as an AI problem. So, investing in strategic partnerships to empower your tech ecosystem will future-proof your AI implementations.

Embrace the Maturity Curve

“We don't just say, 'Come back when you're ready.' We're here to help build your ecosystem."

Elizabeth Samara-Rubio

The most significant gap preventing traditional industries from embracing new AI tech is hiring in-house talent. It's difficult to get a Silicon Valley engineer to want to work for a traditional manufacturing business in the Midwest. Companies like SiMa.ai provide services to integrate their expertise and hardware so their clients never have to develop robust AI talent in-house.

Elizabeth notes the stages businesses undergo with AI integration, from increasing throughput to enhancing productivity, and the role AI infrastructure businesses will play moving forward.

Takeaway: Map out the stages of AI integration in your business. Start small and scale as your team's maturity and understanding of technology grow.

Responsibility in AI Implementations

"An AI model demands continuous monitoring and retraining;

Responsibility isn't just about deployment; it's about ensuring models operate reliably and ethically throughout their lifecycle.”

Elizabeth Samara-Rubio

Takeaway: Develop a robust framework to manage AI models, monitor their performance, and update them as required.

Listen to the full episode to get more details on-premises AI computer hardware and the in-depth technical details of how you get AI into manufacturing and agriculture.

Cheers!

Caden Damiano

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.wayofproduct.com