I enjoy creating the future. You can learn more about me and my work at https://sadiestlawrence.com

Speaking Bio:

Sadie St. Lawrence is the Founder and CEO of the Human Machine Collaboration Institute (HMCI). HMCI pioneers research and education, to optimize human and machine interaction in the new era of knowledge work with AI, serving leading public and private organizations. 

Prior to HMCI Sadie founded Women in Data™, a non-profit organization with representation in 55 countries, and a community of over 70,000 data professionals. Women in Data earned recognition as a Top 50 Leading Non-Profit and the foremost community for Women in AI and Tech in 2021.

With three degrees in piano performance, psychology, and data science, Sadie's journey from neuroscience research to over a decade in data science and AI strategy showcases her diverse expertise. Her accolades include being named one of DataIQ’s Top 100 Most Influential People in Data (2024), Dataleum’s 30 Outstanding Women in Data (2023), Top 30 Women in AI (2022), Top 10 Most Admired Businesswoman (2021), Top 21 Influencer in Data (2021), and Top 30 Most Inspiring Women in AI (2021). She has also received the Outstanding Service Award from UC Davis (2019).

Additionally, Sadie's leadership extends to her role on the White House Council for Equitable Data and AI Training and educating over 700,000 individuals through her data science and AI courses at UC Davis, Coursera, and LinkedIn Learning.

Want to start the year off with a new job? I’m hiring for two roles and would love to meet people who are excited about building the future of work with AI.

1️⃣ AI & Technology Strategy Consultant
You’ll work directly with clients on AI strategy, assessments, and roadmaps, translating emerging tech into real business impact.
View here: https://lnkd.in/g8dj26BB

2️⃣ Social Media & Brand Manager
You’ll help shape and scale our voice across HMCI and my personal brand, telling clear, thoughtful stories about AI, work, and impact.
View here: https://lnkd.in/gVuH23CM

Both roles are hands on, high trust, and ideal for someone who loves ownership, learning fast, and working on meaningful projects.

If this sounds like you, or someone you know, I’d love for you to apply or share 💛

Sadie St Lawrence

For 10 years, Women In Data™️ has been building, supporting, and celebrating women in data, AI, and technology.

On March 6, 2026, we’ll come together in Sacramento for the Women in Data Gala to:
💜 Celebrate a decade of impact
💜 Honor leaders through our Women in Data Awards
💜 Recognize the women and allies who have helped shape this global community

This evening is about more than a milestone.

It’s about the relationships, careers, and opportunities created over the last ten years, and the future we’re continuing to build together.

If Women in Data has ever been part of your journey, I’d love for you to join us.

👉 RSVP here: https://lnkd.in/gDJaE-W2

I can’t wait to celebrate with you.

Feeling incredibly grateful today.

Because of the generosity and thoughtfulness of this community, Becoming an AI Orchestrator made it into the Top 11 AI books on Amazon.

I want to take a moment to sincerely thank the people who took the time to read the book and leave reviews. Your words, reflections, and support truly mean more than you know ❤️

Please give each of these amazing people a follow 👇🏼
Kristi Kunworee Baishya 
Angele Labady
Raghda Elshafie
Flavia Saldanha
Jessica Tappi
Sarah Elahi, CDMP
Sharon Matthews
Amanda Long ✨ Long
Natasha Lalwani
Angela Cao
Jenny Jimenez
Vikas Goyal
Jon Krohn 

I’m deeply appreciative of each of you for helping bring this message forward.

I spend my days doing three very different things:

1️⃣ Building AI strategies with cities and organizations

2️⃣ Teaching people how AI actually works

3️⃣ Explaining to smart leaders why “just using ChatGPT” isn’t a strategy
The biggest surprise?

The technical learning curve isn’t the hard part.

The hard part is unlearning how we’ve worked for the last 20 years.
AI doesn’t reward busyness.

It rewards clarity.

And clarity is uncomfortable.

Anyone else feeling this tension in their work?

Not everything generated by AI should be published.

And not everyone should outsource their thinking.

If AI removes your judgment, your perspective, or your accountability,
then it’s not augmentation.

It’s abdication.

The best AI users I know are sharper thinkers, not quieter ones.

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#11 in AI books on Amazon - WOW THANK YOU 🥰 

My book has been out a little over a month and has stayed in the top 20 AI books on Amazon - so grateful for all the support of my work and reviews, thank you, thank you!

You can check out my book here: https://lnkd.in/gQuTz9ir

One thing I’ve noticed working with strong AI users:

They don’t ask AI to replace their thinking.

They ask it to:

🤓Stress-test ideas
🤓Surface blind spots
🤓Expand possibilities

Then they decide.

AI doesn’t make them quieter.

It makes them more intentional.

That difference matters more than most people realize.

Really honored to have Eric Weber join the Women In Data™️.

Eric has been a consistent supporter of Women in Data from the very early days, starting with the San Francisco chapter. He also personally inspired me early in my own data science journey as one of the first data scientists openly blogging, teaching, and sharing what was possible in this field.

We’re so excited to have his experience and perspective on the board and can’t wait to bring that expertise to our global community. Welcome, Eric 💜

Eric Weber is joining us as a Women in Data™ Board Member and we couldn't be more excited!


Eric is the Head of Data at Superhuman, where he leads data strategy and decision-making at the intersection of product, growth, and user experience.

Eric has been part of the Women in Data journey from the very beginning. As an early supporter of WiD, he sponsored the San Francisco Chapter, helping lay the foundation for the community we continue to grow today.

We’re incredibly grateful for Eric’s long-standing belief in our mission and excited to have his perspective, leadership, and continued support at the board level. Welcome, Eric!

AI ecosystems don’t happen by accident. They’re designed.

In the City of Rancho Cordova, we’re intentionally building one that connects sustainability, education, and research into a system that actually works in the real world.

Our focus is simple and disciplined.

Sustainability through our IR² model, so innovation can fund itself long term.

Education with NVIDIA’s Deep Learning Institute, so AI skills turn into real jobs, not just certificates.

And research and innovation with our university partners, so we keep advancing what’s possible at the intersection of humans and machines.

This isn’t about hype. It’s about infrastructure, talent, and execution.

Proud of what we’re building with the City of Rancho Cordova and our partners. And we’re just getting started.

https://lnkd.in/gG4CX3ie

N8N agent node is a game changer in your workflow. It allows you to choose the model, memory and tools you want your agent to use. 

Check out the full tutorial on my YouTube channel.

If you’re a leader trying to “roll out AI” and feeling stuck, start here 👇🏼

Ask your team three questions:
1️⃣ What decisions do you make repeatedly?
2️⃣ What work feels manual but high-stakes?
3️⃣ Where do errors actually cost us money or trust?

THEN introduce AI.

Not the other way around.

Tools don’t fix broken workflows.

Good orchestration does.

I love this announcement from Amazon Web Services (AWS), because it mirrors something we learned a long time ago in neuroscience.

In the human brain, efficiency comes from specialization. You don’t ask your visual cortex to manage memory storage, and you don’t force motor neurons to hold context they don’t need. When systems are overloaded with responsibilities they weren’t designed for, performance drops and failure rates go up.

That’s exactly what’s been happening in Spark for years.

We’ve been asking computers to also manage storage, capacity planning, and fault tolerance. The result? Overprovisioning, wasted spend, brittle jobs, and failures driven by data skew rather than logic.

With Amazon EMR Serverless storage, AWS essentially “rewired” this pathway.

Amazon EMR Serverless has essentially rewired this pathway by eliminating local storage provisioning for Apache Spark workloads.

By decoupling storage from compute, shuffle data is handled by a dedicated, elastic layer. 

Compute is used when needed and released immediately when done.The system adapts dynamically, just like a well-functioning brain.

The outcome is real-world impact, not just cleaner architecture:
🤓Up to 20% reduction in data processing costs
🤓Fewer Spark job failures from disk constraints and skew
🤓Faster scale-down between stages, especially in funnel-shaped queries
🤓Engineers spending time on insights, not infrastructure tuning

This is one of those changes that looks “under the hood” but fundamentally shifts how teams think about building analytics at scale. Less cognitive load for humans, better resource allocation for machines.

If you care about cost efficiency, resilience, and designing systems that actually behave intelligently under pressure, this is worth your time.

Read the full announcement here and see how serverless storage changes the Spark model:
https://lnkd.in/gHuiv_MT

#AWSPartner

How will I know when AGI has arrived? When it can properly dictate voice to text without me calling people funny names.

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