At SADA, we’ve had the privilege of working hand-in-hand with leading organizations as they embark on their generative AI journeys. These collaborations have not only yielded impressive results for our clients but have also provided us with invaluable insights into what truly drives successful AI implementation. In fact, several of our customers were featured on Google Cloud’s prominent list of “601 real-world gen AI use cases from the world’s leading organizations,” a testament to their innovative spirit. Let’s pull back the curtain and share some of the “behind the scenes” lessons learned from our customer engagements, focusing on how pioneers are harnessing the power of Gemini in Google Workspace.
1. Start with a clear strategic vision and prioritize seamless execution
You can have all the big ideas in the world, but if you can’t translate them into actionable plans and scalable implementations, they’re just thoughts. Our most successful clients approached generative AI with a clear strategic vision that they were committed to executing, with security at the forefront.
Equifax, for instance, had already made a massive $3 billion investment in The Equifax Cloud™ to build a top-tier global technology and security infrastructure. Their vision was to further enhance secure communication and collaboration. They recognized that Gemini, inheriting all their existing Google Workspace security settings, was the ideal solution, giving them peace of mind to deploy generative AI safely across their enterprise.
In the fast-paced world of Hollywood production, SMUGGLER — an award-winning company known for its visionary collaborations — recognized the need to optimize and enhance collaboration while staying at the forefront of technology. As COO Andrew Colón states, “The ethos of SMUGGLER is that we’re very progressive and we cannot lag behind.” Their clear vision was to leverage AI that could seamlessly integrate into their existing Google Workspace, ensuring both efficiency and data security without the need for external tools.
Similarly, apree health, navigating the complexities of fast-moving software development and stringent healthcare regulations, needed a collaboration suite that could satisfy conflicting priorities while maintaining airtight security. Google Workspace provided that simplification, and Gemini, with its embedded security, was a natural fit to accelerate collaboration, even enabling physicians to use it for sensitive patient notes. Pinnacol Assurance also chose Gemini because it was “covered by the same best-in-class security as Workspace.”
In these cases, we collaborated closely to translate ambitious visions into practical, scalable implementations, focusing on getting things done rather than just conceptualizing, with security and integration as non-negotiables.
2. Focus on measurable efficiency and productivity gains
Beyond the hype surrounding generative AI, the most impactful projects we’ve seen consistently focused on delivering measurable business outcomes. Technology was a means to an end, not an end in itself.
Equifax’s primary goal with Gemini was to boost efficiency and productivity, freeing up valuable time for more impactful initiatives. They even conducted a six-month trial with over 1,500 participants to measure the ROI. The results were compelling: 97% of users requested to keep their Gemini licenses, 90% saw an increase in work quality and quantity, and employees from almost every business unit saved more than one hour per day! For example, their help desk representatives now use Gemini to analyze data and improve service, while others leverage “take notes for me” in Google Meet for meeting summaries.
Pinnacol Assurance also realized significant gains: 96% of their employees reported time savings with Gemini, and nearly 90% were satisfied with the tool. Their investigations team now uses Gemini to accelerate repetitive tasks like creating interview questions and summarizing transcripts, acting as a “thought partner.” SMUGGLER, too, saw Gemini automate time-consuming tasks like creating spreadsheets, a task that previously took hours.
Whether it was increased efficiency, enhanced customer experience, or accelerated innovation cycles, these leaders knew precisely what they wanted to achieve, and they built their AI strategies to get there. It was always about the tangible impact on daily work.
3. Embrace an iterative approach and robust change management
Generative AI is not a “set it and forget it” solution; it’s a rapidly evolving field, often with unpredictable nuances. Our successful clients embraced an agile and iterative approach, learning and adapting throughout the implementation process, supported by strong change management. Implementing generative AI isn’t just a technical challenge; it’s a human one that requires careful guidance to ensure user adoption and maximize the value of new tools.
Equifax, with the support of Google Workspace Customer Success and SADA, built a value-driven framework for audience selection, survey design, and measurement. This allowed them to effectively evaluate Gemini’s value and unlock a wealth of insights, understanding how their team was experiencing the power of Gemini on a daily basis. They are now rolling out to nearly 22,000 employees, focusing on requested training topics like prompting and team-specific use cases, with SADA facilitating open office hours.
Pinnacol Assurance provides a prime example of effective change management, where SADA’s team played a crucial role. They actively engaged superusers (like Lina Valencia-Ignatius) and provided weekly tips, videos, and information sessions, creating a supportive environment for employees to learn and adapt to Gemini. They even developed a use case library based on user feedback, demonstrating a commitment to addressing user needs and fostering enthusiasm for the new capabilities. Their leadership’s strong support for AI also played a vital role, with 98% of employees feeling their manager supports their use of Gemini.
SMUGGLER strategically launched Gemini AI with 40 licenses, focusing on enthusiastic early adopters. This intentional phased rollout allowed them to build a core group of Gemini AI users who could champion its benefits and encourage wider, informed adoption within the company. apree health also took a methodical approach, focusing on specific business use cases to ensure effective training and maximize value by showing users immediate, practical benefits, thereby easing the transition and promoting wider adoption.
These clients understood that while the barrier to entry might be low, the path to excellence is a journey of constant adjustment, refinement, and optimization, requiring proactive and human-centric change management.
4. Build on a strong data foundation
This insight is critical. A strong emphasis on high-quality, relevant data proved absolutely crucial for training and fine-tuning generative AI models to achieve desired outputs and accuracy. The old adage, “garbage in, garbage out,” holds even truer with the power of generative AI.
For example, Equifax’s help desk representatives leverage Gemini to dig deeper into data to improve service, showcasing how access to and analysis of good data can directly enhance operations.
At apree health, the ability to easily export data sets from BigQuery into a Google Sheet or directly to Looker allows for controlled access and real-time status updates, empowering both technical and patient care teams with the insights they need to make data-driven decisions.
You can have the fanciest models, but if your data’s a mess, so will your output be. Our clients recognized the fundamental importance of a robust and well-managed data foundation as the bedrock for successful AI implementation.
From possibility to production: Join us at SADA Ground School
Our practical experience, gained through these successful collaborations, positions us as experts in navigating the complexities of generative AI adoption. We’ve seen what works, and perhaps more importantly, what doesn’t. We’re committed to helping businesses like yours leverage this transformative technology to achieve similar breakthrough results.
The AI landscape is filled with endless potential, but the path to a finished, value-driving solution can feel unclear. That’s why we’re hosting SADA Ground School. This isn’t just about saving time; it’s about doing entirely new things, in entirely new ways.
Join us at this virtual event for both the 30,000-foot view of what’s possible and the hands-on guidance you need to build, secure, and scale AI solutions to their end production state.
SADA Ground School
- 🗓️ Date: Sept. 16, 2025
- ⏰ Time: 8 a.m. PT / 11 a.m. ET
- 📍 Location: Virtual
Ready to unlock your AI potential? Register now!
