Lean into the AI Gs: Guidelines, Governance, Guardrails

Reflecting on the current state of AI training across companies, I can't help but be reminded of my days at Intuit when social media and community engagement were just starting to be adopted by B2B companies. Back then, we saw the potential of these new platforms and took proactive steps to lead the way in enabling our teams. Today, I see a similar opportunity in AI, especially in areas like Product Development and Engineering where training investments are still lagging.

Looking at recent industry data from "Growing Up: Navigating Gen AI's Early Years," a report by AI at Wharton and GBK Collective (October 2024), only 33% of Product Development/Engineering teams are getting the opportunity to work on AI learning projects or pilot programs, and just 34% have partnerships with educational institutions for AI training. These numbers present a massive opportunity for organizations to take the lead in AI training and empower their teams for the next wave of innovation. It's important to note that other groups, such as Marketing, Sales, HR, and Operations, can also significantly benefit from targeted AI training. Providing comprehensive training opportunities across all functions can create a cohesive and well-prepared workforce, capable of leveraging AI effectively throughout the organization. This isn't just about getting ahead—it's about equipping your employees with the right skills to create value and drive impactful outcomes. Obviously, multiple groups across the organization could benefit from targeted training initiatives.

When we started integrating social media at Intuit, we implemented a few key strategies to help employees be successful in the evolving social media world. I think these are still relevant today:

1. Center of Excellence: A cross-functional group that was responsible for training employees on how to use social media, what to avoid, and how to leverage it effectively. Imagine doing the same now for AI, pulling in different teams to create a cohesive, empowered learning environment.
2. The Gs of AI: Governance, Guidelines, Guardrails, and Graphics. We established guidelines around Community and Social Media usage. Employees embraced this because they didn't want to make the same misake a senior leader made when posting on Facebook. He had posted his personal phone number so customers started calling his home. We also built style guides, photo libraries, and assets that employees could use safely and consistently. For AI, think of this as establishing best practices for ethical usage, prompt guidelines, and even shared data sets that allow everyone to start from the same page.
3. Hands-On Training: We made sure our team members had the tools and knowledge to succeed. Today, that could translate into "How to write a good prompt" sessions or deep dives into specific AI tools relevant to different functional areas. Or how to integrate AI into your everyday work flow so that you can work more efficiently. We respected the DIYers who wanted to learn themselves and supported those who wanted some guidance.
4. Project Library: Back then, multiple teams were experimenting with social media, so we built a shared repository to prevent overlap and share learnings. Today, AI initiatives are often siloed. Creating a library of ongoing AI projects and prompts can not only save time but also enhance collaboration across different teams, maximizing company-wide impact.
5. Ongoing Information and Training: We published an internal newsletter and built out a section on our internal intranet keeping employees up-to-date on the latest social media trends, highlighting key posts, etc. We also provided ongoing training -- which was updated every month and featured what other employees were doing. How many of you are sharing your own AI success stories with your coworkers.
6. Shared the wealth: We shared employee stories, data about our company programs on social media, community metrics (from both our own community and non-owned communities where our moderators participated). Employees wanted to know what Intuit was doing on Twitter, Facebook, etc.
7. Gamified the experience: We kept track of employees participation in training programs, their community and social media activities, etc.... and gamified the whole process with leaderboards, badging, certification. We celebrated small pilot projects as well as the big heavy lifts like developing a strategy for how to mine the rich data Amazon reviews provides.

Those are just some of the many programs we ran to empower employees to be successful on social media, in our communities and on the web on an ongoing basis.

The organizations that take the lead in empowering their Product Development and Engineering teams with focused AI initiatives today are setting the foundation for success tomorrow. It's not just about keeping up—it's about building a culture of continuous learning and sharing that becomes a competitive differentiator.

For those who've led similar initiatives, what strategies have you found most effective in driving cross-functional learning in emerging tech areas like AI?

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