Building Enterprise Workflows and Spreading AI FOMO

These days, it seems like every executive is trying to figure out how to get their employees to use AI in their day-to-day work. Sure, you can try and mandate from the top down that your teams must use this or that AI tool. But is that really the best way to set up your organization for an AI pilot that will actually succeed in the long term?

At Gumloop, we’ve seen how the best companies manage to go AI-native — successfully. In fact, our CEO, Max Brodeur-Urbas, recently partnered with Cursor, Vercel, and Anthropic to host the AI Champions Sessions, a series of live workshops sharing real insights on deploying AI within organizations successfully. In his session, Max broke down some of the most important insights on what companies can do to make sure that AI pilots fly, rather than flop.

Here are his top takeaways:

• Tackle reliable workflows before autonomous agents: Build robust workflows first; then build agents on top of those workflows later.
• Create and spread AI FOMO: Employees need to be internally motivated. Drive motivation by socializing AI successes.
• Don’t hire “AI people.” Let end users build: People who understand the problems should be building the solutions.
• Run hackathons, and run them well: Successful hackathons require preparation before and promotion after.

Tackle Reliable Workflows Before Autonomous Agents

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Build robust workflows first; then build agents on top of those workflows later.

One of the biggest sources of confusion for companies adopting AI is understanding the difference between agents and workflows. Many organizations jump straight to autonomous agents, only to find themselves disappointed when these systems "go on their own and do their own thing" without predictable results.

“An agent is only as good as the tools it can use or the workflows that it can actually trigger,” Max says. While agents can be powerful orchestrators, workflows provide the reliable, deterministic foundation that makes AI adoption successful.

Despite not watching American football (and being Canadian), Max made his best attempt at clarifying the distinction between workflows and agents using a football analogy: “Workflows are plays, and agents are quarterbacks.” He expands further: “You can't just go straight to agents. It's like trying to be a quarterback without ever studying the playbook or even having a playbook at all.”

Companies that focus on building robust workflows first create the reliable foundation needed for more advanced AI implementations later.

Create and Spread AI FOMO

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Employees need to be internally motivated. Drive motivation by socializing AI successes.

One of Gumloop's most powerful strategies for driving viral AI adoption within organizations is instilling a shared sense of social urgency, one that independently motivates employees to embrace AI tools. In other words: your goal is to create “AI FOMO.”

The best technique for doing this is making AI success stories highly shareable within the organization. "If you can find an amazing use case, record something like a little interview, maybe 10 minutes of the user or the person at the company walking through just how amazing AI revolutionized their workflow," Max explains.

These longer interviews can get clipped into one or two-minute videos that have highlights, quotes, a short demo, and an overview of what's actually happening. From there, these bite-sized success stories spread organically, "from Slack channel to Slack channel, into a channel with 5,000 people in it.” And then everyone has seen how amazing AI can be for this one person, and they want to be that person in the video."

Leadership plays a crucial role by setting clear expectations and following through with rewards. "If you promote someone for using AI, they're going to want to use AI," Max notes. "Once someone sees someone else going up the ranks because they automated 80% of their team's workflow… that makes people really, really want to join that kind of AI movement."

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Don’t Hire “AI People.” Let End Users Build.

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People who understand the problems should be building the solutions.

A common mistake organizations make is hiring dedicated "AI people" to build solutions for everyone else. This approach recreates the same problems that existed before AI-powered tools became accessible to non-technical users.

"The complaint was, people understood a problem deeply, but they didn't have the ability to solve it. They would communicate it via a spec doc to a team of engineers who didn't have time to do it. They would do something half-baked because they didn't fully understand the problem," Max explains.

The primary breakthrough of modern AI tools is that the people who understand the problem can be the ones who build the tool — no need to rely on any middlemen. When companies hire dedicated AI specialists, they risk returning to that old paradigm where solutions are built by people who don't fully understand the problems they're trying to solve. Instead, organizations should empower the actual end users — the people who understand their pain points intimately — to build and customize their own AI-powered solutions.

Run Hackathons, and Run Them Well

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Successful hackathons require preparation before and promotion after.

Structured events like hackathons can be transformative for AI adoption, but success depends heavily on what happens before and after the main event.

"90% of the value of a hackathon actually happens before or after the hackathon," Max emphasizes.

Preparation is crucial. Companies need to spend weeks educating participants about both AI capabilities and limitations. "If someone doesn't understand what AI is not capable of, they'll spin their wheels and waste time trying to do something that's just impossible," Max says.

Equally important is having participants brainstorm use cases well in advance. Without preparation, people default to simple tasks like "summarize a Slack channel" rather than tackling the time-consuming processes that would deliver real value when automated.

The post-hackathon phase determines whether AI adoption takes hold long-term. Leadership should amplify the impact that AI is having by promoting successful use cases and sharing them company-wide. The most impactful solutions often apply across multiple teams, creating knock-on effects throughout the organization.

What AI Champions Can Do to Succeed

Successful enterprise AI adoption isn't about finding and implementing the shiniest new tools; it's about fully understanding the human systems and processes within an organization. Companies that focus on building reliable workflows, creating viral success stories, empowering end users, and running well-structured enablement events see AI adoption spread organically.

The foundation of an AI-native culture is building an environment where employees actively seek out opportunities to automate their work and share their successes with others. As Max demonstrated through Gumloop's own workflows — from hiring talent through open source research to monitoring Reddit sentiment — when done thoughtfully, AI can transform not just individual tasks but entire business operations.

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