The AI Race: Setting the Pace for Adoption
The AI race is underway, but many organizations remain stuck at the starting line, unsure how fast to move or what pace to maintain. Questions swirl: How quickly should we adopt AI? What are others doing? How do we avoid chaos while driving urgency?
For Gregg Johnson, CEO of Invoca, an AI-driven revenue execution platform serving top B2C brands, the key is clarity, discipline, trust, and setting your own pace. “You’re not competing with AI,” Johnson says, “you’re competing with the companies who are using it.” This mindset has guided Invoca’s AI adoption—balancing optimism with structure and clear measurement to deliver real results across the company.
Start with Clarity and Trust
Before launching any AI project, Invoca established clear privacy guidelines. Early on, complex rules proved hard to follow, so they simplified: if data could be published on the website, it’s public; if not, it stays confidential and off open AI platforms. This straightforward rule built trust internally and with customers in regulated industries like finance, insurance, and healthcare.
Invoca split AI tool use accordingly: platforms like OpenAI’s ChatGPT and Anthropic’s Claude are used only for public data, while confidential data stays within Google’s Gemini environment, leveraging existing secure infrastructure.
Let a Thousand Flowers Bloom
Invoca’s AI journey began with experimentation. When generative AI gained attention in late 2022, Johnson encouraged teams to explore and learn freely. This open approach sparked creativity and helped demystify AI.
But enthusiasm wasn’t enough. Different teams weren’t sharing discoveries, so Johnson shifted focus to pattern recognition—spotting successful AI uses and replicating them across departments. He used the metaphor of succulents: you can snip a piece and transplant it somewhere new to thrive. To support this, he appointed a cross-functional AI adoption leader—not a technologist, but a connector who could identify what’s working and help it spread.
Start Where You Can Measure
Johnson advises focusing AI investment where impact is easiest to quantify. Marketing is a prime example, with clear metrics like cost per acquisition and conversion rates. Applying AI here lets you track what moves the needle.
In contrast, areas like engineering or internal communications often lack strong measurement frameworks, making ROI harder to prove. Before applying AI, ask: how well do I measure this area today? Without a solid baseline, measuring improvement is difficult.
This approach is not just operational but strategic. Budget requests for AI must link directly to business outcomes, and clear metrics make that possible.
Align Metrics Before You Scale
AI is most effective when it breaks down silos. At Invoca, Johnson emphasizes connecting teams involved in the customer journey. Many large consumer brands’ buyers interact with ads, websites, and contact centers, but these teams often have separate KPIs.
Invoca’s platform uses AI to unify data across these touchpoints. For example, analyzing contact center conversations feeds insights back into ad targeting, giving marketing teams clear visibility into what’s converting and why. This integration has helped clients improve ad spend efficiency by 20-40%.
The Role of the CEO: Set the Tone, Then Step Back
AI adoption requires leadership, but not micromanagement. Johnson takes a visible, vocal approach—explaining why AI matters to the company and employees’ careers. “It’s not here to replace you. It’s here to level you up,” he tells his teams.
He also leans on cultural optimism, recognizing that people want to do the right thing but can be resistant to change. A little nudge is often enough to get teams trying new AI tools.
The takeaway: The AI race isn’t about sprinting blindly. It’s about setting a steady, aligned pace built on trust, clear metrics, and cross-team collaboration. Invoca’s journey shows that urgency can coexist with discipline—and that’s how you lead the pack.
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