AI Adoption at data.world: Aligning with Industry Trends and Leading in Experimentation
As AI continues to evolve, enterprises worldwide are navigating the early years of generative AI adoption, refining strategies, and tackling the challenges that come with integrating cutting-edge technology. A recent report, Navigating Gen AI’s Early Years by AI at Wharton and the GBK Collective, provides a valuable benchmark for understanding how companies are adopting AI, investing in training, and overcoming initial hesitations.
At data.world, we’re excited to see that our approach aligns closely with the trends highlighted in the report, and in many ways, we’re already leading in the adoption, experimentation, and usage of generative AI tools.
Training and Education: Building a Strong AI Foundation
The report emphasizes that training is a critical component of AI strategy. While many organizations are still in the foundational stages, focusing on courses and hands-on access, our own experience at data.world mirrors this need for a robust training regimen. We’ve prioritized providing access to AI tools, implementing hands-on learning projects, and fostering a culture of continuous learning. We’ve found that both “moderate” and “extensive” training are essential, especially as different teams across data.world adapt to using AI in their day-to-day work.
In fact, this emphasis on training has paid off, as 78% of our team has embraced AI tools like ChatGPT, actively using these platforms to streamline workflows and spark innovation.
Experimentation and Usage: Leading with a Data-Driven Approach
One of the most encouraging findings from the report is the shift in attitudes toward AI usage and experimentation. The industry as a whole has moved beyond initial skepticism, with a strong focus now on proving ROI and scaling effective use cases. At data.world, we’re similarly focused on experimentation, running pilot programs and encouraging teams to explore AI applications in new ways.
Our usage metrics are impressive: with 89 employees actively using ChatGPT Teams accounts, our team is producing approximately 210 Custom-GPT sessions each week, averaging around 2.35 sessions per user. This is notably above the average usage rates cited in the report’s early findings, and we’re optimistic that this rate will continue to climb as our teams find even more applications for Custom-GPTs in their roles. Adding in Chat-GPT usage out side of Custom-GPTs, across our entire employee base, that’s more like four sessions per week—a strong indicator of active and meaningful AI integration.
Investing in AI While Focusing on Adoption
While many companies face challenges around AI implementation, including concerns around data privacy, security, and employee readiness, data.world has taken a proactive stance. We’ve embraced an “internally led” approach, with a clear focus on educating our team and allowing for AI experimentation across departments.
Our investment in AI reflects both the excitement and realism around generative AI. Like many organizations surveyed, we’re conscious of balancing innovation with cautious experimentation. By keeping our AI initiatives led in-house, we can adapt quickly, ensure best practices, and remain agile in a field that’s still rapidly evolving.
Standing Out in the AI Adoption Curve
Reflecting on our progress, it’s clear that data.world is tracking above the industry average in several key areas: experimentation, rollout, education, and consistent usage of generative AI. And with Copilot metrics from our engineering team yet to be added, our overall engagement with AI is only set to grow.
The journey to fully integrated AI is ongoing, but data.world’s strategy aligns well with leading industry practices and, in many cases, exceeds them. Our approach to AI adoption, grounded in strong training, experimentation, and a commitment to responsible innovation, puts us on a promising path toward long-term AI success. As the technology matures, we’re excited to continue refining our AI strategies and leading in the adoption of generative AI.