During a recent conference, I noticed a difference in the levels of AI tool adoption between folks at startups vs. enterprises. Startups are free to explore, and are experimenting rapidly. PMs and engineers elsewhere are struggling with stringent data restrictions, lengthy approval processes, and limited tool access. Any free exploration is left to personal time. It seems like most teams are hampered out there: according to IBM, 75% of companies face barriers to adoption. [1]
This gap isn’t just about access, it’s about the pace of innovation.
Startups enjoy a freedom that allows for rapid iteration and adoption of new tools, fostering a culture of innovation with AI tools. Mature companies are vulnerable to sunk cost fallacy, where commitment to one or few tools has the potential to hamper flexibility and responsiveness to new AI breakthroughs.
Let’s say your team gets approval for one tool, while a startup can experiment with a dozen. You’re going to end up with different-looking workflows. In this rapidly evolving landscape, a few months of sunken cost could leave miles between you and your competition.
Build an AI bridge
So, how can enterprises bridge this gap? It starts with fostering a culture of innovation. Encourage your team to share AI projects and workflows on a dedicated Slack channel. Explore AI tools using non-sensitive company data to experiment (without breaking rules ofc).
Find use cases that work to augment the sh**y parts of your day, teach others, and increase your team’s AI leverage over time.
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*Sources: [1] IBM Global AI Adoption Index 2023