The goal of this report is to examine the adoption of artificial intelligence through the lens of human computer interaction within the context of innovation.

This is a research study focused on understanding the adoption of AI, identifying gaps in knowledge and application, and proposing opportunities for designing for the adoption of AI. There is an opportunity here to distill knowledge and share insights in order for organizations to better adopt and apply artificial intelligence in ways that are successful, scalable, and safe.

For the full list of insights, I encourage you to watch the full presentation. Here I will just highlight a few:

• When AI is used regularly, studies show an 89% productivity boost.

• While 55% of users report being optimistic about using Gen AI at work, 64% report no familiarity with the use of AI.

• Only 31% of organizations have an AI strategy in place.

• The leading driver for adoption is economic: efficiency, increased revenue, productivity, and more.

• The leading barriers to adoption are divided across economic, technical, and social reasons.

• 41% of organizations don’t collect employee feedback on AI tools.

AI adoption isn’t going anywhere; in fact, it is only growing. But many organizations are at a loss of how to integrate and effectively adopt AI at scale.

Our insights provided a clear picture of some key next steps for organizations to take.

• AI Literacy: Low literacy results in low use, a decreased likelihood of sustained adoption, and a lower productivity score. Organizations can combat this by upskilling their workforce and training AI advocates, or by introducing tools that are easy to adopt and integrate into everyday workflows.

• AI Trust & Safety: 47% of workers cite accuracy of AI as a top concern when using Gen AI. Trust and safety are key areas to not overlook when introducing AI, especially at scale. Empathetic design and integration will be key in overcoming this hurdle. Good data is key to this. When building AI systems for your organization, know your data, where it’s coming from, what it’s being used for, the guardrails in place, and more.

• AI Guidelines: AI is unfolding at such a rate, it has been difficult to develop proper channels to support adoption and design. Introducing AI ethics teams, with cross-functional members, is one way to establish strong foundations for scaling AI adoption.