Design your own AI experiment: How to boost AI use in your organization

14 stycznia 2024

2 min czytania

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The future of work is here, and it’s infused with AI. Now is the time for teams to learn and test different AI tools—so they can start using them to drive efficiencies, brainstorm creative solutions, and stay ahead of the competition.

But how can leaders encourage their teams to start using AI, especially when some employees are fearful of the technology? According to research from The Work Innovation Lab, the answer is experimentation. By spending just a few minutes every day experimenting with AI, workers can start to overcome their fear and adopt AI more readily.

That’s exactly what Asana’s marketing team did, in partnership with The Work Innovation Lab.

Czym jest The Work Innovation Lab?

The Work Innovation Lab to „think tank” powołany przez Asanę, który zajmuje się prowadzeniem najnowocześniejszych badań zorientowanych na człowieka. Ich celem jest pomoc firmom w rozwoju, aby mogły sprostać rosnącym wyzwaniom i nieustannym zmianom zachodzącym w obszarze pracy.

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Asana’s “AI Brain Boost”

The Work Innovation Lab, in collaboration with the Marketing AI Institute, designed and conducted a study called the “AI Brain Boost” on Asana’s marketing team. In this study, marketers were tasked with experimenting with AI at least once every day.

The results? Marketers didn’t just save time with AI. They also were able to overcome their fear, use the technology in creative new ways, and share new AI use cases with their colleagues.

How to design your own AI experiment

Any leader can run a similar study on their team. Follow these tips and guidelines to kickstart AI use at your company, plus gather insights to fuel more wide-spread adoption.

1. Recruit volunteers

The Work Innovation Lab wanted employees to experiment with AI on their own terms, so they made their AI Brain Boost study voluntary. To encourage marketers to take the plunge and participate, they set a clear objective—communicating why the study was important and what marketers could gain from it, like time savings, training with AI, helping your team adopt new tools, or all of the above. Sharing the “why” behind the research helps motivate employees and sets the stage for future experimentation.

2. Start small

Ask for a small (5- to 10-minute) time commitment from participants each day. The goal is for employees to spend just a bit of time experimenting with AI, not for them to completely overhaul their work processes (yet). Starting small helps plant the seed of AI experimentation without overwhelming your team.

3. Structure your experiment

To keep things organized, create a research plan with a clear structure. For example, The Work Innovation Lab divided their AI Brain Boost study into three phases:

Phase 1: Brainstorming. They asked participants to pick one generative AI use case to implement to become a more effective marketer, with the flexibility to change their minds later on. To help with brainstorming, The Lab also gave participants a list of common marketing use cases.
Phase 2: Learning. During this phase, The Lab asked participants to implement the use case they identified in phase 1. They shared regular reminders for marketers to spend a small amount of time every day experimenting with AI.
Phase 3: Results and sharing. At the end of the study, The Lab sent participants a short survey to collect data and feedback on their experience. They then compiled this into broader learnings to share across the marketing org.

4. Make it fun

Remember, this exercise is meant to reduce fear and encourage employees to use AI in creative new ways. Think of ways to lighten the atmosphere and encourage participants to have fun with AI. This could look like hosting a special event for participants, offering prizes, or sharing the most creative AI use cases from the study.

The goal of an AI Brain Boost study is to kickstart AI use on your team. Once employees start experimenting, capitalize on that momentum by having participants share what they learned and how they used AI to do their jobs more effectively. That way your team can continue to deepen their knowledge even after the study is over, paving the way for broader adoption across your company.