Let’s talk about things AI can help with and things it won’t make easier
We’ve previously given business leaders an overview of what AI is and how it works in business, but understanding the basics may not be enough to give you a great sense of how to use artificial intelligence to its fullest potential.
As artificial intelligence and tech nerds, we’re always looking into exciting new projects using AI and consulting our clients on how to use and adopt artificial intelligence at scale in their organizations. We’ve collected some of our lessons learned to give you a better idea of how your organization can (and maybe can’t) use AI to improve your work.
Tips for boosting creativity with AI
In a study about AI use in developing new ideas and designs, researchers recently showed that AI can actually increase fixation on a design idea and prevent more ideas from bubbling up in a group (Wadinambiarachchi et al., 2024). However, the research revolved around non-designers collaborating in comparison with using some inspiration through image search, or no inspiration tools at all.
Understanding this doesn’t mean your organization shouldn’t use artificial intelligence in this capacity. An innovative solution to creative problem solving with AI can involve augmenting some of the factors that can cause fixation.
For example, prompts placed toward the AI can dramatically affect the feedback you get in the design process. In the above study, the work revolved around designing a unique robot in a collaborative setting. In that scenario, if you start with a “stick line robot with a helmet and eyes,” you may get a limited set of images that match that categorization from AI.
To broaden the amount of ideas you can use AI to generate in a setting, you would instead focus on less specific feedback. You may even skip the word “robot” altogether and instead type in “give me pictures of different types of helmets worn throughout history.” Getting unstuck from an idea requires a little creativity on the front end, with ideas coming from various viewpoints and angles.
Identifying trends you may be missing in large data sets
A common practice in integrated marketing uses AI to sift through huge amounts of feedback, like a customer opinion survey, to gauge customer satisfaction and group those comments into common themes.
Using this data, a team can flag potential issues for their account management team, collect potential customer advocates for the brand, and keep track of how customer sentiment rebounds over time when patches are put in place.
What makes this approach so effective is understanding the kind of data that is quantifiable and trackable. If customer satisfaction surveys aren’t being pushed out, the AI has a much more limited dataset (ie. tickets, formal complaint letters, etc.) and the results may skew toward those who are happy enough or angry enough to take the time to respond.
Innovating your data collection to optimize it for AI requires thinking through what you’ll be able to track over time, and then adding layers of complexity that make sense in your setting. These layers of information are what make AI insights more powerful than insights your team could generate through a spreadsheet or through pre-packaged programs that aren’t tailored to your data.
For example, while there are marketing programs that track public sentiment for an organization online (are social mentions primarily positive or negative), they are not customized to your processes or workflows. To make this investment of time and collection of data make sense, you can use AI to make this data work across your organization.
A layer of location points as related to location may indicate an outage or other area-specific problem that needs to be addressed. To make this insight more accurate, AI could scrape public information available to fill in location data for customers or other users that wasn’t collected during onboarding.
If your team is focused on making the most out of every data point, AI can identify who is the correct stakeholder for issues that are occurring in real time. Is there an influx of claims from an insurance provider being denied across your healthcare network? AI could notice that trend, provide insights into the specific cases, and send an email to the billing department heads who need to be in the know.
If separate customer service centers are receiving requests that seem disparate but are actually rooted in the same application or system, AI can notice that trend quickly and send an alert to your developers or IT team.
Getting the most out of data trends is all about customizing what you’re tracking, what data you already have and can use to train your AI, and what insights will actually make life easier for your team members.
Conclusion
Whether you are focusing on boosting your team’s creativity or getting the most out of the data you work hard to collect, AI innovation is all about focusing on solving the specific problems your team faces every day. The more AI is customized to your industry, your customer or patient base, and your workflows, the more usable insights you’ll be able to generate for your organization.
Interested in learning how your organization can implement innovative AI to solve your problems? We’d love to help you get the most out of your AI upgrade. Reach out today!
References
Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q., & Velloso, E. (2024). The effects of generative AI on design fixation and divergent thinking. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 380, 1-18. Association for Computing Machinery. https://doi.org/10.1145/3613904.3642919