The transcript below is edited and condensed for clarity.
The best place to start is productivity. We call productivity enhancement a low-hanging fruit model for many of these kinds of ROI discussions. This is an easier place for organizations to lean in, and we see a lot of them taking advantage of it with coding. Copilots, for example, allow us to code faster and be more productive in relation to the development lifecycle while increasing quality.
In general, generative AI is about content production. Where there are a lot of manual processes and people spending time finding things, those are great use cases that can capture high ROI. Creating operational efficiencies and carving out competitive advantage is a big part of leveraging this technology.
Like the innovation cycle, with generative AI we want to come up with a lot of ways to use it, try it out, and then as we validate them and demonstrate ROI we can look at how to repeat those processes. Once we have something that works we can automate it and move toward scaling the solution and applying it to other areas of the business. While it may be a different department, a successful use case in sales may be a pattern we can reapply to our legal department and help us realize ROI even faster. Ultimately these productivity enhancements can make employees happy while also saving the company money, which is great all around.
Not only does it make their job easier, but it can accelerate the time to decisions. Instead of spending my time searching for information, I can get information quicker and move on with my tasks. Employee satisfaction goes way up in those scenarios, especially when they have a good understanding of how to get the right response from the AI. Eventually, we can even automate these processes which makes it even faster, which allows employees to focus on growing their own knowledge and their impact on the organization.
We’re seeing a lot of interest from retail businesses around theft and customer experience in stores. For example, are you driving them to the right kinds of products and what does their movement around the store tell me? That can expand into inventory management, object detection and other ways of ensuring customers have access to the items they want when they want them. With cameras, we have people analytics where we can create solutions to these problems with the help of AI. It can help you enhance business intelligence by recommending alternative layouts or ways of handling loss prevention efforts.
Within the retail space with cameras that are conducting people analytics, we are able to monitor and ensure compliance is met. For example, maybe an employee is supposed to perform certain operations. If they pick up an item they are supposed to sanitize before returning it, then the AI can make sure that happens with object detection. We can get even more specific and put time constraints around the action and it can all be automated to raise an alert if something doesn’t follow the correct process. With businesses that have to worry about, say, contamination of their food source, this can be a huge deal by ensuring compliance with sanitary guidelines.