By now, you probably have seen some proof of concepts around generative AI in various places. Which is great! But how many have actually created tangible business benefits?
Imagine building this wonderful generative AI app only to find out later that the costs are really high, or adoption is really low! Turns out it takes more than a prototype to be successful, and even generative AI didn’t change that.
The following questions can help you separate hype from reality and avoid costly mistakes by finding the right use-case to invest in, so you can reap business benefits more quickly and more reliably:
- Where are the bottlenecks? Which processes take too much time? Which are difficult to scale? Where do your employees have trouble finding the right information at the right time? Take a close look at your core business processes and value flows, and find bottlenecks that result in waiting time, limit your scalability, or can introduce defects. These can become great opportunities for generative AI to resolve them. For example, LexisNexis created their Lexis+ AI platform to enable lawyers to increase their efficiency, effectiveness, and productivity through conversational search, insightful summarization, and intelligent legal drafting.
- Do you have a lot of data, but it’s difficult to use? What data does your company have? Which content did your company create? What data did you collect that is sitting somewhere, waiting to be used? Find sources of data and content, and ask: what if it was easy to find and combine the right information at the right time to generate valuable insights or new content that your customers love? Because it is! As an example, digital marketing platform company Wunderkind found: "We have an unbelievable amount of proprietary data, but it's difficult looking across our multiple data 'silos' to find the right answer and distill the information into quick, actionable insights. Adding Amazon Q as a topline layer over our various content and data repositories brings a whole new level of efficiency to our customer success and marketing teams. Based on initial estimates, we expect the time spent on content discovery alone to be reduced by over 30%, which empowers our success team to service clients faster, and with better accuracy."
- How can you measure success? What business metrics and KPIs are affected by your generative AI solution? How can you calculate a tangible business benefit that can help you understand exactly how much value you get our of your generative AI solution? This is important, because it helps you bring your generative AI project from PoC to reality by building a strong business case. With clear metrics that translate into saved cost or increased revenue, you can show your leadership exactly how valuable your generative AI project is, and how your company is reaping its benefits. Lonely Planet developed a generative AI solution on AWS to help customers plan epic trips and create life-changing experiences with personalized travel itineraries. By building with Claude 2 on Amazon Bedrock, they reduced itinerary generation costs by nearly 80% percent.
The closer your generative AI project is to key business processes and your company’s data, and the more you focus on clear success metrics, the bigger your chances are to build a useful generative AI app that delivers tangible value for your company.
