Business AI Initiatives Should Focus On Use Cases First
Choosing an AI technology stack is getting more complicated. Not a day goes without the announcements of new AI models, tools and frameworks. Whichever ones you choose to power your business AI initiatives, keep in mind that you are likely to be using a much better AI tech stack next year and again the year after that.
Rather than getting bogged down in the specifics of today's AI models and platforms, businesses should start by focusing on understanding how AI, in general, can create value for them. This means being aware of what capabilities are on offer and how these could be applied to improve existing business processes. Where are the bottlenecks and inefficiencies? What tasks are eating up valuable employee time and attention? How can the organizations products or services be made more valuable to its customers? As the business grows and the competitive landscape shifts, what new business process capabilities will be required to get or stay ahead? Answering these questions will highlight areas where AI may be able to drive significant improvements.
Once potential use cases have been identified, it's important to prioritize them based on their alignment with key business objectives. Not every application of AI will be equally impactful. Businesses should focus their efforts on the use cases that have the greatest potential to move the needle, whether that's through cost savings, increased revenue, improved market insights or customer experience. It's also critical to validate the feasibility and potential ROI of each use case before investing significant resources.
With high-value use cases identified, attention can turn to the data required to power them. Many businesses are sitting on troves of data that they aren't fully utilizing. Dormant data represents an untapped opportunity to drive value with AI. Organizations should take inventory of their data assets across different functions and consider how this information could be leveraged.
Data collection should also be thoughtfully integrated into business processes. By capturing the right information at the right time, businesses can ensure a steady flow of data to feed their AI initiatives. Of course, all of this data must be properly organized and governed. Sound data management practices are foundational for successful AI deployment.
In addition to data, another key area where businesses should focus their efforts is prompt engineering. The prompts used to interact with AI models have a direct impact on the quality of outputs. Rather than starting from scratch with each new use case, businesses should develop a library of reusable prompts that are known to elicit good results. Prompts should be regarded as integral part of defining business processes.
By developing a robust pipeline of use cases, ensuring the right data is in place to support them, and crafting prompts that can stand the test of time, organizations can create a strong foundation for their AI initiatives. This foundation will serve them well regardless of which specific AI models and tools they end up using. As new and improved models become available, businesses will be well-positioned to take advantage of them. They'll be able to plug these models into their existing use cases and data, realizing the benefits of the latest technology without starting from scratch.