Identify use-cases and ground AI in real business needs

- As companies unveil evermore remarkable generative AI capabilities in the form of new products and services, it's understandable to be eager to test the innovative possibilities they present. I'm the voice in the room telling you to wait. AI should not drive strategy. Your business strategy should drive AI implementation. This means identifying the business problems and goals first, then evaluating if and how AI can add value. Beware of starting a generative AI initiative simply because it's the hot new thing right now. Adopting AI technology for technology's sake rarely delivers value. The key is to ground your AI initiatives in practical business needs. In this video, I'll show you how to identify and prioritize AI use cases based on real business goals and challenges. This will set up impactful and relevant AI adoption across your organization. Here's how you can go about doing it. First, clarify your key organizational objectives. What business challenges are you facing? Increased cost pressures, poor customer retention, supply chain disruptions. The goal here is to outline tangible problems. Next, analyze where inefficiency bottlenecks, or waste exist in your core operations. Where could automation or optimization drive dramatic improvements? Now using your investment in AI literacy and increased understanding of generative AI capabilities and solutions, which we covered in the previous lesson, you can begin to brainstorm how AI can alleviate pain points or help you capitalize on opportunities. For instance, if reducing response times for customer service requests is your priority, AI powered chatbots might be a helpful solution to explore. Or perhaps you want a conversational AI assistant that can improve the closing ratios of your sales execs by 5% by providing product expertise in real-time. The idea is to align AI solutions to your specific business problems. You should screen use cases against criteria like feasibility, business impact, and effort required. It can also be helpful to categorize use cases into types. For example, revenue boosting, cost saving, risk reduction, employee assistance, etc. The crucial thing to remember is that not every problem is best solved with AI, and it's important to evaluate whether the expected return on investment justifies the resources and effort needed. Throughout this process, be sure to include stakeholders from different functions to tap into diverse perspectives. Their insights can reveal opportunities you might not have considered and will help ensure that AI solutions resonate with the people using them. To put this into practice, consider hosting brainstorming session with team leaders to identify pain points that AI could alleviate. Be sure to stay informed about industry trends and explore use cases to understand how businesses similar to yours are utilized in AI. And more importantly, engage with AI experts or consultants who can provide an external perspective on the technical and strategic feasibility of your AI initiatives. By focusing on identifying AI use cases that are closely aligned with your businesses' challenges and goals, you ensure that your AI initiatives are not just technologically sound, but also strategically valuable.