AI in 2024 is indeed capable of unlocking substantial productivity gains across various use cases like CS- think 20-40% or even more, just look at Klarna and their 2/3 of customer service chats leading to a 25% drop in repeat inquiries – link to the study in the comments.). Not without reason we specialise in selecting such gains in customer service. This area is full of inefficiencies and reinventing the wheel.
But…you should not delude yourself that AI can replace thousands of employees or 10x your profit overnight. Rather than jumping on the AI bandwagon simply because it’s a hot trend, organisations should take a more strategic approach.
Here are some actionable steps to that we advise to our clients to help them identify and prioritise potential AI use cases:
1. Conduct a thorough analysis of your business processes, identifying bottlenecks, inefficiencies, and areas with high volumes of repetitive tasks. These are often prime candidates for AI automation.
2. Evaluate the data landscape within your organisation. AI solutions thrive on large, high-quality datasets. Identify processes or functions that generate or rely on substantial amounts of structured or unstructured data.
3. Engage with subject matter experts and front-line employees to understand their pain points and gather insights on which tasks or processes could benefit from AI assistance or automation.
4. Prioritise use cases based on their potential impact on key business metrics such as cost savings, revenue generation, customer satisfaction, or employee productivity.
5. Start small with pilot projects focused on high-priority, well-scoped use cases. This allows you to test the waters, measure the results, and build trust and experience with AI before scaling up.
6. Develop a comprehensive change management plan that includes training and education for employees on how to effectively collaborate with and leverage AI tools.
7. Continuously monitor and refine your AI implementations, adjusting as needed based on performance metrics and evolving business needs.
And while AI offers immense potential, it’s not a silver bullet. Sometimes, you may find that simpler solutions like process improvements are more appropriate than fancy AI models. The key is to take a data-driven approach to identifying and implementing AI use cases that truly move the needle for your business.
Hope this helps 🙂
