Source: HBR
Artificial intelligence (AI) has gained significant traction in recent years, transforming business operations across various industries. As companies witness the potential benefits, even those previously hesitant to adopt AI are now exploring its applications. However, determining where to invest in an AI project that will yield favorable outcomes can be a challenge. It is crucial for leaders to assess the real potential and risks before committing to AI implementation.
To determine if AI will create value for your company, consider the following:
- Assess the pain points: Identify operational challenges or inefficiencies you wish to address with AI. Focus on processes that impact cost, revenue streams, or resource allocation.
- Identify good candidates: Look for tasks that are time-consuming, labour-intensive, or involve image scanning or voice analysis. Areas like predictive accuracy in customer behaviour or general forecasting also hold potential across various industries.
It is essential to be realistic about the role of AI. While it can offer valuable insights, there may be limitations where human judgment is critical or where AI may not provide significant value.
Choosing the right entry point for AI implementation:
Start with a specific task rather than a comprehensive project. Select a high-value task that relies on data and can be effectively addressed using AI. For instance, in the healthcare sector, identifying patients at risk of falling can offer substantial value by mitigating potential injuries and improving care.
Consider data and integration requirements:
Ensure you have access to the necessary data for the AI project. Investigate the data requirements, including privacy regulations and data formats. Collaboration with data engineers can help streamline this process. Additionally, verify if the AI output can seamlessly integrate with your existing systems to achieve automated operations.
Managing expectations:
AI is a powerful tool but not a magical solution. Different AI methods, available data, and the nature of the task impact accuracy and return on investment. Understand the type of AI being deployed and set realistic expectations for success based on the task’s requirements.
Deploy AI wisely:
An AI solution that works well for one task may not be suitable for others. Deployment should be tailored to specific functional procedures and corresponding data. Consider engaging data scientists, data engineers, and IT personnel to ensure ongoing maintenance and optimization of the AI system.
Evaluating returns on investment:
It is important to recognize that AI is primarily a decision support mechanism, enhancing specific stages of a process rather than changing the ultimate outcome. Assess the overall impact on your bottom line, considering factors such as improved efficiency and the redeployment of resources to more knowledge-intensive tasks.
In conclusion, successful AI implementation requires thorough due diligence. Collaborative brainstorming among data engineers, data scientists, process subject matter experts, and executives can enhance the likelihood of successful rollouts. Finding the right approach tailored to your company’s needs can unlock new opportunities and optimize resources, offering a competitive advantage in today’s evolving business landscape.
