Common mistakes when applying AI to operations
Implementing AI in operations requires careful consideration of process mapping, data quality, and user access to avoid common mistakes and ensure effective insights, as seen in the example of a shift supervisor using AI to identify recurring maintenance issues and inform data-driven decisions.
A maintenance log filled with unexplained downtime and unactioned recommendations is a common sight in many operations teams, and the decision to apply AI to such a system can seem like a straightforward solution to uncover hidden insights and improve efficiency. However, this enthusiasm often overlooks critical pitfalls that can render the entire effort ineffective.
Automating a process nobody mapped
One of the most significant mistakes made when applying AI to operations is attempting to automate a process that has not been thoroughly mapped. This oversight can lead to the AI system being trained on incomplete or inaccurate data, resulting in suboptimal performance and potential errors. For instance, a shift supervisor checks the maintenance log to identify recurring issues, only to find that the data is inconsistent and lacks crucial details, making it challenging for the AI to provide meaningful insights.
A thorough process mapping exercise is essential to identify all the steps involved, the data required, and the potential exceptions that may arise. This exercise helps ensure that the AI system is designed to handle the complexities of the real-world process, rather than a simplified or theoretical version of it. By taking the time to map the process accurately, operations teams can avoid the pitfalls of automation and create a more effective AI-powered system.
Treating process and exception records as complete when it is not
Most people get wrong the assumption that process and exception records are complete and accurate, which is wrong. In reality, these records often contain gaps, inconsistencies, and biases that can significantly impact the performance of the AI system. For example, a category manager compares two suppliers based on their historical performance data, only to discover that the data is incomplete or inaccurate, leading to flawed decisions.
To mitigate this issue, operations teams must implement robust data validation and cleansing processes to ensure that the data used to train the AI system is accurate and comprehensive. This may involve manually reviewing records, implementing data quality checks, and using techniques such as data imputation to fill gaps in the data. By acknowledging the limitations of their data and taking steps to address them, operations teams can create a more reliable and effective AI-powered system.
Giving the capability only to the analyst who already had it
Another common mistake is giving the AI capability only to the analyst who already had the ability to perform the task, rather than extending it to other team members who could benefit from it. This approach can limit the potential benefits of the AI system and create a bottleneck in the process. For instance, an operations analyst uses the AI system to analyze production data, but the insights are not shared with other team members, such as the production manager, who could use them to make informed decisions.
By making the AI capability available to a broader range of users, operations teams can democratize access to insights and enable more informed decision-making across the organization. This may involve providing training and support to help users understand how to use the AI system effectively and ensuring that the system is integrated with existing workflows and processes. To learn more about how to implement AI in operations, visit our operations page.
Declaring success against a metric with no baseline
Declaring success against a metric with no baseline is a mistake that can lead to misleading conclusions and a lack of accountability. Without a clear understanding of the current state of the process, it is impossible to determine whether the AI system has had a positive impact. For example, an operations team implements an AI-powered predictive maintenance system and claims a reduction in downtime, but without a baseline, it is impossible to verify this claim.
To avoid this mistake, operations teams must establish clear baselines and metrics before implementing the AI system. This may involve collecting historical data, establishing key performance indicators (KPIs), and defining clear targets for improvement. By taking a data-driven approach to evaluation, operations teams can ensure that their AI system is having a meaningful impact and make informed decisions about future investments.
When AI is the wrong choice
AI is not a panacea for all operational challenges, and there are situations where it is the wrong choice. For instance, in situations where the process is highly variable or requires a high degree of human judgment, AI may not be the best solution. In such cases, alternative approaches, such as process re-engineering or training programs, may be more effective.
In other cases, the costs and complexity of implementing an AI system may outweigh the potential benefits, particularly for small or simple processes. In these situations, operations teams must carefully weigh the potential benefits against the costs and consider alternative solutions. If you are unsure about whether AI is the right choice for your operations, contact us to discuss your options.
Frequently asked questions
What are the most common mistakes when applying AI to operations?
Attempting to automate unmapped processes and assuming complete data records are common mistakes that can lead to ineffective AI systems.
How can operations teams ensure accurate data for AI training?
Implementing robust data validation and cleansing processes, such as manual reviews and data quality checks, can help ensure accurate and comprehensive data.
What is the benefit of extending AI capabilities to multiple team members?
Democratizing access to AI insights enables more informed decision-making across the organization and avoids bottlenecks in the process.
Why is establishing a baseline important when evaluating AI success?
Without a clear baseline, it is impossible to determine the impact of the AI system, making it essential to establish clear metrics and targets before implementation.