How long before AI shows results in operations?
Implementing AI in operations reduces downtime by identifying causes of equipment failure, allowing teams to ask specific questions and receive insights to inform decisions, such as "what are the top causes of equipment failure?" and then act on the information to improve overall efficiency.
A dusty dashboard in a corner of the operations room is often the first sign that a new technology has not yet shown its value, and the question of when AI will show results in operations is one that founders and managers ask every day. The answer lies in understanding the realistic sequence of events from connection to measurable change. In my experience, the first fortnight of implementing AI in operations is spent on connecting systems and data sources, a necessary but time-consuming task that lays the foundation for future success.
Connection and setup
The initial period of AI implementation is dominated by technical setup and data integration. This involves linking various systems, such as enterprise resource planning and customer relationship management, to the AI platform. A significant amount of time is spent on data mapping, ensuring that the AI system can read and understand the organisation's data. This process can be tedious, but it is essential for the AI to provide accurate and relevant insights. As the operations team works to connect the systems, they must also ensure that the data is clean and consistent, which can be a challenge in itself.
During this phase, the operations team must also define the scope of the project, identifying which areas of the business will be impacted by the AI implementation. This requires close collaboration with various stakeholders, including department heads and subject matter experts. The goal is to create a clear understanding of what the AI system is intended to achieve and how it will be measured. By taking the time to get this right, the organisation sets itself up for success in the long run.
The first question worth answering
Once the technical setup is complete, the focus shifts to identifying the first question worth answering. This is a critical step, as it will determine the initial direction of the AI implementation. A good starting point is to identify a business problem that has a significant impact on operations, such as reducing downtime or improving supply chain efficiency. The operations team should work with stakeholders to define a specific question that the AI system can help answer, such as "what are the top causes of equipment failure?" or "how can we optimise our inventory levels?". By focusing on a specific question, the organisation can begin to see the value of the AI system and build momentum for further implementation.
In the case of a manufacturing company, for example, the first question might be "what is the most common reason for production delays?" The AI system can analyse data from various sources, such as production logs and maintenance records, to provide insights into the causes of delays. This information can then be used to inform decisions about resource allocation and process improvements. By answering this question, the organisation can begin to see the potential of the AI system to drive business value.
When a leading measure should move
A leading measure is a metric that indicates the potential for future success, such as a reduction in equipment failures or an improvement in supply chain efficiency. A leading measure should move within a few weeks of the AI system being implemented, as the organisation begins to see the initial benefits of the technology. For example, if the AI system is designed to reduce downtime, the leading measure might be the number of hours of unplanned downtime per month. As the AI system begins to provide insights and recommendations, the organisation should start to see a reduction in this metric. This is a sign that the AI system is having a positive impact on operations and that the implementation is on track.
It is essential to track leading measures closely, as they provide an early indication of the AI system's effectiveness. By monitoring these metrics, the operations team can identify areas where the AI system is having a positive impact and make adjustments as needed. This might involve refining the AI model or providing additional training to stakeholders. By taking a proactive approach to tracking leading measures, the organisation can ensure that the AI system is delivering the expected benefits.
What makes it take twice as long
One of the most common mistakes that organisations make when implementing AI is underestimating the time and effort required to integrate the technology with existing systems and processes. This can lead to delays and cost overruns, as the organisation struggles to get the AI system up and running. In my experience, this is often due to a lack of clear communication and collaboration between stakeholders, including IT, operations, and business leaders. When everyone is not on the same page, it can take twice as long to implement the AI system, as misunderstandings and miscommunications lead to false starts and rework.
Another factor that can slow down the implementation process is the lack of high-quality data. If the data is incomplete, inaccurate, or inconsistent, the AI system will struggle to provide accurate insights and recommendations. This can lead to a vicious cycle, where the organisation is forced to spend more time and resources cleaning and preparing the data, rather than using the AI system to drive business value. By taking the time to get the data right and ensuring that all stakeholders are aligned, the organisation can avoid these common pitfalls and ensure a smoother implementation process. For more information on how to get started with AI in operations, visit our operations page.
When AI is not the answer
While AI has the potential to drive significant business value in operations, there are times when it is not the right solution. If the organisation is struggling with basic data management or lacks a clear understanding of its business processes, it may be better to focus on these fundamentals before implementing AI. In these cases, the organisation should focus on building a strong foundation in data management and process improvement, rather than trying to implement a complex AI system. By taking a step back and addressing these underlying issues, the organisation can ensure that it is ready to get the most out of AI when the time is right. If you are unsure about whether AI is right for your organisation, contact us to discuss your options.
Frequently asked questions
How long does it take to see results from AI in operations?
The first fortnight is spent connecting systems and data sources, then the focus shifts to identifying the first question worth answering, with leading measures moving within a few weeks.
What is the most common mistake organisations make when implementing AI?
Underestimating the time and effort required to integrate the technology with existing systems and processes, often due to a lack of clear communication and collaboration between stakeholders.
How can organisations ensure a smoother AI implementation process?
By taking the time to get the data right, ensuring all stakeholders are aligned, and defining a clear understanding of what the AI system is intended to achieve and how it will be measured.
When is AI not the right solution for operations?
When the organisation is struggling with basic data management or lacks a clear understanding of its business processes, it may be better to focus on these fundamentals before implementing AI.