Adoption and change for operations
Operations teams use AsscherAi to identify bottlenecks in their workflows by asking questions about project status and resource allocation, then adjust their plans to optimise throughput and meet deadlines, which reduces the need for expensive expediting and improves overall efficiency.
A operations manager sits at their desk, staring at a screen displaying the latest project status updates, wondering why the new enterprise AI system is not being used to its full potential, despite its ability to answer complex questions about the organisation's data in real time.
Attaching new capability to a routine that already exists
The most effective way to drive adoption of a new system is to attach it to a routine that already exists, rather than trying to create a new habit. For an operations manager, this might mean using the AI system to inform daily meetings, where project status and exceptions are discussed. By incorporating the AI system into this existing routine, it becomes a natural part of the workflow, rather than an add-on that is easily forgotten.
This approach also helps to build trust in the system, as users see the value it provides in a context they are already familiar with. It is wrong to assume that a new system will be adopted simply because it is available - people need to see a clear benefit, and attaching it to an existing routine provides that benefit.
Who needs to be able to ask, not just who needs the answer
Most people get wrong the idea that only senior managers or technical experts need to be able to ask questions of the AI system. In reality, the ability to ask questions should be available to anyone who needs to make decisions based on the data, regardless of their level or role. This might include shift supervisors, team leaders, or even external partners.
By giving more people the ability to ask questions, organisations can unlock the full potential of their data, and make better decisions at all levels. This is particularly important in operations, where decisions need to be made quickly and accurately, and where the ability to ask questions can mean the difference between meeting a deadline and missing it.
The operations data this depends on
The ability to ask questions of the AI system depends on having high-quality operations data, including process and exception records, project status, budget consumption, and supplier performance. This data needs to be accurate, up-to-date, and accessible, in order for the AI system to provide useful answers.
For example, a category manager might use the AI system to compare the performance of different suppliers, and identify areas where improvements can be made. This requires having detailed data on supplier performance, including metrics such as on-time delivery, quality ratings, and cost.
Having this data in place also enables the AI system to provide insights that would be difficult or impossible to gain through manual analysis, such as identifying patterns and trends in supplier performance over time.
What it changes about
The ability to ask questions of the AI system changes the way organisations approach anomalies and exceptions, by making it possible to identify patterns and trends that would be invisible through manual analysis. It also changes the way organisations manage projects, by providing real-time visibility into budget consumption and progress.
For instance, a project manager might use the AI system to identify areas where a project is consuming budget faster than progress, and take corrective action to get the project back on track. This requires having detailed data on budget consumption and project status, as well as the ability to ask questions and get answers in real time.
Measured with
The effectiveness of the AI system can be measured using a range of metrics, including on-time delivery, exception rate, and expediting cost. These metrics provide a clear indication of whether the AI system is having a positive impact on operations, and where further improvements can be made.
By tracking these metrics over time, organisations can see the tangible benefits of using the AI system, and make adjustments as needed to optimise its use. For more information on how to get started with the AI system, visit our operations page.
What this does not do
The AI system is not a replacement for human judgement and expertise, but rather a tool to support and augment it. It is also not a solution for organisations that do not have their data in order, or that lack a clear understanding of their operations and goals.
In these cases, the AI system may actually create more problems than it solves, by providing answers that are based on incomplete or inaccurate data. If you are unsure whether the AI system is right for your organisation, contact us to discuss your options and determine the best course of action.
Frequently asked questions
How can we increase adoption of our new AI system in operations?
Attach the AI system to existing routines, such as daily meetings, to build trust and demonstrate its value.
Who should be able to ask questions of the AI system?
Anyone who needs to make decisions based on data, including shift supervisors, team leaders, and external partners.
What data is required to support the AI system?
High-quality operations data, including process and exception records, project status, budget consumption, and supplier performance.
How can we measure the effectiveness of the AI system?
Track metrics such as on-time delivery, exception rate, and expediting cost to see the tangible benefits of using the AI system.