• Written by Muhammed Abdulla NC, CEO

A dashboard gathers dust in the research and development department, its charts and graphs a reminder of a capable system that has failed to gain traction. The problem is not the technology itself, but how it has been integrated into the existing workflow.

Attaching new capability to a routine that already exists

Most people get wrong the idea that a new system can be simply dropped into an organisation and expected to be used. This approach is wrong because it ignores the existing routines and processes that employees are accustomed to. A more effective approach is to attach the new capability to a routine that already exists, allowing employees to gradually become familiar with the new system. For example, a research and development team may already have a routine of reviewing test results and design reviews on a weekly basis. By incorporating the new system into this existing routine, employees can begin to see the value of the new capability and start to use it regularly.

A shift supervisor checks the maintenance log and notices that a particular machine has been experiencing frequent breakdowns. By using the new system to analyse the test results and failure analyses, the supervisor can identify the root cause of the problem and schedule maintenance accordingly. This approach not only improves the efficiency of the maintenance process but also helps to reduce downtime and increase overall productivity.

Who needs to be able to ask, not just who needs the answer

Another common mistake is to focus solely on who needs the answer, rather than who needs to be able to ask the question. This approach is wrong because it ignores the fact that employees at all levels of the organisation may have valuable insights and questions that can inform the research and development process. By giving employees at all levels the ability to ask questions, organisations can tap into a wider range of knowledge and expertise, and make more informed decisions.

For instance, a junior engineer may have a question about the design of a particular component, but may not have the necessary access or permissions to obtain the answer. By using a system like AsscherAi, the engineer can ask the question and receive an answer in real-time, without having to navigate complex bureaucratic processes.

The research and development data this depends on

The success of an enterprise AI adoption for research and development depends on the quality and availability of certain types of data. This includes test results and failure analyses, design review records, timesheets and allocation, and supplier assessments. By having access to this data, organisations can use the new system to identify trends and patterns that may not be immediately apparent, and make more informed decisions about their research and development efforts.

For example, a category manager compares two suppliers based on their performance data, and uses this information to inform procurement decisions. By using a system like AsscherAi, the manager can quickly and easily access the necessary data, and make more informed decisions about which suppliers to use.

What it changes about failures rediscovered across programmes and effort invisible against project stage

One of the key benefits of an enterprise AI adoption for research and development is that it can help to identify failures and inefficiencies that may be occurring across multiple programmes and projects. By using the new system to analyse data from different sources, organisations can identify patterns and trends that may not be immediately apparent, and take steps to address them.

For instance, a programme manager may notice that a particular type of failure is occurring across multiple projects, but may not have the necessary data to identify the root cause. By using a system like AsscherAi, the manager can quickly and easily access the necessary data, and use this information to inform decisions about how to address the problem. To learn more about how AsscherAi can support your research and development efforts, visit our research and development page.

Measured with failures found before validation, effort against stage, estimate revision count

The success of an enterprise AI adoption for research and development can be measured in a variety of ways, including the number of failures found before validation, the amount of effort required against each project stage, and the number of estimate revisions. By tracking these metrics, organisations can get a clear picture of the impact of the new system, and make adjustments as needed to optimise its use.

For example, a project manager may use the new system to track the number of failures found before validation, and use this information to inform decisions about how to improve the testing process. By reducing the number of failures found before validation, the organisation can reduce the amount of time and resources required to bring a product to market, and improve overall productivity.

What this does not do

While an enterprise AI adoption for research and development can be a powerful tool for improving productivity and reducing costs, it is not a silver bullet. It will not automatically solve all of an organisation's problems, and it will not replace the need for human judgment and expertise. To get the most out of the new system, organisations must be willing to invest time and resources in training and support, and must be prepared to make changes to their existing processes and workflows. If you have questions about how to get started with an enterprise AI adoption for research and development, or would like to learn more about how AsscherAi can support your efforts, please contact us.