• Written by Sonali Subhadarsini, Product Manager

While AI process automation for research and development is often touted as a way to streamline decision-making, in practice it tends to focus on automating the steps around a decision rather than the decision itself, and this subtle distinction can make all the difference in its successful implementation. What this means is that rather than trying to automate the complex and nuanced decision-making process, organisations are instead using AI to automate the more mundane and repetitive tasks that surround it, such as data collection and analysis.

Mapping the current process

Before automating any part of the research and development process, it is essential to have a thorough understanding of the current workflow, including all the various tasks and handoffs involved. This requires mapping out the entire process, from initial concept to final product, and identifying all the different stakeholders and their roles. For example, a shift supervisor may be responsible for overseeing the testing process, while a category manager may be responsible for sourcing materials and supplies. By understanding how these different roles interact and depend on each other, organisations can begin to identify areas where automation can have the greatest impact.

This process mapping exercise can be time-consuming and requires a significant amount of effort and resources, but it is a crucial step in ensuring that automation is targeted at the right areas and that it is effective in streamlining the overall process. It also helps to identify potential bottlenecks and areas where automation may not be suitable, such as tasks that require a high degree of creativity or human judgment.

The handoffs that create most of the delay

One of the main areas where automation can have a significant impact is in the handoffs between different teams and stakeholders. These handoffs can often create significant delays and bottlenecks in the process, as information and tasks are passed from one person to another. For example, when a design team completes a prototype, it may need to be reviewed and approved by a separate team before it can be moved on to the next stage of development. By automating these handoffs, organisations can reduce the time and effort required to complete each stage of the process and improve overall efficiency.

Automating these handoffs also requires a high degree of standardisation and consistency in the process, which can be challenging to achieve, especially in complex and dynamic environments. However, by using AI to automate these tasks, organisations can begin to build a more streamlined and efficient process that is less prone to delays and errors.

The research and development data this depends on

The automation of research and development processes depends on a wide range of data, including test results and failure analyses, design review records, timesheets and allocation, and supplier assessments. This data is used to inform and guide the automation process, and to ensure that it is targeted at the right areas and that it is effective in streamlining the overall process. For example, a category manager may use data on supplier performance to inform sourcing decisions, while a shift supervisor may use data on test results to identify areas for improvement in the manufacturing process.

Having access to this data is critical to the success of AI process automation for research and development, and organisations must ensure that they have the necessary systems and infrastructure in place to collect, store, and analyse this data. This may involve investing in new technologies and systems, such as data analytics software and cloud-based storage solutions. For more information on how to implement AI process automation for research and development, visit our research and development page.

What it changes about failures and effort

AI process automation for research and development can have a significant impact on the way organisations approach failures and effort. By automating the steps around decision-making, organisations can reduce the likelihood of failures being rediscovered across programmes, and can make effort more visible against project stage. This can help to improve overall efficiency and effectiveness, and can reduce the time and resources required to complete projects.

For example, a shift supervisor may be able to use automated systems to track and analyse data on failures and effort, and to identify areas where improvements can be made. This can help to reduce the likelihood of failures being repeated, and can improve overall quality and reliability. By making effort more visible against project stage, organisations can also better allocate resources and prioritise tasks, which can help to improve overall project outcomes.

Measuring success

Measuring the success of AI process automation for research and development requires a range of metrics and indicators, including failures found before validation, effort against stage, and estimate revision count. These metrics can help organisations to understand the impact of automation on their processes, and to identify areas where further improvements can be made. For example, a category manager may use data on failures found before validation to identify areas where the automation process is having a significant impact, and to target further improvements.

By using these metrics and indicators, organisations can begin to build a more comprehensive understanding of the impact of AI process automation on their research and development processes, and can make more informed decisions about where to target further improvements. If you have any questions about how to implement AI process automation for research and development, or would like to learn more about how it can benefit your organisation, please contact us.

What this does not do

While AI process automation for research and development can have a significant impact on the efficiency and effectiveness of organisational processes, it is not a silver bullet and there are limitations to its use. For example, it may not be suitable for tasks that require a high degree of creativity or human judgment, and it may not be effective in complex and dynamic environments where processes are constantly changing.

Most people get wrong the idea that AI process automation can replace human decision-making entirely, which is wrong. AI process automation is best used to support and augment human decision-making, rather than replacing it entirely. By understanding the limitations of AI process automation, organisations can begin to build a more realistic understanding of what it can achieve, and can make more informed decisions about where to target its use.