• Written by Sonali Subhadarsini, Product Manager

Some people object to the idea of using enterprise AI governance for research and development, arguing that it will stifle innovation by imposing too many controls on the research process, but this objection is based on a misunderstanding of how these controls actually work, and what they are intended to achieve, which is not to restrict the questions that can be asked, but to ensure that the answers are reliable and trustworthy, and that the data used to generate them is properly managed and secured.

Enforcing permission at retrieval

One of the key challenges in implementing enterprise AI governance for research and development is ensuring that the right people have access to the right data, and that they are using it for the right purposes, which is why it is so important to enforce permission at the point of retrieval, rather than trying to instruct the model itself about who should be allowed to see what, because this approach is more flexible and easier to manage, and it allows for much more fine-grained control over who can see what, and under what circumstances.

This approach also makes it much easier to audit who has accessed what data, and when, which is essential for maintaining the security and integrity of the research and development process, and for ensuring that sensitive information is not leaked or misused, and that the organisation is complying with all relevant laws and regulations, such as those related to intellectual property and data protection.

Auditability

Auditability is another critical aspect of enterprise AI governance for research and development, because it provides a clear record of what was asked, what was returned, and on what data, which is essential for tracking the progress of research projects, and for identifying any potential errors or biases in the data or the models used to analyse it, and for ensuring that the results are reliable and trustworthy.

This information can also be used to improve the performance of the models over time, by identifying areas where they are struggling, and providing additional training data or tweaking the algorithms to improve their accuracy, and to provide a clear audit trail in case of any disputes or disagreements about the results, or if there are any allegations of misconduct or misuse of the data.

Research and development data

The effectiveness of enterprise AI governance for research and development depends on having access to high-quality data, including test results and failure analyses, design review records, timesheets and allocation, and supplier assessments, which provide a detailed picture of the research and development process, and allow for much more accurate and informed decision-making.

This data can be used to identify trends and patterns that may not be immediately apparent, and to anticipate potential problems or challenges before they arise, which can help to reduce the risk of costly delays or setbacks, and improve the overall efficiency and effectiveness of the research and development process, and to provide a clear understanding of how different projects and programmes are related, and how they can be optimised and improved, as described in more detail on our research and development page.

What it changes

Enterprise AI governance for research and development can have a significant impact on the way that research and development is conducted, by providing much more detailed and accurate information about the progress of projects, and the challenges and risks that they face, which can help to identify potential failures before they occur, and to take steps to mitigate them, and to provide a much clearer understanding of the effort required to complete different projects and programmes, and how this effort can be optimised and improved.

This can also help to make invisible effort visible against project stage, which can be a major challenge in research and development, where it can be difficult to track progress and identify potential bottlenecks or roadblocks, and to provide a clear understanding of how different projects and programmes are related, and how they can be optimised and improved, which can help to reduce the risk of costly delays or setbacks, and improve the overall efficiency and effectiveness of the research and development process.

Measured with

The effectiveness of enterprise AI governance for research and development can be measured in a variety of ways, including the number of failures found before validation, the effort required against stage, and the estimate revision count, which provide a clear picture of the impact of the governance framework on the research and development process, and allow for much more accurate and informed decision-making.

This information can be used to identify areas where the governance framework is working well, and where it needs to be improved, and to provide a clear understanding of the return on investment in the governance framework, and how it can be optimised and improved over time, and to get in touch with us to discuss how AsscherAi can help with this, please visit our contact us page.

Limitations

While enterprise AI governance for research and development can be a powerful tool for improving the efficiency and effectiveness of the research and development process, it is not a panacea, and it has its limitations, which need to be carefully considered before implementing a governance framework, and it is not suitable for all organisations or research and development projects, and it requires careful planning and implementation to be effective.

Most people get wrong about this that it is just a matter of implementing a few controls and procedures, and then everything will be fine, but this is not the case, and it requires a much more nuanced and sophisticated approach, that takes into account the specific needs and challenges of the organisation and the research and development project, and that is tailored to the specific requirements and goals of the project, and that is continuously monitored and improved over time.