Common mistakes when applying AI to research and development
Applying AI to research and development requires careful process mapping, otherwise automation efforts will fail to deliver expected results, as seen in a project where undocumented testing processes led to incomplete data and suboptimal AI performance, highlighting the need for thorough planning.
How many times have we sat in meetings where someone asks "can we use AI to automate our research and development process" only to be met with a room full of nodding heads and a complete lack of understanding of what that actually entails? The truth is, applying AI to research and development is not a straightforward task, and there are several common mistakes that can lead to disappointing results.
Automating a process nobody mapped
The first mistake is trying to automate a process that has not been properly mapped out. This is a recipe for disaster, as AI systems rely on well-defined processes and data to function effectively. Without a clear understanding of the existing process, it is impossible to identify areas where AI can add value. I have seen this mistake happen time and time again, and it is a judgement I am willing to make: if you have not taken the time to map out your process, you are not ready to apply AI.
A good example of this is a project I worked on where the goal was to use AI to optimise the testing process for new products. However, when we started digging into the existing process, we realised that it was not well documented and varied greatly from one team to another. We had to take a step back and map out the process before we could even think about applying AI.
Treating test results and failure analyses as complete when it is not
Another common mistake is treating test results and failure analyses as complete when they are not. This can lead to AI systems being trained on incomplete or inaccurate data, which can result in suboptimal performance. Most people get wrong the idea that test results and failure analyses are always complete and accurate, but they are wrong: in reality, these processes are often iterative and require multiple rounds of testing and refinement.
For instance, a shift supervisor checks the maintenance log and assumes that all the necessary tests have been run, but in reality, some tests may have been skipped or not properly documented. This can lead to incomplete data being fed into the AI system, which can have serious consequences.
Giving the capability only to the analyst who already had it
A third mistake is giving the capability to apply AI to research and development only to the analyst who already had it. This can lead to a lack of buy-in and adoption from other team members, who may feel that the AI system is not relevant to their work. It is a mistake to assume that only certain individuals have the necessary skills or expertise to work with AI, when in reality, many people can benefit from using these systems.
In fact, one of the key benefits of using AI in research and development is that it can democratise access to complex data and analytics, allowing more people to contribute to the decision-making process. By giving the capability to apply AI to a wider range of team members, organisations can unlock new insights and ideas that may not have been possible otherwise.
Declaring success against a metric with no baseline
A fourth mistake is declaring success against a metric with no baseline. This can lead to unrealistic expectations and a lack of understanding of what constitutes real progress. It is a mistake to assume that simply applying AI to a process will automatically lead to improvements, when in reality, it is necessary to establish a baseline and track progress over time.
For example, a category manager compares two suppliers using an AI system, but has no baseline to compare the results to. Without a baseline, it is impossible to know whether the AI system is actually providing any value, or if the results are simply a reflection of the existing process. To learn more about how to apply AI to research and development effectively, it is necessary to take a more nuanced approach.
When AI is not the right choice
Finally, it is worth considering when AI is not the right choice for research and development. There are certain situations where the use of AI may not be appropriate, such as when the data is too complex or nuanced for an AI system to handle. In these cases, it may be better to rely on human judgement and expertise, rather than trying to force an AI system to work with incomplete or inaccurate data.
If you are considering applying AI to your research and development process, but are not sure where to start, contact us to learn more about how we can help. We can work with you to identify areas where AI can add value, and help you avoid the common mistakes that can lead to disappointing results.
Frequently asked questions
What are the most common mistakes when applying AI to research and development?
Common mistakes include automating unmapped processes, treating incomplete data as complete, and giving AI capabilities only to select individuals, leading to disappointing results and poor adoption.
How can incomplete data affect AI performance in research and development?
Incomplete data can lead to suboptimal AI performance, as AI systems rely on accurate and complete data to function effectively, and incomplete data can result in inaccurate insights and poor decision-making.
What are the benefits of giving AI capabilities to a wider range of team members?
Giving AI capabilities to a wider range of team members can democratise access to complex data and analytics, allowing more people to contribute to the decision-making process and unlocking new insights and ideas.
Why is it important to establish a baseline when measuring AI success in research and development?
Establishing a baseline is crucial to measuring AI success, as it allows organisations to track progress over time and understand what constitutes real progress, rather than simply declaring success without a clear understanding of the impact.