Five Criteria to Selecting a Successful AI Use Case
Despite recognizing the importance of integrating AI capabilities to stay competitive, many businesses do not know how to jumpstart their AI journey. Below are five guidelines that can enable organizations to embark on a successful AI journey.
1. Provides an opportunity to create savings and generate revenue
First and foremost, shortlisting processes with high Operational Expense (OPEX) provides ample opportunities to generate value – whether it creates cost or productivity savings. One way to identify such functions is to measure the nature of the tasks and human intervention involved.
In some cases, manual and repeatable tasks can be a great use case for AI to automate – whether it’s AI with human intervention or AI-powered automation. AI can make specific processes intelligent and empower organizations to utilize their workforce in more productive work.
2. Identify the process where you have been collecting data
Make sure to select a process whereby assets are already instrumented and therefore a lot of data is being collected. The success of any AI deployment depends on the quality of the data. The process that you select for the AI use case not only needs to have sufficient data volume, but it must relate to the process or asset that you want to derive impact.
Just because a process delivers plenty of data does not mean it is useful for your AI use case. For example, the data may include scheduled downtime and may not be representative of your everyday operations.
3. Choose a business case where ROI is measurable
One of the most significant problems with AI is that the management is typically unclear about what they would like to achieve by this technology implementation.
To set the right expectations, enterprises should identify a measurable outcome or KPI that the use case will contribute towards. By clearly articulating your problem statement and what you want to achieve will ensure that you can articulate the benefits and your ROI on your AI investment is measurable.
4. Ensure it does not jeopardize existing processes
It should go without saying – but we will say it! Make sure that that your use case does not have the potential to jeopardize existing processes and workforce safety. There are many levers that can be put in place to ensure the safety of your processes and workforce. In one case, AI is being used to automate the optimization of gas turbines. However, because changes in fuel setpoints are being automated and the plant’s control system still maintains its safe operating window. Alarms will be activated if thresholds are breached due to predictions deviating outside of the expected operating conditions – providing you an extra safeguard.
5. Scalable to other processes
Finally, the selected use case should have the potential to scalable the AI framework to other processes and production lines across an organization. Many learnings and processes, such as turbine optimization or quality control, can be applied to other areas of the plant, therefore creating a snowball effect of value across the operations line.
The above-listed steps delivered exceptional AI success for a leading global food ingredient solutions company. By using Canvass AI, the company achieved over $333,000 worth of energy savings and reduced CO2 emissions by 9 million pounds. The company is now exploring opportunities to use AI in other processes and plants.