In a nutshell:
- Start with existing business processes to introduce machine learning predictions.
- Use a benchmark to measure the improvement of ML predictions.
- Start small and gradually expand the use of ML across your organization.
- The goal is to augment human decision-making, not replace it entirely.
Zohar Bronfman, Pecan's CEO and co-founder, discusses how to gain ROI from AI in your business in the video above — or read on for more!
Are you looking to get started with machine learning predictions but don't know where to begin? A common question for beginners is: what's the best way to dip my toes in the ML waters?
Let's explore a practical approach that can help you kickstart your machine-learning journey while delivering tangible value to your organization.
Begin with Existing Business Processes
The key is to start with an existing business process that already has some logic or rules embedded in it. For example, many companies already have ways of identifying employees at risk of leaving the company. Maybe it's if someone hasn't shown up for work for a period of time, hasn't been promoted recently, or has been consistently working overtime.
Take an existing process like that, understand how well it currently performs, and then see what happens when you add HR predictive analytics around employee attrition. This approach allows you to leverage your domain knowledge and existing data while introducing the power of machine learning.
Use a Benchmark for Improvement
By starting with an existing process, you can use its performance before adding machine learning as a benchmark. This gives you a clear point of comparison. You can then compare the ML-enhanced process to the previous process to see how much better the predictions are compared to your existing rules-based system.
For instance, if your current system identifies 60% of employees at risk of leaving, you can measure how much that percentage improves with ML predictions. Maybe you'll be able to identify 75% or even 80% of at-risk employees, allowing for more targeted retention efforts.
With a solid benchmark in place, you have a concrete basis for evaluating if and how much ML can improve your business processes. This approach also makes it easier to demonstrate the value of ML to stakeholders who may be skeptical about its benefits.
Start Small, Win Big
Starting small with existing processes is a great way for beginners to start exploring machine learning predictions. Instead of launching something completely new and difficult to assess, you can rapidly determine where AI is making a difference for your organization and quantify its impact.
This method also allows you to learn and iterate quickly. You can experiment with different ML algorithms, feature engineering techniques, and data preprocessing methods to see what works best for your specific use case. As you gain confidence and experience, you can gradually tackle more complex problems and expand the use of ML across your organization.
Remember, the goal is not to replace human decision-making entirely, but to augment and enhance it. By starting with familiar processes, you can more easily integrate ML predictions into your existing workflows and help your team understand and trust the new AI-driven insights.
Ready to get started now? Choose a process, identify the right benchmark, and then try a free trial of Pecan to start improving your business processes with machine learning!
