5 Tips To Improve The Performance Of AI Projects
There has been an increase in the use and the consequent implementation of artificial intelligence (AI) projects in all industries. Some of the common reasons for adopting AI are improving efficiency, productivity, customer satisfaction and looking to have a competitive edge. For others, it’s pressure from the fear of missing out on the trend. However, regardless of the motivation behind your need to implement AI projects in your business, it’s crucial that your project performs and solves the needs for which you build it. (1)
Among the top things that adversely affect performance in AI is data. That’s why you need to be careful with collecting, labelling and annotating your data. You need an excellent annotation tool platform to ensure your data is annotated and labelled appropriately before feeding it into your project. This article shares this and other ways to improve your AI projects.
Align your project with your business goals
Even with the fear of missing out being a possibility, avoid starting an AI project just because it’s trending or you see other businesses doing it. Ensure your business has a clear use case and application, with clearly defined goals and objectives. Ensure you can convince yourself and others that an AI project is necessary and it’ll add value to the business in the long run. (2)
You can use AI to improve the business in many ways. It could be that you want to boost customer satisfaction, save costs or improve sales and revenue. The point is to objectively evaluate your business to identify a real problem to solve. You can start by doing a thorough business analysis of your internal and external processes. Ultimately, it won’t matter how good the AI project is but how well it performed in solving an identified problem.
Create a data strategy early
Quality data sets are at the heart of successfully training machine learning (ML) models and the performance of your AI project. However, it’s not just the quality that matters but the quantity as well. The amount of data you input also plays a significant role in ensuring that your AI project gives an accurate outcome.
Therefore, you can’t afford to make your data strategy an afterthought. Figure out before beginning the project where you’ll gather your training data from; how you’ll segregate, label and use it and the amount and quality you need. Be careful about introducing data bias by ensuring that you use the highest quality data. You can do this by carrying out data quality assessments and data cleaning procedures to improve the accuracy of your AI projects. (3)
Use the right algorithm
The algorithm you choose also plays a vital role in training your ML models and the ultimate performance of your AI project. When you feed data sets to your ML model, it’s the work of the algorithm to decipher, interpret and predict the result accurately. That’s how the algorithm you choose influences the performance of your AI project. (4)
However, your choice of algorithm isn’t as important as how you tune it to work for your project. While it’s tempting to keep upgrading to improve performance, it won’t help if you haven’t fine-tuned the algorithm correctly. This is an excellent way to improve project accuracy.
Get the right team for the project
You need data scientists in your AI project team, but you also need other professionals to make a good team. They include domain experts for the subject matter, ML engineers and project testers. Having a monolithic team can hinder the success of the project from getting ideas from only one point of view without anyone to challenge or validate them. Ensure that your team’s skills, experience and expertise complement to give your project a better chance of success.
Build from the ground up
No matter how fast you want the project to be up and running, don’t be in a hurry to go for the big win immediately. Take your time and take it a step at a time. Let the small initial successes work at the motivation for what lies ahead. In addition, these early wins might serve as a springboard for gaining the support of those who were not on board initially.
The best approach is to break the project down into smaller achievable goals and set timelines to avoid tallying at one stage for more than you should. This will also help in identifying challenges and problems with the project early to rectify the issues as you move along. Otherwise, you risk finishing a project and then realising it’s full of errors that negatively impact its performance.
Conclusion
A successful AI project can transform your business and help you achieve the set business goals. However, AI projects aren’t always successful and can invariably fail. The tips above are some of the things you can do to ensure that your project performs better and helps you solve a need in your business.
References:
“Deloitte Survey: Artificial Intelligence Delivers, but Missteps Can Yield ‘Bridges to Nowhere’”, Source: https://www.prnewswire.com/news-releases/deloitte-survey-artificial-intelligence-delivers-but-missteps-can-yield-bridges-to-nowhere-300734738.html
2. “Strategy For and With AI”, Source: https://sloanreview.mit.edu/article/strategy-for-and-with-ai/
“Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms”, Source: https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/
“Artificial Intelligence Algorithms: All you need to know”, Source: https://www.edureka.co/blog/artificial-intelligence-algorithms/
