8 Common Mistakes in Implementing AI/ML

Interest in AI/ML is at an all-time high due to the influence of generative AI solutions, but deciding where to invest your time and resources and how to scale up into production can be a challenge. These are common mistakes that mid-market and enterprise organizations encounter during this process.

1. Lack of clear objectives: Undefined objectives or a lack of alignment with your business goals can lead to a POC that fails to deliver value. The lack of focus makes it nearly impossible to demonstrate tangible value that can be scaled up into production.
2. Not enough skilled talent: If your in-house team has insufficient expertise in developing an operational framework in data science, AI, and ML, you may encounter project delays, suboptimal models, and inefficiency that can erode trust with business partners.
3. Overlooking change management: AI/ML solutions can drastically change end user workflows and entire roles. Without sufficient change management in place, you’re likely to encounter resistance, lack of adoption, and misunderstandings about the value of AI/ML.
4. Poor infrastructure scalability: Can your infrastructure even keep up with scaling AI/ML solutions into production? Neglecting infrastructure planning results in limitations in your performance and overall scalability.
5. Insufficient model evaluation and monitoring: Your AI/ML models need proper assessment and ongoing monitoring to ensure they’re a good match for your use case and are not suboptimal or outdated. Models that appear correct but lead to incorrect results can be difficult to spot and fix once significant investments are made.
6. Unrealistic expectations: Be honest – are you expecting your AI/ML POC to deliver immediate and miraculous results? The process will likely be iterative and will include and require experimentation and continuous improvement to reach your business goals.
7. Lack of ethical considerations: The ethical implications of AI are a hot topic and deserve consideration with every deployment. Biases, privacy concerns, and societal impact are three important areas to think about in this process.
8. Data quality and governance issues: The “Garbage In, Garbage Out” principle still applies to AI solutions. When foundational data quality gets neglected, it can have a material impact on the accuracy and reliability of models. Additionally, without the right data governance in place, compliance violations become a significant risk.

Ready to learn more about the challenges facing AI/ML adoption and preparing your data for it? Get our white paper “Is Your Data Ready for AI/ML” to get started on the right foot with your pilot and proof of value initiatives.