Don’t Start from Scratch

Starting from scratch is not the best of ideas most of the time.

Too often, we assume innovative ideas and meaningful changes require a blank slate. When business projects fail, we say things like,  “Let’s go back to the drawing board.” When we consider the things we’d like to change, I assume each of us thinks, “I just need a fresh start.” However, creative progress is rarely the result of completely erasing all previous ideas and completely redoing the work from zero.

For the sake of argument…

There are theories which state that the feathers of birds evolved from reptilian scales. Ages, millennia after, scales gradually became small feathers, which were used for warmth and insulation at first. Eventually, they evolved/developed into larger feathers capable of flight.

There wasn’t a magical moment when the animal kingdom said, “Yup; the idea of changing our arms into wings has its merit; let's try creating a species that flies.” The development of flying birds was a gradual process of iterating and expanding upon ideas that already worked.

Back to our “fresh start”; before throwing out everything you did – just stop and think again for a moment.

The modern business landscape hasn’t quit the quest for effective performance management systems (PMSs). PMSs have been, and still are, an important tool for the success of any organization. And one solution was to reshape the traditional approaches and performance management practices through the use of AI and machine learning.

Much like the example of bird feathers evolving for optimized flight, the performance management system has integrated AI and machine learning to develop a more modern, data-driven approach. Traditionally, performance management systems emphasized alignment with company goals. Now, however, machine learning is enhancing this framework by moving beyond static Excel files to robust data analytics, enabling deeper insights collection. This shift supports data-driven decision-making and fosters a performance-oriented culture.

It’s not a secret that we’re living in a data-driven age – socially, politically and economically. AI and machine learning are processing huge amounts of data, which leads to the discovery of new trends and patterns that would’ve otherwise remained hidden. AI and machine learning-driven processes facilitate not only continuous data collection but also its interpretation, enabling real-time feedback and supporting decision-making that aligns with organizational goals. These technologies assist in identifying performance trends, uncovering skill gaps, and predicting areas where improvements can be proactively implemented.

But before moving further, let’s be extremely clear about one thing: the success of AI and ML models is tied to both data quality and quantity and their integration into Performance Management Systems. That’s no easy feat!

Undoubtedly the benefits of integrating AI and ML are real. However, much like our feathers example, it is far from being without flaws and here are some of the most frequent issues that always need to be considered:

1. Data privacy

The adoption of AI and machine learning involves first and foremost processing and analyzing large amounts of data, including sensitive information. Ensuring there’s no breach in relation to existing data privacy regulations and safeguarding against unauthorized access are maybe the most daunting challenges. To deal with them, companies should implement strong data privacy measures like encryption and access controls. In addition, to build trust among users, data handling policies must be first and foremost transparent.

2. Model interoperability

AI and machine learning models, such as deep learning models, often require more interoperability and having a clear picture of how these models interact in specific decisions is definitely challenging. Consequently, logical concerns are to be raised on accountability and transparency. To tackle this issue, making sure all stakeholders understand and trust the insights generated by the models must be a priority.

3. Continuous learning

No AI or machine learning model is stable and independent forever; changes within the company environment must be reflected in the models as well. Implementing mechanisms for ongoing model training involves regular monitoring, feedback loops, and the insertion of new data to improve the model's relevance over time.

4. Integration into existing systems

Integrating AI and machine learning with existing systems may require some redesigning to accommodate specific needs. This takes up time and other resources as companies need to assess the existing infrastructure, start planning for the necessary modifications, while at the same time, making sure that the existing infrastructure is completely compatible with the AI/ML solutions.

5. Low quality or insufficient data

AI and machine learning models depend on high-quality, diverse, and objectively useful data. Low quality and insufficient data produce inaccurate models and predictions; therefore, companies should prioritize data quality through data cleaning, processing, and validation processes – this also takes time. Organizations should establish data governance practices as well to maintain the reliability and integrity of their datasets.

6. Underfitting of training data

Particularly for machine learning, this process occurs when data is unable to establish an accurate relationship between whatever data is inserted and output variables. It’s like trying to get into the clothes you wore when you were a kid. Data is too simplistic and too scarce to pinpoint a clear relationship. But there are solutions to avoid getting into this kind of trouble: (a) extend the training time until the results are optimal; (b) improve the model; (c) add more variables to your data sets.

7. Overfitting of training data

Overfitting is the opposite case. If previously we’ve spoken about lack of data, overtraining it with huge data sets can reduce the model’s performance. Usually, it signals that the algorithm is based on redundant and/or insignificant data, ending up in a reduced overall performance. As with the previous point, this issue can also be fixed by using some of the following suggestions: (a) use data augmentation techniques – AKA analyzing the datasets from different perspectives or contexts; or (b) you can simplify the model by removing some features from it.

8. Integrating AI and machine learning in performance management systems

The evolution of the performance management system has been enhanced through the infusion of more agile solutions brought by AI and ML technologies. They have allowed organizations to entail new complex predicting, optimizing and automating processes in real-time. Additionally, performance management went through a series of framework adjustments: moving from periodic to continuous evaluations and data-driven maturity assessments.

The new enhancements brought on by both AI and machine learning equipped organizations with real-time intelligence into performance metrics, therefore making it possible for them to improve their accuracy and leading to better strategic decision-making.

Performance management is now a strategic asset.

If your company isn't quite ready to implement AI and machine learning solutions for evaluating internal processes and tools, a maturity assessment service can be an effective starting point. We can help you assess your organization’s current maturity level and provide a clear roadmap for improvement, addressing performance challenges and building on your resilience together.

And as always, to get the latest news and developments in Performance Management and Maturity Assessments follow us at

Linkedin and Meta.