4 Benchmarks for Your AI Adoption

Disclaimer: This article was co-authored by human writers and OpenAI’s ChatGPT. At EIT, we pride ourselves on activating the potential in people and technology. To do this, we strive to use cutting-edge tools like Generative AI to their fullest potential. 


Artificial Intelligence (AI) has transformed industries. AI will continue to revolutionize how businesses run and make decisions. But effectively using AI requires strategic planning.

Organizations plan for AI implementation by establishing benchmarks, key performance indicators (KPIs) to industry excellence. These KPIs vary based on your company’s goals, and they provide vital feedback to help understand progress and position in the industry.

Here are AI transformation benchmarks one may consider tracking that are vital when understanding AI performance.

  1. The quality of the data AI draws from.
  2. The AI’s accuracy when using that data.
  3. The ethicality and transparency of the AI.
  4. The AI’s effect on business metrics.

By adopting these benchmarks, businesses position themselves for successful AI adoptions.

Data Quality and Preparation

AI cannot conjure material out of thin air. Rather, the model is fed the program data it uses to generate material. Because of this, high-quality data is the foundation of successful AI transformation.

Data quality KPI will comprise data accuracy, completeness, and relevance. All three are all crucial to ensure that your AI model work from a solid foundation.

Data accuracy is perhaps the most straightforward. Data accuracy refers to how well your data reflects reality.  A precise dataset is without errors and will be more likely to provide useful results.

Data completeness, comparable to data accuracy, focuses on whether a data set has all of the information the AI model needs. Gaps in a dataset challenges the model’s ability to produce accurate results.

Pretend you were training a model to help predict health outcomes. Now imagine that there was a gap in your data from 2018-2021. That would seriously affect how your model perceives trends.

Finally, data relevance asks if all the data is useful for the AI to make decisions. While more data may seem better, it only is when that data isn’t distracting to the AI.

Let’s say a healthcare company wants to predict the odds of someone developing heart disease. The organization would want to train the AI with data from real-world patients. That would include the age, cholesterol levels, lifestyle habits, and family history.

But, what if they included a variable like eye color in their dataset? The model may get confused and falsely identify patients one way or another based on that eye color.

A high data quality benchmark requires considering how accurate, complete and relevant your data is. Tracking these KPIs ensures that your AI is set up to deliver valuable results.

Performance and Accuracy

Of course, the quality of the data only matters if the AI can use it properly. So, measuring performance and accuracy are crucial benchmarks for evaluating AI effectiveness.

Organizations should set up benchmarks to measure AI model performance. According to DeepAI, a common metric to measure this is an F1 score.

An F1 score considers an AI’s precision and recall when generating results. Precision measures how likely an AI-generated result is to be correct. Recall measures how much of the correct data a model can produce.

Imagine you have a new product that you estimate interests 1000 people on your mailing list. You ask an AI model to identify those people on your list and send them a coupon for the product.

Let’s say the AI only sent coupons to 100 people, but all of them were interested. That would indicate high precision and low recall.

By contrast, say the AI sent coupons to everyone interested, but also 1000 people who weren’t. that would indicate high recall and low precision.

An F1 score close to 1.0 would show that your model had very high precision and recall. Yet, depending on your specific needs, it may be more important to focus on one over the other.

You might decide it’s more important to reach your interested customers than to avoid uninterested ones. In that case, you value recall over precision.

But, let’s say you want your AI to filer out spam emails instead. While you want minimal spam in your inbox, you also don’t want the system to filter out any important emails. So, you may prioritize recall over precision.

When benchmarking for your AI transformation, you should track your model’s F1 score. It can give you a good idea of how accurate it is on the whole.

Yet, you should also track precision and recall separately. This can help you focus on getting results that are more useful to you.

Transparent and Explainable AI

As we discussed earlier, AI learns from large amounts of data fed to it by humans. While this allows AI to draw upon a range of information, bias from its training data can bleed into results.

For example, Amazon trained a resume-screening AI using data from a decade’s worth of job applications. Most of these applications had come from males. The result? The AI model assumed that females were worse candidates and discriminated against them.

Now, it wasn’t anyone’s intention at Amazon to create a misogynistic AI. Yet, it reminds us how important setting ethical benchmarks are when implementing AI. In particular, there are 2 metrics well worth tracking, transparency and explainability.

Transparency is how easy it is to see what data sources an AI uses in generating results. Consider a bank using an AI to approve or reject loan applications. If employees and regulators can see the algorithms that the AI uses and its training data, that model is transparent.

Explainability goes a step further. It refers to how easy it is to understand why an AI came to a particular decision. If someone applying for a loan can understand the exactly why the AI approved or rejected them, that model is explainable.

Transparency and explainability-focused benchmarks involve explaining how AI models arrive at their decisions. This includes tracking what sources the AI draws from and how it prioritizes results. This helps us interpret the AI’s decision-making process and make necessary adjustments.

Business Impact and ROI

Measuring the business impact of AI initiatives is crucial for evaluating their effectiveness. Organizations should define benchmarks for assessing the value created by AI.

Unlike the other benchmarks that we’ve discussed, which KPIs you choose to track will depend on your specific goals. For example, lets say your goal was to improve customer experience by implementing a chatbot on your website. Measuring things like customer satisfaction, the time it takes to solve a problem, and more would be appropriate.

The key to this is defining what success looks like for your organization up front. Asking questions like “what would have to change to consider this transformation a success?” are crucial to choosing which KPIs to track.


Effectively using AI requires organizations to set up meaningful benchmarks. These should measure data quality, accuracy, ethicality, and business impact.

By checking these benchmarks, organizations can drive continuous improvement in their AI implementations. This unlocks the full potential of this transformative technology.

If you’re interested in implementing AI in your organization, ExperienceIT can help. Now a part of Globant’s network of AI experts, ExperienceIT can find the AI solution that is right for you. To learn more about the solutions we offer, visit us here.