The purpose of AI initiatives is to leverage advanced technologies to solve complex problems, enhance decision-making, and drive innovation within organizations. By integrating AI, businesses aim to improve efficiency, reduce costs, and create new opportunities for growth. AI initiatives are designed to transform data into actionable insights, enabling companies to stay competitive in an ever-evolving market.

The primary objectives of AI initiatives include:

1. Enhancing Operational Efficiency: Streamlining processes and automating repetitive tasks to save time and resources.
2. Improving Decision-Making: Providing data-driven insights to support strategic decisions and reduce uncertainty.
3. Driving Innovation: Developing new products, services, and business models that leverage AI capabilities.
4. Increasing Customer Satisfaction: Personalizing customer experiences and improving service quality through AI-driven solutions.
5. Achieving Business Goals: Aligning AI projects with organizational objectives to ensure they contribute to overall business success.

1. Overview

In today’s fast-paced business environment, integrating AI into your operations is no longer a luxury—it’s a necessity. But how can you be sure your AI initiatives are truly making a difference? It’s not enough to simply hope for the best; you need concrete evidence that your efforts are paying off.

In this article, we’ll demystify what it means to achieve success with AI. By the end, you’ll have the tools to evaluate whether your AI projects are delivering real value to your organization. With these insights, you’ll be able to make informed decisions about which initiatives to continue and which to reconsider.

2. Diagram

3. Defining Success in AI Initiatives: Beyond Accuracy and Speed

What makes an AI initiative truly successful? Is it about achieving 99% accuracy, lightning-fast performance, or skyrocketing revenue? While these factors are important, a successful AI initiative is one that meets its intended business goals and satisfies its users. Let’s break down the three core pillars of AI success:

1. Model Success: This pillar focuses on whether your AI model performs well both in development and production. It’s about ensuring the model’s accuracy, reliability, and robustness.
2. Business Success: Here, the emphasis is on whether the AI is helping achieve your organizational objectives. This could include improving efficiency, increasing revenue, or enhancing decision-making processes.
3. User Success: The final pillar is about user satisfaction. Are the users happy with the AI solutions? Do they find them valuable and effective?

Although these pillars reinforce each other, the metrics, targets, and minimum acceptable thresholds for each will differ. To assess the strength of each pillar, you need to define specific criteria and measure them consistently.

At any given time, whether during testing or when your AI initiative is live, you should be able to confidently answer the following questions for each success pillar:

• Model Success: Is the model performing adequately in both development and production?
• Business Success: Are you observing tangible business improvements?
• User Success: Are users satisfied with the solution?

If the answer is YES to all three questions, it means your AI initiative is truly successful. However, if one of these pillars is weak, you will likely encounter issues such as customer complaints or stagnant business metrics.

4. Defining Success in AI Initiatives: Beyond Accuracy and Speed

Measuring the success of AI initiatives can vary depending on the circumstances. Let’s explore four common scenarios:

1. Building from Scratch

When developing a solution from the ground up, it’s essential to ensure that it works as expected, starts creating value for your business, and is ready for deployment. Success measurement in this scenario helps you decide whether to iterate on the current solution, fully deploy it, or start over. This evaluation begins during deployment and continues through post-development testing.

2. In Production Without Prior Evaluation

Ideally, you should evaluate the success pillars before your AI solution goes into production. However, businesses often miss this opportunity. Many companies develop and deploy models only to realize later that they may not be delivering value. This is when panic calls from clients come in, often overwhelmed by customer complaints due to poor model performance or failing business metrics. Fortunately, it’s never too late to evaluate success. In this scenario, success measurement helps you decide whether to keep the solution, iterate on it, or start over. If the evaluation results are disappointing, you may choose to pursue an alternative solution or build a new AI solution with different assumptions.

3. Evaluating an Off-the-Shelf Solution

AI vendors often showcase impressive model performance numbers and persuasive case studies. However, their solutions may not have been tested with your data, employees, and customers. Therefore, it’s crucial to ensure that the solution works with your company data and within your workflow. Even for off-the-shelf solutions, you need to ensure that the three success pillars—model success, business success, and user success—are strong.

4. After Full Deployment

Post-deployment, some of the evaluations initiated during post-development testing will continue to be instrumental for ongoing success. These evaluations help inform your company about how well the AI solution continues to create value for your business. 

5. Evaluating AI Model Success: From Development to Production

Model success refers to how well your AI solutions perform specific tasks. For instance, how accurately does your model diagnose lung cancer patients, or what is its false positive rate in detecting duplicate content?

To measure model success, collaborate with your AI experts to establish the best metrics for assessing performance in both development and production. It’s crucial to distinguish between these two stages because a model that performs well in development doesn’t automatically translate to success in production. You need to track performance in both scenarios.

Depending on the application, the way you measure success in development can differ from how you assess it in production. Additionally, you may want to use several distinct metrics in production.

Consider a product recommendation engine that generates personalized product recommendations as shoppers browse. During development and evaluation, you might use metrics like precision and recall on a static development dataset. However, once the model is tested in production, the dataset becomes dynamic. You need to evaluate the recommendation engine in a real-world setting with dynamic recommendations.

In production, you could conduct user studies where specific users rate their recommendations on a scale. You can also track the click-through rate to see if users are engaging with the recommendations. The click-through rate serves as a key metric to measure production performance. It’s important to have at least one metric that you can track on an ongoing basis to ensure model consistency and detect any degradation

Model success refers to how well your AI solutions perform on specific task.  For example how accurate is your model in correctly diagnosing the patients in lung cancer.  Or what is the model’s false positive rate I detecting the duplicate content?

To Measure model success, collaborate with your AI experts to establish the best metrics to assess model performance both in development and production. The reason I make this distinction because a successful model does not automatically translate to a successful model in production. So you need to be sure you are tracking model performance in both scenarios. Plus depending on the application, the way you measure success in development can be different from how you assess model success in production- not always in many cases. Also, you may want to use several distinct metrics in production. 

Lets take a product recommendation engine that generates personalised product recommendations as shoppers are browsing. In development and evaluation. They may use metrics such as precision and recall on the development dataset because its static. 

However, when the model goes testing in production the dataset is no longer applicable. You need to evaluate the recommendation engine in a real setting with dynamic recommendation. In production you could potentially perform a user study where specific users rate their recommendation on a rating scale/ you can also tack the click-through to see if the users are engaging with recommendations in this case. The production perform is measured using click-through rate. It is important to have at least one metric that you can track on an ongoing basis to ensure model consistency and track and degradation.

6. Achieving Business Success with AI: Measuring Impact and ROI

Business success in AI is about understanding how automation impacts your organization. Does it save time, reduce human errors, or prevent customer churn? To measure business success, you’ll use the Return on AI Investment (ROAI) established during the planning phase of your AI initiatives. ROAI is tracked both during testing and after deployment. Here are the steps to measure business success once you’ve determined the relevant business metrics:

1. Establish a Baseline: Determine the starting values for each metric you aim to improve.
2. Set Expected ROAI: Define the expected return on AI investment.
3. Continuously Track ROAI: Monitor the ROAI consistently to see if there’s progress from the baseline.
4. Track Percentage of Expected ROAI Reached: Measure how close you are to achieving your target improvement.

Baseline measurements provide the initial values from which you hope to improve. ROAI indicates whether there’s progress from the baseline. The percentage of expected ROAI reached shows how far you are from your target improvement, guiding the path forward for future iterations

7. Connecting Business and Model Success in AI

Model success is a key driver of business success. For instance, achieving 80% model accuracy might result in a 20% ROAI, while 90% accuracy could lead to a 25% ROAI. As you improve your models, the ROAI typically improves as well.

However, minor model improvements may not significantly impact your ROAI and can make it seem stagnant. In such cases, you might need a substantial accuracy boost to see a change in ROAI. This could involve building new models with better assumptions, data, and algorithms rather than iterating on existing ones. Sometimes, the problem may be too complex to solve with current AI tools and technologies, requiring you to wait for advancements.

Conversely, in some scenarios, even low-performing models can sufficiently move your ROAI towards its target. For example, a recommendation system with a 30% recall at 10 in development means that out of ten items recommended, only three are relevant. Despite appearing low, this could still push your sales-related ROAI beyond expectations.

The bottom line: Don’t wait for a perfect model to test business success, but ensure models meet minimum thresholds.

8. Ensuring User Success in AI Initiatives

Most AI systems interact with people—your customers, employees, and vendors. Why is user success crucial for AI initiatives? Regardless of how well your model performs or how much of the expected ROAI is achieved, if the AI users are not satisfied with the solution, there will be a risk of poor user adoption. Evaluating user success can also reveal a range of unknown model and non-model issues through user feedback.

Interviews

One way to collect qualitative evaluations is through user interviews. Select a diverse group of users and develop a series of questions in collaboration with your development teams. The interview participants should ideally come from different business units, use the solutions differently, or be at varying seniority levels.

Surveys

Another approach to collecting qualitative feedback is through surveys. If you have many users consuming the AI output, surveys can help you gather feedback at scale. Like interviews, you can formulate different questions, including open-ended ones. Analyze the survey results to find common themes in user feedback and use these themes to guide improvements.

For both surveys and interviews, if the feedback is filled with major complaints or concerns, it indicates that the user success pillar is still weak, and your AI initiative is not ready for deployment. If the feedback is mostly positive with minor issues that can be easily fixed, then you know your user success path is on the right track. The bottom line is to assess the problems and risks to determine if the user success pillar is strong enough to warrant deployment. 

9.Addressing Non-Model Factors in AI Success

While model success significantly impacts business success, other confounding factors often influence the overall success of AI initiatives. These factors can manifest as issues in the different success pillars.

Companies often tend to blame models when users are unhappy or the ROAI is disappointing. This is an easy way out since models don’t get offended, but it’s usually incorrect. Most of the time, if models are well-developed and tested, poor ROAIs are often caused by non-model factors. Additionally, while some user complaints may require model improvements, many are related to non-model issues. Common non-model factors impacting AI success include:

Poor User Interface: A complicated or unintuitive interface can hinder user adoption and satisfaction.

Lack of Training: Users may not be adequately trained to consume and interpret AI outputs.

Wrong Metrics: Using incorrect metrics to measure ROAI can lead to misleading conclusions.

Network Latency: Delays in accessing AI outputs due to network issues can frustrate users.

There can be many other non-model issues that will arise as you evaluate your business and user success. Non-model issues are unavoidable, but by evaluating the three success pillars, these issues will naturally surface, allowing you to address them promptly and improve your AI outcomes.

10. Conclusion

In conclusion, the success of AI initiatives hinges on a holistic approach that considers model performance, business impact, and user satisfaction. By evaluating these three pillars—model success, business success, and user success—organizations can ensure their AI projects deliver real value. Additionally, addressing non-model factors and continuously measuring success across different scenarios will help in refining AI solutions and achieving sustainable growth. As AI continues to evolve, staying adaptable and responsive to feedback will be key to maintaining a competitive edge and driving long-term success.