Lately, we have seen a significant number of discussions regarding the potential of AI and strategies to incorporate it into the product development process. Specifically, how can we enhance, innovate, and scale in ways we were not able to do before? In this interview, we asked Samra Tanovic, our VP of Analytics and Services, to share her experience and insights on this topic.

Q: Could you tell us about your journey in incorporating AI into products? 

A: Several years ago, when the need for AI functionalities arose within one of our projects, we were faced with a series of questions: How do we enable our teams and build the expertise needed for such tasks? How long will it take? How do we spot opportunities for the use of AI faster? What are the costs?

The key takeaway from this experience is that it’s essential to start with an explicit, well-defined goal that aligns with business and user needs and then to consider how AI could assist in achieving this goal. It’s important to recognize that not all problems require an AI solution. There’s a risk of wasting significant time and resources trying to utilize AI just for the sake of using it. Instead, we learned to carefully examine existing challenges and ask: Can AI solve this problem more efficiently? Is there potential for scalability? Does the benefit of using AI outweigh the cost?

Our journey also taught us that data science projects, unlike standard software engineering, involve a series of experiments that may or may not lead to discoveries or entirely new lines of research. The inherent uncertainties in this process make it difficult to define and control, which is why a unique management approach is required.

Another key aspect we realized is the importance of the data that AI uses and produces. While the size of the training data does impact the model’s performance, data quality often plays a considerably more influential role. Investing in clean, relevant, and diverse data preprocessing turned out to be essential for accurate AI outcomes, making it an investment worth prioritizing.

Finally, in order to build expertise and prepare our company for the rapidly evolving AI landscape, we set up a team dedicated to the research, development, and implementation of cutting-edge AI solutions within existing and new projects. This step allowed us to continue innovating and incorporating AI where it made sense, enhancing both our products and our ability to adapt to future technological advancements.

Q: Could you provide examples where your teams used AI to enhance problem-solving efficiency?

A: The main criterion is to consider processes that are excessively manual, do not scale well with product growth, or areas where customer experiences could be improved through personalization or automation. ML is mostly required in cases when the targeted behavior cannot be accurately expressed in software logic without relying on external data sources.

A few years back, we used AI to analyze text and extract common business attributes, such as business category, address and operating hours from an unstructured text, a process that was previously time-consuming for humans. The efficiency of the validation of the business attributes, which was our main use case, was increased by 80%.

An interesting and simple example involved removing a common UI element, a dropdown menu, and using classification to determine the correct option automatically. Previously, selecting the wrong option led to downstream problems and complicated user experience. However, by leveraging ML to accurately determine the appropriate dropdown option based on the user’s input, we managed to reduce mistakes by 30%. 

Moreover, an intriguing area to consider is threshold replacements. Traditional business rules with set thresholds are often used to trigger specific actions. However, these thresholds can be too rigid and may not adapt well to changing circumstances. AI models, in contrast, can learn from data patterns and make more flexible, dynamic decisions. 

Q: Lastly, drawing from your experience, what are some obstacles that businesses might encounter while implementing AI, and what strategies can they employ to overcome them?

A: One of the biggest challenges is the lack of trust in AI-powered solutions. Many businesses are hesitant to rely on AI for critical decisions because they don’t fully understand how algorithms work or the resulting accuracy is not meeting their expectations. 

AI systems are designed to learn but can make errors or provide inaccurate results based on various factors, such as the quality of the training data, complexity of the task, and limitations of the algorithms used. 

In scenarios where explicit and deterministic results are critical, businesses should carefully assess the suitability of AI models. Hybrid approaches that pair AI with deterministic methods should be considered, along with models with high interpretability, to maintain control and build trust. It is also useful to be transparent about limitations and potential biases. 

Speaking of biases, if not designed and trained with fairness and ethical considerations in mind, AI systems have the potential to perpetuate biases and discrimination. Ensuring compliance with regulations, regular audits, testing for bias and seeking legal advice can help.

Q: Thank you. It was a pleasure discussing this topic.

A: Thank you. I’m excited to see how AI continues to shape and enhance the field of product development.

With over 6 years in the AI field, Atlantbh can help you utilize the potential of AI to optimize existing business operations and products. Find out how we can help, contact us.

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