Artificial Intelligence is shaping the future of business innovation, driving the development of products with capabilities that were unimaginable until recently. At Keepler, we are well aware of this.

As in any sector, every technological advancement requires an adaptation of all the areas it affects.

Managing teams that develop AI products is something that Keepler has taken very seriously from the beginning. That is why, from the “Delivery” area, we consider the combination of highly specialized profiles, the dependency on high-quality data, and the need for a constant experimentation environment.

This combination makes managing these teams particularly complex.

Team Structure and Roles

In AI teams, business profiles are highly involved throughout the development process. Defining key roles properly is essential:

• Data Scientists: Responsible for data analysis and for creating and training predictive models.
• Data Engineers: Extract, filter, and prepare data so that it can be properly leveraged.
• Machine Learning Engineers: Positioned at the intersection of AI/ML knowledge and software development. Their main role is to bridge the gap between data scientists and the architecture and data engineering teams, translating models and insights generated by data scientists into robust and scalable ML pipelines.
• Cloud Engineers: Experts in cloud infrastructure, ensuring scalability and automation of the model lifecycle. They are essential for continuous monitoring and reliable deployment.
• AI Product Managers: Align developments with business strategy, defining objectives and ensuring that AI delivers real value to the company.
• Data Experts: Ensure the quality, availability, and ethical use of data, a critical aspect in avoiding biases in AI models.

Culture and Work Methodology

Managing these types of teams requires flexible methodologies, as AI development involves constant experimentation. Therefore, the following should be considered:

• • Adapting Agile: It is crucial to understand that developing AI products involves a significant amount of time dedicated to research. Therefore, a consensus agreement must be reached among all involved parties, stating that in many cases, the value delivered will result from this research rather than a functional piece of software.
  • Encouraging Communication: Technical and business profiles must be perfectly aligned to ensure models are useful and viable. This is essential to translating business requirements into achievable technical objectives.
  • Promoting a Culture of Experimentation: Uncertainty is inherent to AI. Failing fast and learning is essential for product evolution. Encouraging curiosity and innovation within the team can make the difference between a mediocre and a disruptive model. Additionally, the heavy reliance on data must be considered. It should be recognized that data may not always allow business objectives to be met (either due to insufficiency or a misalignment between patterns and objectives). Therefore, teams must be open to pivoting and leveraging insights gained to propose alternative cases or scope changes.

Common Challenges and Strategies to Overcome Them

• Data Dependency: Data quality and availability can be a bottleneck. It is crucial to establish data management strategies from the outset, including proper storage, quality annotation, and continuous dataset updates.
• Misalignment with Business Goals: To avoid unrealistic expectations, AI Product Managers must translate business needs into viable solutions. An effective strategy is involving key stakeholders at every stage of development.
• Scalability of Models: Moving from prototype to production requires the right infrastructure and robust MLOps processes. Tools like Kubernetes and TensorFlow Serving can facilitate this transition.
• Ethics and Algorithmic Bias: Incorporating fairness and transparency reviews to prevent biases in AI models. Implementing regular audits and ethical evaluation frameworks is a recommended practice.

Metrics and Team Success

At Keepler, we do not measure the success of an AI team solely by the accuracy of the model but also by its impact on the business. Key metrics include:

• User Satisfaction Index: Measures user acceptance. As with any other software product, high user satisfaction indicates that the delivered product meets real user needs.
• Training and Inference Time: The more we optimize model efficiency, the better the user experience, and the lower the computational costs.
• Business Impact (ROI – Return on Investment): Reduction of costs, improved decision-making, process optimization, and revenue growth are some of the parameters measured to ensure a positive business impact.
• Model Quality: For Keepler, this is one of the core metrics. Special attention must be paid to possible model degradation over time. Therefore, implementing periodic retraining processes helps maintain model accuracy in changing environments.

Conclusion

Managing teams developing AI products requires special attention to synchronization and understanding between the technical and strategic sides. To achieve this, it is advisable to rely on agile methodologies that foster a creative and continuous learning environment.

Keeping the team motivated by making them the protagonists of their achievements and providing them with the necessary tools to develop a strong understanding of user needs is essential.

The use of Artificial Intelligence is proving that organizations can gain a competitive advantage by enhancing innovation and increasing efficiency in various business areas.

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