Recent studies indicate that the majority of investments in artificial intelligence (AI) do not meet production. This presents a significant challenge as negative experiences may discourage stakeholders from supporting future AI-based projects, and those less experienced may not adequately assess associated risks. In future posts, we will delve into the underlying causes of low yield in AI-driven projects and propose methods for mitigating such issues. In this post, I would like to highlight the main factors that contribute to the successful implementation of an AI project that reaches production.
To ensure a successful AI project that reaches production, I believe that there are five important factors that must be taken into account:
Finding Talent - Task fit. An effective AI project team should consist of individuals with a range of skills, such as applied science, software development, domain expertise, product understanding, and more. However, it's also essential to match team members' qualifications and experience to avoid over- or under-qualification. Note, that while AI projects may involve scientific research, the ultimate goal is to solve a problem in a practical and straightforward manner. A production-focused mindset, rather than a purely research-oriented mindset, is necessary to ensure success in delivering practical solutions.
Total complexity analysis is an essential process for evaluating and comprehending the entirety of a problem or situation, including all its interconnected components. It is critical to consider not only the technical aspects, such as algorithms and the code hosting framework, but also the legal and domain-related factors that may impact the project. By adopting a holistic perspective, it becomes possible to identify potential bottlenecks, risks, and opportunities to leverage existing tools. This comprehensive understanding of the problem at hand allows for the development of effective and efficient solutions, while also providing a clear understanding of the resources and expertise that may be required to overcome any obstacles and achieve the desired outcome.
Clear and well-defined expectations are a must-have for an AI project, not only for stakeholders but also for all the disciplines involved in its execution. By setting specific goals, actionable tasks, and outlining responsibilities, all parties can understand their role and what is expected of them.
Have a return of investment (ROI) perspective, AI projects require a careful evaluation of the actual value that deep models can bring to the table. This approach forces decision-makers to consider what kind of AI capability is necessary for the task at hand, and if a simpler alternative would suffice. For example, one could determine whether a large custom made deep neural network model is necessary, or if simpler models can be used initially until customer traction is gained. This approach allows for a more informed investment decision, and helps to mitigate the risk of expensive investments becoming obsolete either due to change in demand or as as new technologies emerge.
A clear and well-defined working method is essential for a successful project. It helps to ensure that everyone involved understands their roles, responsibilities, and ownership rights. However, there can be tension between having clear rules and maintaining a sense of ownership and creativity. Finding the right balance between structure and flexibility is key. As the field of AI is relatively new, there is no "textbook" approach and organizations may need to experiment and learn through trial and error to find a method that works best for their specific setting.
In future posts we will break down those elements as well as propose methods to increase the yield in AI-driven projects on their way to production.
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