While working with our clients, we have seen the transformational effect artificial intelligence (AI) has on customer experience, cost reduction and profitability. Considering the opportunities and advantages that AI delivers, it’s not surprising to witness its growing adoption globally. Results from Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning, showed that 76% of enterprises prioritize AI and machine learning (ML) over other IT initiatives in 2021.
However, we have also seen how AI deployments can run into headwinds. Executives start with many hopes and expectations but eventually struggle to put their models into production or ensure that the end users are actually using the intelligence to drive actions and impact. According to a white paper published by Pactera Technologies in 2019, about 85% of AI projects fail eventually.
Based on experience, we have realized that most executives start with a “data science lab” approach to launch their AI project. In their minds, AI is about developing some ML models which one of their data analysts or data scientists can easily accomplish in a few months.
However, because this is a “lab” siloed from other key components of the entire AI implementation chain, soon they realize that what started as a few-months-long project with an OPEX budget now stretches into years in some cases and is not delivering actual value despite overshooting the budget.
Here are a few key observations about why some AI projects face deployment delays and budgetary overruns and fail to meet business goals:
A successful ML implementation requires all the talent and resources in place. This includes a data scientist to create and deploy models, a data engineer to align the model with IT, a developer to deploy logic, validate and test and a UI developer to present business insights. Then business users need to use the insights and make quick decisions daily.
Most companies do not have the necessary skills in-house. Even when an organization has all the resources, they may be aligned differently, functioning under different teams. Such a fragmented structure may lack coordination, resulting in inferior outcomes, increased costs and deployment delays.
My recommendation is the AI leader must set up a dedicated, multifunctional team to drive the AI project. This team can comprise data scientists, business analysts, an IT engineer, a data engineer and a full-stack UI developer. The team strength can vary based on project scope but inclusion of all these roles creates a synergy that is paramount for success of any AI project.
Enterprises should upskill their in-house talent and build an AI team with a long-term vision. They should expand and diversify their AI talent and explore how they can leverage their vendors.
Developing and deploying ML models and ensuring that they drive real actions require a considerable time investment and are an ongoing process. It involves multiple stages and many associated challenges.
