By: Matt Fornito, Head of AI, Trace3
As we continue to see the Healthcare and Financial Services industries advance in artificial intelligence (AI), many other verticals are beginning to follow suit. But like any digitally transformative journey, the AI Maturity Journey requires a deep understanding of your current hardware, software, data, talent, and workflows to know what is necessary to be successful.
When it comes to the Five Stages of the AI Maturity Model (a framework I created to help AI and IT leaders gauge how advanced their AI strategy and resources are), most people don’t realize it’s often broken processes that keep plans from evolving. In this blog, I will uncover the five most common pitfalls that businesses encounter when trying to implement AI technology and advance digital transformation within their organization.
- Data and people are often siloed. Business teams don’t necessarily know what data is collected, and data teams aren’t always sure what business problems need to be resolved. To overcome these silos, everyone must first align on the business problem and understand the key components to analyze and solve it. Once aligned, it is critical to execute a data integrity and AI readiness check. This process allows you to make sure the infrastructure is performant enough and the dataset is large enough and reflects the features to build an accurate AI model.
- There is a data science talent shortage. With the increasing demand for data science professionals, organizations are implementing programs to create their own ‘citizen’ data scientists or are hiring new graduates. However, there is still a gap when it comes to finding exceptional talent – especially in terms of deep learning expertise.Moving forward with a dedicated team of experts, like those at Trace3, will help you accomplish your objectives. Our data scientists can develop both machine learning (ML) and deep learning (DL) models. They are experts in GPU acceleration and high-performance compute who can identify and understand the business problems, identify optimal software stacks and hardware architectures, and build highly performant models.
- Just getting started with GPUs. Even when businesses have big data, complex models, and a team of data scientists, getting started with GPUs can be challenging. If you have several data scientists, you will likely need GPU acceleration and faster storage and networking. Data scientists (with GPU acceleration) can more rapidly collect and explore data to help drive and answer a problem. If done correctly, this time-consuming process leads to incredibly accurate insights, but understanding how long these processes take— and how to optimize code for GPU acceleration—is crucial for a successful implementation.
- Building deep learning algorithms and scaling AI. Qualified data scientists and data engineers are critical when building and scaling models into production environments. Even if a data scientist could build a model on a small subset of data, being able to scale that model with proper processes in place prevents them from making it into production. Re-evaluation of the model may need to happen every few months—depending on the amount of data collected and changes to the underlying data—to ensure optimal accuracy and speed.
- Implementing AI into the organization. Even when the right hardware, software, and data science talent is in place, an organization’s acceleration of AI can still fail without an AI Champion’s support. An AI Champion is someone that understands their business exceptionally well and cares enough about AI’s advancement to ask the big questions like: How can we build better models? How can all of us leverage this platform to be more successful in a much faster way? And what infrastructure or software will help drive faster insights? The most common AI Champions that Trace3 collaborates with are usually in a C-suite, VP, SVP, or Business Unit Leader role but do not always need to be senior management to serve as vital support when implementing AI into the organization.
As a former Data Scientist and someone who ran their own company, I’ve seen how experiencing these pain points while trying to implement an AI strategy can deter IT leaders from moving forward, which is why I created the AI Processes Pipeline. It is a process flow to help organizations understand all the steps in an AI Journey, how to be successful, and how to mitigate risk when implementing AI. To learn more about this process, check out my presentation from the Trace3Virtual Innovation Summit, where I take a closer look at these common pitfalls and failures that prevent successful AI integrations across organizations.