Building your AI team: The roles your enterprise needs
Artificial intelligence (AI) isn’t just about frameworks, data sources and pipelines—it’s also about people. Enterprises embarking on an AI journey have a much greater opportunity for success when they have executive leadership support and the right talent in key AI roles.
You know your business best and are in a position to make the right choices for your company. To help you think about your AI journey, here are suggestions from IBM concerning who should be on your AI team. Our experience suggests that these specific roles should be filled to get buy-in on the project and create a successful solution.
Enterprises that have successfully implemented AI have strong executive leadership support for the new technology. A C-suite sponsor can take an active role to ensure that AI projects are aligned with the strategy of your company. The executive sponsor is responsible for obtaining startup funding and can partner with your solution provider to act as project director.
Sometimes referred to as a cognitive solutions architect, the person in this role is responsible for operationalizing the entire suite of machine learning and deep learning models within the IT framework and systems at hand. Like data scientists, systems architects have a solid knowledge of these models and their application, but with a more systems-oriented focus. In addition, they need to understand the business of your company from an operational perspective.
The value of the output of machine learning models is highly dependent on the quality, depth and breadth of data. Ensuring data quality means the data engineer role is vital to your AI success. Data engineers are responsible for defining and implementing the integration of data into the overall AI architecture. Given the complexity of this role, a team approach is recommended. Skills include experience with data platforms including SQL and NoSQL databases.
At its heart, data science is focused on exploring data to extract actionable information for making business decisions. Data scientists generally have a broad technology background, and often they have a degree in a science, technology, engineering and mathematics (STEM) field. The job calls for math aptitude and coding skills as well as critical thinking and problem-solving abilities.
Development, quality assurance and operational engineers—collectively referred to as DevOps—are professionals with experience in both application development and deployment. DevOps engineers work with architects, developers, data engineers and the data scientist to ensure solutions are rolled out and managed.
Business analysts use results from the data science models and often act as “translators” between the business users and the machine learning team. For example, a business analyst might work with the marketing team to understand what the team needs to target customers, and then work with the machine learning team to guide their work.
The role of education and training
In many companies, these core AI roles can be filled through education and training of existing employees and recruitment of others as needed. Employees may also need help in using the results of machine learning as part of their day-to-day activities. Employee education can be accomplished through a combination of in-person and online courses in addition to hands-on experience. Bringing in experienced experts to work with employees on delivering the first use cases is recommended.
IBM offers end-to-end solutions including consulting services, training, tools, platforms and support for enterprises, no matter where they are at in their AI journey. IBM Power Systems offers a portfolio of AI platforms, including accelerated servers with built-in GPUs for faster time to AI and insight discovery.
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