For a long time, AI was mainly a playground for researchers and scientists. The focus was on creating tools for researchers to build better models and explore new areas rather than operationalizing AI. Now that organizations across various industries have adopted and implemented new generative AI technologies, IT teams are having to learn new processes and technologies at a pace more rapidly than ever before.
IT professionals, developers and data scientists need to be empowered with the skills to overcome the challenges associated with developing and deploying AI/ML applications that organizations may face. Without the proper tools and practices in place that are obtained from specialized training for the specific AI applications or platforms being used, the skills gap can potentially have a negative impact on AI innovation.
Effects of the skills gap
As IT complexity continues to increase, the skills gap is also increasing across many organizations that are trying to innovate. This complexity demands a higher level of IT skills to plan and maintain, which can inject risk and technical debt into organizations that lack the required expertise. IT teams are now faced with the challenge of how to adapt to changing IT landscapes while being limited in budgets and IT skills.
According to a 2024 study, 81 percent of IT professionals think that they can use AI, but only 12 percent actually have the skills to do so and 70 percent of workers likely need to upgrade their AI skills. There are a number of negative impacts that organizations are seeing as a result of the skills gap, including lower levels of governance, increased risk of cyberattacks, and a greater chance that the AI applications aren’t being optimized for the best results. Having scalable, flexible, citizen-enabling IT platforms and the right expertise to maximize the impact of the technology is crucial to keeping pace with the rapid evolution of advancements like cloud and AI.
Upskilling your IT team
To set your IT team up for success, the first step should be to provide thorough training on the specific technology that they will be using so that they can familiarize themselves with the main features, architecture, and components of the platform. A good training program will allow IT professionals to build core skills for using the AI platform, which will help them learn how to train, develop, and deploy machine learning models. Giving your team access to hands-on experience will equip them with the knowledge and resources to gain the competency to manage and troubleshoot issues as they arise.
Many teams already have flourishing AI models, but many of them have not yet operationalized solutions with robust AI model training, versioning, and deployment backend. When it comes to operationalizing an AI/ML model, bringing it from training to deployment is not a trivial task, and it often requires organizations to bridge the skills gap between data scientists, engineers, and business stakeholders, which is key to fostering alignment and effective collaboration.
Organizations will often have a division of expertise where data scientists, engineers, and business stakeholders possess distinct skills and knowledge. This division can cause problems when models are transferred between teams, as the necessary skills and understanding may not be uniformly distributed.
Addressing these gaps requires ongoing education and cross-training, so all team members have a basic understanding of each other’s roles and tools. Investing in cross-team skill building and training will make the model development and deployment processes smoother and more effective.
Whether you have members on your team who are new to AI or have years of experience with it, it’s likely that they need to learn more about the current IT landscape and the hurdles they may face when developing and deploying AI/ML applications before taking on new AI initiatives. Businesses have the responsibility to elevate their workforce with the skills and knowledge to thrive in this era of AI.
