Ask any MSP, and chances are they will have a list as long as their arm (possibly both arms) of customers asking for AI-powered services. MSPs are under increasing pressure to meet this worldwide demand—and even for those who were ahead of the AI curve 10 minutes ago, staying up to date with its rapid evolution has revealed a stark reality: a skills gap.
What do we mean by that? Think of the scene in the James Bond film Skyfall when Q is trying to decrypt Raoul Silva’s computer: “Every time I try to gain access, it changes.” The overall premise is the same for MSPs today: AI is constantly evolving, and it’s an ongoing mission for us humans to keep up with the pace of developments. (The myth of Sisyphus comes to mind as well, but the 007 image is more fun.)
With this challenge comes a great opportunity for MSPs: as customers increasingly expect expert AI consulting and seamless integration of intelligent tools with their systems, MSPs are in a unique position to upskill themselves and get ahead of the game.
The AI Skills Gap in MSP Operations
MSPs who offer AI services report an average increase in service revenues of 20-30% year-on-year, according to a recent report by Lansweeper. Yet many MSPs find themselves caught in a dual struggle: meeting heightened customer expectations for AI coupled with a lack of knowledge within their own internal workforce—from staying up to date (and even ahead) of innovation to coordinating AI disciplines like data science, software development and cyber security.
The numbers prove that the struggle is real. 80%t of MSP respondents to Barracuda Network’s The Evolving Landscape of the MSP Business 2024 report said they needed significant or notable improvements in their knowledge and application of AI products and services, while Lansweeper’s survey of 195 MSPs across North America and Europe reported 51.8% of respondents citing a shortage of skilled AI professionals as a significant challenge.
With MSPs at the forefront of the AI revolution and rollout, it’s understandable they’re keen to provide not only the tools, but also understand the tools, too. The most effective digital transformation leaders understand that MSPs who proactively address the AI skills gap have the power to transform a potentially debilitating weakness into a competitive strength.
What Do We Mean By ‘AI Skills’?
AI skills are a mix of the hard and soft skills necessary to understand and confidently—not to mention ethically— apply AI. Hard skills include technical knowledge, which requires mathematical and scientific-related understanding; as well as expertise in programming languages (like Python, C# and R) as well as AI concepts and algorithms. There’s the analytical element too—knowing how to understand and interrogate unstructured data, for example—as well as knowledge of software engineering and systems.
Then there are the soft skills. This expertise is critical when it comes to communicating complex AI concepts to those customers who may have limited technical understanding, thinking critically to solve complex problems on behalf of those customers, and future-focused decision-making. Yep, the machines may be learning, but humans are still in the driver’s seat—so understanding AI ethics, privacy issues, and the human implications of both is essential.
Building the (AI Skills) Bridge
As the saying goes, you gotta build a bridge. But how to do it? A whole heap of training, development, and investment in skills-building programs that are both agile and comprehensive, and that integrate continuous learning, practical experience, and strong alignment with business objectives. Here are seven ways MSPs can do it:
- Comprehensive curriculum coverage. Many training programs provide detailed instructions on everything from the basics of machine learning to the intricacies of advanced AI applications. These courses often combine theoretical frameworks with practical, hands-on projects that allow learners to tackle real-world challenges.
- Modular and adaptive learning paths. Training solutions often take a modular approach, meaning MSPs can tailor courses to specific roles or projects. Flexible learning modules make sure that both entry-level team members and seasoned professionals can find value in the training. After all, AI, machine learning, and deep learning are complex beasts which involve math, algorithmic thinking, and advanced programming skills—which can be intimidating for those without a strong technical background. Courses that strike a balance between breaking down fundamental concepts and diving into advanced techniques boost team competency.
- Vendor management and training software. Effective AI training isn’t limited to content alone: it’s also all about the tools. Think tools for scheduling, enrollment, and performance tracking—all essential for ensuring that training initiatives are executed smoothly. Managed learning services help automate administrative tasks, allowing MSPs to focus on the impact of the learning outcomes.
- Integration with business strategy. Topics such as AI strategy, risk management, and change management are increasingly included in AI training. This ensures that the learning process is not just about technical proficiency but also about aligning AI initiatives with broader business objectives—a critical requirement for MSPs serving diverse client needs.
- Foster a culture of continuous learning. AI advancement moves really fast, so MSPs should create an environment where ongoing education is part of the day-to-day. This means investing in regular refresher courses, microlearning sessions and subscription-based platforms that continuously update content in line with the latest advances. Establishing a culture that values knowledge sharing and lifelong learning can help employees stay current and adaptable… and who doesn’t want to stay current?
- Embrace blended learning models and hands-on labs. Theoretical knowledge alone often falls short when it comes to AI implementation. Successful programs integrate classroom learning with hands-on experiments, simulation labs and real-world projects. Programs that blend project-based learning, hackathons or in-house innovation labs give employees the opportunity to apply concepts in controlled yet realistic environments.
- Encourage internal mentorship. Building internal networks where experienced practitioners mentor less experienced colleagues can reinforce formal training with on-the-job guidance. Establishing AI champions will help you spread best practices, foster collaboration and build a support system that encourages continual innovation and problem-solving.
Bridge the gap today, lead tomorrow
While the breakneck pace of AI has shone a harsh light on a significant skills gap, it has also created fertile ground for innovation in training and education—a way for MSPs to grow their skills and lead from the front.
As the landscape of AI continues to evolve, MSPs that proactively address the skills gap today will be best positioned to lead as trusted advisors tomorrow. By investing in AI training and development courses and partnering with managed learning service providers, MSPs can elevate their operational capabilities and boost revenue.
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