AI Training an Art for MSPs

While there are many uncertainties around the use and planning for artificial intelligence, training has emerged as one of the key areas in which companies are trying to find their way.

“AI has become a critical new function and our customers are just beginning to scratch the surface,” said Vinod Paul, president of Align Managed Services, No. 60 on the 2023 Channel Futures MSP 501.

“It’s kind of the wild west right now,” agreed Wade Yeaman, CEO of Plano, Texas-based Fluid IT Services, No. 30 on the MSP 501.

Align's Vinod Paul

As is the case with many new and rapidly emerging technologies, one must consider AI training on multiple levels. Not only must the hands-on users be trained, separate curricula need to be developed for mid-level managers, technologists and other people who will be involved in planning, selecting and implementing AI solutions, policies and procedures. The training needs of each group varies based on their function.

To complicate matters still further, expect that most of your people may have very different levels of knowledge from the outset, requiring a delicate balance to avoid disenfranchising the newbies without boring more knowledgeable team members to the point of disengagement.

In short, AI training is an art as much as it is a science.

The Starting Point

Train your mid-management people to align the capabilities and potential of AI with the goals and objectives of your business. You should considered this at a long-range strategic level as well as the more tactical aspects in which AI can make a more immediate impact. A special focus on your vertical market, and how competitors are using AI, can be particularly useful.

You can then develop these aspects into an effective policy that guides your future decision-making processes for the selection and use of AI capabilities without undue redundancy.

Fluid IT Services' Wade Yeaman

“If we look at a business from the front to the back, you usually have sales, marketing, your core services, and your administrative services,” said Yeaman. “For each one of those, there can be many AI tools that will work really well for those business processes. You have to have a good understanding of your internal business processes, but mapping the tool to the process and what are you actually trying to achieve.”

The key, therefore, is to train your mid-management team to select the best tool for the job, and then standardize on that tool in order to avoid unnecessary duplication. While your company might opt for a different alternative in the months or years ahead, this needs to be done with full focus on the benefits of doing so, relative to the disruption caused by making the change.

Other areas of training for mid-level managers should include resource allocation, effective project management, risk management, performance monitoring and also compliance, as well as any international regulations that may require attention. Security is an aspect of training that needs to be a focus at both the manager level and the user level. Each level of the organization should be aware of the unique security considerations related to AI, especially potential threats to AI models, and the importance of data privacy in AI applications. Policies for the prompt reporting of any suspicious activities or security incidents must also be covered.

Know Your Audience

The underpinning of your training curriculum should ensure that presenters have a solid understanding of their audiences, and the degree to which the trainees are already grounded in AI, from a strategy point of view. This may mean that different training curricula must be built for different levels of existing expertise, as well as for different functions.

“This is very difficult because you won’t have the time or the resources to go one-on-one with everyone,” said Yeaman. “But I think you can add a review process and then go back to mentor the people who need more help.”

Communicating AI concepts to non-technical stakeholders requires the ability to convey complex ideas in a clear and accessible manner. Avoiding technical jargon, where feasible, can facilitate understanding among diverse audiences – especially among end users who are not particularly focused on technologies. Similarly, visuals, charts and analogies can help non-technical stakeholders grasp abstract ideas and unfamiliar concepts. Understand that individuals absorb information in different ways; thus, you’ll want to have all the available media represented, including, text, audio and video.

“We are putting together a learning campaign via our blog, and through our communications with our general clientele,” said Paul, whose team also builds training messaging into follow-up emails, social media, webinars and newsletters. “We’re putting out short, weekly snippets that focus on things like how to use Microsoft Teams and Copilot. We’re also taking feedback to help us improve future deployments. We are building a continuous learning culture.”

Collaborate with cross-functional teams in development of the training − particularly IT, legal, marketing and operations − to address diverse perspectives and requirements. To enhance efficiency, fully document all of this work and make it available to the targeted audiences for future reference.

It's also important to be careful around messaging the company’s anticipated savings which, in many cases, is built around potential layoffs.

“I think that the reality is, we just don't know, so don't make any bold statements,” added Yeaman. “I think the responsible approach is to say, ‘We're new to this, and we want to explore it. We want to be able to leverage the good in it, but we also understand there's a scary bad in it. We don't want that. And we certainly don't want it to adversely impact our culture.’ If I were to lose just part of my staff to AI, it would destroy our culture overnight. So it's got to be thought-out by leadership, and you’ve got to be careful about bold statements.”

Continuous learning in AI involves staying abreast of the latest advancements in the field. This includes activity with AI community groups, online courses, research papers, industry publications, and news related to breakthroughs in AI algorithms, models and applications. Regularly monitoring industry trends and case studies helps professionals understand how AI is being applied in different sectors. This awareness facilitates the identification of emerging opportunities and challenges that can be instrumental in the ongoing development of your education tracks.