Only 5% of mid-market organizations offer structured AI training, yet employees who get it use AI tools daily at nearly three times the rate of those who don't. This guide shows how to close that gap with a practical, low-cost upskilling approach built around the people, tools, and culture you already have.
Every business in the country is, to some degree, reckoning with the reality of artificial intelligence. Right now, your organization might be exploring how AI can improve your customer experience, expand your service offering, or otherwise drive revenue. Almost certainly, you're thinking about how to reshape your organization to make the most of this new technology.
Most leaders are now realizing that AI adoption is similar to previous waves of digital transformation, in that it's not enough to focus solely on the technology itself. Instead, you need plans that address three core aspects of your organization: systems, processes, and people.
People can be the most challenging part of the equation. Professional development is expensive and time-consuming, and may not even be the best option given the pace of AI's evolution. With these challenges, how can any leader build an AI-ready team?
The good news is that it's not only possible, but you can do so with minimal expenditure. All you need is the right approach.
Identifying the AI training gap
The Helios HR 2026 Mid-Market AI Workforce Trend Report shows a high level of both formal and informal AI use in the workplace. Even in companies without a formal AI strategy, employees are looking at ways to improve their workflows with AI.
Our research shows that:
- Nearly half of respondents (46%) use AI tools at least once a day
- Only 5% of mid-market organizations offer structured AI training programs
- 48% rely entirely on self-directed learning
- 44% have offered no AI training at all in the past six months
- 80% of employees are already using unauthorized AI tools at work, creating compliance and data security risks most organizations haven't accounted for
- In organizations where structured training does exist, 80% of employees use AI tools daily, compared to fewer than 30% where no program is in place
This data is both encouraging and troubling. IT and compliance may have some concerns about informal AI usage, which risks exposing sensitive data or even creating legal risk, as happened in the recent Workday lawsuit.
On the other hand, it shows that there is a great appetite to learn more about AI. Employees want tools that help them work both faster and smarter. That positive energy can be the basis for a dynamic, collaborative culture of learning.
5 ways to build an AI training program
1. Start by understanding where your people are
When you speak to your team about AI, you might find a surprising range of opinions and experience levels. Some people will already be proficient; others might never have used an AI tool. Some might be excited about the possibilities; others might harbor strong doubts.
Our data shows that most employees fall into one of four groups:
- AI enthusiasts: Enthusiasts are excited about AI's potential, and they are already using AI tools in their workflows. This group is most likely to act as champions and encourage broader AI adoption.
- AI optimists: This group has less experience with AI technology, but they believe it will be beneficial to their role. The optimist group is most likely to engage with training initiatives and be willing to join AI projects.
- AI skeptics: Skeptics play a crucial role in AI projects. They are often the voice of reason, keeping teams grounded in material business concerns, such as process reliability and customer expectations.
- AI neutrals: Finally, there is a group of people who simply haven't had a chance to see AI in action. This group often responds to a guided introduction to AI, especially when it focuses on how AI can fit into their processes.
The ideal team includes a mix of people, with enthusiasts driving change while skeptics ensure a diligent approach to transformation. The most important thing is to get an idea of where your people are, so you can develop a training strategy that addresses weaknesses and builds on strengths.
2. Identify the tools your employees already have
Many organizations already have access to sophisticated AI tools, often as part of another service, such as Microsoft or Google. If your team has these tools, then this represents a great place to start. These platforms are already familiar, which makes it easier for employees to see how they can fold AI tools into their existing workflows.
Using familiar tools also allows you to build a training plan that focuses on role-specific use cases. For example, if a team works with lots of Excel spreadsheets, they can run exercises on using Copilot to perform data analysis. This gives a much more hands-on learning experience than a general "Introduction to AI" training module.
When people start using Artificial Intelligence on a daily basis, they will become more optimistic about including AI in their workflow. With that enthusiasm, your team will be equipped to embrace more sophisticated AI processes and tools in the future.
3. Encourage people to build
Using AI doesn't have to mean following pre-defined workflows. The best results often come when people build tools and processes of their own, tailored to the specific challenges they face every day. That kind of ownership is hard to replicate with a standard training module.
Give your team time and space to experiment within safe guardrails. This might mean a dedicated session each month, a shared channel for testing ideas, or simply making clear that experimentation is encouraged. Clear boundaries around data handling and approved tools mean people can explore freely without creating compliance risks.
This approach sends a clear signal that you want your people to be part of your AI future. According to our report, in organizations with a well-defined AI strategy, positive employee sentiment toward AI training reaches 94%. An AI budget, however modest, reinforces that message further, framing upskilling as a professional investment rather than an obligation.
4. Build a shared AI use case library
An AI use case library can turn individual learning into team learning. A use case library is simply a way for people to share their work with each other. This can exist as a shared folder on the network, as an intranet site, or as a channel on Teams or Slack. As people contribute, the library will grow, and eventually it will become a valuable training resource in itself.
When your team is using AI every day and running their own experiments, they will start to have breakthroughs and innovations. One person might find a prompt that automates a repetitive task; another might create a sophisticated agent that handles complex tasks.
A resource such as this must have visible leadership support, or else people won't take the time to contribute. Some ideas here include monthly acknowledgments for the best contributions, regular seminars to share ideas in person, and a newsletter highlighting interesting shares. Most importantly, leaders need to get involved and share their own ideas!
5. Make AI skills part of your employer brand
Organizations that invest in AI upskilling gain a meaningful recruiting advantage. Candidates are actively seeking employers who will help them grow their AI capabilities, and mid-market companies competing for talent against larger organizations can use this to their advantage.
Include AI learning as a named benefit in job postings by being specific, such as noting access to AI tools and a personal learning budget, rather than a generic mention of "professional development." In career development conversations, help employees see how building AI capabilities connects to their own growth and progression. When people see a clear path forward in an AI-enabled workplace, engagement and retention rise.
For your existing team, the opportunity is equally compelling. Our report found that 54% of employees describe themselves as AI Enthusiasts or Optimists. They're not afraid of this technology; they want to understand it. A learning program, however simple, gives them a concrete reason to stay and grow with your organization rather than looking for that opportunity elsewhere.
Ready to build your AI-ready workforce?
AI upskilling requires a clear signal from leadership, some structure to channel the curiosity that already exists in your workforce, and a willingness to let people learn by doing. The organizations that move forward now, even with small and low-cost steps, will build a meaningful advantage over those still waiting for the perfect program to arrive.
Our 2026 Mid-Market AI Workforce Trend Report offers a detailed look at where mid-market organizations stand today and what the highest-performing adopters have in common.
Helios HR can help you design a practical AI workforce strategy tailored to your organization's size, culture, and goals:
- AI consulting to develop a people-first AI adoption strategy
- Training and development services to build AI literacy across your workforce
- Strategic HR consulting to align your talent strategy with the future of work
- HR consulting to assess your current capabilities and identify the right next steps
Book a call with a Helios HR consultant to start building an AI-ready workforce without breaking the bank.
FAQ
How much does it cost to start an AI upskilling program?
Far less than most leaders assume. The first phase relies on tools you already license, peer-led experimentation, and a shared use case library. A modest budget for protected learning time and a few external workshops is usually enough to build real capability.
How long until we see results from AI training?
Most mid-market companies see meaningful daily adoption within three to six months when training is paired with role-specific tools and clear governance. Confidence builds fastest when employees apply AI to work they already do, rather than learning AI in the abstract.
Do we need a formal AI policy before we start training?
Yes, at least in draft form. Training without guardrails creates compliance and data security exposure, and only 12% of mid-market organizations have a finalized AI policy. A short set of rules covering approved tools, data handling, and disclosure is enough to begin.
How do we get skeptical employees on board?
Skeptics are not the obstacle they appear to be. Treat their concerns about reliability, accuracy, and customer impact as design inputs for your program. When skeptics see AI applied responsibly to their own workflows, many shift into thoughtful adopters whose voice protects quality.
What roles should we prioritize first?
Start with teams generating the most revenue or handling repetitive, high-volume work, and the managers who lead them. Manager fluency is the strongest predictor of team adoption, so any pilot should include the leaders who will model and reinforce AI use day to day.
How do we measure whether AI upskilling is working?
Track usage frequency, the number of role-specific use cases in active production, contributions to your shared library, and time saved on defined tasks. Sentiment matters too. Employees who feel supported in learning AI are far more engaged in the wider strategy.
Related resources
World Economic Forum: The Future of Jobs Report 2025
McKinsey & Company: The state of AI
Deloitte Insights: State of Generative AI in the Enterprise
MIT Sloan Management Review: Artificial Intelligence and Machine Learning