Artificial intelligence is no longer a future trend, it’s reshaping how training providers design, deliver, and measure their programs today.
By 2026, AI in training and development will be a defining factor in how training businesses scale operations, deliver value to clients, and differentiate themselves in a competitive market.
From our discussions here at Arlo with experts in the training industry, it’s clear that AI is moving from experimental use into core training operations, reshaping how providers build courses, support learners, and run their businesses.
And from these discussions, we’ve identified eleven key use cases that show how training providers will use AI in their businesses to drive success, without losing the core pillar behind successful learning outcomes: expert instructors.
These insights come from our recent webinar, Beyond the Hype: The Truth about AI in Training, which featured a panel of training experts:
- Dr. Stella Lee — Founder of EdTech consultancy Paradox Learning
- Elena Agaragimova — Trainer and CEO of Shiftwell.ai
- Scott D’Amico — President of Communispond, a global leader in communication skills training
- Donald H. Taylor — Chair of the Learning Technologies Conference, who moderated the discussion.
Watch the webinar below, and read -on for the use-cases 👇
Use Case 1: AI-generated training content
For many training providers, building courses starts with messy source material: PowerPoint decks, instructor notes, or compliance manuals that aren’t designed for delivery.
Turning these into professional training resources normally takes weeks of effort from instructional designers.
Generative AI changes the workflow. With the right elearning tool, providers can upload existing resources and instantly generate draft modules, complete with text, images, and even quiz questions.
This doesn’t replace the trainer’s expertise, it accelerates the first draft so providers can focus their time on refining the narrative, checking accuracy, and adding context.
The result is faster course production without sacrificing quality.
Providers can respond quickly to client requests, refresh existing courses, or create digital versions of classroom material in a fraction of the time it once took.
The AI handles structure and formatting, while human instructors safeguard accuracy, tone, and relevance.
As Elena pointed out, the real value isn’t in pressing a button to produce generic material — it’s in combining AI’s speed with the provider’s own intellectual property and expertise to create training that learners can’t get anywhere else.
“Anybody can create a training right now… but what makes you unique is your intellectual property, your research, the expertise you bring to the table. Don’t be so quick to just outsource that part, because that’s the part AI cannot do yet.” — Elena Agaragimova
Use Case 2: Personalizing training delivery through adaptive technology
Traditional training can sometimes struggle with a one-size-fits-all problem. Learners arrive with different backgrounds, levels of confidence, and speeds of progress, but the course moves forward at the same pace regardless.
Adaptive platforms change that by adjusting the learning path in real time based on how each person is performing.
As Stella Lee explained during the webinar, the opportunity isn’t simply to “do things faster,” but to augment human learning intentionally.
She pointed out that AI can finally deliver on the long-promised idea of adaptive learning at scale, offering feedback that feels more personal and relevant: “It could mean helping learners to ask better questions, not just to get answers fast… to actually provide real feedback, to have more personalized or objective feedback.”
In practice, this means a participant who struggles with a scenario might automatically be directed to extra practice modules, while another who demonstrates mastery can move ahead.
Over time, the system shapes a path that feels unique to each learner, keeping them engaged and preventing either boredom or overwhelm.
For training providers, adaptive platforms don’t replace the instructor but extend their reach.
Instructors remain in control of the overall experience, while AI takes on the role of monitoring progress, adjusting activities, and highlighting where human intervention may be needed.
The outcome is a learning journey that feels responsive and individual, while still grounded in the provider’s expertise and instructional design.
Use Case 3: Learner Engagement and Experience
Even the best-designed course will fall short if participants feel detached or passive. One of AI’s most immediate contributions to training is in shaping experiences that keep learners actively engaged rather than simply receiving information.
During the webinar, Stella Lee stressed that the goal of using AI isn’t just faster delivery but giving learners a stronger sense of support, curiosity, and agency.
In her words, success means learners “feel more supported, more curious, more empowered, more agency and not just having them feel like they’re being surveyed or confused or disengaged.”
AI-powered tools can play a role here in subtle but powerful ways. They can tailor case studies so that examples resonate with different industries, localize references for international audiences, or vary the delivery format to match the learner’s style.
Instructors remain central to guiding the session, but the technology ensures that what learners see and do feels relevant and timely.
The impact is twofold: participants are more motivated to stay involved in the learning process, and providers can demonstrate higher levels of learner engagement to their clients.
Instead of a static experience, training becomes responsive, meeting learners where they are and holding their attention throughout.
Use Case 4: Real-time support and feedback
In traditional training settings, learners often have to wait until the next session, or until they can catch the instructor, to get answers to their questions. That delay can stall progress and reduce confidence. AI can bridge this gap by providing support and feedback the moment learners need it.
AI-powered chatbots are one example. Embedded into a course platform, they can field routine questions, clarify instructions, or point learners to relevant resources without interrupting the flow of the session. For participants, it feels like instant access to a teaching assistant who is always available.
Beyond answering questions, AI systems can also givereal-time feedback on practical exercises. As Scott D’Amico noted in the webinar, one application is public speaking practice: an AI tool can pick up on filler words, pacing, or lack of gestures and immediately suggest improvements.
Learners can then rehearse repeatedly until they feel confident, developing their skills faster than waiting for scheduled coaching.
Another use case, is to put transcripts directly into an LLM for analysis. Someone looking to improve their public speaking for example, could record a speech they give, drop the transcript into an LLM and ask it for direct feedback e.g. how can I lessen the use of filler words such as ‘er,’ or ‘like’, in this speech.
For training providers, this technology doesn’t replace instructors but supplements them. Instructors can focus on higher-value teaching moments, while AI handles the immediate, repetitive queries and basic feedback.
The outcome is learners who feel more supported and who can practice and refine their skills continuously, an important factor in effective employee development.
Related read: 8 Best Training Evaluation Tools to Measure Training Effectiveness
Use Case 5: Predictive analytics to analyze skill gaps
Training providers often face the challenge of aligning their offerings with what clients actually need.
Relying on learner surveys or manager feedback alone can leave gaps, as these methods acan be subjective.
AI-powered analytics provide a more reliable view by analyzing performance data, learner interactions, and assessment results to highlight both current skill gaps and the skills needed in the future.
Capabilities like these allow providers to go beyond simply responding to client requests. They can predict future learning needs with greater confidence and design targeted training programs that directly address workforce challenges before they become critical.
For example, if data shows a trend of employees struggling with a new technology, a provider can package a course in advance rather than waiting for the problem to escalate.
The outcome is more strategic learner development with training businesses positioning themselves as proactive partners that help organizations stay ahead of industry change.
Use Case 6: Knowledge retention and continuous learning
A recurring theme in the webinar was that a single training event is rarely enough to drive lasting change.
Skills fade quickly if they aren’t reinforced, and learners often return to old habits once the classroom session ends.
As Scott explained, “We might be limited to a day or two with a group… if people aren’t immediately practicing those skills when they leave the class, the retention drops off precipitously very, very quickly.”
AI opens up new ways to address this long-standing challenge. Providers can use AI-powered tools to create reinforcement activities that learners can access after a course, keeping concepts fresh and supporting continuous learning.
For example, Scott described how his organization uses AI trained on decades of communication skills material to act as a practice coach: learners rehearse presentations, receive instant feedback on pacing or filler words, and are directed back to relevant workbook sections or reinforcement videos.
This combination of automated practice and targeted nudges helps learners strengthen their knowledge over time, rather than letting it fade. Elena added that the scalability of AI means this kind of support isn’t reserved for executives: “With AI, we can scale coaching. It’s accessible to everybody, it’s available… whenever that person needs it the most.”
For training providers, the benefit is clear. They can extend the life and impact of their courses, prove stronger learning outcomes to clients, and support ongoing skill development that aligns with long-term business needs.
Use Case 7: Automating routine training admin tasks
Much of the work in a training business isn’t about teaching, it’s the repetitive administration behind every course. From scheduling and sending registration emails to pulling reports, these routine tasks consume valuable time that could be spent improving learning outcomes.
AI is increasingly being used to take this load off providers. As Scott D’Amico noted, the real gains come when AI is applied across the full cycle of operations, not just content: “It’s going to be hard for AI to really be successful… if it’s just random people in little pockets.
You need that holistic view—from creating content to marketing it, and then critically, how can it help support instructors pre-class, during class, or post-class reinforcement?”
By embedding AI into workflows, whether that’s generating automated communications, suggesting schedule adjustments, or creating draft reports, providers can save hours of manual work.
Scott highlighted how organizations are already linking AI to tools like Zapier to automatically draft emails, schedule posts, and even produce social media campaigns.
The result is that trainers and administrators reclaim time and energy for higher-value work: designing better learning experiences, coaching learners, and partnering with clients.
Automation isn’t about replacing the human role in training, it’s about removing the friction so experts can focus on making training impactful.
Use Case 8: Using AI to power marketing and sales campaigns
For many training providers, running effective marketing and sales campaigns is as demanding as delivering the courses themselves.
Creating email cadences, writing blog posts, and maintaining a social media presence can quickly overwhelm small teams. AI is now being used to ease this pressure.
During the webinar, Scott described how training companies are connecting AI with workflow tools like Zapier to automate content generation.
A single training resource can be repurposed into a blog post, turned into a LinkedIn campaign, and scheduled for distribution without manual rewriting each time.
“You can pull from a piece of content and now you have multiple marketing items that can be pushed out,” he explained.
AI doesn’t replace the need for a clear message or strong positioning, but it helps providers produce more campaigns, faster. It can draft copy for upcoming training programs, create variations for different audiences, and test responses across channels.
This allows training providers to keep their name in front of prospective clients while spending more of their own time on relationship building and delivery.
Used well, AI becomes a force multiplier in marketing, giving providers the ability to reach more people, keep participants engaged between sessions, and maintain visibility in crowded markets without adding headcount.
Use Case 9: Simulation and roleplay
One of the most promising areas for AI in training is the use of simulations and roleplay.
Instead of waiting for the classroom or coaching session, learners can practice scenarios in an AI-driven environment that mimics real workplace challenges—whether it’s handling a difficult sales call, managing a safety drill, or responding to a client objection.
In the webinar, Scott D’Amico highlighted how these tools are becoming more accessible, noting that advances in AI are lowering both the cost and complexity of running simulations. Learners can now engage in realistic practice exercises without the heavy setup once required.
The advantage is twofold. First, learners receive instant, AI-powered feedback on their performance, helping them refine responses and build confidence before they ever sit down with an instructor.
Then, when the live coaching session takes place, instructors can focus on higher-order skills and nuanced feedback rather than basic corrections.
For training providers, this approach combines scalability with quality.
Simulations extend learning beyond the classroom and give participants a safe environment to test decisions, make mistakes, and improve, leading to stronger learning experiences and measurable improvements in employee performance.
Use Case 10: Pairing AI with human skills and emotional intelligence
While AI can generate content, analyze data, and automate tasks, it cannot replicate the human qualities that define great training. Emotional intelligence, interpersonal awareness, and critical thinking remain the foundation of effective learning experiences.
The panelists cautioned against losing “the humanity of training,” reflecting the concern that overreliance on automation risks undermining what makes instructor-led learning effective.
For providers, the use case is about balance. AI can accelerate production and personalize delivery, but it is trainers who bring empathy, facilitation, and leadership, the qualities that transform information into genuine development.
Use Case 11: Proving ROI
Training providers are under constant pressure to demonstrate the value of their programs. Traditional evaluation methods, surveys, attendance rates, or anecdotal feedback, often fall short in showing real business impact.
AI-powered analytics provide a stronger foundation by analyzing learner behavior, assessment results, and long-term performance data.
During the webinar, Elena Agaragimova stressed that success comes from being a strategic partner to clients, not just delivering content. By using AI-driven insights, providers can speak the language of business leaders, showing clear links between training, productivity, and organizational outcomes.
Scott D’Amico added that business leaders care most about productivity, revenue, expenses, and reputation. AI enables providers to track and present evidence across these dimensions, moving beyond vague claims of effectiveness and actually proving training ROI.
Conclusion: Future of AI in Training and Development
Artificial intelligence is no longer an experiment on the sidelines, it is becoming central to how training providers design, deliver, and measure learning.
From AI-powered tools that accelerate content creation, to predictive analytics that identify skill gaps, to adaptive learning platforms that create personalized paths, the applications are wide-ranging and practical.
But success does not come from technology alone. The real impact will come when AI systems are paired with human expertise—trainers applying emotional intelligence, critical thinking, and industry knowledge to guide the learning process. This combination ensures training remains relevant, engaging, and effective.
For training providers, the future lies in integrating AI to deliver training that is scalable, engaging, and aligned to client needs.
Those who embrace this shift will be able to optimize training programs, demonstrate measurable business outcomes, and strengthen their role as trusted partners to learners in their field of expertise.