"Should we add an AI agent to our platform?"
If you're asking this question, you might actually be behind. The real question product leaders (in e-learning and EdTech) should ask is: "How do we monetize our AI agent effectively?"
As education technology evolves, AI agents have moved from a nice-to-have feature to a core component of modern learning platforms. But of course adding an AI agent isn't enough—you need a clear strategy to turn this technology into revenue.
I've spent the last five years or so helping EdTech companies integrate and monetize AI solutions. What I've learned is that success doesn't come from just dropping an AI chatbot into your LMS and calling it a day. It comes from understanding how AI agents can fundamentally transform your product's value proposition.
In this post, I'll cover…
- The value of AI agents in e-learning and EdTech;
- Key monetization and pricing strategies;
- Recommendations for building a business case;
By the way, I recently recorded a webinar on this very topic—watch it on-demand here.
The proposition: The strategic value of AI agents in EdTech
First, let's break down why AI agents really matter in education technology. Traditional LMSs excel at content delivery and basic assessment but often fail to provide personalized, adaptive learning experiences with always-on performance support. This is what users actually want and are happy to pay for—and this is where AI agents make a massive difference.
Let’s examine the benefits of AI agents for you, the platform provider, and your product's users.
Benefits for your platform’s users
24/7 on-demand performance support
Users receive immediate, personalized feedback at any time. When a learner struggles with a concept at 10 pm, they don't have to wait until an instructor comes online at 9 AM the next day. The AI agent can explain the concept in detail, provide additional examples, or break down the problem into smaller steps.
Scale personalization
While humans can personalize their approach for 20-30 students, AI agents can do it for thousands simultaneously. Each learner gets a customized experience based on their pace, preferences, and performance. For example, one of our customers connected their agent to their 5,000-course library so the agent can serve perfectly personalized content to learners. Accurate recommendations from a library of this size would be a tall ask for a human.
Coaching and role-play exercises
AI agents can function as personal coaches, practice specific scenarios, or engage in role-play. For example, Mindtools offers an interactive AI Conversations feature: an agent that can help managers rehearse and refine their approach to difficult workplace conversations through dynamic characters, real-world scenarios, and adaptive responses,
Search and *actually* find
For a learner, there’s nothing more frustrating when they know they had read “something about a topic somewhere,” and now that they need that piece of information, they can’t find it. AI agents are much more efficient in finding information than traditional search engines, and they can also summarize content (and include source references) for easier consumption. Search less, find fast, and act more.
Benefits for you
Convert engagement into subscription
When people love to use something, they are happy to pay for it. A learner experience with on-demand performance support, hyper-personalized journeys, interactive exercises, and convenient content discovery is the sort of experience users love—and pay for. Launching an AI agent to your platform will convert engaged users, into paying members.
Save time and take some workload off
Take Persimmon Life's example: Before implementing their FAQ agent to support their employees, the Regional Growth Managers spent 60-80% of their time answering questions and providing performance support. Their LMS could not recommend the right content at the right time, which resulted in non-stop questions from users. Within the first month of adding the AI agent to their platform, they reported saving 40% of their time.
Support data-driven decision-making
AI agents collect valuable data about how people learn. They track which explanations work best, identify common misconceptions, and highlight areas where learners need more support. This information helps you improve your content and gives instructors and content creators insights they can use to adjust their strategies.
When implemented thoughtfully, AI agents create a positive feedback loop. Better learning experiences drive higher engagement, generating more data that enables better personalization, leading to even stronger learning experiences. This cycle doesn't just improve outcomes—it builds a moat around your product.
The strategic value of AI agents isn't just about automation or efficiency. It's about transforming your product from a content delivery platform into an intelligent learning partner. This transformation opens new monetization opportunities, which we'll explore in the next section.
Can AI agents make money for your brand? Key pricing strategies
After helping dozens of EdTech companies roll out AI agents, I've found that the most successful monetization strategies align with how institutions and learners use these tools. Let me share what works—and importantly, what doesn't.
The foundation: Subscription-based
Subscription models work particularly well for AI agents, but the trick is in the tiering. Rather than creating arbitrary divisions, build your tiers around genuine use cases.
Image source:
5app
Ideally, you would look at user behavior and understand what features and solutions should be available on which tier. Most SaaS companies see the best results with three or four pricing tiers—enough options without decision fatigue.
The key? Each tier solves specific problems for specific users. They're not just paying for "more AI" – they're paying for clear solutions to their problems.
The flexible option: Pay-per-interaction
Some providers prefer more flexible pricing, especially when AI technology is new to their audience. Pay-per-interaction models can work well, but need careful structuring to prevent bill shock.
For example, it could use a token system:
- Companies buy L&D tokens in bulk
- Different interactions cost different numbers of tokens
- Unused tokens roll over to the next billing cycle
- Analytics help the company predict usage and budget accordingly
Since this model can be unpredictable, it is crucial to experiment early on, run pricing tests, iterate, and learn quickly what works and what doesn’t.
The bold move: Performance-based pricing
This is the most interesting pricing model I’ve seen—and one of the most challenging to implement. It ties pricing directly to measurable outcomes. For example, a professional training platform could charge based on average training score and learner assessment improvements or completion rates.
For this to work, you need:
- Clear, measurable metrics
- A strong correlation between AI usage and outcomes
- Buy-in from stakeholders
- Powerful tracking systems
While performance-based pricing can align everyone's interests and justify premium pricing, it’s also the hardest to execute and communicate clearly to your platform users.
The revenue multiplier: Value-added services
Think of your AI agent as a platform for additional services, not just a standalone product. The most successful companies are building entire ecosystems around their AI agents:
- Custom grounding using business-specific materials
- Integration services with existing tools
- Analytics dashboards for admins
For example, you could set up an AI coach that has access to your own professional training content library as the foundation, plus, you could upload each client’s company policies and documentation to their dedicated agent’s knowledge bank. Then each client can have their own dashboard with agent message themes, content gap reports, sentiment analysis, and other essential engagement data that makes the basic AI coach 10x more valuable.
The most successful implementations often combine elements from multiple strategies. The key is matching your pricing strategy to your users' needs and your product's strengths.
Building the business case
Every time our team presents an AI agent initiative to a prospective client's executive team, the first question is always the same: "What's the bottom line?"
Most product leaders know they need AI capabilities, but quantifying the return on investment is a challenge. The reality is, implementing an AI agent isn't cheap—but when done right, the returns can be massive.
Let's start with the investment side.
Investment: Buy or build?
First things first: should you build an AI agent yourself or buy an off-the-shelf solution? Building your own AI agent is really time-consuming and expensive. This includes:
- Build cost: How big of a team you have vs. need, and what special skills you need to hire (ML/AI experts, data scientists, engineers). What is the opportunity cost?
- Maintenance cost: How much success will you need before scale becomes a challenge? Can you model out LLM & indexing costs?
- Customizability: How close can your future AI feature get to the core of your business? Is your use case unique enough to justify a custom build or are there existing, flexible solutions that can facilitate it well?
- Reliability: Security and compliance considerations.
Here’s a breakdown of what you will need to build and how long it might take—depending on the team size.
On the other hand, off-the-shelf agents are faster and cheaper to deploy, but they're often not tailored for your exact use case. If the ROI suffers, is the cost savings really worth it?
In my opinion, the question isn't buy or build—you should do both. Buy the foundational platform where the heavy lifting has already been done for your industry and use cases, build and customize your own agents on top of it. But of course, I'm biased—that's exactly what Mindset AI enables.
Return on investment
There are two parts to the return on investment piece: the ROI of 1) not having to build the agent platform yourself and 2) monetizing the AI agent itself.
By purchasing an AI PaaS that lets you launch highly customized AI agents quickly and easily, you will achieve higher ROI because…
- Your team can work on other revenue-generating features;
- You can move your Profit and Loss (PNL) 6-7 months forward whilst reducing R&D costs;
- You can launch Ai features while they are unique and worth paying for—instead of expected by default.
Here’s an ROI table you can reference in the context of a build-buy decision.
Now, let’s cover the AI agent ROI. The ROI journey with AI agents typically unfolds in three phases:
- Early adaption (0-2 months): Expect modest returns as you optimize the system and users get accustomed to it.
- Mass adoption (2-4 months): By month four, many platforms see a clear uptick in revenue, driven by improved user engagement, reduced churn, and shorter time to purchase.
- Growth engine (4+ months): This is where real acceleration happens. By this stage, you've gone to market, refined your positioning and pricing, and proven the value of your AI agents. Early customers are seeing results, marketing channels are set up and ready to scale, and word-of-mouth is kicking in—driving organic growth. At this point, the agentic experience becomes an expectation, and platforms without AI start to feel outdated.
The time to positive ROI varies. Some Mindset AI customers see returns as early as month one, while others take a few months. For those with longer sales cycles, ROI takes a little longer—but the deal sizes are typically larger.
Here are some examples:
Now, while I’m proud of what our customers have achieved, I’m not sharing these stats to brag—but to give you a tangible sense of what’s possible.
The key to these results is the network effect. When users get immediate, helpful responses to their questions, they use the platform more. Increased usage leads to better learning outcomes, making them more likely to upgrade to premium courses or purchase memberships. Then they tell their colleagues or friends, and the cycle continues.
What else to consider
Once your business case is approved and you are ready to set the wheels in motion, here are some next steps to think about during the implementation phase:
- Integration with existing pricing: Your AI agent shouldn't disrupt your current pricing structure—it should improve it. When adding AI features to your platform, your pricing should reflect the additional value while maintaining consistency with your existing pricing structure.
- Running an insightful Proof of Concept: The proof of concept is your opportunity to validate assumptions and refine your approach before a full rollout. The key is to start small but think big. Aim to close at least one new deal during your AI agent PoC so you have a case study by the time you start the full rollout.
- Planning for your platform upgrade: Create a transition timeline that gives users enough time to adapt while maintaining momentum. If you want a phased launch, consider offering early access to some customers. Have documentation ready and your support team on standby. Mostimportantly, build excitement for this excellent upgrade to your offering!
Adding an AI agent to your EdTech platform isn't just about keeping up with technology trends—it's about creating actual value and new revenue streams for your business. The success of your AI monetization strategy will depend on thoughtful implementation, a clear pricing structure, and a solid understanding of your users' needs.
Remember that monetizing AI agents isn't about charging premium prices for basic automation—it's about creating sustainable value that actually improves learning outcomes while generating revenue for your business.
So, are you ready to start your AI agent journey?