Master the AI middle ground and unlock crazy ROI
The best examples of businesses using AI aren’t million-dollar moonshot projects, but not basic automation either.
Everyone's focused on the big AI stories. The huge projects with even huger budgets.
But here's something worth thinking about… What sits between those massive projects and basic automation?
What options exist for businesses that want to do more than automate simple tasks, but aren't ready to rebuild everything from scratch?
The answer lies in what the internet is calling mid-tier AI initiatives. They're not moonshots trying to solve every problem at once. They're not basic automation either, like having AI sort your emails.
They're the practical middle ground that most businesses overlook (but really shouldn’t).
What’s the middle ground?
Picture it like this: at one end, you have companies pouring millions into building their own version of ChatGPT to serve their customers.
Don’t get me wrong—there’s a time and place for that.
But projects like these cost millions, take up precious time and resources, and at the end of it all, don’t guarantee a good ROI.
On the other end, you have teams using basic AI to only handle repetitive tasks like sorting support tickets.
Again, not harmful and for sure effective. But often they have a tendency to exist in a standalone way—working on isolated use cases, unscalable, and failing to impact at a company-wide level.
Both approaches have their place, but both miss opportunities.
The middle ground is where AI gets practical. It's where you find projects that:
Target one clear business problem (like pricing or hiring)
Use data you already have
Start small but can grow with you
Work alongside your existing systems
Show real results within months, not years
Scale across teams without a massive disruption
Key differences between basic and mid-tier AI initiatives:
We looked at a bunch of businesses using AI effectively and noticed something interesting. The ones getting the best results weren't trying to reinvent their entire operation. Instead, they picked specific problems that affected multiple teams, solved them well, and built from there.
Here are four mid-tier AI projects that are making a real difference right now, plus practical ways you can implement them.
4 mid-tier AI projects that you can use in your business
1. Personalising marketing for higher conversion rates
Most businesses blast the same marketing emails to everyone. Maybe they swap in a name or company—but that's about it.
Mid-tier AI takes a completely different approach. Instead of treating everyone the same, it builds a rich understanding of each customer through their interactions.
Take Doe Lashes, a beauty brand that wanted to move beyond basic email marketing. They implemented an AI-powered quiz system that:
Learns detailed preferences from each customer
Creates personalised product recommendations
Segments customers for targeted messaging
Adapts email content based on behaviour
The results transformed their marketing. They now collect 3x more email subscribers than before, with 44% of all their email signups coming through their AI quiz system.
They also discovered that 35% of potential customers had never used false lashes before, leading them to create targeted educational content for first-time buyers.
Want to try something similar? Start small, with just one customer segment and one type of interaction—like new customer onboarding or sales outreach emails. Let the AI learn from those conversations before expanding to other areas.
2. Chatbots that actually understand the context
Basic chatbots are everywhere. You know the ones—who answer simple questions but either get stuck in a loop or pass you to a human the moment you need real help.
Mid-tier AI chatbots are different. They focus on being genuinely helpful by remembering context and learning from past interactions.
Here's what most support systems get wrong:
They treat every conversation as new
They can't connect related issues
They stick to rigid scripts
They pass too many tickets to human agents
The key difference with context-aware AI is memory. These systems:
Learn from your previous interactions
Understand your preferences
Adapt responses over time
Know when to bring in human help
Like HubSpot, for example. They implemented their own AI-powered support bot to direct customers to appropriate resources. The bot was trained using existing content from the company's knowledge base, help sites, and blogs, so it could deliver accurate and contextually relevant responses.
This initiative led to an 80% increase in sales team efficiency. Pretty impressive.
Implementing context-aware chatbots in your business might sound complex, but it doesn’t have to be. Begin with trying to solve just one small problem, like reducing support queries or boosting lead generation. Get comfortable with that first, then expand into other areas of your business.
3. Smart hiring starts with better screening
"This candidate looks great on paper, but they're not quite what we need."
Sound familiar? Traditional resume screening can sometimes be a game of chance. You might spot the perfect hire, or miss them completely because their experience didn't match your keyword search.
Using AI during early screening phases can dramatically reduce the time to hire. As seen by Hilton, who use AI-powered recruitment software to standardise their screening process and conduct initial interviews more quickly.
The numbers tell the story: after implementing AI-powered video interviews, Hilton’s time to hire dropped from 42 days to just five.
Wondering where to start implementing AI into your hiring process in an impactful way? Here’s a list of practical ideas to get you started.
4. Automated lead scoring: Spend time selling only where it matters
Before using AI, HES FinTech’s marketing team was bringing in leads—a lot of them. Too many for the sales team to sift through and qualify and sell to.
So their solution was to use AI (specifically a product called GiniMachine) to spot the best leads before a sales rep ever touched them.
Their approach worked because it combined:
Three years of past lead data from their HubSpot CRM
Multiple data points (like company size and industry)
Clear success metrics (actual conversions to customers)
Automated scoring through their CRM
The results changed how their sales team worked. Low-quality leads (scoring under 0.25) got automated responses with helpful resources. High-scoring leads went straight to sales reps. Everything in between got sorted based on specific factors like budget and project type.
Here’s the case study if you want to read more on HES FinTech.
A few ways you could get started using AI for sales:
Using AI where it matters
There's a sweet spot between basic automation and complete AI transformation. It's where you'll find practical projects that solve real problems without requiring massive budgets or complete system overhauls.
The companies we've looked at aren't trying to build the next ChatGPT. Each started small, focused on one clear problem, and used data they already had. That's the key to making AI work in the real world.
Want to build something similar? Have a look through our extensive course catalog—we help companies navigate exactly this kind of practical AI implementation.
This post was written by Shanice.