4 Ways Teams Are Using Flowingly & AI Right Now
4 Ways Teams Are Using Flowingly & AI Right Now
Let’s be honest—everyone’s banging on about AI, but how many organisations are actually using it to solve real problems?
The gap between AI hype and practical implementation is wider than most vendors want to admit. That’s why we’re cutting through the noise to share how teams are combining Flowingly with AI to create genuine improvements in both customer and employee experiences.
In our recent webinar with Liam from Incendo, we explored use cases that aren’t theoretical “someday” implementations—they’re happening right now, delivering real value. No fancy computer science degree required.
1. Submitting Paper Forms into Flowingly
Ever tried to convince people to stop using paper forms? It’s about as easy as getting your nan to understand Instagram Stories.
The reality is stark: organisations spend months (sometimes years) trying to wean staff off paper, burning through budget and patience. Projects close, benefits aren’t realised, and you’re back to square one—with a stack of paper forms still sitting on the counter.
Here’s an approach that’s actually working:
Stop fighting the paper battle
Let people use the formats they’re comfortable with
Use AI to do the heavy lifting
Document processing models extract information automatically and accurately
Allow multiple entry points, one workflow
Whether it’s email attachments, front desk submissions, or website forms
What’s really happening behind the curtain: This AI use case works by mapping out where fields like “name,” “address,” and “phone number” typically appear on your standard forms. Unlike generic text recognition tools that can get confused by different layouts, this approach knows precisely where to look for each piece of information on your specific forms.
The mind-blowing part? You only need to show the AI model about five examples of completed forms—some neat, some messy, some typed, some handwritten—and suddenly they’re capturing information with 98% accuracy. That means your team can stop manually entering data and start handling the parts of their job that actually require human judgment.
The system is smart enough to know that scribble in the name field is still a name, even when someone’s messy ‘z’ looks suspiciously like a ‘2’. It pulls all this information straight into your workflow without anyone having to squint at bad handwriting or manually type things into multiple systems.
The key insight? Change management doesn’t have to mean forcing people to abandon familiar processes—it can mean making those processes work better behind the scenes.
2. Smart Email Triage & Responses
Many organisations face a perfect storm: improve customer experience while cutting budgets. All the while customer queries are becoming more complex, volumes have increased, and customer expectations keep getting higher and higher.
Meanwhile, teams are stretched thin, with less time to carefully consider each message. The inevitable result? Rushed, generic responses that leave customers feeling like an afterthought. Staff end up spending precious mental energy just deciding where to route messages, leaving little bandwidth for crafting thoughtful, personalised replies.
Here’s an approach that’s actually working:
Auto categorisation
AI reads incoming emails and identifies the appropriate department
Content summarisation
Key points are extracted, ensuring nothing gets missed
Response recommendations
Suggested replies maintain consistent service quality
What’s really happening behind the curtain: This isn’t just glorified keyword searching that sees “dog” in a subject line and mindlessly routes it to animal control. We’ve all experienced that kind of “smart” system, and it’s about as smart as a goldfish with amnesia.
Instead, it’s using natural language processing to actually understand the content and intent of messages. In the demo, someone deliberately tried to trick the system with a misleading subject line about dogs, but the content was clearly about litter in parks. The system analysed the full message context, ignored the red herring in the subject line, and correctly routed it to the parks team.
The AI reads through the entire message, identifies the main topics being discussed, and makes intelligent decisions about categorisation. It also extracts the key points that need addressing, ensuring nothing gets missed in lengthy emails.
The game-changer? Everyone from the brand-new temp to your most experienced staff can now focus on giving great answers instead of playing email detective. The system suggests where messages should go and offers response ideas, but humans still bring the judgment, empathy, and decision-making that no AI can match.
It’s not just pulling canned responses either. The system actually crafts suggestions based on what the person is specifically asking about—capturing your organisation’s voice while addressing the particular points raised. No more copy-pasting the same generic template and hoping it sort of fits.
This approach means consistent customer service without the staffing overheads—and without the risk of points being missed in busy inboxes.
The key insight? You don’t need to hire more people to improve service quality—you need to give your existing team tools that eliminate the low-value work so they can focus on what matters: the human connection.
3. ID Document Processing
Let’s face it—manually checking IDs and inputting data is about as exciting as watching paint dry. It’s also a breeding ground for errors that can have serious consequences.
Think about how many places you handle identification documents: new employee onboarding, customer verification, contractor certifications. When humans manually process these, mistakes happen—dates get mistyped, documents get misfiled, and suddenly someone’s been driving company vehicles with an expired licence for two years.
Here’s an approach that’s actually working:
Leverage pre-built AI models
No need to train systems on every possible ID type
Handles imperfect inputs
Process photos with glare, odd angles, and poor resolution
Automate document management
Save to SharePoint with consistent naming conventions
What’s really happening behind the curtain: Unlike the paper form solution that needs training on your specific documents, this implementation leverages pre-built AI models that already understand identification documents from around the world.
The system uses computer vision technology that’s been trained on thousands of different ID types—passports, driver’s licenses, national ID cards—from dozens of countries. It knows where to find key information on each document type, regardless of the format.
What makes this particularly clever is that it doesn’t just capture text—it understands document structure. It knows the difference between an issue date and expiry date. It recognises when a string of characters is a license number versus an address. And it does this even with imperfect images that have glare, weird angles, or poor lighting.
When verification from third parties (like police) is needed, Flowingly’s external actor feature can give those outside your organisation a secure way to contribute to the workflow without the usual email ping-pong.
Instead of monitoring mailboxes and manually attaching returned documents, external parties simply click a link that pulls them directly into the workflow, where they can submit their verification. The workflow continues automatically once they’ve contributed.
The result? A process that’s faster, more accurate, and creates a proper audit trail without anyone having to manually save documents or remember naming conventions.
The key insight? The most expensive mistakes are often the most mundane ones—a mistyped date, a misfiled document, a missed verification. Letting AI handle these details isn’t just about efficiency; it’s about reducing significant organisational risk.
4. Sentiment Analysis That Actually Tells You Something Useful
We’ve all been there—staring at pages of survey responses or feedback submissions trying to make sense of what people are actually saying. By the time you’ve made sense of it all, it’s usually too late to do anything meaningful with the insights.
That’s where the fourth use case comes in: real-time sentiment analysis and theme extraction from public submissions, surveys, or feedback.
Every organisation has talented people stuck wading through mountains of feedback—whether it’s council submissions, customer surveys, or internal staff sentiment. These folks could be identifying critical insights and taking action, but instead they’re drowning in a process that’s typically slow, subjective, and backward-looking.
Here’s an approach that’s actually working:
Automated sentiment detection
Understand emotional tone beyond simple positive/negative
Geographic sentiment mapping
Visualise patterns by location to spot regional concerns
Real-time dashboards
See results as they come in, not weeks after collection ends
What’s really happening behind the curtain:
This implementation uses multiple AI technologies working together to make sense of qualitative feedback—the kind of free-text responses that traditionally require hours of human analysis.
The system uses sentiment analysis algorithms to determine the emotional tone behind submissions. But it goes beyond simple positive/negative classification—it can detect nuances like “concerned but supportive” or “strongly opposed on specific grounds,” giving you a much more nuanced understanding of public opinion.
The AI doesn’t just look at individual submissions in isolation. It analyses patterns across all responses, using natural language processing to identify recurring themes and topics. This is not simple keyword counting—it’s understanding concepts even when people express similar concerns using completely different words.
What’s particularly clever is how it condenses complex feedback into actionable insights. The system automatically generates thematic summaries—short phrases that capture the essence of what people are saying about specific aspects of your proposal. This transforms pages of feedback into digestible insights that decision-makers can actually use.
The kicker? This happens in real-time, not weeks after your consultation period ends. Most survey results or submission analyses are produced after everything’s finished—when it’s too late to adjust your approach or messaging. With live insights, you can course-correct while your consultation is still active, ensuring you get more representative feedback.
The key insight? The true value of feedback isn’t in collecting it—it’s in how quickly you can understand and act on it. Real-time analysis transforms feedback from a retrospective exercise into an active guidance system for decision-making.
These four examples show how organizations are using AI today – not in some distant future. The key is using AI to handle repetitive tasks while keeping humans in control of decisions and customer relationships.
The most successful implementations:
- Free people from data entry to focus on customer service
- Improve consistency while preserving human judgment
- Automate document handling to reduce costly mistakes
- Turn feedback into actionable insights in real-time
Want to see these solutions in action? Watch our webinar with Incendo where we demonstrate exactly how these use cases work and how they’re built. Or get in touch for a demonstration of how Flowingly and AI can transform your processes without the usual implementation headaches.
