SMEs Secretly Ask These 5 Questions Before Paying for AI
Insights from Tat Banerjee's discovery calls with SMEs evaluating AI solutions
Today, I had a call with someone who works with aged care organisations helping Italian-speaking communities. She has been searching for an AI translation tool for months.
Generic AI translation tools weren't working for her clients because elderly patients revert to regional dialects, not standard Italian. The conversation revealed something crucial about what SMEs actually need from AI, and it's not what most vendors think:
Small and medium enterprises aren't looking for an AI strategy or innovation theatre. They're looking for something far more practical: tools that close loops in their workflows without creating new headaches.
Here's what I've learned separates AI that gets deployed from AI that sits in pilot purgatory.
1. “Does It Solve a Specific, Painful Problem?”
At the start of the call, my potential client clearly stated their problem:
Carers can’t communicate with elderly Italian clients who speak in regional dialects. This is because many of them have dementia.
As their condition progresses, they revert to the language and dialect they learnt in childhood. An elderly Italian who's spoken English for 50 years in Australia might suddenly only speak Sicilian, the dialect of their village from decades ago.
They can measure the cost: missed health issues, incident reports that never get filed, and families complaining about poor care.
Compare this to prospects who say "we need AI to stay competitive" or "everyone's using it." Those conversations rarely go anywhere.
What I've learned:
SMEs who can quantify their problem in time, money, or risk are the ones who actually convert. My job isn't to convince them they need AI. It's to find clients who already know what problem needs solving.
2. “Can We Test This Before Committing?”
My potential client wanted to test the solution at a workshop next week. She needed something clients could try with real dialects before making decisions.
Early in building our company, I pushed for annual contracts and enterprise commitments upfront. I lost deals that way.
What I've learned:
SMEs can't commit six months and $50,000 to find out if something works. They need to test small, learn fast, scale only if it proves valuable.
3. “What Does It Cost When I Actually Use It?”
When she asked about pricing, I told her: $2-5 Australian dollars per hour of actual usage, not per-user licensing.
Her response: "Wait, you don't charge per user?"
She looked surprised, but in a good way.
What I've learned:
Usage-based pricing resonates with SMEs in a way seat-based licensing doesn't. Traditional software charges per seat. If they have 50 carers, they pay for 50 licences even though only 15 use it on any given day. With usage-based pricing, they pay for value received, not seats purchased.
4. "Is the Interface Simple Enough for Actual Users?"
The aged care client was worried.
"What if the elderly residents refuse to use it? They're not exactly early adopters."
I laughed.
Here's something nobody tells you about elderly users: they're not a monolith.
You've got two camps. Camp A genuinely gets excited about new tech. Camp B won't touch the TV remote without written instructions and a prayer.
The mistake? Trying to convince everyone at once.
The trick? Find your Giuseppe.
Every aged care community has one. Giuseppe, Rosa, whoever—the resident who actually wants to try new things. Let them test the AI translation tool first. Get them excited about speaking Sicilian again and having the carer actually understand.
Because once Giuseppe tells his friends "this thing is amazing" you've basically won. Peer recommendation from someone they trust beats any demo I could ever run. The tech-hesitant won't listen to me. They'll absolutely listen to Giuseppe bragging at lunch.
This is now my standard adoption advice for aged care: find your technology champion among the residents first. Let them do your marketing.
What I've learned:
Complexity kills adoption in SME environments. The current version is almost stupidly simple. One button to start. One button to stop. Everything else happens automatically. Power users sometimes complain it's too basic. But adoption rates are higher.
5. "Can This Work With Our Existing Systems?"
The aged care clients all use different management systems. Our solution needs to integrate with multiple platforms, not require everyone to switch to one system.
What I've learned:
"Integration is possible" and "integration is straightforward" are very different things.
Now I ask specifically:
What systems do you use?
How locked-in are you?
What's your tolerance for integration complexity?
Because what truly matters is:
- How long does integration typically take
- What information do we need from their systems
- Who owns the integration work
The deals that close fastest are where we integrate with what they already have, not where we require them to change.’
The Pattern I Can't Unsee
After dozens of these discovery calls, the pattern is clear. SMEs convert when they have:
- A specific problem they’re already spending money on. Not innovation theatre. Concrete problems with measurable costs.
- Willingness to test with minimal commitment. Pilots before contracts. Real scenarios before purchase decisions.
- Pricing that scales with value, not seats. Usage-based models that match their actual use patterns.
- Teams that will actually use simple tools, not complex powerful ones.
- Existing systems we can integrate with, not requirements to change everything.
Tools That Close Loops Without Creating New Headaches
Here's what I've noticed across nearly every discovery call:
SMEs often start by exploring AI chatbots. They've heard about ChatGPT. They've played with translation tools. They've tested customer service bots.
But the conversation always shifts to the same question:
"What happens after the chatbot responds?"
The aged care client didn't just need translation. She needed the conversation to become documentation.
The logistics company didn't just need a chatbot answering shipment questions. They needed those queries to update tracking systems.
The legal firm didn't just need contract analysis. They needed extracted terms to populate their case management software.
What I've learned: Chatbots that don't trigger actions are just expensive conversation partners.
The real value emerges when AI chatbots become the interface that closes operational loops. This is why we built our solution the way we did.
When a carer uses our platform to speak with an Italian-speaking patient, the AI doesn't just translate the conversation in real-time. It transcribes everything that was said in both languages. It generates a summary that captures the key points. And crucially, that summary is downloadable, ready to go straight into whatever system the aged care provider uses for record-keeping.
The loop closes. The carer pressed one button to start the conversation, one button to end it, and walked away with documentation that would have taken 20 minutes to create manually.
This is what I keep seeing in discovery calls:
SMEs don't want AI that makes their job different. They want AI that makes their job done.
A customer service team handling multilingual enquiries doesn't just need translation. They need the conversation logged, categorised, and ready for follow-up.
A healthcare provider conducting patient consultations across languages doesn't just need interpretation. They need clinical notes that satisfy regulatory requirements.
The shift I'm seeing? SMEs are moving from "Can your AI understand our questions?" to "Can your AI complete our workflows?"
This is why transcription, translation, and downloadable summaries aren't separate features for VideoTranslatorAI. They're the minimum viable loop.
Because what's the point of a brilliant conversation if someone still has to sit down afterwards and recreate what happened?
The prospects who get excited about our platform are the ones who immediately see this.
"Wait, so the summary captures both languages?" Yes. "And I can download it straight into our system?" Yes. "So the carer doesn't have to do any paperwork afterwards?" Exactly.
The chatbot isn't the destination. It's the interface to the automation they've been trying to build for years, finally packaged in a way that actually closes loops rather than creating new manual steps.
VideoTranslatorAI Real-time Interpreter Chatbot
What I Wish I'd Known Earlier
Most AI vendor advice tells you to lead with capabilities. "Here's what our AI can do."
After 50+ discovery calls, I've learned to lead with understanding.
"What problem are you trying to solve, and can our AI help?"
That shift changes everything. It moves from technology-first to problem-first. It focuses on closing loops in real workflows rather than showcasing impressive capabilities.
The prospects who become clients are the ones where I can honestly answer yes to most items on this checklist.
The ones who ghost me are usually the ones where I tried to force-fit our solution to their needs.
This checklist emerged from watching potential clients test our solutions, ask difficult questions, and sometimes walk away.
It's not what I wished SMEs cared about. It's what they actually evaluate when deciding whether to deploy AI in their organisations.
And honestly? They're usually right to be this demanding. Because the gap between impressive demos and deployed solutions is where most AI projects go to die.