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Insights on AI automation
Expert advice on workflow optimization, building smarter systems, and driving real business results with AI.
Expert advice on workflow optimization, building smarter systems, and driving real business results with AI.

You’ve probably felt it already, the pressure to “do something with AI.”Your board is asking. Your competitors are experimenting. Your team is already using half-approved tools behind your back.
And yet… every time you try to push an AI initiative forward, something jams:
If you’re honest, it feels less like innovation and more like babysitting another unreliable system.
This blog is for you, the person who gets blamed when things break.We’re going to cut through the noise and walk through the AI-related challenges SMEs face, why they happen, and what you can do to avoid burning money, time, or reputation.
By the end, you’ll know exactly what’s at stake, what’s realistic, and how companies like Kuhnic build AI that actually works inside real operations.
Enterprises have larger budgets, cleaner data, dedicated AI teams, and entire departments dedicated to change management.
You don’t.
That means when you adopt AI, you’re doing it while:
And this is exactly why AI-related challenges for SMEs appear and function differently. You’re not fighting the technology, you’re fighting the context around it.
Let’s break down the biggest friction points and what to do about each.
A lot of businesses start their AI journey backwards:
Tool first. Problem later.
If you’ve ever heard someone inside your company say:
The risk:You invest in automation, but nothing actually changes.Your team wastes time. The system gets abandoned. Leadership loses trust in AI.
A better way:Start with one operational bottleneck that costs you real money or real time, e.g.:
AI only works when it’s solving a problem that’s already costing you something.
Most SMEs underestimate how messy their data is until an AI system tries to use it.
Common issues:
AI amplifies whatever it gets.Good data → great outputBad data → chaos
Example:A consulting firm we worked with had 15 versions of “final_final_updated_report_v7.pdf.”Their AI system kept referencing outdated information.The fix wasn’t “better AI.”It was cleaning the source.
The lesson:Before implementing AI, stabilise the environment it depends on.
Every leader trying AI for the first time hits this wall:
“Why is the model confidently giving me wrong information?”
Hallucinations happen when:
SMEs often try to adopt generic models, then expect enterprise-grade accuracy.
Generic AI = generic answers.Custom AI = reliable answers.
At Kuhnic, we fix this by:
You don’t have to trust the model. You just need to trust the system around it.
AI that lives in a silo is basically a toy.
But here’s the problem: most SMEs run 10–30 tools across the business —
Each one stores data differently, labels fields differently, and structures information differently.
If your AI can’t connect to everything, it can’t automate anything.
This is one of the biggest AI-related challenges because SMEs often assume:
“We’ll plug in the AI and it will just work.”
In reality, 70% of AI automation issues come from broken or incomplete integrations.
Here’s the truth most vendors won’t tell you:
AI doesn’t replace thinking. It replaces repetitive work.
What AI can do reliably:
What AI cannot do reliably (yet):
If your team expects “the AI will figure it out,” you’re guaranteed disappointment.
Imagine giving a robot a map of a maze with missing paths and mislabeled exits.
That’s how AI feels in most SMEs.
Book a discovery call to discuss how AI can transform your operations.
When your workflows aren’t:
AI can’t automate them.
Example:A law firm wanted an AI-powered contract review.But their lawyers all used different templates and different naming conventions.The AI couldn’t recognise what was what.
We rebuilt the process first.Then AI automated it flawlessly.
AI isn’t just a technical shift — it’s a behavioural one.
You’ll run into:
This is one of the most overlooked AI-related challenges for SMEs.
A successful rollout requires:
SMEs often have weaker security policies than enterprises, which increases:
AI systems introduce:
Kuhnic solves this by designing AI with:
Security isn’t optional — it’s part of the architecture.
Here’s the straightforward path we use with clients:
Look for processes that are:
Examples:
Solve one problem → prove value → expand.
If you can’t map it, you can’t automate it.
Use a simple structure:
We can workshop this with you in 20 minutes.
AI works best when it can:
No data → no automation.
This includes:
Guardrails turn AI from “creative” to “reliable.”
AI should live where your team works:
Zero extra dashboards.Zero extra friction.
Examples from recent clients:
If it doesn’t make life easier, it’s not worth implementing.
They were drowning in:
Their biggest AI-related challenge?
Their data was inconsistent, and their processes weren’t standard.
Instead of forcing AI onto a broken system, we:
Within 8 weeks:
This is what “AI that works” looks like.
AI can absolutely transform your operations, faster turnarounds, fewer mistakes, reduced admin, and clearer insights. But only when it’s built on solid data, stable processes, clear workflows, and realistic expectations. Understanding the real AI-related challenges SMEs face is the difference between wasting money and unlocking meaningful, measurable efficiency.
Want to see how this works inside your business? Book a 20-minute walkthrough with an expert at Kuhnic. No fluff. Just clarity.
The biggest challenges include messy data, unclear processes, poor integrations, unrealistic expectations, and lack of internal alignment. At Kuhnic, we help SMEs diagnose these issues upfront so they avoid overspending or deploying AI that doesn’t deliver results.
We create guardrails around the model: grounding it in your verified data, adding logic-based checks, and building approval layers. This dramatically reduces errors and makes the AI reliable inside your real workflows.
Yes, but only after stabilising the environment. Kuhnic often starts with data clean-up, standardisation, and integration so your AI systems have the right foundation. Without this step, results are inconsistent.
For most SMEs we work with, the first measurable outcome appears within 30–60 days. Once the first workflow is automated successfully, the compounding ROI becomes significant because every additional workflow becomes faster to deploy.
Generic tools can’t integrate deeply with your systems or reflect your operational realities. Kuhnic builds custom, high-impact automations tailored to your workflows, your data, and your business model, giving you accuracy, scalability, and real value.
Book a 20-minute walkthrough and see exactly what we can streamline inside your business.
Join 100+ businesses that have streamlined their workflows with custom AI solutions built around how they actually work.

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