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LogiNet International, 20 Farringdon Street, London (2026)

06/03/2026

Many AI projects don’t fail at the start.

They fail right after the pilot.

The first use case works.
A small team uses it.
Everyone gets excited.

Then the company tries to scale it.

And things start to break.

What worked for 5 users doesn’t work for 50.
Costs rise.
Workflows don’t fit.
The “quick solution” suddenly needs rebuilding.

The pattern shows up again and again:

1️⃣ Teams scale before proving real value
2️⃣ What works in one team breaks organisation-wide
3️⃣ Scaling bad assumptions just makes them expensive

The AI tools that actually scale follow one rule:
They disappear into the workflow.

If it still feels like a “special project,” it’s probably too early to scale.

Curious → have you seen an AI pilot succeed, then struggle when it was rolled out to a bigger team?

20/02/2026

Ever notice how an AI tool looks brilliant in the demo…
and then quietly dies three weeks after rollout?

Not because it’s broken.
Not because the tech doesn’t work.
But because no one is really using it.

One pattern kept showing up across projects:

The tool wasn’t the problem.
The training was.

Most teams underestimate this part completely.

They budget for licenses.
They budget for integration.
They even budget for development.

But they don’t budget for the learning curve.

And here’s the uncomfortable truth:

If a tool needs hours of training before someone can use it on real work, it’s probably the wrong tool.

In practice, what works looks much simpler:

1️⃣ One tool at a time
Rolling out five “AI initiatives” at once guarantees confusion. Pick one use case. Solve it properly.

2️⃣ Teach people to automate their own boring task
Not “AI literacy workshops.” Not theory about models.
Sit next to someone and fix the task they hate doing every day.

3️⃣ Keep it practical
If someone can’t see how this saves them time within a week, adoption drops fast.

4️⃣ Adoption > sophistication
A simple tool used daily beats an advanced system nobody trusts.

We’ve seen projects where the technology was solid, the ROI case made sense, but the rollout failed because people were overwhelmed.

And we’ve seen the opposite:
basic tools, minimal training, clear use case → strong adoption and real impact.

When AI fails, it’s rarely because the model wasn’t smart enough.
It fails because the team never fully integrated it into their actual workflow.
And once that happens, even a good tool becomes “that thing we tried once.”

So here’s the real question:
When you introduce a new AI tool, do you measure technical performance, or do you measure whether people actually changed how they work?

Curious how you handle this in your team.

13/02/2026

Most e-commerce platforms are built for B2C. Then companies try to force B2B logic into them. That’s where things start to break.

We’ve launched the new website for Logishop → https://logishop.io/

Logishop was built for structured, complex sales processes and for serving B2B partners from day one:
→ custom pricing
→ deep integrations
→ large product catalogues
→ bulk ordering and fast reordering
→ quote management

And when needed, it supports B2C and hybrid models without turning your system into a workaround machine.

It’s already trusted by online store operators like Mirbest Group, Szimpatika or Libri Booklove, who rely on it for stable, scalable operations.

On the new site, you’ll find:
• Core capabilities explained clearly
• How B2B, B2C and hybrid models work in practice
• Industry-specific use cases
• Transparent pricing
• Our roadmap and upcoming developments
• The team behind it → LogiNet, with 18 years in e-commerce development

If your business model is structured, multi-layered, or partner-driven, you need more than a “standard” online store engine.

Take a look: https://logishop.io/

29/01/2026

Whenever AI security comes up, the conversation usually jumps straight to vendors, models, and regulations.

But in practice, that’s rarely where things actually go wrong.

What we’ve seen across projects is much simpler and more uncomfortable:
most AI risks don’t start with technology. They start with people.

Not because teams are careless.
But because AI slips into everyday work faster than rules and habits can catch up.

Someone pastes sensitive data into the wrong tool.
A prompt gets reused where it shouldn’t.
Access rights are broader than anyone remembers setting up.

And suddenly “AI security” becomes a problem, even though the model did exactly what it was supposed to do.

One thing became clear very quickly for us:
locking down vendors and ticking compliance boxes is necessary, but it’s not enough.

What actually reduces risk looks far less dramatic:

✅ Clear rules on what can and can’t be shared
✅ Role-based access instead of “everyone can try it”
✅ Basic training on how AI tools should be used in daily work
✅ Knowing where data flows, not just where it’s stored

Most issues don’t come from malicious intent.
They come from uncertainty and assumptions.

Teams assume the tool is safe by default.
Managers assume someone else thought about governance.
IT assumes usage is limited.

AI doesn’t break these assumptions, it exposes them.

We’ve learned that if people don’t understand the boundaries, no amount of security documentation will help.
And if teams are afraid of getting it wrong, they’ll either avoid AI entirely or use it in ways no one sees.

In the end, AI security isn’t just a technical topic.
It’s an organisational one.

So we are curious:

Where do you think the biggest AI risk actually sits today?
The tools themselves, unclear rules, lack of training, or something else entirely?

23/01/2026

At some point in almost every AI project, someone asks a simple question.
And the room usually goes quiet.

“Did this actually help?”

Not is it live.
Not did we deploy it.
But did it make work easier, cheaper, or better in a way anyone can clearly point to?

When we looked back at our own AI projects and client work, a pattern stood out. Teams often had dashboards, reports, and adoption numbers, yet still couldn’t say whether the AI was worth keeping.

You don’t need 20 KPIs.
You need three things you can explain without a slide deck:

1️⃣ Time saved
If a task took 5 hours and now takes 1, that’s value.

2️⃣ Money saved or earned
Lower costs, fewer errors, avoided hires, recovered revenue. If none of these move, something’s off.

3️⃣ Would people be angry if you took it away?
If the team wouldn’t care, the AI never really landed.

Everything else is usually noise.

We’ve learned that if you need a complex dashboard to prove AI is working, it probably isn’t. When AI actually delivers value, people notice it immediately. Finance sees it, operations feel it, and teams stop complaining about the task it replaced.

We also apply a hard rule:
if ROI is still negative after 6 months, we stop and reassess. Sometimes that means fixing the process. Sometimes it means dropping the tool entirely.

Not every AI experiment deserves to be scaled.

In the end, most teams don’t struggle with building AI.
They struggle with knowing whether it was worth it.

So we are curious:

How do you decide if an AI initiative actually worked?
Time saved? Cost reduced? Team adoption? Something else?

We’ve found that answering this honestly is harder than it sounds. And it’s one of the reasons many projects stall after the pilot phase.

16/01/2026

If AI “didn’t work” in your last project, there’s a good chance the problem wasn’t AI at all.

In most cases, it was the data.

This was one of the hardest questions we had to answer honestly in our own projects:
is the data actually usable, or do we just assume it is?

Every company has data problems.
Most just don’t like looking at them too closely.

Spreadsheets that have been copied for years.
CRMs where half the fields are empty or inconsistent.
Different teams tracking the same thing in slightly different ways.

Then AI gets added on top, and suddenly everyone expects clarity.

What we learned the hard way: AI doesn’t clean this up. It amplifies it.
If the input is messy, the output will be too → just faster and more confident.

The teams that actually get value from AI don’t start with a massive data clean-up.
They start much smaller.

What works in practice:
• “Clean enough” beats perfect data
• Fixing one workflow beats fixing everything
• Knowing where data comes from matters more than how much you have
• Improving how data is captured often creates more value than changing models

We’ve seen teams lose months trying to prepare all their data for AI.
We’ve also seen teams get results in weeks by focusing on a single flow: invoices, support tickets, product data, call logs.

Data readiness isn’t about being perfect.
It’s about being honest.

We included this in the guide because it shows up in almost every project that actually moves forward.

👉 https://campaign.loginet.com/ai-implementation-reality-check

07/01/2026

Most AI initiatives don’t fail at the model stage.
They fail much earlier.

Not because the tech isn’t good enough, but because teams get stuck before anything meaningful ships.

When we started pulling together the 25 questions that show up in every AI project, one pattern kept coming back again and again:
teams spend months talking about AI, but very little time actually using it.

What gets in the way isn’t a lack of strategy decks or frameworks.

It’s this mix of expectations pulling in opposite directions:
• Teams worry AI will replace them → it won’t.
• Leadership expects instant results → it doesn’t work like that.
• IT sees security and compliance risks → often for good reason.

So nothing moves. Or worse, everything moves at once.

From our projects, a few things became very clear:
→ Big “AI strategies” slow teams down more than they help.
→ Trying to roll out AI everywhere at once creates resistance.
→ Tools don’t fail first. Adoption does.

What works looks much less impressive on paper.

Teams that make progress usually start with one boring, expensive problem.
Something repetitive that everyone already hates doing.

They pick the simplest tool that can help.
They try it with a small group.
They measure time saved, not hype generated.

If it works, they keep it.
If it doesn’t, they drop it and move on.

No transformation programme.
No steering committee.
Just one problem fixed properly.

Most teams don’t need a perfect AI roadmap.
They need permission to start small and learn fast.

That’s why we built the guide around the questions people actually ask mid-project, not the ones consultants like to open with.
👉 We wrote down all 25 questions that slow AI projects down and the answers we learned the hard way: https://campaign.loginet.com/ai-implementation-reality-check

19/12/2025

Every December, we pause for one evening and get most of the team together to celebrate the year behind us.

Good food, sweets, drinks, and DJs setting the mood → a solid start.

Then came the quiz, put together by our colleagues, full of funny questions and small details only people from inside the company would recognise.

We marked a few milestones too. Colleagues celebrating 5 and 10 years with us, a small team who baked cookies week after week for everyone, and a special moment for a colleague reaching retirement.

Thanks to everyone who joined and helped make it a relaxed, friendly evening. A nice way to close the year together and head into the next one on a good note.

11/12/2025

Ever wondered why so many teams pour money, time, and enthusiasm into AI… and still end up disappointed?

It usually comes down to one thing:

They try to automate chaos.

Teams rush into AI hoping for a quick edge, but if the workflow underneath is messy, inconsistent, or full of bottlenecks, AI won’t fix it.
It won’t simplify the chaos.
It will speed it up.

That’s why many “AI transformations” fail before they even start.
Not because the tech is bad, but because the process it sits on is already broken.

Here's what actually works in practice:

1️⃣ Start with one repetitive, annoying task
The daily task your team keeps patching with manual workarounds.
This always beats starting with a big, abstract strategy.

2️⃣ Test AI on your real workflow
Not on demo data.
Not in a polished sandbox.
Use the version you deal with every day.
If the tool can’t survive real conditions → it’s the wrong tool.

3️⃣ Measure only what matters
Did the task get faster?
Did errors drop?
Did you reclaim hours?
These answers tell you when to continue, not vanity metrics or dashboards.

4️⃣ Then move on to the next fix
The strongest AI gains don’t come from trying to “AI-fy” everything.
They come from improving one process at a time.

Because the truth is still the same:
If the process is broken, AI just helps you break it faster.
Fix it first, then automate it.

We built the guide around this principle and 24 other real lessons from implementations that actually worked: https://campaign.loginet.com/ai-implementation-reality-check

🔑 Critical success factor
Start with something small today.
Your detailed AI strategy will be outdated long before it's finished.
The teams making real progress aren’t the ones with the most polished plans: they’re the ones who started last month while everyone else was still in meetings.

02/12/2025

We’ll say the quiet part out loud: we earn money building custom AI, and we still tell most teams not to build it.

That honesty turned into one of the key answers in our 25-question AI guide.
Here’s the decision logic we actually use at LogiNet before writing a single line of code:

🔍 Can an existing tool handle about 80% of the job?
If yes, use it. Customise the rest.
Most companies need better spreadsheets, not custom AI.

💰 Is the problem worth more than $200K per year?
If the financial impact isn’t that big, custom development rarely pays off.

🧹 Do you have clean, usable data?
If not, no AI model, custom or boxed, will magically fix it.
Clean the data first.

⏳ Will this process still matter in two years?
If the answer is no, don’t build. Use an off-the-shelf tool and move on.

And here’s what the cost side actually looks like in real projects:
• Smart API wrapper → $5–15K, 2–4 weeks
• Custom ML model → $50–200K, 3–6 months
• Full custom AI platform → $200K+, 6–12 months
• Existing tool + integration → $5–20K

When do we finally say “yes, build it”?
• ROI can be measured within 12 months
• There’s genuinely no existing tool that solves the problem
• The team has good data, a realistic budget, and patience
• The workflow is core to the business, not a side process

One example:
Our water-industry project. Demand prediction.
No SaaS did it properly.
So we built a custom model and it saved them millions.
That’s when custom makes sense.

When do we recommend sticking to boxed tools instead?
• HR, basic CRM, standard analytics
• Budget under $50K
• You need the solution “by next week”
• Or the reason is simply “our competitors use AI”

Custom AI works but only when the problem is big enough, stable enough, and measurable enough to justify it.

It’s one of the 25 questions we answered honestly in our AI reality check.
👉 See all 25 practical answers: https://campaign.loginet.com/ai-implementation-reality-check

25/11/2025

Last week our team spent an evening together for something a little different from our usual work: preparing and donating food and everyday essentials for people who need a bit of help right now.

With the guidance of Budapest Bike Maffia, we made and packed sandwiches in the office and gathered toiletries.

All of this went to the South-Buda Social Centre of the Hungarian Charity Service of the Order of Malta, a facility that provides a safe place, food, and accommodation for people in need.

Seeing the boxes fill up, table by table, reminded us how small actions add up when many people get involved.

A big thank you to everyone at LogiNet who joined, donated, or helped with the delivery.

21/11/2025

One thing we hear a lot:
“Which AI tools are actually worth paying for?”

After enough projects, we noticed the same pattern: teams try 10 tools before finding the one that actually solves their problem.
So we made it one of the 25 questions we answered in our AI guide.

Here’s a quick snapshot from what we’ve seen work:

💬 Customer support
• Intercom – good fit if you handle 100+ chats a day
• Tidio – solid for smaller teams
• Help Scout – when you need a proper support desk, not just chat

🔄 Automation
• Zapier – easiest to start with, but gets pricey as usage grows
• Make.com – more complex, often cheaper once you scale
• n8n – great if you have developers and want more control

✍️ Content & research
• ChatGPT Plus – does most of what the “AI copy” tools offer
• Perplexity – much better than searching the web manually
• Midjourney – when you really need custom visuals
• Buffer – scheduling with usable AI suggestions

📊 Data & documents
• DocuSign CLM – helpful if you handle a high volume of contracts
• Parsio – pulls data out of emails and PDFs
• Obviously AI – no-code predictive analytics if your data is in half-decent shape

💻 Development & IT
• GitHub Copilot – pays off if your devs code several hours a day
• Cursor – editor + AI that works well for full projects
• Pieces – saves and organises code snippets with context

And the rule we use internally:
If a tool doesn’t improve one specific task 5–10x, don’t scale it.
Trying more tools isn’t the strategy, measuring one properly is.

We collected 25 real questions like this and answered them based on what actually worked in the field.
See all 25 practical answers: https://campaign.loginet.com/ai-implementation-reality-check

LogiNet International

06/03/2026

20/02/2026

13/02/2026

29/01/2026

23/01/2026

16/01/2026

07/01/2026

19/12/2025

11/12/2025

02/12/2025

25/11/2025

21/11/2025

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