LogiNet International

Good prompts become even more valuable when teams can reuse, improve, and share them.

That's exactly why we built StickyPrompts. This free Prompt Catalogue includes 33 practical templates our team uses in real client projects and everyday work, across six business categories.

If you're looking for prompts that go beyond generic examples, it's well worth a look.

👉 Download your free copy: https://stickyprompts.com/ai-prompt-catalogue

🤓 And if you'd like to use these and many more prompts directly in a collaborative multimodel AI workspace, sign in to StickyPrompts and start for free: https://app.stickyprompts.com/

Business AI Prompt Catalogue Download a free catalogue of reusable AI prompt templates for marketing, HR, software development, productivity and business. Compatible with ChatGPT, Claude, Gemini and more.

Many companies come to us already knowing what kind of system they want: a new CRM, customer portal, or e-commerce platform.

In many cases, though, the conversations reveal that the system itself isn't the real problem.

These are the situations we encounter most often:

→ preparing quotes takes days
→ too much data has to be copied manually between systems
→ teams manage processes in Excel
→ retrieving a simple piece of information requires multiple phone calls
→ approval processes get stuck and slow down operations

In these situations, it's worth understanding what's really causing the problem before making technology decisions. Our experience shows that, in many cases, processes, roles, or information flows need to be clarified and improved first for a technology solution to deliver meaningful results.

In the article, we explain how software consulting helps organisations move from symptoms to the underlying problem, and from there to a well-functioning digital system: 👇

https://loginet.com/blog/it-consulting-technology-decisions

One of the most expensive moments in a software project is when, halfway through development, you discover that an important business process was left out of the planning phase.

At that point, you're no longer dealing with a misunderstanding, but with lost time, budget, and resources.

When business requirements haven't been properly explored, key rules are missing, or different teams interpret the same requirements differently, development can easily head in the wrong direction.

That's why we place so much emphasis on the specification phase. In practice, this involves far more than producing documentation:
→ uncovering business processes and the real problems behind them
→ defining functional and technical requirements
→ documenting both the current and the desired future state
→ building a working prototype that stakeholders can actually try out
→ identifying critical questions and risks before development begins

Our experience is that a working prototype prevents far more misunderstandings than a lengthy document on its own. Decision-makers, users, and developers all see the same system, allowing feedback and clarification to happen before development starts.

In the article, we explain how a software specification is structured, what a complete specification package contains, and how it helps reduce development risk: 👇

https://loginet.com/blog/functional-specification-software-project

Software design used to revolve around specifications, flowcharts, and long documentation. A significant portion of problems only surfaced during development.

Across our projects, we increasingly see that the biggest risk is often not the development itself, but discovering too late that users do not actually behave the way the specification assumed.

AI tools and low-code platforms have significantly changed this process.

Today, working prototypes can be built much faster, which leads to a very different approach to software design:
→ problems become visible much earlier
→ teams work from the same functioning system, not just specifications and mockups
→ workflows can be tested during actual usage
→ user feedback can be incorporated much earlier
→ it becomes clear much sooner if an expensive development direction will not work in practice

Our experience is that an early prototype often helps teams make better decisions than another round of documentation or planning workshops.

In the article, we break down how AI is changing software design, and why working prototypes have become one of the most important planning tools: 👇
https://loginet.com/blog/ai-software-design-rapid-prototyping

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?

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.

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/

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?

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.

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