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AI Solutions
I’m all about digital growth - for the past 10 years I’ve been working on digital innovations and building dozens of software products. I know each stage of the cycle like the back of my hand. As the CEO at Appunite I coordinate creating exceptional products with entrepreneurs and companies. The combination of my sales and tech experience makes me capable of understanding our clients needs and challenges, and it has helped companies from a variety of industries grow digital. My main goal is always to enable our client to focus on their industry transformation and the core of the business, while Appunite is responsible for building and delivering the products.
Every leader I talk to has the same complaint: "I know something's wrong in our operations. I just can't prove it."
They feel it in the friction. The delays that shouldn't happen. The workarounds their team has normalized. The sense that things could run smoother, faster, cheaper - but the data to back that intuition? It doesn't exist. Or it's scattered across twelve different systems that don't talk to each other.
So they make decisions based on gut feeling. In companies that claim to be data-driven.
I know this problem intimately. I lived it.
A few years ago, I made mistakes building my own leadership team. I didn't equip them with the right tools to observe reality and build processes. We created a culture where opinion mattered more than fact. Where we couldn't talk numbers. Where everything became emotional instead of evidence-based.
What I didn't understand then: this wasn't a leadership failure. It was an infrastructure failure. Every department had its own metrics, its own definitions, its own version of reality. When those realities collided in a leadership meeting, the result was not a decision. It was a negotiation between opinions. The loudest voice won, not the best argument.
I lost a lot of money. I lost authority. I made decisions I regret because I was operating blind, just like the leaders I now want to help.
The problem isn't intelligence. It isn't effort. It's visibility. And the silo that causes blindness is not between departments. It's between their data.
You can't fix what you can't see.
And the tools most companies use? They don't help you see. They help you do things faster; the wrong things, often. Your CRM tracks activities. Your project management tool tracks tasks. Your accounting software tracks transactions. Each system gives its department a view of its own fragment. But none of them answer the question that actually matters:
Why is this taking so long? And how much is it costing us?
Here's what changes when you can actually see your operations:
Instead of "we should probably improve our hiring process," you get: "Our average time-to-hire is 47 days. Industry benchmark is 28. 32% of delay happens between interview scheduling and feedback collection. This costs us approximately €15,000 per month in lost productivity and extended contractor costs."
The first is a discussion. The second is a decision.
And that distinction matters more than most leaders realize. When two competent people argue in a meeting, the problem is rarely ego. It's that they're reasoning from different datasets. Sales says the hiring pipeline is fine because they track offer acceptance rates. Operations says it's broken because they track time-to-productivity. Both are right within their own fragment of reality. Both are wrong about the whole picture.
Data creates urgency that sales pitches never can. When you see your own numbers, not someone else's case study, but your actual operations, you can't unsee them. The cost of inaction becomes concrete. The path forward becomes obvious. And when every leader looks at the same data, defined the same way, disagreement shifts from "whose numbers are right" to "what should we do about it." That shift turns meetings from defensive posturing into collaborative problem-solving.
This is why visibility must come first.
Not because it's interesting. Because it's the foundation for everything else. You cannot make good decisions about what to fix, what to build, what to change, until you know what's actually happening.
Most companies try to solve operational problems by buying software. They research options. They sit through demos. They pick the tool that promises the most features. They implement it.
And then they adapt their processes to fit the software.
This is backwards. And it deepens the silo problem.
You end up paying for 100% of features while using 10%. You force your team to work the way the software dictates, not the way your business actually operates. Each department picks the tool that fits its own workflow, building yet another isolated data reality. "Project margin" means something different in finance than in delivery. "On time" means something different in sales than in operations. When core concepts have no shared definition, alignment is structurally impossible.
The cost of this incoherence compounds silently. Sales promises what operations cannot deliver. Operations builds capacity for clients finance never approved. Finance cuts budgets on projects delivery considers essential. Each decision makes local sense. The aggregate produces organizational friction, wasted resources, and strategic drift. And the cost stays invisible until a crisis exposes it: a lost client, a failed project, a board meeting where the numbers don't add up.
Meanwhile, you're running your business on the same tools as everyone else. Your operations become commoditized. Your competitive advantage disappears into generic workflows.
There's a phrase I keep seeing in analyst reports: "The SaaS model depends on customers not being able to build their own solutions. That assumption is weakening."
The moat that protected software vendors for twenty years - technical complexity - is draining. AI has changed the economics of building custom software. The question is no longer "can we build exactly what we need?" The question is "do we understand what we need to build?"
And you can't answer that question without visibility.
In 1978, Dan Bricklin sat in a Harvard Business School classroom watching his professor build financial models on a blackboard. Erasing. Recalculating. Rewriting. The person who understood the business couldn't build the model without technical help. They were hostage to arithmetic.
Then came VisiCalc. Suddenly, business managers could build their own financial models. The constraint shifted from "can we build it?" to "do we understand what should be built?"
Accountants didn't disappear. They moved up the complexity ladder.
We're living through the same shift with software. AI coding tools can now build 80% of any application. A yoga studio owner can describe their booking system and get something functional. An HVAC company can integrate scheduling with inventory. This is genuinely transformative.
But here's what the hype misses: "functional" isn't "production-grade."
Gary Marcus, the cognitive scientist, calls it the 80/20 problem: "It's often easy to get a solution that is 80% correct, and extremely difficult to get the last 20%."
That last 20% is where systems actually work. Where edge cases don't break production. Where security isn't an afterthought. Where the software integrates with everything else you run.
AI handles the 80%. The question is: who handles the 20%?
And before you can answer that question, you need to know where the 20% matters most. Which brings us back to visibility.
Once you can see your operations clearly, a different kind of solution becomes possible.
Not software that you adapt to fit. Software that's built from your actual processes.
We call this "Process-Native Software." It's built from and for your specific workflows, not adapted to fit generic patterns. It captures the way your business actually works, not the way some product manager in San Francisco imagined businesses work.
The economics have changed. AI-assisted engineering means custom solutions can be delivered much cheaper than before, and iteratively, so you're not betting everything on a single big build. For a process that's critical to your business, one where generic tools cause constant friction, that's often worth it.
But you should only build custom where it matters. Most processes don't need it. Email doesn't need to be custom. Basic accounting doesn't need to be custom. The commodity functions should stay on commodity software.
It's the 3-5 critical processes that generate 80% of your value - those are the ones worth examining. Those are the ones where visibility reveals whether generic is good enough, or whether you're paying the hidden costs of forced adaptation.
Here's what I believe:
1. Start with visibility. Before you buy anything, build anything, or change anything, see what's actually happening. Create a baseline. Understand your processes as they are, not as you imagine them.
2. Quantify the cost of inefficiency. Don't settle for "this feels slow." Calculate what it costs. Compare to benchmarks. Make the case concrete.
3. Build custom only where it matters. For critical processes where generic tools cause friction, custom solutions make sense. For everything else, keep it simple.
4. Measure the improvement. After you change something, prove it worked. Track the impact. Build a feedback loop.
This is what we're building at Appunite. Shugyo.ai provides the visibility, not through more dashboards (dashboards visualize data; they don't unify the meaning behind it), but through a shared ontology: a structured definition of what objects exist in your business, how they relate, and what the metrics actually mean. It creates a digital twin of your operations, reveals where things break down, and quantifies the cost. When the data shows that custom software would solve a problem worth solving, we build it. Process-Native Software. Fixed price. Measured outcomes.
Small companies don't need this. The founder sees everything. But when you cross 50 people, no single person can hold the full picture anymore. You either build a shared operating model or you accept that your leadership team will govern by negotiation between partially informed opinions.
We always start with seeing. Because you can't fix what you can't see.
I don't want any other leader to be in the situation I was in - making decisions blind, losing money and credibility because the data wasn't there.
Every leader deserves to operate with facts, not feelings.
Every organization deserves to see itself clearly.
See clearly. Fix precisely.
That's what we're building. That's what I believe.
