Andrei Mellas

Founder Adapt Grow Fitness & Ai Interdisciplinary Collective

I'm passionate about human optimization unlocking ways to enhance physical recovery, cognitive performance, and overall resilience through evidence-informed approaches.

After years exploring mindset, strength training, neuroplasticity, and alternative therapies (from HRV tracking to psychedelic research), I've pivoted to building resources that empower informed decisions in emerging fields.

In 2026, I launched The Peptide List (www.thepeptidelist.com)—an independent, unbiased directory of 550+ verified peptide providers across the US (physicians, 503A/503B pharmacies, clinics, telehealth, labs) and an encyclopedia of 55+ research peptides.
Key features:

Comprehensive, searchable provider listings (no affiliates or endorsements)
Detailed peptide profiles (mechanisms, research insights, categories like weight loss or tissue repair)
Community-driven reviews and submissions

My goal: Cut through the noise in the peptides space with transparency and neutrality. 

This is for informational purposes only not medical advice. Always consult licensed professionals.
Previously, I founded/built ADAPT GROW FITNESS, focusing on physical and mental strength coaching.
Open to collaborations in biohacking, longevity, and health tech. 

Let's connect if you're researching peptides or optimizing performance!

#Biohacking #Peptides #Longevity #HumanOptimization #ResearchChemicals

Founder

Conducting research and analysis on the applications and implications of data analysis and artificial intelligence, and to use the findings of this research to promote the public good and benefit society through the development and deployment of ethical, responsible, and effective Al technologies and practices.

With extensive experience in IT Technology Leadership, Enterprise Architecture, and IT Management, I have committed my career to advancing innovation and driving impactful transformation. As a Technology Architect at IAG Transform, I play a critical role in shaping digital transformation, aligning technology strategies with business goals, and implementing innovative, scalable solutions.

My expertise lies in navigating the complexities of enterprise IT to deliver secure, maintainable, and future-ready systems that meet strategic objectives. I am passionate about harnessing digital capabilities to redefine business landscapes, fostering agility, innovation, and sustainable growth.

Beyond technical expertise, I excel at building strong relationships, influencing stakeholders, and promoting best practices to ensure excellence in digital solutions. My leadership is driven by a dedication to bridging the gap between technology and business, empowering organizations to achieve their strategic vision.

Nadzeya Stalbouskaya

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝗯𝘁 𝗜𝘀 𝗡𝗼𝘁 𝗮 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗜𝘁’𝘀 𝗮𝗻 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗯𝗹𝗲𝗺
There’s one uncomfortable truth about architecture debt that we don’t talk about enough.

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗯𝘁 𝘀𝘂𝗿𝘃𝗶𝘃𝗲𝘀 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗻𝗼 𝗼𝗻𝗲 𝘁𝗿𝘂𝗹𝘆 𝗼𝘄𝗻𝘀 𝗶𝘁.

Not developers. Not product teams. Not architects. And definitely not “the organisation”.

In many companies, architecture debt lives in the gaps between roles. Delivery teams optimise for speed. Product optimises for outcomes. Architecture advises, reviews, recommends. And debt quietly accumulates in between.

When something breaks, ownership is suddenly very clear. When something slows down, it becomes “everyone’s problem”. And that’s exactly where architecture debt likes to hide.

I’ve seen organisations with strong engineers, capable architects, and mature processes still drowning in architectural debt. Not because they didn’t understand the risks. But because there was no explicit moment where someone said: this trade-off is owned, tracked, and revisited.

Shared responsibility sounds collaborative. In practice, it often means deferred responsibility.

Architecture debt isn’t created by bad intent. It’s created by reasonable decisions made without a clear owner for the long-term consequences. Once the project ends, the context disappears. The system remains.

And later, when the cost shows up, everyone is surprised.

This is why tools and frameworks alone don’t solve architecture debt. You can document it perfectly and still do nothing about it. Without ownership, even the best visibility becomes noise.

In my experience, the turning point is not a new process. It’s a decision: 𝘄𝗵𝗼 𝗶𝘀 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗯𝗲𝘆𝗼𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆.

Until that question has a real answer, architecture debt will keep growing quietly, politely, and very efficiently.

𝘞𝘩𝘰 𝘰𝘸𝘯𝘴 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘥𝘦𝘣𝘵 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘴𝘢𝘵𝘪𝘰𝘯 𝘵𝘰𝘥𝘢𝘺? 𝘈𝘯𝘥 𝘪𝘴 𝘵𝘩𝘢𝘵 𝘰𝘸𝘯𝘦𝘳𝘴𝘩𝘪𝘱 𝘦𝘹𝘱𝘭𝘪𝘤𝘪𝘵, 𝘰𝘳 𝘫𝘶𝘴𝘵 𝘢𝘴𝘴𝘶𝘮𝘦𝘥?

#ArchitectureDebt #EnterpriseArchitecture #Ownership #Governance #TechLeadership

undefined undefined

First, the idea that AI adoption would automatically lead to productivity gains.
Sources:
https://www.gartner.com/en/articles/ai-value-realization
https://mitsloan.mit.edu/ideas-made-to-matter/why-ai-doesnt-automatically-make-teams-more-productive

Second, predictions around “autonomous enterprises” proved overly optimistic.
Source:
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Third, the expectation that governance would become lighter with smarter technology did not hold.
Source:
https://www.weforum.org/agenda/ai-governance/

https://www.pcquest.com/tech-explained/is-your-enterprise-architecture-ready-for-ai-10944385

Technology Architect at IAG Transform | Architecture Debt Advocate | Enterprise Architecture | Data & AI & Innovation | Digital Transformation

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝗯𝘁 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗦𝗹𝗼𝘄 𝗧𝗲𝗮𝗺𝘀. 𝗜𝘁 𝗦𝗹𝗼𝘄𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀
Here’s a pattern I keep seeing across very different organisations.

When architecture debt grows, teams don’t necessarily get slower.
𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗱𝗼.

Suddenly, every change needs a meeting.
Every initiative needs an exception.
Every roadmap discussion turns into a negotiation with the past.

“What happens if we change this?”
“Well... it depends.”
Depends on undocumented integrations. Depends on legacy constraints. Depends on a decision made five years ago that no one remembers approving.

This is how architecture debt quietly leaks into the business layer.

Business leaders don’t experience it as “bad architecture”. They experience it as uncertainty. Inability to get clear answers. Plans that constantly shift. Delivery that looks unpredictable even when teams are working hard. From their perspective, technology becomes unreliable not because it fails, but because it 𝗰𝗮𝗻’𝘁 𝗰𝗼𝗺𝗺𝗶𝘁.

And this is where many organisations misdiagnose the problem.

They invest in more governance.
More alignment meetings.
More process to “coordinate” change.

But the real issue is structural.
Architecture debt turns simple questions into complex ones. And no amount of facilitation fixes that.

I’ve seen cases where a business request was perfectly reasonable, but the architecture underneath made it risky, expensive, or slow. Not because the idea was wrong, but because the system was never designed to evolve in that direction. At that point, architecture debt is no longer technical. It becomes a strategic constraint, whether anyone labels it that way or not.

This is why measuring architecture debt purely in technical terms misses the point.

𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝗮𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗶𝗴𝗻𝗮𝗹𝘀:
– 𝘏𝘰𝘸 𝘭𝘰𝘯𝘨 𝘥𝘰𝘦𝘴 𝘪𝘵 𝘵𝘢𝘬𝘦 𝘵𝘰 𝘢𝘯𝘴𝘸𝘦𝘳 “𝘤𝘢𝘯 𝘸𝘦?”
– 𝘏𝘰𝘸 𝘰𝘧𝘵𝘦𝘯 𝘥𝘰 𝘱𝘭𝘢𝘯𝘴 𝘤𝘩𝘢𝘯𝘨𝘦 𝘭𝘢𝘵𝘦?
– 𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦𝘴 𝘥𝘦𝘱𝘦𝘯𝘥 𝘰𝘯 𝘦𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯𝘴?

When architecture stops enabling decisions, it starts negotiating them. And that’s a very expensive place to be.

Curious how this shows up for you.

𝘋𝘰 𝘺𝘰𝘶 𝘴𝘦𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘥𝘦𝘣𝘵 𝘮𝘰𝘳𝘦 𝘪𝘯 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘺 𝘥𝘦𝘭𝘢𝘺𝘴, 𝘰𝘳 𝘪𝘯 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘱𝘢𝘳𝘢𝘭𝘺𝘴𝘪𝘴? 𝘈𝘯𝘥 𝘸𝘩𝘢𝘵 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘩𝘢𝘴 𝘺𝘰𝘶𝘳 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘮𝘢𝘥𝘦 𝘩𝘢𝘳𝘥𝘦𝘳 𝘵𝘩𝘢𝘯 𝘵𝘩𝘦𝘺 𝘴𝘩𝘰𝘶𝘭𝘥 𝘣𝘦?

#ArchitectureDebt #EnterpriseArchitecture #BusinessAndIT #DecisionMaking

Happy to share that my latest article has been published in Architecture & Governance Magazine.

The piece is titled:
“When Excellent Technology Architecture Fails to Deliver Business Results.”
https://lnkd.in/g4-twnsz

This topic is very close to my daily work.

I’ve seen strong architectures. Clean diagrams. Solid technology choices.
And still no real business impact.

Because architecture does not fail on paper.
It fails in decision-making, in ownership gaps, in governance under pressure.

In the article, I explore why technically sound solutions sometimes don’t translate into outcomes, and what architects can do differently if we want to stay relevant to business leaders.

It’s not about blaming technology.
It’s about understanding the invisible forces that shape how architecture behaves in real organisations.

If you care about architecture that actually delivers not just looks impressive I’d be very interested in your thoughts.

What do you think breaks the connection between good architecture and real business results most often?

#EnterpriseArchitecture #ArchitectureDebt #Governance #BusinessAlignment

View company: Architecture and Governance Magazine

Check out the latest piece from Nadzeya Stalbouskaya over at A&G (link in the comments), where she writes about "When Excellent Technology Architecture Fails to Deliver Business Results"

𝗧𝗼𝗺𝗼𝗿𝗿𝗼𝘄 𝗜’𝗺 𝘀𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝗮𝘁 𝘀𝘄𝗶𝘁𝗰𝗵𝗢𝗡 𝗘𝘂𝗿𝗼𝗽𝗲 𝗮𝗻𝗱 𝘁𝗵𝗶𝘀 𝘁𝗼𝗽𝗶𝗰 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹 𝗳𝗼𝗿 𝗺𝗲.
My session is based on my EAPJ article: “𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘪𝘯𝘨 𝘧𝘰𝘳 𝘙𝘦𝘶𝘴𝘦: 𝘞𝘩𝘺 𝘗𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘛𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘐𝘴 𝘵𝘩𝘦 𝘕𝘦𝘸 𝘌𝘈 𝘐𝘮𝘱𝘦𝘳𝘢𝘵𝘪𝘷𝘦.”

And honestly, this is not just about platforms. It’s about why so many “good” architectures fail to scale.

We say we build enterprise platforms. But in practice, every program negotiates its own exceptions. Every domain optimizes locally. Every deadline wins over reuse. 

And then we are surprised that integration costs explode.
I’ve seen this pattern many times.

A company invests millions into a “strategic platform.” Two years later, half of the new initiatives bypass it. Not because engineers are lazy. But because incentives, ownership, and governance were never aligned to support reuse.

So tomorrow I will focus on three uncomfortable but very practical theses:
1️⃣ Reuse does not happen naturally. It must be architected deliberately.
If reuse is not measurable, incentivized, and protected by governance, it will collapse under delivery pressure.

2️⃣ Platform thinking is an operating model, not a technology choice.
You can buy the best tools and still fail if decision rights, funding models, and accountability stay project-centric.

3️⃣ Architectural debt grows fastest where reuse is optional.
Every “temporary” workaround becomes tomorrow’s integration layer. And those layers accumulate silently.

switchON is interesting to me because it brings practitioners. People who deal with delivery pressure, funding cycles, political trade-offs not just clean diagrams.

If you care about architecture that scales beyond one roadmap and one funding cycle, join the discussion.

𝘞𝘩𝘢𝘵 𝘣𝘭𝘰𝘤𝘬𝘴 𝘳𝘦𝘶𝘴𝘦 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯? 𝘋𝘰 𝘺𝘰𝘶 𝘮𝘦𝘢𝘴𝘶𝘳𝘦 𝘳𝘦𝘶𝘴𝘦 𝘢𝘴 𝘴𝘦𝘳𝘪𝘰𝘶𝘴𝘭𝘺 𝘢𝘴 𝘺𝘰𝘶 𝘮𝘦𝘢𝘴𝘶𝘳𝘦 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘺 𝘴𝘱𝘦𝘦𝘥?

#EnterpriseArchitecture #PlatformThinking #DigitalTransformation #ArchitectureDebt

Architecting for Reuse Why Platform Thinking is the New EA Imperative

𝗠𝗼𝘀𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗮𝗶𝗹 𝗻𝗼𝘁 𝗼𝗻 𝘃𝗶𝘀𝗶𝗼𝗻, 𝗯𝘂𝘁 𝗼𝗻 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀
Architecture strategy is one of those things almost every organisation claims to have.

There is usually a document. Sometimes very well written. Sometimes aligned with business language. Often approved and stored in the right place. And yet, when real decisions start, the strategy is rarely referenced.

From what I’ve seen, architecture strategy doesn’t fail because it’s wrong. It fails because it’s disconnected from how decisions are actually made.

When strategy is not tied to the portfolio, it has no leverage. It can’t influence investment choices, because nothing translates it into priorities. When it’s not tied to the roadmap, it has no time dimension. It describes direction, but not sequence, pace, or limits.

As a result, strategy becomes aspirational. It explains what the organisation wants to become, but not what it is willing to stop, delay, or decommission in order to get there.

This is where architecture debt quietly grows. Strategic principles say one thing, portfolio decisions say another, and the roadmap tries to satisfy both. In practice, it satisfies neither.

I’ve been in discussions where everyone agreed with the strategy, while continuing to fund solutions that directly contradicted it. Not because people were careless, but because the strategy had no mechanism to intervene.

Here is a position I strongly believe in. Architecture strategy is not a vision statement. It is a system of constraints. If it doesn’t limit choices, it doesn’t guide them.

For me, a working architecture strategy is visible in small, uncomfortable moments. When a solution is rejected. When an investment is postponed. When someone asks “does this fit our strategy?” and the answer actually changes the outcome.

That’s when strategy stops being a document and starts being architecture.

𝘞𝘩𝘦𝘳𝘦 𝘥𝘰𝘦𝘴 𝘺𝘰𝘶𝘳 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘢𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘵𝘰𝘥𝘢𝘺? 𝘞𝘩𝘦𝘳𝘦 𝘪𝘴 𝘪𝘵 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘪𝘨𝘯𝘰𝘳𝘦𝘥? 𝘈𝘯𝘥 𝘸𝘩𝘢𝘵 𝘸𝘰𝘶𝘭𝘥 𝘩𝘢𝘷𝘦 𝘵𝘰 𝘤𝘩𝘢𝘯𝘨𝘦 𝘧𝘰𝘳 𝘪𝘵 𝘵𝘰 𝘣𝘦𝘤𝘰𝘮𝘦 𝘢 𝘳𝘦𝘢𝘭 𝘤𝘰𝘯𝘴𝘵𝘳𝘢𝘪𝘯𝘵, 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘢 𝘳𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦?

#EnterpriseArchitecture #ArchitectureStrategy #TechnologyStrategy #ArchitectureDebt

𝗠𝗮𝗿𝗰𝗵 𝟭. 𝗙𝗶𝗿𝘀𝘁 𝗱𝗮𝘆 𝗼𝗳 𝘀𝗽𝗿𝗶𝗻𝗴. There’s something symbolic about this day.
Not dramatic. Not revolutionary. Just a quiet shift. More light. More air. A feeling that something is starting again.

In architecture and leadership, we rarely get “new seasons.” We carry legacy decisions, unfinished transformations, inherited constraints.

But spring is a reminder that renewal doesn’t require a full reset. Sometimes it’s just one honest conversation. One outdated assumption questioned. One small constraint removed.

Progress rarely arrives as a big launch. More often, it’s subtle recalibration.

A new habit. A new boundary. A clearer direction. Spring doesn’t erase winter. It builds on it.

Maybe today is a good moment to ask: 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘪𝘯𝘨 𝘵𝘰 𝘱𝘭𝘢𝘯𝘵 𝘵𝘩𝘪𝘴 𝘴𝘦𝘢𝘴𝘰𝘯 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘸𝘰𝘳𝘬 𝘰𝘳 𝘺𝘰𝘶𝘳 𝘭𝘪𝘧𝘦? 𝘈𝘯𝘥 𝘸𝘩𝘢𝘵 𝘥𝘰 𝘐 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘭𝘦𝘵 𝘨𝘰 𝘰𝘧 𝘵𝘰 𝘮𝘢𝘬𝘦 𝘴𝘱𝘢𝘤𝘦 𝘧𝘰𝘳 𝘪𝘵?

𝗛𝗮𝗽𝗽𝘆 𝗳𝗶𝗿𝘀𝘁 𝗱𝗮𝘆 𝗼𝗳 𝘀𝗽𝗿𝗶𝗻𝗴 🌿

#NewBeginnings #Leadership #Growth #Mindset

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝗱𝗼𝗻’𝘁 𝗳𝗮𝗶𝗹 𝗼𝗻 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗼𝗻 𝗵𝗼𝗻𝗲𝘀𝘁𝘆
Once a portfolio exists, the next artefact usually appears very quickly. The roadmap.

And this is where many organisations make a quiet but expensive mistake. They build a roadmap without grounding it in portfolio decisions.

The result looks familiar. Clear phases. Nice timelines. Everything seems achievable. Almost too achievable.

From my experience, an architecture roadmap without a portfolio is not a plan. It’s a collection of good intentions. It shows what we would like to do, but not what we are actually prepared to prioritise, delay, or stop.

A real roadmap is uncomfortable by design. It makes trade-offs visible. It forces sequencing. It exposes capacity limits. And most importantly, it connects future change to today’s architectural reality.

When the roadmap is not anchored in the portfolio, a few things usually happen. Dependencies are underestimated. Legacy complexity is ignored. Architectural debt is postponed again, just with a new date attached to it. And delivery teams inherit promises they never helped shape.

I’ve seen roadmaps that failed not because teams couldn’t deliver, but because the roadmap assumed an architecture that didn’t exist yet. Or ignored the one that already did.

Here is a simple rule I use. If your roadmap doesn’t clearly show what you are not doing, it’s not a roadmap. It’s a wish list.

And one more opinion that is rarely popular. A roadmap that everyone agrees with is usually too optimistic. Real roadmaps create tension, because they turn strategy into limits.

For me, the value of an architecture roadmap is not alignment. It’s honesty. Honesty about capacity, about risk, and about the architectural debt we are willing to carry, for now.

𝙌𝙪𝙚𝙨𝙩𝙞𝙤𝙣𝙨 𝙩𝙤 𝙮𝙤𝙪:
𝘸𝘩𝘢𝘵 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘥𝘰𝘦𝘴 𝘺𝘰𝘶𝘳 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘳𝘰𝘢𝘥𝘮𝘢𝘱 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘧𝘰𝘳𝘤𝘦 𝘵𝘰𝘥𝘢𝘺
𝘸𝘩𝘦𝘳𝘦 𝘥𝘰𝘦𝘴 𝘪𝘵 𝘧𝘦𝘦𝘭 𝘰𝘷𝘦𝘳𝘭𝘺 𝘰𝘱𝘵𝘪𝘮𝘪𝘴𝘵𝘪𝘤
𝘢𝘯𝘥 𝘸𝘩𝘪𝘤𝘩 𝘵𝘳𝘢𝘥𝘦-𝘰𝘧𝘧𝘴 𝘢𝘳𝘦 𝘮𝘪𝘴𝘴𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘪𝘵 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺

#EnterpriseArchitecture #ArchitectureRoadmap #TechnologyStrategy #ArchitectureDebt

I rarely recommend architecture books. Not because they are bad. But because many of them stay safely in the world of frameworks and theory. Stuart Dee’s Practical IT Architecture is different.

I had the pleasure of being one of the reviewers of this book, and I can honestly say it focuses on what architects really deal with every day:
How do you communicate with stakeholders who don’t speak “architecture”?  How do you explain trade-offs without oversimplifying? How do you manage debt and risk without sounding like the department of “no”? How do you turn structure into influence?

This is not a glossy framework guide. It is a working architect’s handbook.
Clear. Direct. Practical.

What I appreciate most is that it treats architecture as a decision discipline, not a diagramming exercise. And anyone who has tried to negotiate between business urgency and technical reality knows how important that shift is.
If you are practicing architecture in complex environments, especially in large enterprises, this book will feel familiar in a good way.

And yes, I recommend it. Not politely. Genuinely.

Have you read architecture books that actually helped you in real conversations? Or are most still living in PowerPoint land?

#EnterpriseArchitecture #ArchitecturePractice #ITArchitecture #ArchitectureLeadership

Author of Practical IT Architecture - A Concise Guide to Effective Practice, Enterprise and Solutions Architect Leader, Strategist, Thought Leader

Most architecture books focus on EA frameworks, models, and strategy, but few address the fundamentals that architects need every day: 

How do I communicate effectively with stakeholders? 

How do I manage debt, assess risks, and articulate trade-offs? 

How do I create compelling presentations and negotiate successfully?

That's why I wrote Practical IT Architecture - A Concise Guide to Effective Practice.

It is built for those who lead with clarity, structure, and impact. Whether you're scaling enterprise platforms or refining modular microservices, this guide delivers 154 pages of actionable insight grounded in real-world complexity.

✔️ Designed for practitioners
 ✔️ Structured for strategic clarity
 ✔️ Written to elevate your architecture conversations

This isn’t theory. It’s the architecture playbook your actually need.

You can visit the website http:/https://lnkd.in/eEZvcTBJ for more details or purchase on Amazon(https://amzn.eu/d/01K3sRQE)  in multiple formats. If you find it valuable, I'd be grateful for a review.

I will also be launching in-depth Udemy courses that explore the book's topics in greater detail so watch this space.

𝗜’𝘃𝗲 𝗕𝗲𝗲𝗻 𝗣𝗼𝘀𝘁𝗶𝗻𝗴 𝗔𝗯𝗼𝘂𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝗯𝘁 𝗳𝗼𝗿 𝟮 𝗠𝗼𝗻𝘁𝗵𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗜 𝗟𝗲𝗮𝗿𝗻𝗲𝗱
Two months ago I started a weekly series on 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗯𝘁. Not because I needed more content. Honestly, LinkedIn has enough content to last us all until retirement. I did it because I kept seeing the same quiet pattern across organizations: we treat “debt” like a technical cleanliness topic, and then we act surprised when transformation becomes slow, expensive, and emotionally exhausting.

Here’s my main point, and I’ll stand by it even if it annoys people: 𝗺𝗼𝘀𝘁 “𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗯𝘁” 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮 𝗰𝗼𝗽𝗶𝗻𝗴 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺. They make problems feel manageable. Tickets, refactoring, more tests, new standards. All good things. But very often the real blocker is structural. Wrong boundaries. Hidden dependencies. Missing ownership. Systems that were never designed to change, only to run. Calling that “tech debt” is like calling a broken foundation “paint issues”.

So I started writing about architecture debt in different angles, because language matters. When we label everything as tech debt, we solve the wrong problem really efficiently. We get great sprint reports. And then the business asks a simple question like “can we change this flow by next quarter?” and the honest answer is “no, because everything touches everything”. That’s not a backlog problem. That’s an architecture debt problem.

What surprised me most is what people argued about. Not “is debt bad?” Everyone knows debt exists. The hottest discussions were about 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗮𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲. Some people said debt can be a conscious trade-off, and I agree. But here’s the uncomfortable part: in many companies debt is not “accepted”. It’s just… ignored. No owner. No decision. No review date. It becomes normal, and normal becomes policy. That’s how transformations get stuck in “almost done” mode for years.

Another discussion that kept coming back was 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁. Someone said “if you can’t estimate repayment cost, it’s not debt”. I get the logic. But if we only call it debt when we can fully price it, we will call it debt only when it’s already painful. Early architecture debt is like a health issue. You rarely get a perfect cost model upfront. You get signals. And you either treat it early, or you enjoy the full experience later, with interest.

If you followed this series, thank you. It wasn’t written for architects only. It was written for anyone who is tired of “doing transformation” while the system structure quietly fights back.

And one more question, slightly provocative on purpose: 𝘋𝘰 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘮𝘰𝘴𝘵 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘳𝘦𝘥𝘶𝘤𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘥𝘦𝘣𝘵… 𝘰𝘳 𝘵𝘩𝘦𝘺 𝘫𝘶𝘴𝘵 𝘸𝘢𝘯𝘵 𝘢 𝘯𝘪𝘤𝘦𝘳 𝘭𝘢𝘣𝘦𝘭 𝘧𝘰𝘳 𝘪𝘵?

#ArchitectureDebt #EnterpriseArchitecture #DigitalTransformation #SystemsThinking

Glad to be part of The Enterprise Architecture Show 2026 with Sofa Summits.
I’ll be speaking about “Architecting for Reuse: Why Platform Thinking Is the New EA Imperative.”
 This topic comes straight from practice. Not reuse as a slogan, but reuse as a hard architectural discipline that forces better decisions about ownership, boundaries, and long-term cost.
Platform thinking sounds attractive. In reality, it challenges comfort zones.
 It exposes where organisations rely on shortcuts, local optimisations, and invisible dependencies that don’t scale.
If you’re working with enterprise architecture beyond diagrams especially in complex, fast-moving environments this will be a very practical conversation.
See you online on 26 February.

We’re thrilled to feature Nadzeya Stalbouskaya, Technology Architect at IAG Transform, presenting “Architecting for Reuse: Why Platform Thinking is the New EA Imperative”.  This is your chance to learn from one of the leading voices in Enterprise Architecture at:

🌐 The Enterprise Architecture Show 2026 (Virtual)
📅 Date: 26th February
🕑 Time: 14:00 CET

Don’t miss the biggest edition of #switchOn Europe yet! Click below for speaker line-up, event info and registration 👉 https://lnkd.in/eEez_dp6

𝗜𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝘄𝗮𝘀 𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽, 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘀𝘁𝗶𝗹𝗹 𝘀𝘄𝗶𝗽𝗲 𝗿𝗶𝗴𝗵𝘁?
At some point in my career, I realised something uncomfortable. If I treated personal relationships the same way I sometimes treated work, my friends would have staged an intervention.

Forget flowers and chocolates. Let's talk about corporate 𝗿𝗲𝗱 𝗳𝗹𝗮𝗴𝘀 and 𝗴𝗿𝗲𝗲𝗻 𝗳𝗹𝗮𝗴𝘀.

🚩 𝙍𝙚𝙙 𝙛𝙡𝙖𝙜𝙨 𝙄 𝙡𝙚𝙖𝙧𝙣𝙚𝙙 𝙩𝙤 𝙨𝙩𝙤𝙥 𝙣𝙤𝙧𝙢𝙖𝙡𝙞𝙨𝙞𝙣𝙜:
– No feedback, just silence.
– Meetings about meetings. And then another meeting.
– Leaders who disappear when things get hard.
– “It’s just a phase, it will calm down later”
– Being busy confused with being important
– Respect discussed in values, but missing in practice
– Commitment expected without clarity

🟢 𝗚𝗿𝗲𝗲𝗻 𝗳𝗹𝗮𝗴𝘀 𝗜 𝗻𝗼𝘄 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗹𝗼𝗼𝗸 𝗳𝗼𝗿:
– Calm, not chaos
– Clear ownership instead of heroic improvisation
– Honest conversations, even when they’re uncomfortable
– Time respected by default, not as a reward
– Growth that is visible, not motivational

𝗧𝗵𝗲 𝗽𝗿𝗼𝘃𝗼𝗰𝗮𝘁𝗶𝘃𝗲 𝘁𝗿𝘂𝘁𝗵? A lot of corporate cultures survive only because people are patient, loyal and afraid to leave. Not because the relationship is healthy.

I've seen both. Way too many 𝙧𝙚𝙙 𝙛𝙡𝙖𝙜𝙨 hiding behind trendy perks. Here's 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵: the best teams look like the healthiest relationships. You feel safe to speak up, try, fail, and get back up.

These days, I don’t look for “𝗹𝗼𝘃𝗲” at work. I look for trust, consistency and adult behaviour. Everything else is just decoration.

It's 𝗩𝗮𝗹𝗲𝗻𝘁𝗶𝗻𝗲'𝘀 𝗗𝗮𝘆. Don't settle for a workplace that only loves you on paper.

𝘞𝘩𝘢𝘵 𝘸𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘥𝘥 𝘵𝘰 𝘵𝘩𝘪𝘴 𝘭𝘪𝘴𝘵 𝘧𝘳𝘰𝘮 𝘺𝘰𝘶𝘳 𝘰𝘸𝘯 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦?
𝘐𝘧 𝘺𝘰𝘶𝘳 𝘫𝘰𝘣 𝘸𝘢𝘴 𝘢 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱,𝘸𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘤𝘰𝘮𝘮𝘪𝘵?

#CorporateRedFlags #HealthyWorkCulture #ValentinesDay #WorkLifeBalance

Happy to share that I’ll be joining the 3rd Annual International Future of BPO, Shared Services & GBS Conference as a Workshop Moderator this June in Prague.
I’ll be moderating a workshop on Overcoming Enterprise Architecture Challenges & Building a Digital-Ready Enterprise Architecture.
 Very close to the reality many of us live in: architecture that looks good on paper, but struggles under pressure, scale, and real organisational constraints.

What I’m most looking forward to is an honest, practical discussion. Not about frameworks for the sake of frameworks, but about what actually works in large, complex GBS and shared services environments.
If you’re working with enterprise architecture, digital transformation, or operating models in global organisations, this should be a very grounded and useful conversation.

See you in Prague.

Workshop Moderator Announcement | International Future of BPO, Shared Services & GBS Conference – Year 3

We are delighted to welcome Nadzeya Stalbouskaya, Technology Lead at IAG Global Business Services, as a Workshop Moderator at the 3rd Annual International Future of BPO, Shared Services & GBS Conference.

Nadzeya Stalbouskaya leads technology strategy across IAG Global Business Services, overseeing delivery hubs in Barcelona, Dublin, Kraków, London, Madrid, and Newcastle. With deep expertise in IT leadership, enterprise architecture, and digital transformation, she designs secure, scalable technology ecosystems that drive innovation, agility, and tangible business impact across global enterprises.

🎤 Workshop:
 Overcoming Enterprise Architecture Challenges & Building a Digital-Ready Enterprise Architecture

📅 June 9–10, 2026
 📍 Kings Court Hotel, Prague, Czech Republic
      https://lnkd.in/gAY5xvvz

Join Nadzeya Stalbouskaya to learn how enterprise architecture and digital innovation are empowering Shared Services and GBS to build world-class, future-ready enterprise operations.

Learn more about the conference:
 https://fobpossgbs.com/

Discover other global industry leaders speaking at the event:
 https://lnkd.in/ekW5C4_T

Book early bird discounted pass:
https://lnkd.in/ejN4mnRw

#FutureOfBPO #SharedServices #GlobalBusinessServices #DigitalTransformation #BusinessTransformation #EnterpriseArchitecture #DigitalReadyEnterprise #TechnologyLeadership #ITLeadership #TechLeadership #ITStrategy #GBSLeadership #InnovationAtScale #InnovationInGBS #FutureOfWork #WorkshopModerator #ConferenceSpeaker #PragueBusinessEvents #PragueConference #IndustryEvents #WomenInTech

I had a deep conversation with Mustafa Qizilbash on the real role of Enterprise Architecture in making AI scalable in large organizations.
Most companies still think AI fails because the models are weak.

They don’t.

AI fails because architecture was an afterthought.

In large enterprises, especially those with legacy systems, multiple domains and cross-country operations, AI is not just a technology initiative. It is an architecture decision. If data ownership is unclear, if standards are optional, if integration is inconsistent, AI will amplify the chaos. It will not fix it.

We spoke openly about a few uncomfortable truths:
– 80%+ of AI pilots fail not because of algorithms, but because there is no architectural foundation
– You cannot “automate the house” before understanding its structure
– Reusability is not optional in enterprise AI. It is survival
– Centralized does not automatically mean scalable. Decentralized does not automatically mean autonomous

Architecture is not bureaucracy.

It is the operating system of scalable AI.
If you are leading AI transformation, I would honestly ask you to pause and reflect:

Are we building intelligence?
Or are we layering AI on top of architectural debt?
And who really owns the data that our models depend on?

If these questions feel uncomfortable, this episode is probably for you.

#EnterpriseArchitecture #AI #DataArchitecture #DigitalTransformation

Author | Enterprise Data & AI Professional | CDO-Track | CDMP | Driving AI-Ready Organizations

🎙️ Episode #84: The Role of Enterprise Architecture in Making AI Scalable in Large Organizations with Nadzeya Stalbouskaya (Technology Architect, Poland)

Watch Episode #84: https://lnkd.in/gywVZdww

Most organizations think AI fails because the models are weak.

They don’t.

AI fails because architecture was an afterthought.

In this conversation with Nadzeya, we went deep into something many companies are now realizing — you cannot scale AI on top of fragmented systems, inconsistent standards, and uncontrolled experimentation.

Especially in large enterprises with decades of legacy systems, multiple domains, and cross-country operations, AI is not just a tool decision.

It is an architecture decision.

Some hard truths we explored:

• 80%+ of AI pilots fail not because of AI — but because there is no architectural foundation
• You cannot “automate the house” before understanding its structure
• Reusability is not optional in enterprise AI — it is survival
• Centralized does not mean scalable — and decentralized does not mean autonomous
• Federated architecture requires strong standards, not freedom without guardrails
• AI on unstructured data is easier to start — but structured enterprise data demands real modeling discipline
• Vendor convenience can quietly become vendor lock-in

One powerful insight from Nadzeya:

Before implementing AI, analyze your real situation.

Large organizations cannot “pause the airport” to upgrade systems overnight. Transformation must respect operational reality.

We discussed how enterprise architecture:

– Creates shared standards across domains and countries
– Enables API-first integration
– Defines data ownership clearly
– Forces technology evaluation beyond hype cycles
– Uses architecture review boards to prevent future technical debt
– Balances centralized governance with federated execution

AI is not a front-end feature.

It is a consequence of disciplined architecture.

Another critical takeaway:

Autonomy without standards is chaos.
Standards without autonomy is stagnation.

The future is federated — small, domain-aligned data products operating within enterprise-wide governance frameworks.

And perhaps the most important message for leaders:

You cannot scale AI if you are still debating who owns the data,
Which system is authoritative,
Or which standard applies.

Architecture is not bureaucracy.

It is the operating system of scalable AI.

If you are leading AI transformation in a large organization, pause and ask:

Are we building intelligence?
Or are we layering AI on top of architectural debt?

#EnterpriseArchitecture #AI #AgenticAI #DataArchitecture #DataGovernance #DigitalTransformation #DataLeadership #FederatedData #TechnologyStrategy #LetsTalkAboutData

With Nadzeya Stalbouskaya (Technology Architect, Poland) Watch Episode #84: https://youtu.be/WynImb3v5G0 Most organizations think AI fails because the

Open article: 🎙️ Episode #84: The Role of Enterprise Architecture in Making AI Scalable in Large Organizations by Mustafa Qizilbash • 2 min read

🎙️ Episode #84: The Role of Enterprise Architecture in Making AI Scalable in Large Organizations

𝗠𝗼𝘀𝘁 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗔𝗿𝗲 𝗡𝗼𝘁 𝗥𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗔𝗜. 𝗡𝗼𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹𝗹𝘆, 𝗯𝘂𝘁 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹𝗹𝘆
This is an uncomfortable topic. Which is probably why it’s rarely discussed.

From my experience, the biggest blockers to AI adoption are not models, data platforms, or tooling.
They are human expectations and organizational psychology.

AI requires a very specific mindset:
💠 trust in data
💠willingness to delegate decisions
💠acceptance of probabilistic outcomes

But many organizations still expect the opposite:
💠100% predictability
💠full control at every step
💠a clearly named person to blame

This mismatch creates tension long before any technical limitation appears.

𝟭. 𝗔𝗜 𝘄𝗼𝗿𝗸𝘀 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗮𝗻𝘁 𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆
AI produces likelihoods, not guarantees.
Many leadership teams still expect deterministic answers.
This is not a tooling problem. It’s an expectation problem.

𝟮. 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗜 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝘁𝗿𝘂𝘀𝘁 𝗶𝘀𝘀𝘂𝗲𝘀
Delegating decisions to AI means trusting data, processes, and prior design choices.
In organizations with weak ownership or political decision-making, AI becomes threatening rather than helpful.

𝟯. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗮𝗱𝗮𝗽𝘁𝗲𝗱 𝘁𝗼 𝗔𝗜 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿
When outcomes are probabilistic, accountability must shift from “who approved this?” to “how was the system designed, governed, and monitored?”
Most companies are not there yet.

This is why many AI initiatives stall quietly.
Not because the models fail.
But because the organization isn’t ready to accept how AI actually behaves.

Until expectations change, AI will remain a controlled experiment, not a real capability.

𝘐𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘤𝘶𝘭𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘢𝘤𝘤𝘦𝘱𝘵 𝘱𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘴𝘵𝘪𝘤 𝘰𝘶𝘵𝘤𝘰𝘮𝘦𝘴?
𝘞𝘩𝘰 𝘰𝘸𝘯𝘴 𝘈𝘐 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘪𝘯 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦, 𝘯𝘰𝘵 𝘰𝘯 𝘱𝘢𝘱𝘦𝘳?

#ArtificialIntelligence #EnterpriseArchitecture #ChangeManagement #SystemsThinking

𝗧𝗵𝗲 𝗨𝗻𝘀𝗽𝗼𝗸𝗲𝗻 𝗥𝘂𝗹𝗲𝘀 𝗼𝗳 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗖𝘂𝗹𝘁𝘂𝗿𝗲
Every company has policies. Handbooks. Values written on slides and walls.
And then there are the rules no one explains, but everyone eventually learns.

You learn them when you see who really gets promoted.
When you notice which topics are safe to raise and which ones quietly close doors.
When you realise that “open feedback culture” still has an invisible boundary.

These rules are rarely malicious. Most of the time, they are the result of history, power dynamics, fear, and habits that formed long before you arrived. But they shape behaviour far more than any formal policy ever will.

No document tells you how fast is “too fast” to disagree with your manager.
No onboarding explains when silence is interpreted as professionalism and when it’s seen as disengagement.
No policy clarifies how much initiative is welcomed before it becomes uncomfortable.

So people learn by watching. By testing. By making small mistakes and adjusting. By observing who is listened to, who is tolerated, and who is quietly sidelined.

The problem starts when we pretend these rules don’t exist. When organisations talk about transparency but never acknowledge the informal systems that actually run the place. That gap creates confusion, frustration, and unnecessary self-doubt especially for those who take written rules at face value.

Understanding unspoken rules doesn’t mean accepting them blindly.
But it does mean navigating reality instead of an idealised version of it.

And often, professional maturity is less about knowing the policies and more about learning how the invisible system really works.

𝘞𝘩𝘪𝘤𝘩 𝘶𝘯𝘴𝘱𝘰𝘬𝘦𝘯 𝘳𝘶𝘭𝘦 𝘵𝘰𝘰𝘬 𝘺𝘰𝘶 𝘵𝘩𝘦 𝘭𝘰𝘯𝘨𝘦𝘴𝘵 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘤𝘢𝘳𝘦𝘦𝘳? 𝘋𝘰 𝘺𝘰𝘶 𝘵𝘩𝘪𝘯𝘬 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘴𝘩𝘰𝘶𝘭𝘥 𝘵𝘢𝘭𝘬 𝘮𝘰𝘳𝘦 𝘰𝘱𝘦𝘯𝘭𝘺 𝘢𝘣𝘰𝘶𝘵 𝘵𝘩𝘦𝘴𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘭 𝘥𝘺𝘯𝘢𝘮𝘪𝘤𝘴?

#CorporateCulture #CareerReality #WorkplaceDynamics #ProfessionalGrowth

𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗜𝘀 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗦𝗮𝗺𝗲 𝗮𝘀 𝗕𝗲𝗶𝗻𝗴 𝗔𝗯𝗹𝗲 𝘁𝗼 𝗨𝘀𝗲 𝗜𝘁
Almost everyone I talk to today has “done an AI course.” Some even have several certificates.

And yet, inside organizations, the picture looks very familiar:
AI pilots don’t scale
Models never reach production 

Business leaders still ask: “So what does this actually give us?”

From my experience, this gap is not about intelligence or motivation.
It’s architectural.

𝗧𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗜 𝗸𝗲𝗲𝗽 𝘀𝗲𝗲𝗶𝗻𝗴 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀:

𝟭. 𝗔𝗜 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
Teams know how models work, but not how they fit into existing processes, data flows, ownership models, and operating rhythms. Without that, AI stays isolated.

𝟮. 𝗔𝗜 𝗳𝗮𝗶𝗹𝘀 𝘄𝗵𝗲𝗿𝗲 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝘂𝗻𝗰𝗹𝗲𝗮𝗿
Who owns the model in production?
Who owns data quality?
Who is responsible when outcomes drift?
If architecture doesn’t answer these questions upfront, AI quietly dies after the demo.

𝟯. 𝗔𝗜 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗯𝘆 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗶𝗻
Most AI problems are not algorithm problems.
They are integration, governance, data, and decision-making problems. This is where architectural thinking matters more than another course.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝘆 𝗜 𝘀𝘁𝗿𝗼𝗻𝗴𝗹𝘆 𝗯𝗲𝗹𝗶𝗲𝘃𝗲:
AI will not replace people.
But people who understand how AI becomes an enterprise capability will replace those who only understand the theory.

So the real question is not “Do we know AI?”
It’s “Can our architecture actually absorb it?”

𝘐𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯, 𝘪𝘴 𝘈𝘐 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘢 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘺, 𝘰𝘳 𝘴𝘵𝘪𝘭𝘭 𝘢𝘯 𝘦𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵? 𝘞𝘩𝘢𝘵 𝘩𝘢𝘴 𝘣𝘦𝘦𝘯 𝘵𝘩𝘦 𝘣𝘪𝘨𝘨𝘦𝘴𝘵 𝘣𝘭𝘰𝘤𝘬𝘦𝘳 𝘸𝘩𝘦𝘯 𝘮𝘰𝘷𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘱𝘪𝘭𝘰𝘵 𝘵𝘰 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯?

#EnterpriseArchitecture #ArtificialIntelligence #ArchitectureDebt #DigitalTransformation

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼𝘀 𝗳𝗮𝗶𝗹 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝘁𝗿𝘆 𝘁𝗼 𝗯𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗵𝗼𝗻𝗲𝘀𝘁
After many discussions about architecture portfolios, I often hear the same follow-up question. What exactly should be inside a portfolio if it’s meant to be useful, not just presentable.

This is where many portfolios quietly turn into documentation exercises. Everything gets included. Every system, every interface, every dependency. The result looks thorough, but it rarely helps anyone make a decision.

In my experience, a real architecture portfolio is selective on purpose. It doesn’t try to describe everything. It focuses on what influences decisions. What drives cost, risk, change speed, and long-term sustainability.

A useful portfolio makes a few things explicit. It shows which systems are truly business-critical and which are just historically important. It makes ownership visible, including where ownership is missing or blurred. It exposes where multiple solutions solve the same problem and where architectural debt is being consciously accepted rather than accidentally ignored.

What matters is not the level of detail, but the ability to answer uncomfortable questions. If this system fails, what breaks first. If we stop investing here, what do we actually lose. If we want to simplify the landscape, where do we start without hurting the business.

This is also where many portfolios fail politically. Because once these questions are visible, someone has to decide. Someone has to say yes, no, or not now. And that’s much harder than maintaining another spreadsheet.

One opinion that may not be popular. A portfolio that avoids tension is not doing its job. If it doesn’t create difficult conversations, it’s probably optimised for comfort, not for change.

For me, the value of an architecture portfolio is simple. It turns abstract strategy into concrete choices. And it makes architectural debt visible before it becomes expensive.

𝘞𝘩𝘢𝘵 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴 𝘥𝘰𝘦𝘴 𝘺𝘰𝘶𝘳 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘱𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘵𝘰𝘥𝘢𝘺? 𝘞𝘩𝘪𝘤𝘩 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘥𝘰𝘦𝘴 𝘪𝘵 𝘢𝘷𝘰𝘪𝘥 𝘢𝘴𝘬𝘪𝘯𝘨? 𝘞𝘩𝘦𝘳𝘦 𝘥𝘰 𝘺𝘰𝘶 𝘧𝘦𝘦𝘭 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘳𝘦𝘴𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘸𝘩𝘦𝘯 𝘱𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰 𝘥𝘪𝘴𝘤𝘶𝘴𝘴𝘪𝘰𝘯𝘴 𝘴𝘵𝘢𝘳𝘵?

#EnterpriseArchitecture #ArchitecturePortfolio #TechnologyStrategy #ArchitectureDebt

𝗠𝘆 𝗻𝗲𝘄 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗶𝘀 𝗹𝗶𝘃𝗲 𝗶𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗝𝗼𝘂𝗿𝗻𝗮𝗹:
“𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜: 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗛𝗶𝗱𝗱𝗲𝗻 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆.”
https://lnkd.in/dmEcGsWK

This piece is personal.
Over the last two years, I’ve seen the same pattern repeat across organizations. AI projects move fast. Pilots appear quickly. Demos look impressive. Expectations rise even faster.

And then something breaks. Not because the model failed. But because no one defined the boundaries.

Who is responsible for AI decisions? Who owns the data quality? Who monitors unintended consequences? Where does human judgment stop and automation begin?

These questions are rarely answered clearly.

In my experience, AI does not reduce complexity. It redistributes it. And often hides it.

Architects are not becoming less relevant in this environment. They are becoming more accountable. Not for building models, but for shaping decision structures, ownership models, escalation paths, and system coherence.

AI amplifies what already exists.

If governance is weak, AI scales weakness.
If architecture debt exists, AI accelerates it.
If responsibility is unclear, AI makes that ambiguity visible.

This article is my reflection on what the role of an architect truly becomes in the AI era. Less about drawing diagrams. More about holding the system together under pressure.

𝘞𝘩𝘦𝘳𝘦 𝘥𝘰 𝘺𝘰𝘶 𝘴𝘦𝘦 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘨𝘢𝘱𝘴 𝘪𝘯 𝘈𝘐 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦𝘴? 𝘏𝘢𝘴 𝘈𝘐 𝘴𝘪𝘮𝘱𝘭𝘪𝘧𝘪𝘦𝘥 𝘺𝘰𝘶𝘳 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘰𝘳 𝘦𝘹𝘱𝘰𝘴𝘦𝘥 𝘩𝘪𝘥𝘥𝘦𝘯 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺? 𝘈𝘯𝘥 𝘸𝘩𝘰, 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯, 𝘪𝘴 𝘵𝘳𝘶𝘭𝘺 𝘢𝘤𝘤𝘰𝘶𝘯𝘵𝘢𝘣𝘭𝘦 𝘧𝘰𝘳 𝘈𝘐 𝘰𝘶𝘵𝘤𝘰𝘮𝘦𝘴?

#EnterpriseArchitecture #ArtificialIntelligence #ArchitectureDebt #TechnologyLeadership

eapj.org

Architect in the Age of AI Responsibility, Boundaries, and Hidden Complexity

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗪𝗼𝗿𝗹𝗱 𝗜𝘀 𝗡𝗼𝘁 𝗔𝗯𝗼𝘂𝘁 “𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝗔𝗜”
It’s about holding the system together. The real question is not “Will architects become unnecessary because of AI?”

From what I see, it’s the opposite.
AI accelerates everything:
💠decisions
💠delivery
💠mistakes
💠technical and architectural debt

Speed is no longer the problem. Coherence is.

From my experience, the architect’s role is shifting fast.
Not from “drawing diagrams” to “prompt engineering”.
But from designing to maintaining system integrity under pressure.

𝗜 𝘀𝗲𝗲 𝘁𝗵𝗿𝗲𝗲 𝗰𝗹𝗲𝗮𝗿 𝘀𝗵𝗶𝗳𝘁𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴.

𝟭. 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗿𝗲𝗱𝘂𝗰𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆. 𝗜𝘁 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝗶𝘁
AI works across data, processes, platforms, and teams.
If boundaries are unclear, ownership weak, or dependencies hidden, AI doesn’t smooth them out. It makes them visible. And painful.

𝟮. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘇𝗲𝗿𝘀, 𝗻𝗼𝘁 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗿𝘀
In an AI-driven environment, things change faster than governance cycles.
Someone has to hold context.
Someone has to protect long-term coherence when short-term acceleration is tempting.
That role doesn’t disappear. It becomes critical.

𝟯. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗯𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝗿𝗶𝘀𝗸 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿
AI scales decisions instantly. That means every unresolved architectural shortcut now has a bigger blast radius.
The architect’s job shifts from “creating structure” to preventing systemic collapse.

This is not about knowing every AI tool. It’s about understanding how systems behave under extreme speed.

That’s where architecture still matters. Maybe more than ever.

𝘐𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯, 𝘸𝘩𝘰 𝘪𝘴 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦 𝘧𝘰𝘳 𝘩𝘰𝘭𝘥𝘪𝘯𝘨 𝘴𝘺𝘴𝘵𝘦𝘮 𝘤𝘰𝘩𝘦𝘳𝘦𝘯𝘤𝘦 𝘢𝘴 𝘈𝘐 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦𝘴 𝘤𝘩𝘢𝘯𝘨𝘦?
𝘋𝘰 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘴 𝘩𝘢𝘷𝘦 𝘵𝘩𝘦 𝘢𝘶𝘵𝘩𝘰𝘳𝘪𝘵𝘺 𝘵𝘰 𝘴𝘭𝘰𝘸 𝘵𝘩𝘪𝘯𝘨𝘴 𝘥𝘰𝘸𝘯 𝘸𝘩𝘦𝘯 𝘵𝘩𝘦 𝘴𝘺𝘴𝘵𝘦𝘮 𝘯𝘦𝘦𝘥𝘴 𝘪𝘵?

#EnterpriseArchitecture #ArtificialIntelligence #ArchitectureDebt #SystemsThinking

𝗦𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗼𝘃𝗲 𝗜𝘀 𝗡𝗼𝘁 𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲, 𝗯𝘂𝘁 𝘁𝗼 𝗦𝘁𝗼𝗽
In many organizations, optimization has become a reflex. If something doesn’t work, we refine it. If it’s inefficient, we redesign it. If it’s painful, we automate it.
But after years working with enterprise architecture and transformation programs, I’ve learned something uncomfortable: some projects and processes should not be optimized at all.

They should be stopped. Here are five signals I personally use to recognize that moment.

𝙏𝙝𝙚𝙨𝙞𝙨 1: 𝗜𝗳 𝗻𝗼 𝗼𝗻𝗲 𝗰𝗮𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝘄𝗵𝘆 𝗶𝘁 𝗲𝘅𝗶𝘀𝘁𝘀, 𝗶𝘁 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘀𝗵𝗼𝘂𝗹𝗱𝗻’𝘁
When a process survives only because “it’s always been there”, optimization is just prolonging confusion. Purpose comes before efficiency.

𝙏𝙝𝙚𝙨𝙞𝙨 2: 𝗜𝗳 𝗶𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝘀 𝗹𝗼𝗰𝗮𝗹 𝗰𝗼𝗺𝗳𝗼𝗿𝘁 𝗯𝘂𝘁 𝗵𝘂𝗿𝘁𝘀 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺, 𝗶𝘁’𝘀 𝗮 𝗻𝗲𝘁 𝗹𝗼𝘀𝘀
Many initiatives make one team’s life easier while increasing complexity everywhere else. Architecture exists to protect the whole system, not local convenience.

𝙏𝙝𝙚𝙨𝙞𝙨 3: 𝗜𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆-𝗯𝗮𝘀𝗲𝗱, 𝗻𝗼𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱, 𝘀𝘁𝗼𝗽𝗽𝗶𝗻𝗴 𝗶𝘀 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁
More reports, more steps, more checkpoints none of these guarantee value. When outcomes are unclear, optimization creates the illusion of progress.

𝙏𝙝𝙚𝙨𝙞𝙨 4: 𝗜𝗳 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗲𝘅𝗶𝘀𝘁𝘀 𝗺𝗮𝗶𝗻𝗹𝘆 𝘁𝗼 𝗰𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗲 𝗳𝗼𝗿 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗯𝗿𝗼𝗸𝗲𝗻 𝗼𝗻𝗲, 𝗶𝘁’𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗱𝗲𝗯𝘁
Fixing symptoms with layers of process is how complexity quietly accumulates. At some point, the bravest move is removal, not refinement.

𝙏𝙝𝙚𝙨𝙞𝙨 5: 𝗜𝗳 𝗻𝗼 𝗼𝗻𝗲 𝘄𝗼𝘂𝗹𝗱 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝘁𝗼𝗱𝗮𝘆, 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗰𝗹𝗲𝗮𝗻 𝘀𝗹𝗮𝘁𝗲, 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗱𝗲𝘀𝗲𝗿𝘃𝗲 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁
This is my simplest test. If we wouldn’t consciously design it now, optimizing it is not discipline, it’s avoidance.

Architecture is not only about building better things. It’s also about having the courage to say: this no longer serves us.

Stopping is not failure.
Stopping is design.

𝘐𝘧 𝘺𝘰𝘶 𝘤𝘰𝘶𝘭𝘥 𝘴𝘸𝘪𝘵𝘤𝘩 𝘰𝘧𝘧 𝘰𝘯𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘰𝘳 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘵𝘰𝘥𝘢𝘺, 𝘸𝘩𝘢𝘵 𝘸𝘰𝘶𝘭𝘥 𝘪𝘵 𝘣𝘦?

#EnterpriseArchitecture #ArchitectureDebt #OrganizationalDesign #TechLeadership

taplio

The Battle

Andrei Mellas

Nadzeya Stalbouskaya

Followers

Posts

Engagements

Likes

Performance ScoreNadzeya Stalbouskaya's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Andrei Mellas

Nadzeya Stalbouskaya

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Nadzeya Stalbouskaya's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreNadzeya Stalbouskaya's LinkedIn Performance