DEV Community: Rylko Roman

How We Decide Whether AI Software Is Worth Paying For

Rylko Roman — Tue, 23 Dec 2025 08:22:57 +0000

Everyone says “you must invest in AI in 2026” – but no one wants to admit how many AI licenses are already sitting unused.

At Pynest, we build software and augment engineering teams for clients in the US and Europe. That means we sit on both sides of the table: as a buyer of tools like Microsoft 365 Copilot, and as a partner who has to make sure these tools really fit into daily engineering and business workflows.

This is how we actually decide when AI software is worth paying for – and when it is smarter to wait.

The context: AI budgets are under pressure

Analysts are already warning that the first wave of “buy AI everywhere” is cooling down. A recent Forrester view suggests enterprises are postponing a significant share of planned AI budgets because returns are not matching promises.

At the same time, research summarized by Microsoft and IDC shows that where generative AI is integrated deeply into operations, early adopters can see strong ROI, not just nice demos.

In other words: AI software is not automatically good or bad. It depends entirely on integration, use case selection, and how people actually work with it.

Our basic rule: start from workflows, not from logos

We do not begin with the question “Should we buy Copilot or Agentforce?”. We start with “Which workflows are currently painful or expensive?”

Typical examples inside Pynest:

Drafting and refining internal docs, RFCs, and client emails.
Sifting through long project threads and meeting notes to understand “what really happened”.
Helping engineers explore a new codebase faster, without turning them into prompt operators.

Only after we identify the top 3–5 painful workflows do we map them to tools. Sometimes an AI feature inside software we already pay for is enough; sometimes we need a dedicated product; and often we decide to build a small internal agent instead of buying a big platform.

For us, buying AI is never “strategic” in the abstract. It must be brutally tactical.

A simple evaluation framework we actually use

When we look at tools like Copilot-style assistants or AI-enhanced CRMs, we use three lenses:

Value per active user, not per seat.

We estimate how many hours a typical engineer, recruiter, or manager would realistically save per week. If the annual license cost is higher than 20–30% of that time value, we are already cautious.
Integration friction.

Does the tool plug into our existing stack (Git, ticketing, docs, HRM) or will it create yet another silo? If it cannot see the same context our people work in, we know adoption will stall.
Data and risk profile.

We check where prompts and outputs are stored, what training the vendor does on our data, and how they handle access control and audit trails. If this is unclear, the deal pauses – even if the UX is beautiful.

We treat vendor promises with healthy skepticism. A Microsoft-sponsored IDC report may show strong ROI averages, but we always translate that into our own numbers and workflows before committing.

What other experts are seeing

We are not alone in this more cautious approach.

Forrester VP research director Emily Collins notes that 2025 for CMOs is “less sensationalized and more operationalized”: AI investments must prove real efficiency and effectiveness, not just generate headlines.

Data scientist and author Daniel Gutierrez has highlighted the same IDC study: generative AI delivers substantial ROI only when embedded into core operations, not as a side experiment.

KPMG’s recent “Future of Work” research also points out that companies get far greater financial benefit from AI when employees themselves feel it helps them in daily work – not when it is pushed only from the top.

These outside views match what we see on the ground: tools succeed when they sit in the flow of work and are trusted by the people using them.

How we decide to adopt, delay, or avoid an AI tool

When we adopt

We tend to adopt when all of the following are true:

There is a clear, repeatable workflow with measurable pain (for example, engineers writing repetitive test boilerplate or recruiters cleaning up CVs).
The tool integrates with our stack with minimal extra steps.
We can define simple, concrete success metrics within 3–6 months (time to complete a task, ticket volume, lead time, etc.).
At least one “product owner” inside the business is personally invested in making it work.

For example, before buying a Copilot-type license at scale, we run a small internal pilot with detailed measurement: which teams use it, how often, and what changes in their commit patterns, defect rates, or lead times.

When we delay

We delay when:

The business case is vague: “everyone in the market is buying this, we’ll figure it out later.”
The vendor’s data policy is unclear or evolving too fast.
Our people do not yet have basic AI literacy – they need training first, not more tools.

Given that some reports show a significant share of enterprises are postponing AI budgets to later years because value is unclear, we are comfortable saying “not now” even if a tool is trendy.

When we avoid completely

We avoid tools that:

Lock our data in a proprietary format with no realistic export path.
Require sending sensitive client code or personal data to a black-box environment.
Promise to “replace entire teams” instead of augmenting them.

In our experience, anything that starts with “this will replace your engineers / analysts / SDRs” almost never delivers – and almost always creates cultural damage.

Budgeting and ROI: how we talk about numbers

On the budgeting side, we treat AI tools like any other operational investment:

We cap AI licenses as a share of our total SaaS spend, so they do not silently swallow the entire budget.
We tie renewal to observed behavior, not to vendor roadmaps: if usage and impact drop, we downgrade or cancel, even if the vendor is promising amazing new features “next quarter.”
We measure “time to useful output”, not just raw hours saved. If a tool makes a developer faster but increases review time and error rates, we count that as a net negative.

External stats are useful as a sanity check: for instance, some analyses show that many businesses now allocate at least 5% of their digital budget to generative AI and report measurable productivity gains – but these are averages, not a prescription.

What this looks like inside Pynest

Concretely, in 2025–2026 our approach at Pynest looks like this:

We run small, focused AI pilots in real teams (engineering, HR, sales ops) with clear owners and metrics.
We prefer platforms that extend tools we already use (IDE, office suite, CRM) over adding more standalone products.
We invest heavily in people and process: training, internal champions, and guidelines on when not to trust AI output.
We regularly review all paid AI tools twice a year and are not afraid to cut what does not prove its value.

For us, AI software is not a magic line item. It is one more tool in the toolbox. The only real question is: does it make our people, and our clients’ teams, better at the work that actually moves the business forward?

If the honest answer is “yes, and we can prove it,” then we sign the contract. If not, we wait.

Decentralization, Sovereign Clouds and the AI Hangover: A CTO’s View

Rylko Roman — Sat, 20 Dec 2025 09:58:46 +0000

In the last two years, three themes keep popping up in conversations with CIOs and CTOs I work with:

1) we are too dependent on a handful of cloud and SaaS giants,

2) regulators are pushing harder on data sovereignty,

3) AI is powerful, but nowhere near the magic “replace everyone” story we were sold.

Put together, this is exactly the mix behind today’s talk about decentralization, sovereign clouds and the “AI hangover”.

I'm the CTO at Pynest, a software development and staff-augmentation company working with distributed teams in the US and Europe. We sit in the middle of all of this: modern cloud architectures, strict data rules, and clients who want AI everywhere but are suddenly more cautious than 12–18 months ago.

Centralization Got Us Far — And Into Trouble

For a decade, the default answer to almost any infra question was: “Just put it on a hyperscaler”. The same happened in security tooling: endless “platform consolidation” via acquisitions, fewer vendors, bigger suites.

The result is very strong capabilities, but also massive single points of failure and huge targets. When one of the big providers suffers a breach or a regional outage, the blast radius is now entire sectors, not single apps. That “too much power in too few hands” problem is exactly what the Mozilla Foundation has been warning about for years in its Internet Health work.

From a CTO chair this now looks less like “efficiency” and more like concentration risk.

Sovereign Clouds: Control, Compliance… And Fragmentation

In Europe the answer is not just “more multi-cloud”, but “more sovereign cloud”. EU initiatives like Gaia-X and national sovereign cloud programs in Germany and France are trying to ensure that sensitive data lives under local laws, not only under US CLOUD Act and similar regimes.

For our EU-based clients this already shows up in requirements:

Critical workloads must run on EU-controlled infrastructure, or at least in EU-only regions with clear legal separation.
There must be an exit strategy: data formats, APIs and contracts that allow migration away from a single hyperscaler.
Security and compliance teams want clear answers to “who can touch this data, from which jurisdiction, and under what process”.

From the engineering side, that means more work on abstraction layers, standard interfaces and data-layer design. Instead of “one big cloud”, we design for a fabric: some workloads on a sovereign provider, some still on AWS/Azure/GCP, tied together with clear contracts and strong identity.

It is less “move everything to sovereign cloud” and more “treat sovereignty as a first-class constraint in architecture”.

The AI Hangover: From “Replacement” to “Augmentation”

A lot of the centralization push came from AI as well. The promise was: plug your data into a huge model hosted by a huge vendor and watch the magic. In reality, we got something more mundane but still useful.

Even big industry voices are pushing back on the “AI will replace developers” hype. AWS CEO Matt Garman recently called plans to replace junior staff with AI “one of the dumbest things I’ve ever heard”, arguing that you kill your future senior talent if you remove entry-level roles.

From what we see at Pynest:

AI coding tools absolutely speed up repetitive tasks, code search and experiments.
They do not replace the hard parts: architecture, debugging in messy systems, trade-offs under constraints, working with real stakeholders.
Teams that tried to “replace juniors with AI” quickly ran into a simple issue: nobody is growing into the next generation of seniors.

So yes, there is an “AI hangover”: expectations are being corrected. AI is moving into a more realistic place — as an accelerator, not a magic outsourcing of thinking.

Decentralization in Practice: Architecture, Not Slogans

What does “decentralization” actually mean for a CIO or CTO, beyond slogans?

From my perspective, there are four practical shifts:

Data and workloads become region-aware by design

You model where data is allowed to live and which services can talk across borders. Sovereign cloud zones, EU-only storage, “US only” partitions for some clients — this becomes part of your domain model, not just a hosting checkbox.
You reduce deep lock-in to a single stack

You do not have to go full multi-cloud with everything, but you do design for portability in critical paths: neutral data formats, open standards, portable CI/CD, Identity as a central layer instead of provider-specific glue.
AI is brought closer to the data, not the other way around

Instead of shipping all your sensitive datasets to some central “AI factory”, you bring models to where the data already safely lives — via private endpoints, on-prem deployments or sovereign providers that support AI workloads.
Resilience beats “one big platform”

The goal is not a perfectly unified tool, but graceful failure: if a provider, region or product dies, you degrade but do not go dark.

This is less about ideology and more about operational survival in a world of sanctions, new laws and very creative attackers.

How We Approach It at Pynest

On our projects at Pynest we see these themes from two sides: as a development partner and as a staff-augmentation provider embedded into client teams.

A few practical patterns we use:

“Soft multi-cloud” for regulated clients

For EU fintech or health projects we often design with a primary sovereign/EU cloud and a secondary hyperscaler, with data clearly split: PII and core transactions stay in the sovereign zone, anonymized or aggregated data can go to global AI services.
Data contracts instead of one “mega warehouse”

Rather than copy everything into one central place, we use lakehouse-style setups with strong data contracts between domains. That makes it easier to move or re-host pieces without breaking the whole system.
AI as a co-pilot on top of that fabric

Our AI work (observability, test generation, support automation) sits on top of this distributed architecture instead of dictating it. If a provider changes pricing or regulations shift, you should be able to swap out an AI component without re-building your entire platform.

Inside Pynest itself, we follow the same logic: critical HR and candidate data lives in controlled locations; our internal AI agents (for resume parsing, onboarding workflows, internal support) work with those datasets under strict access rules rather than sending everything to random external tools.

What CIOs Should Be Asking in 2026

If I had to reduce all of this to a short checklist for 2026, it would be:

“Where are we too centralized?”

Which single vendors, clouds or products can take down key parts of our business if they fail or change terms?
“Where does sovereignty really matter?”

For which datasets and workloads do we need legal and technical control over where data lives and who can see it?
“Is AI driving our architecture, or the other way round?”

Are we bending our systems around the latest AI product pitch, or fitting AI into a deliberate, resilient design?
“Do we have an exit plan?”

If tomorrow we had to leave a provider or move a region for political, regulatory or cost reasons, do we know roughly how we would do it?

The tension between centralization and decentralization is not going away. Sovereign clouds will grow, AI will keep evolving, and big platforms will still be there. The job of modern technology leaders is to design systems that benefit from scale without becoming hostages to it.

For me, that is the real story underneath the buzzwords: not “centralized vs decentralized” as a religion, but “how do we build systems and teams that can survive the next wave of change without a full rebuild every three years?”

How AI-Powered Observability Actually Changes Life For CIOs

Rylko Roman — Tue, 02 Dec 2025 08:34:02 +0000

Over the last two years I’ve watched observability tools quietly turn into something else. They stopped being “more dashboards” and started behaving like a junior SRE who never sleeps: spots anomalies, suggests root causes, even drafts post-mortems.

As a CTO working with multiple SaaS and data platforms at Pynest, I see the same pattern: once environments become cloud-native and globally distributed, humans alone cannot keep up. That’s where AI-driven observability — Dynatrace, New Relic, Grafana and similar platforms — stops being “nice to have” and becomes basic survival.

The New Relic Observability Forecast, for example, reports that adoption of AI technologies is already the top driver for observability initiatives, and organizations that use AI-driven observability see materially higher business value and ROI.

Why We Moved To AI-Driven Observability

Before we brought AI into the stack, our setup looked like many others:

APM in one tool
Logs in another
Custom business dashboards in something home-grown

During a major incident, we would open six tabs and start manual correlation. MTTR was often measured in hours, not minutes.

We decided to experiment with AI-enabled observability for three reasons:

Too many moving parts. Microservices, serverless, multiple clouds — no one person could hold the dependency graph in their head.
Alert fatigue. People either ignored alerts or tuned them so aggressively that real problems slipped through.
Expensive war rooms. Every serious outage meant half the senior team on a call at 2 a.m.

Platforms like Dynatrace now ship AI engines such as Davis® AI that continuously analyze dependencies and telemetry to detect anomalies and pinpoint root causes, aiming to move customers from reactive to preventive operations. That matched exactly what we needed.

How AI-Powered Observability Works In Practice

In our case, the AI layer sits on top of metrics, logs, traces and events coming from OpenTelemetry and vendor agents. Day to day, it changes work in a few concrete ways:

Automated anomaly detection. Instead of static thresholds, the system learns “normal” for each service and flags deviations.
Intelligent alerting. We get one incident with a causal graph rather than 200 near-identical alerts.
Root-cause hints. The platform suggests where the regression likely started — a specific deployment, database, or external dependency.
AIOps hooks. For a handful of well-understood scenarios, incidents trigger runbooks or rollback pipelines automatically.

On a real client project (a payment platform with strict SLAs), that combination cut the time to identify the root cause by roughly 40% and reduced noisy alerts by more than half. The important part is not that “AI solved everything”, but that humans now spend more time deciding what to do, and less time just figuring out what is happening.

Observability leaders have been pushing in this direction for years. As Charity Majors, cofounder and CTO at Honeycomb, likes to emphasize, real observability is about answering new, unanticipated questions about your system — not just staring at three fixed “pillars”. AI simply helps teams explore those questions faster when the data volume is beyond human scale.

Similarly, in a recent Grafana Labs article, Ben Sully, Senior Software Engineer, describes how their AI assistant helps teams resolve incidents faster, reduce alert fatigue and guide investigations step by step, instead of leaving engineers alone with a wall of charts.

Has AI Lived Up To The Hype?

Short answer: yes, but only if you treat it as augmentation, not autopilot.

Where AI delivers:

Noise reduction. Correlated, de-duplicated alerts are a huge win for SRE teams.
Faster incident triage. Suggested root causes are often “good enough” to start remediation immediately.
Better cloud cost conversations. When you can quantify how a noisy service hurts both reliability and spend, it’s much easier to prioritize fixes.

Where it still struggles:

Garbage in, garbage out. If telemetry is poor or incomplete, AI will confidently point you in the wrong direction.
Business context. The system doesn’t know that “checkout in Germany is more critical than an internal admin panel”, unless you encode that explicitly.
Trust. Engineers must understand why a suggestion was made, not just see a probability score.

In that sense, AI-powered observability is forcing us to do the unglamorous homework: better instrumentation, cleaner data models, clear priorities.

The Organizational Change No One Talks About

Most articles focus on tools. In reality, AI observability changes teams first.

What we had to adjust at Pynest and with our clients:

SRE as product managers. SRE and platform teams now spend more time designing signals and workflows for the AI, not just maintaining dashboards.
On-call as “AI pair programming”. On-call engineers learn to interrogate the AI — “show me similar incidents”, “what changed in the last 15 minutes?” — instead of clicking charts by hand.
New skills. We look for people who are comfortable with both telemetry data and business impact, because they are the ones who can tune the models meaningfully.

There are good external examples too. Air France-KLM’s IT leadership publicly highlighted how Dynatrace’s AI and prediction capabilities give them early warnings, reduce operational impact and support more sustainable operations. That is exactly the kind of story CIOs want to tell to their boards.

Lessons Learned, Pitfalls And What We’d Do Differently

If I were starting again, I’d structure an AI-observability journey around three phases:

Stabilize the basics.
- Standardize telemetry (OpenTelemetry where possible).
- Define a small set of truly critical user journeys and SLOs.
- Turn off 60–70% of legacy alerts before adding anything new.
Add AI in narrow, high-value paths.
- Start with anomaly detection around those critical journeys.
- Use AI-driven RCA suggestions, but always confirm with raw data.
- Let the platform draft post-mortems, with humans editing them.
Automate carefully.
- Automate only well-understood fixes (safe rollbacks, cache flushes).
- Keep a human approval step for anything that can touch customer data or money.
- Track metrics like MTTR, number of incidents per month, and cloud spend, so you can prove value.

The biggest pitfall I see is treating AI observability as “yet another dashboard project”. It isn’t. It is a long-term change in how teams see and operate systems.

How We Approach This At Pynest

At Pynest, we rarely “sell tools”. Instead, we help clients redesign how they run critical workloads.

On one engagement with a European fintech, we combined:

A commercial AI-enabled observability platform for deep automatic analysis
An open-source stack (Prometheus, Loki, Tempo, Grafana) for flexibility
Custom SLO and error-budget logic tied to business KPIs

The result wasn’t just fewer alerts. It was a new operating model: product teams owning their SLOs, SREs curating signals and AI workflows, and leadership finally seeing a single, trusted picture of reliability and cost.

That, in my view, is where AI-powered observability really pays off. Not when it magically “finds bugs with machine learning”, but when it becomes the shared language between engineering, operations and the business.

For CIOs looking at 2026, I’d summarize it this way:

Don’t start with “Which tool should we buy?”. Start with “Which decisions do we want this system to help us make faster and with more confidence?”

If you can answer that clearly, AI-driven observability has a good chance to deliver on its promises — and maybe even let your SRE team sleep a bit more.

SAP, Microsoft, Capgemini and Sanctions: What This Deal Really Means for CIOs

Rylko Roman — Thu, 27 Nov 2025 10:53:40 +0000

Written from the perspective of a European CTO.

Recently SAP, Microsoft and Capgemini announced a deal “to ensure business continuity if sanctions restrict Microsoft’s cloud services in Europe.”

On the surface it looks like another big-vendor partnership press release. But if you read between the lines, it’s one of the clearest public signals yet that sanctions against a hyperscaler are no longer treated as a remote edge case – they’re being planned for as a real continuity scenario.

I’m CTO at Pynest, a software development and staff augmentation company that works a lot with fintech, healthtech and other regulated clients. For our customers, the question “what happens if our chosen cloud suddenly becomes constrained in our region?” is not theoretical. This announcement puts that question front and center for any CIO in Europe.

In this article I’ll break down what this deal really implies and what I would do as a CIO/CTO in 2025–2026.

What This Deal Probably Really Says

If you strip away the marketing language, the underlying logic is roughly:

SAP wants to guarantee that its cloud products and customer workloads are not “hostage” to one provider (Azure) if sanctions or political restrictions hit.
Microsoft knows European customers are increasingly asking about sanctions, data sovereignty and “what if” scenarios. Partnering on an explicit continuity story is a way of saying: we hear you.
Capgemini acts as the integrator and “alternative infrastructure” specialist: helping to lift SAP workloads out of Azure and stand them up on an alternative European platform if something breaks politically.

In plain English, this looks like a pre-negotiated exit and continuity plan in case the political environment makes parts of Microsoft’s cloud unavailable in Europe. Not a promise to move everyone off Azure, but a structured way to keep critical SAP workloads alive if the worst happens.

Why This Matters Specifically for Europe

In Europe we’ve been living with three tensions for years:

Strong privacy and data-protection laws (GDPR and friends).
Extraterritorial regulations from outside the EU (like the US CLOUD Act).
Increasing talk about “digital sovereignty” and strategic dependencies.

Until now, much of that stayed in strategy decks and policy papers. There were initiatives like EU-only regions, Gaia-X, and national cloud certification schemes, but most CIOs still treated hyperscaler sanctions as a low-probability scenario.

This deal changes the tone. When SAP and Microsoft themselves publicly talk about a continuity path in case sanctions hit Microsoft’s cloud services in Europe, the message to European CIOs is:

“If our own partnership now has a sanctions continuity plan, you probably need one too.”

You don’t need to panic. But you can’t honestly call this risk “too exotic to plan for” anymore.

The Architecture Idea Hiding Behind the Marketing

If you ignore the brand names, there is a simple architectural principle underneath:

Your critical applications and data should be as decoupled from any single cloud provider as is realistically possible.

In practice that means:

Favoring common layers like containers and Kubernetes over deeply proprietary PaaS features where it really matters.
Managing infrastructure and configuration as code (Terraform, Bicep, Pulumi, etc.) instead of by hand in one provider’s portal.
Keeping portable representations of your state – database exports, configuration bundles, secrets and policies – in a form that can be bootstrapped elsewhere.

What SAP, Microsoft and Capgemini are effectively saying is:

“We will help you build a serious, tested emergency path out of Azure for your SAP workloads, not just talk about multi-cloud on a slide.”

This is multi-cloud for survival, not multi-cloud for discount negotiations.

How This Looks from the Trenches

At Pynest, working with European clients, I see a few recurring patterns that this news will only accelerate.

1. “One primary cloud, but a real Plan B”

Most mid-to-large organizations still don’t want three clouds in parallel. They want:

One primary platform (often Azure in SAP-heavy environments).
A practical continuity plan for a narrow set of critical workloads:
- documented export/restore procedures,
- tested infrastructure-as-code templates for an alternative provider or sovereign environment,
- and clarity around what “reduced but acceptable service” looks like in a crisis.

This SAP–Microsoft–Capgemini announcement validates that thinking. If the big players now talk publicly about sanctions continuity, it gets much easier for a CIO to argue for budget to design and test that plan B.

2. “Sovereign core, global edge”

Another pattern is splitting the estate into:

A sovereign core for EU citizen data, logs, and regulated analytics – in EU-only regions or EU-based providers.
Less sensitive services (marketing sites, some internal tools, non-critical analytics) in global regions of hyperscalers.

The new deal essentially formalizes the idea that even in a Microsoft-centric SAP landscape, you might need a parallel European-run landing zone that can host key workloads if sanctions clip Azure’s wings.

3. Contracts and accountability

Large customers are increasingly asking for:

clear exit clauses for data and workloads in contracts,
named responsibilities for migration and stand-up in case of a sanctions or jurisdiction issue,
and measured RTO/RPO targets for “sanctions scenarios”, not just technical outages.

Again, the SAP–Microsoft–Capgemini story gives legal and procurement teams a concrete precedent: “If they model this scenario, we should too.”

Roman’s Take: What Changes for CIOs

As a CTO, I see three shifts this will push forward.

1. Multi-cloud stops being a buzzword

For years, “multi-cloud” was mostly a slide for conferences and a driver of complexity. Now the angle changes:

Less “we run everything everywhere”.
More “we have one main cloud, plus a tested path to run the truly critical 10–20% elsewhere if geopolitics goes sideways.”

It’s not glamorous, but it’s responsible.

2. Exit planning becomes part of design reviews

Going forward, every major initiative that leans heavily on one cloud in Europe will have to address a question like:

“If this provider becomes partially unavailable in our region due to sanctions, what do we do in the first 30 days?”

If your architecture team cannot answer that without hand-waving, this news is your wake-up call.

3. Integrators and specialist partners become more central

There is a reason this deal includes Capgemini. Real continuity is not just a legal clause, it’s applied engineering:

building portable deployment templates,
rehearsing failover to a secondary environment,
keeping alternative platform knowledge up to date.

Many internal teams simply don’t have the bandwidth for that on top of their day job. Some will lean on big integrators. Others, especially SMEs, will work with smaller engineering partners or staff augmentation teams that bring this expertise in.

What I Would Do as a CIO in 2026

If I were sitting in the CIO chair of a European organization, here is the pragmatic checklist I’d start from:

Identify truly critical systems.

Not everything needs a sanctions continuity plan. Focus on workloads that:
- are business-critical,
- are heavily tied to one cloud,
- and carry regulatory or reputational risk if disrupted.
Audit your “exit readiness”.

For those critical systems, ask:
- Can we export data in a format that another provider can realistically consume?
- Do we have infra-as-code to stand up a minimal viable version elsewhere?
- Have we tested any of this beyond a slide deck?
Talk to your vendors and partners.
- What do your current cloud and application vendors commit to in sanctions scenarios?
- Are they prepared to support migration or dual-landing patterns?
- Can your integration partners actually execute an emergency move?
Update your architecture standards.
- Prefer portable components in critical paths.
- Restrict deep lock-in services to non-critical, easily rebuildable workloads.
- Make “documented and tested exit plan” part of your design gate for major systems.

Final Thought

The SAP, Microsoft and Capgemini deal is not a call to abandon Azure or hyperscalers in Europe. It is a public acknowledgment that:

Cloud continuity is now as much about geopolitics as it is about uptime.

If you’re a CIO or CTO in Europe and you still treat sanctions as an edge case, this is your nudge to update that mental model. You don’t need to go full multi-cloud. But you do need a serious, engineered plan B for the parts of your estate that your business and regulators truly care about.

That, in my view, is the real message behind this “strategic partnership” headline.

When A CIO Should Restructure Their Tech Teams (And How Not To Screw It Up)

Rylko Roman — Thu, 20 Nov 2025 15:08:04 +0000

I talk to a lot of CIOs and business leaders who feel a similar pain:

"We have smart people and plenty of tools, but everything still moves slowly. Maybe the problem is how we are structured?"

This article is my view as a CTO on when to shake up your team structure, how to do it without burning people out, and how to know it worked.

The audience here is business and product leaders, not just engineers. I will use a bit of tech language, but keep it practical.

1. The real signal: busy people, slow outcomes

For me, the leading sign is very simple:

Everyone is busy, yet important outcomes crawl.

You start seeing the same patterns:

Every decision has to go through one or two "heroes"
Teams cannot clearly answer a basic question: "What end to end result do you own?"
Projects jump between three or four departments before anything useful reaches a customer
Incidents require half the company on one Slack call

This is usually not a "people problem".

It is a structure problem.

Conway's law says that the systems you build are shaped by how your teams communicate. In practice it works the other way too: if your structure is fragmented, your products will feel fragmented.

When the org chart no longer matches how you want value to flow to customers, it is time to rethink it.

2. Start from value streams, not from boxes on a slide

Most restructuring fails because it starts from the wrong side.

Typical anti pattern: a small group of leaders draws a new org chart in PowerPoint, renames some roles and announces a "new operating model".

Nothing really changes.

A healthier way to plan:

2.1 Map how value should flow

Forget titles for a moment. Start with questions:

What are our core products or services?
For each one, what is the value stream from idea to revenue?
Where does work get stuck? Which handoffs are always painful?
Where do we see constant escalations because "no one really owns this"?

This gives you a picture of how work should flow and where your structure fights reality.

The "Team Topologies" approach by Matthew Skelton and Manuel Pais is very useful here. They suggest designing teams around clear streams of change and keeping each team's cognitive load at a sustainable level, instead of building huge "do everything" departments.

2.2 Design responsibilities first, structure second

Only after you understand value streams, move to responsibilities:

Which end to end outcomes should each team own?
Where do we need platform teams that provide internal services to others?
Where do we need enabling teams that help others adopt new practices?

Then and only then turn this into:

Teams and reporting lines
Product owners or business leads
Shared services (security, data, finance integration and so on)

If you go straight to "who reports to whom", you will rebuild your current problems with new labels.

3. What you actually need to make restructuring work

Restructuring is not free. It needs resources and some political courage.

3.1 Executive cover

Someone at the top (CEO, COO, board sponsor) must say clearly:

"This change will create noise for a few months. We support it and will not roll it back after the first conflict."

Without this, the first big escalation will kill the new structure.

McKinsey interviews with CIOs show the same pattern: operating model changes work only when there is explicit alignment in the whole leadership team, not just in IT.

3.2 Strong HR and people partners

You need HR and people managers who can:

Explain the "why" of the change in simple language
Work with managers on new roles and growth paths
Support people who are anxious or unhappy with the new setup

Restructuring is emotional. Pretending it is a purely rational exercise is a good way to lose your best people.

3.3 Time and a transition plan

A realistic restructuring needs:

2–3 months of overlap between old and new structure
Extra budget for training and sometimes for severance or new hires
Clear "do not touch yet" areas to avoid changing everything at once

If you try to flip the whole company in one week, you will create more chaos than value.

4. When is the right time to shake up your teams?

There is no perfect moment, but some times are better than others.

Good time

Перед запуском новой стратегии or a big product shift
After you have seen repeated failures with the current structure and have data, not only feelings
When there is a natural inflection point: new market, new line of business, merger

It is much easier to align structure with a fresh story:

"Here is our new direction. Here is how we change teams so this direction has a chance."

Bad time

Right in the middle of a major incident or regulatory crisis
As a reactive move after one or two people leave
Only because a new leader wants "their own org"

In crisis, you usually want to change the process and ownership around the specific problem, not redraw the entire company.

5. The biggest mistakes CIOs make in restructuring

I keep seeing the same five mistakes.

5.1 Cosmetic change

Teams get new names and managers, but:

Ownership does not change
All decisions still go through the same "hero" leaders
Old reporting lines continue informally

If after six months everyone still runs to the same three people for every decision, you did not restructure. You only added confusion.

5.2 No story for people

Big announcement in all hands:

"We have a new structure. Here are the boxes."

No explanation:

What problem were we solving?
What will be better for customers?
What will be better for employees?

Without a clear story, people assume the worst: "this is just politics" or "this is a way to hide layoffs".

5.3 Ignoring cognitive load

Many org designs look nice on a slide but ignore a simple fact: a team has a limited mental bandwidth.

If one team has to:

Own five unrelated systems,
Support three business units,
And learn three new technologies in a year,

it will burn out or slow down. That is why Team Topologies and many modern org design approaches talk a lot about limiting cognitive load and creating loosely coupled teams.

5.4 Forgetting about structure and architecture together

DORA research on high performing engineering organizations shows that loosely coupled teams plus loosely coupled architecture go hand in hand. Organizational structure cannot be fixed separately from systems structure.

If your architecture is one giant monolith, no team structure will fully save you. But the reverse is true as well: even with a modular architecture, if teams are organised around old department lines, you will see friction.

5.5 Treating restructuring as punishment

Sometimes restructuring is used as a way to "punish" underperformers or certain groups:

Unpopular teams get broken and spread around
Leaders are demoted without explanation
Whole domains are moved around every few months

After this, any future change will be met with resistance. People learn that "new structure" means "someone will be hurt", not "we will work better".

6. How to measure if the new structure works

Pretty slides mean nothing. Look at the flow.

Some simple metrics that work well:

Time from idea to first value in production Is it shorter than before?
Number of cross team escalations Do we still need three VPs in a room to ship a small feature?
Incident response How long does it take to detect and fix issues? How many teams are pulled in?
Ownership clarity Ask random people: "What is your team responsible for end to end?" If they can answer in one clear sentence, you are moving in the right direction.

You can connect this with business metrics:

Time to launch a new offer or pricing change
Onboarding time for new customers or partners
Satisfaction scores from internal "clients" of tech (marketing, ops, finance)

If both tech and business metrics improve, the structure is probably helping.

7. How we approach this at Pynest

At Pynest we work with clients that are often in the middle of this journey.

On our side we try to live the same principles:

We structure teams around client value streams, not just technologies. For example, a team that owns onboarding for a fintech client end to end, instead of "one backend team for everything".
We use a Team Topologies inspired model for internal platform teams (data platform, infrastructure, DevEx) and enabling teams that help product teams adopt new practices like observability or better testing.
When we augment a client team, we ask blunt questions about ownership and flow first, and only then talk about specific roles or headcount.

Internally we also measure:

How long it takes for a new developer to make a meaningful change in a client project
How often work gets blocked on "waiting for another team"
Whether each team can explain their mission in one clear sentence

This is not perfect, and we adjust the structure roughly once a year instead of waiting for a big crisis.

8. Final thoughts for CIOs

If you are a CIO or tech leader, here is my short version:

Do not wait for a full blown crisis. If everyone is busy and outcomes are slow, start exploring structural issues.
Do not start with boxes. Start with how value should flow and what outcomes teams should own.
Do not ignore cognitive load and architecture. Team design, system design and leadership culture live together.
Do not treat restructuring as a one time event. It is an ongoing process of adjusting how people, systems and goals fit together.

In 2026 and beyond, your technology stack will probably look modern enough.

Your real competitive advantage will be how you design the people side around it.

Why CIOs and CTOs Need to Own Corporate Recycling

Rylko Roman — Tue, 18 Nov 2025 06:57:03 +0000

Most days I think about data pipelines, infrastructure and delivery speed — not trash bags. But in 2025, you can’t separate IT from waste anymore, especially when you look at the numbers.

The Global E-waste Monitor 2024 estimates that the world generated 62 million tonnes of e-waste in 2022, up 82% since 2010, and we’re on track to hit 82 million tonnes by 2030, while only about 22% is formally collected and recycled.

At the same time, Circle Economy’s latest analysis shows that only 6.9% of all materials used globally come from recycled sources, and that share has actually been falling.

If you’re a CIO, CTO or IT director, a non-trivial chunk of that problem sits directly under your responsibility: laptops, phones, servers, network equipment, peripherals, cloud usage patterns, plus the data that describes all of it.

This is why I believe corporate recycling is no longer a “facilities” topic. It’s an IT governance topic, an ESG topic, and frankly a competitiveness topic.

Below I’ll walk through the key questions TechTarget asked — from why IT should be involved to how we’re planning to implement this at Pynest.

Why IT Leaders Should Be Directly Involved in Recycling Initiatives

There are three very practical reasons why CIOs and CTOs need to be in the core team — not just “support” — for any serious recycling initiative.

1. IT owns the asset lifecycle

Every device passes through IT at least twice: procurement and retirement.

Refresh cycles, vendor selection, leasing vs buying, redeployment policies — all of this decides whether a laptop:

Gets a second life internally;
Goes to a refurbisher or donation program;
Or quietly becomes hazardous e-waste.

IT asset management is already a structured process in most enterprises. Extending that into a circular IT mindset (repair, reuse, certified recycling) is a logical next step, not a separate project. Analysts and CIO reports now talk explicitly about “circular IT as a strategic imperative,” not a side benefit.

2. IT already runs the data stack

Modern recycling is data-heavy:

Tonnage by waste stream and location
E-waste volumes by device type
Vendor performance and diversion rates
Scope 2 and 3 emissions link-backs

That data lives across facilities, procurement, HR, ESG tools, and external providers. Integrating it into a single, reliable model is exactly what IT is good at.

3. Sustainability is now part of IT performance

The role of sustainability leaders has shifted from narrow reporting to board-level strategy, and IT is central to how they execute. :contentReference[oaicite:3]{index=3}

As Ivonne Bojoh, CEO at Circle Economy, puts it, even perfect recycling on its own won’t fix the “triple planetary crisis” — we need systemic change around how we design, use and retire products.

IT leaders are in a unique position to turn that systemic conversation into hard architecture choices: device strategy, cloud strategy, data strategy.

How Recycling Ties Into ESG, Compliance and Digital Transformation

For most enterprises, recycling sits at the intersection of three big themes:

1. ESG reporting and compliance

Under frameworks like CSRD, GRI or ISSB, companies are expected to report resource use, waste and circularity with the same discipline they apply to financials.

That means:

Audit-ready data on waste streams and e-waste
Proven chain-of-custody for devices (especially with sensitive data)
Evidence that suppliers and recyclers follow environmental and social standards

This is very much a data-model and integration problem — which is why IT needs a seat at the table from day one.

2. Digital transformation

Once you start treating recycling as a data product, it naturally becomes part of digital transformation:

Smart bins and sensors produce time-series data
Hauler and recycler portals expose APIs and machine-readable reports
Cloud analytics turn those streams into dashboards and alerts

The smart waste management market itself is growing fast — some reports estimate it at around $2.2–3.3B in 2023, projected to reach $8–11B by 2032.

So this is not just an environmental topic; it’s a genuine technology category.

3. Circular economy and risk

The circular economy is increasingly framed as a multi-trillion-dollar business opportunity, not a cost centre.

Derek Mak, Founder & CEO of 99Bridges, summarises it neatly: the circular economy is “a multi-trillion-dollar market by 2030 and growing rapidly,” which means there is room for many innovators.

If you run IT and ignore this, you’re not just missing a sustainability checkbox — you’re potentially behind on where your industry is going.

What a Modern Waste Audit Looks Like (and Why IT Should Care)

Classic waste audits were manual: people in gloves, bags on tarps, and spreadsheets.

That’s still a useful starting point, but a modern waste audit usually combines:

Manual sampling and sorting (to understand composition)
Digital capture of results (app or tablet, standard schema)
Integration into a central analytics platform

Sustainability & diversion experts like Kerstin Mayer, who works with Busch Systems, emphasise that regular waste audits help organisations “identify opportunities for improvement, reduce waste, and enhance sustainability.”

From an IT architecture perspective, a modern audit typically includes:

A standard taxonomy of waste streams across sites
APIs or data exports from haulers and recyclers
Optional IoT sensors on high-volume bins (fill level, weight, temperature)
A single dashboard where sustainability, facilities and IT see the same numbers

In other words: it looks like any other data integration project — with the difference that your “events” are movements of material, not user clicks.

Setting Recycling Goals and Aligning Them With Corporate KPIs

You can’t just say “we’ll recycle more” and call it strategy. Goals need to connect to the metrics your board already understands.

Typical patterns I see in mature programmes:

Environmental goals
- “Achieve 90–95% certified recycling for IT hardware by 2028.”
- “Reach 70% total waste diversion from landfill across core campuses.”
Financial goals
- “Reduce waste disposal costs per FTE by 20% in three years.”
- “Cut new device spend by 15% through redeployment and extended lifecycle.” :contentReference[oaicite:10]{index=10}
Risk and compliance goals
- “100% of retired devices processed through certified partners with verifiable data destruction.”
- “Full audit trail for e-waste export / handling to reduce regulatory exposure.”

For IT leaders, it helps to phrase goals in familiar language:

SLAs around asset redeployment time
Error budgets for missing or untracked devices
Coverage metrics for certified e-waste handling

This is how recycling becomes part of normal IT governance, not an exotic side project.

Tools and Platforms to Digitise and Automate Recycling

You can easily drown in tools here. My rule of thumb: use as few platforms as possible, but cover the full lifecycle.

1. ITAM and lifecycle tools

These are your source of truth for IT assets:

Every device has an ID, owner, location, and lifecycle status
Decommission steps include secure wipe, physical handling, and disposition (reuse / resale / donation / recycling / destruction)

When you connect this to recycling partners, you can show exactly how many devices took each path.

2. Smart waste management systems

These platforms handle:

Bin- and site-level data
Pickup optimisation and route planning
Contamination alerts and trend analysis

The rapid growth of the smart waste management market shows how fast this space is professionalising.

3. ESG and reporting platforms

These tools:

Map your waste, emissions and resource data to frameworks like CSRD/GRI/ISSB
Provide workflow for approvals and audit trails
Generate investor-ready reports and regulator-ready disclosures

4. Security and compliance tooling

For IT, one part is non-negotiable: secure device retirement.

You need:

Certified wiping tools and processes
Chain-of-custody logs and certificates of destruction
Integration with vendors’ take-back programmes (e.g., device makers who refurbish and recycle returned hardware)

This is where large vendors like HP, Dell and others have built robust “take-back + refurbish + recycle” programmes that CIOs can plug into rather than reinvent from scratch.

How Big Companies Are Already Handling IT-Driven Recycling

A few patterns from large enterprises and infrastructure players:

Device makers use take-back and circular programmes
- HP, for example, promotes device return for restoration, reuse or responsible recycling, tying it directly to lower environmental impact.
Tech giants integrate circularity into IT strategy
- Google’s Chief Sustainability Officer Kate Brandt has talked for years about treating hardware, data centres and operations through a circular economy lens — designing for reuse and recycling by default.
Recycling and waste leaders invest heavily in technology
- WM’s Chief Sustainability Officer, Tara Hemmer, frames investments in sorting and recycling technology as central to the company’s long-term strategy — not just compliance.
Circular tech startups show what’s possible
- Platforms like 99Bridges, led by Derek Mak, use software, AI and tracking to manage reusable packaging at scale for large retailers and cities, proving that digitally managed circular systems can handle enterprise-class volumes.

The common thread: IT is not just a back-office enabler; it’s at the centre of how recycling and circularity actually work.

Engaging Employees: From Poster Campaigns to Data-Driven Gamification

Even the best tools fail if people act as if nothing changed. Here, IT can again play an enabling role.

Effective programmes usually combine:

Clear internal communications

Simple, repeated messages about what goes where, why it matters, and what happens to the material afterwards — ideally endorsed by leadership, not just facilities.
Gamification

Studies on gamification and sustainability show that points, badges, team challenges and real-time feedback can significantly increase participation in recycling programmes and pro-environmental behaviour at work.

In practice, that might mean:

Team-level leaderboards for contamination rates or diversion
Challenges between offices (“which site can cut landfill waste by 15% first?”)
Rewards that link back to ESG commitments (donations, volunteering days, etc.)

Data visualisation in the flow of work

IT can push live dashboards into:

Office screens and digital signage
Slack/Teams channels
Intranet widgets next to operational KPIs

When developers see “bin contamination down 40% this quarter” next to deployment metrics, recycling stops being an abstract CSR statement.

Metrics, KPIs and Making Recycling Data Audit-Ready

To answer TechTarget’s question about metrics: here’s what I’d consider “table stakes” for a serious programme.

Core waste metrics:

Total waste and recycling by stream, by site, per FTE
Diversion rate (% of waste kept out of landfill/incineration)
Contamination rates for key streams (e.g., recycling, organics)

IT-specific metrics:

Number and % of IT assets:
- Redeployed internally
- Refurbished / resold / donated
- Recycled via certified partners
- Destroyed (with justification and certificate)
Average device lifetime by category
Scope 2/3 emissions linked to IT hardware lifecycle where possible

Data quality and auditability:

Each record has a source (sensor, vendor report, ITAM export, manual audit)
Periods (months, quarters) are closed and immutable once reported
Data is mapped to the right ESG tags and stored with proper access control

If you do this well, external assurance for ESG reports becomes much less painful — because your waste data behaves like proper operational data, not a side spreadsheet.

How We Plan to Implement This at Pynest

At Pynest we build data and software systems for clients in fintech, healthcare, e-learning and other regulated industries. Our own physical footprint is relatively small, but our IT asset footprint and data capabilities are large — which means we should practice what we preach.

Here’s how we’re structuring our own corporate recycling initiative from the IT side.

1. Start with e-waste and device lifecycle

We’re treating every device as part of a circular asset pool:

Extending lifetimes where it makes sense (RAM/SSD upgrades, not automatic refresh)
Making redeployment-first the default before buying new hardware
Partnering only with certified refurbishers and recyclers, and pushing them for machine-readable certificates and reports, not PDFs

2. Build a waste and circularity data model

We’re integrating:

Our ITAM data (devices, owners, lifecycle)
Vendor and recycler outputs (take-back reports, certificates of destruction)
Basic office waste data (recycling, landfill, organics)

into a small internal “circularity” data mart. The goal is to see:

Per-employee device footprint
Reuse / redeployment rate
E-waste flows by partner and geography

in the same dashboards as energy and cloud usage over time.

3. Turn internal patterns into client-ready architectures

Many of our clients now ask how to make waste and ESG data reliable, auditable and useful.

So we plan to turn our own experience into:

Reference data models for waste streams and IT lifecycle
Standard ingestion patterns for IoT sensors and vendor APIs
Reusable ESG dashboard templates that combine waste, energy and IT metrics

The idea is simple: if we can do this in our own modest offices, we can scale the pattern to large campuses and multi-site enterprises.

4. Tie recycling data back into IT decision-making

Finally, we don’t want recycling to live only in an ESG slide deck.

We’re planning to connect our circularity metrics back into:

Procurement (preferred vendors with strong circularity and take-back programmes)
Risk registers (data destruction, e-waste export risks)
Client RFP responses (demonstrating that our own IT operations follow the same principles we recommend)

If we do our job right, recycling and circularity will show up not as a separate initiative, but as another dimension of how we design and operate systems.

Closing Thought

Corporate recycling used to mean “buy more blue bins.”

Now it’s about how we design and run the entire digital infrastructure of a company — from device strategy and IT contracts to data models and analytics.

IT leaders are already responsible for the systems that create and track waste.

The next step is to use the same engineering mindset to reduce it, prove it, and turn circularity into a normal part of IT architecture — not an afterthought.

Building a Ransomware Playbook That Actually Works

Rylko Roman — Tue, 11 Nov 2025 08:00:10 +0000

Ransomware is not just a “security incident.” It is an operations, finance, legal, and reputation event that must be handled with rigor. A good playbook makes roles and decisions predictable under stress, turns technical steps into repeatable procedures, and measures recovery in business terms. Below are the essentials for CISOs, CSOs, CIOs, and CTOs: how to plan through tabletops, how to staff, which remediation steps matter, and how to recover reliably. I also share what we implemented at Pynest and the results.

Planning: Build Muscle Memory With Realistic Tabletops

Plan as if you’ll be hit tomorrow. Keep plans short, specific, and owned by people with names. Each scenario should include: entry vector, first signals, containment approach, decision gates, roles, notifications, recovery route, and success criteria.

Three core scenarios to cover

Credentialed compromise that spreads quickly
Double extortion with data theft
Backup corruption or tampering

Run tabletops quarterly. Rotate times (including nights/weekends). Invite business owners, not only security and IT. Keep scenarios slightly ambiguous to force evidence-based decisions. End with a “hot wash,” extract 5–10 improvements, assign owners and due dates, and track closure.

“Security is not a product, but a process.” — Bruce Schneier

Decide authority in advance. Specify who can isolate identity providers, revoke OAuth apps, shut down SSO or VPN for affected segments, block command-and-control, and trigger clean-room recovery. Name alternates.

Staffing and Skills: Design for Coverage, Not Heroics

Your roster should cover identity, endpoints, backups, networks, and communications, with clear primary and backup owners.

Minimum functional roles

Incident Commander: runs the war room, keeps a decision log, reports to executives
Identity Owner: AD/Entra/Okta, conditional access, token revocation, service principals, vault rotation
Endpoint/EDR Lead: quarantine at scale, tuning, clean forensics collection
Backup & DR Owner: immutability policy, restore tests, service restore priorities
Network/SecOps: emergency segmentation, C2 blocking, clean networks for rebuild
Legal & Communications: notification rules and language for regulators, customers, partners
External IR Retainer: pre-contracted DFIR with access and NDAs in place

“We cannot allow avoidable cyber disruption to cost human lives.” — Jen Easterly

Shift skills left. Train non-security owners on the exact runbooks they must execute. A backup admin who can run a restore drill on their own is more valuable than any policy binder.

Remediation: Contain Fast, Eradicate Thoroughly, Verify Like a Skeptic

Containment checklist

Isolate compromised devices and accounts
Suspend risky trust: SSO, high-risk OAuth apps, unused service accounts
Block known command-and-control routes and lateral movement protocols where feasible
Preserve evidence: memory, disk, identity audit logs, cloud admin actions
Communicate on a pre-agreed, safe channel and timestamp decisions

Eradication checklist

Rebuild from golden images
Rotate everything: passwords, API keys, OAuth secrets, certificates, service credentials
Patch initial access vectors and exploited weaknesses
Hunt for persistence: scheduled tasks, GPO backdoors, rogue identity apps, modified conditional access, startup services

Verification checklist

Rescan and risk-rate results
Use canary files and tokens to catch residual encryption behavior
Run contract tests at service boundaries to confirm critical flows work

“Quantify the risk. Identify key assets, make sure they are backed up and that the backups are secure.” — Kevin Mandia

This is the right mental model for remediation: measure what matters, protect what pays the bills, and assume attackers targeted your recovery path as well.

Recovery: Engineer for Predictability

Backups that survive the attacker

Enforce 3-2-1-1-0: three copies, two media, one off-site, one immutable, zero errors in restore tests
Keep at least one copy that cannot be altered even by admins
Test restores on a schedule and publish pass/fail to executives

Clean-room rebuild

Stand up an isolated environment, restore core identity and networking, then a minimal set of business services
Validate data integrity and configurations before reconnecting
Reintroduce segments in phases and run contract tests after each step

Measure recovery like operations

Time to isolate risky accounts
Time to restore crown-jewel services
Restore pass rate and integrity checks
User-visible service levels during staged return

Governance and Communications: Decide Before the Crisis

Ransom stance: document your position on paying or not paying, who advises, and applicable legal or insurance constraints
Notification logic: decision trees for regulators, customers, partners, and the board; pre-approved language
Executive reporting: a short, consistent dashboard (detection time, isolation completeness, restore pass rate, data exposure status, next milestones)

What We Implemented at Pynest

A year ago, we rewrote our ransomware playbook to cut containment time and raise confidence in recovery. We focused on people, drills, and measurable outcomes.

Tabletops that change behavior

Quarterly runs, including nights/weekends
Each ends with a hot wash and an improvement backlog with owners and deadlines
Leadership reviews closure monthly
Cultural outcome: teams expect ambiguity and ask for evidence first

Identity and endpoint readiness

Standard runbooks to revoke tokens, disable suspicious identity apps, rotate vault items, and quarantine at scale
Pre-approved authority to shut down SSO/VPN for affected segments
Backup and network owners trained to act in parallel with security

Backups built for real incidents

3-2-1-1-0 with immutable copies
Weekly restore tests into a clean room, with results visible to the board
Crown-jewel catalog mapped to explicit RTO/RPO for business-first recovery order

Runbook-as-code

Versioned, clickable runbooks with decision gates and owner call trees
“Dry-run” scripts for identity and network controls to practice safely and often

What changed

Time to isolate high-risk accounts in drills dropped from hours to under 20 minutes
Restore test pass rate climbed from the high seventies to the high nineties
Executives get a consistent one-pager during exercises and discuss tradeoffs by business impact
The board sees trend lines, not anecdotes, and backs investments tied to measurable gains

The Playbook, Section by Section

Preparation and tabletops

Map business processes to systems, data, and owners
Define triggers that start the ransomware playbook
Publish roles with names, alternates, and escalations
Run quarterly tabletops and track findings to closure

Staffing and skills

On-call rotations for identity, endpoint, backup/DR, network, and communications
External DFIR retainer with access and NDAs ready

Detection and containment

Isolate devices and accounts
Suspend risky trust for affected segments
Block command-and-control and protect clean networks
Collect and store evidence away from rebuild paths

Eradication

Rebuild from golden images
Rotate keys, tokens, and certificates
Patch initial access and exploited weaknesses
Hunt for persistence
Verify with rescans and canaries

Recovery

Prioritize services by business impact
Use a clean room to restore and validate
Reconnect in phases with contract tests

Governance and communications

Decide ransom policy and advisors
Pre-write regulatory and customer notifications
Report the same small set of metrics every time

A Short Checklist You Can Adopt Today

Name your Incident Commander and alternates. Grant authority to isolate identity, revoke trust, and trigger clean-room rebuilds.
Schedule the next tabletop now. Vary time and scenario. Run a hot wash and track fixes to closure.
Assume monitoring gaps. Layer controls across identity, endpoints, networks, and data.
Enforce 3-2-1-1-0 backups. Test restores on a schedule and report results to leadership.
Measure recovery in business terms. Track time to isolate, time to restore crown jewels, and restore pass rates.
Decide your ransom stance and notification rules ahead of time. Keep legal and communications in the loop.

Bottom line: a strong ransomware playbook creates predictable behavior under pressure. Clear owners, tested decisions, and verifiable clean rebuilds turn a chaotic breach into a managed recovery. Prepare now so your first real rehearsal is not the day your business is on the line.

How We Integrate Legacy Systems With Modern Stacks

Rylko Roman — Mon, 03 Nov 2025 15:18:48 +0000

Integrating with legacy isn’t “old vs. new.” It’s gravity. You have a system that has carried the business for years—imperfect, undocumented, but revenue-critical. Then leadership asks: “We need a mobile app, partner APIs, real-time analytics, maybe an LLM search—without breaking what works.” Familiar?

Across insurance, logistics, fintech, e-commerce, and B2B platforms, we’ve integrated COBOL/AS-400, Oracle Forms, “Spring 3 monoliths,” and homegrown CMSs with modern services. Tooling differs, but the pattern that works for us is surprisingly consistent. Below is what actually moves projects forward in production. At the end, I’ll share the single approach I recommend to nearly everyone facing legacy integration.

The Core Philosophy: “Squeeze Gently”

We don’t “rewrite everything.” We wrap legacy in an Anti-Corruption Layer (ACL) and expose a thin, stable contract (API + events). Around that contract, we build new services and strangle capabilities out of the monolith step by step (the classic strangler fig pattern). The devil is in the details: stopping direct DB coupling, keeping transactions honest, preserving history, and hitting SLAs.

Common Traps (and What We Do Instead)

1) “Let’s design the perfect domain model first.”

Don’t. There’s always a column like status_code=42 that only “Peter who left in 2017” remembers. Start contract-first from business needs: what must the outside world see (mobile app, partner portal, reports)? Fix that API first; then map it to legacy.

Practice: Contract-first + Consumer-Driven Contracts (CDC tests). We agree a JSON/gRPC schema and ship contract tests. Any accidental field change breaks CI.

2) “Reading the legacy DB directly is fastest.”

Fast, yes. Fragile, absolutely. Legacy schemas change without notice, transactions are sticky, and “two joins” turn into a six-hour nightly ETL. We avoid binding new components to raw tables. Between “them and us” sits an ACL/Legacy Adapter that translates the legacy dialect into a clean domain model.

Practice: A small legacy-adapter service. Inside: gnarly mappers and vendor quirks. Outside: clean DTOs. It’s “another layer,” but it localizes chaos.

3) “We must stay synchronous or we’ll lose consistency.”

False dichotomy. For money paths (payments, policy issuance) we keep synchronous + idempotent flows with detailed auditing. For everything else (search, notifications, work queues, analytics) we go event-driven and eventually consistent.

Practice: Change Data Capture (CDC) via Debezium/GoldenGate/LogMiner → Kafka/Pub/Sub → normalization → read-optimized projections. You don’t crack open legacy code; you stream changes from the DB log and feed the new world.

4) “Let’s build the perfect lakehouse first.”

Build only what the product needs now. For Monday morning reports, start with a nightly pipeline and a “dirty but labeled” mart. For real-time UX, create a narrow projection for the 3–5 entities that matter. After a month you’ll know what deserves hardening.

Practice: Lightweight data model: 2–3 CDC streams → 1–2 projections for UX/BI; schema versioned with migrations; data contracts documented.

5) “The new service can reuse legacy credentials—it’s practical.”

Practical, but risky. Put legacy behind an API gateway, scrub access, and upgrade authn/z: mTLS, OAuth2/OIDC, short-lived tokens; secrets in a vault, not in configs. Internally: RBAC/ABAC. Externally: rate limits and scoped keys for partners.

Step-By-Step: How We Actually Integrate “Old” With “New”

Step 0 — Map What Exists

Flow map: who writes/reads DBs, which nightly jobs run, which reports are “wind-sensitive.”
Non-negotiables: where you truly need strong consistency (money, inventory) and where eventual consistency is fine.
Risks: heroic tables with one keeper, batch scripts with no owner, Excel exports powering finance.

Step 1 — Contract-First and a “Thin Neck”

Freeze the external contract (REST/gRPC/GraphQL + events).
Ship contract tests and real consumer scenarios (app, partner, BI).
Build the ACL/Legacy Adapter translating between the contract and legacy.

Step 2 — Observability First

Tracing and SLOs: success rate, latency, error budget; dashboards for event flow.
Sandwich logging in the adapter: inbound contract ↔ legacy dialect.
Idempotency keys, de-duplication, outbox pattern for safe publishes.

Step 3 — Data Without Surgery

Turn on CDC from the production DB (with DBA blessing).
Normalize change events into domain topics; build projections for reading.
For LLM/RAG search, create a separate index; the source of truth remains legacy.

Step 4 — Strangle Capabilities Outward

Pick one zone (e.g., stock availability). Build a new service + cache around it.
Shift a slice of traffic with feature flags/canary. Watch SLOs.
Repeat. In 6–12 months the monolith quietly shrinks; the business already runs on the new perimeter.

Step 5 — Change Hygiene

Version contracts (v1/v2 with end-of-life dates).
Change management: RFCs, a clear owner, a rollback plan.
Keep short ADRs (1–2 pages) for architectural decisions.

A Real Story: Insurance + Warehouse + Mobile

Monolith on Java 6, heavy Oracle schema, nightly ETLs, Excel reports. The business needed a mobile app for agents and a partner API, but full rewrite was a non-starter.

What we shipped:

External contract: 14 endpoints + 6 events (“policy.created,” “status.changed,” “reservation.released”).
Legacy Adapter: a separate service. Inside—PL/SQL mappers and manual transactions. Outside—clean DTO, idempotency, audit.
CDC: redo log capture → Kafka → two projections: mobile (low-latency reads) and BI (fresh reporting).
Security: OAuth2, short-lived tokens, partner scopes, mTLS to the adapter; old point-to-point integrations moved behind the gateway.
Strangling: first read-only statuses, then requests (with idempotency), then tariff calculation via a new microservice + cache.
Results: in 4 months we delivered the partner API and mobile app on a modern stack, had zero money incidents, cut night ETL from 6h to 1h, and kept legacy changes minimal.

The “Modern” Tools That Actually Help

CDC + events instead of reading someone else’s tables: Debezium or cloud alternatives → Kafka/Pub/Sub.
Anti-Corruption Layer as a microservice translator. You don’t need the “perfect monorepo.” You need isolation of weirdness.
Short-TTL caching + idempotency. A 60–120s cache can raise UX quality dramatically without pretending everything must be strongly consistent.
Feature flags, canary, shadow traffic. Don’t pull a big switch; flow into the new path.
Observability as a product. Business SLOs at the top (“issue policy < 3s”); technical metrics at the bottom; simple alerts to avoid pager hell.
Security by default. Short-lived credentials, signed events, least privilege scopes.

The Single Approach I Recommend to Almost Everyone

Strangler + ACL + CDC.

In plain terms: a clean, stable external contract; a translator layer shielding the mess; and change events streaming from the DB log.

Why it works:

Minimal revenue risk. You don’t operate on the heart; you build arteries around it.
Predictable cost. Small, measurable iterations. Each slice pays for itself.
Decoupling from internal quirks. Legacy can evolve or wobble—your external contract stays stable.
Future-ready. Today you add mobile; tomorrow, LLM search or anti-fraud—contracts and events are already there.
Team discipline. ACL localizes dirt, CDC provides a shared truth bus, contract tests stop accidental breakage.

A 6–8 Week Starter Plan:

Agree the external contract for the first 3–5 flows.
Stand up the ACL/Legacy Adapter with idempotency and basic authz.
Enable CDC for two or three high-value tables; build one read projection.
Expose the first read-only scenario via API + events.
Turn on feature flags + canary; practice rollback.
Lock SLOs and basic observability (tracing + 5 actionable alerts).

After this, you own a thin neck through which you can pull functionality out of the monolith without shutting down the business.

“What If It Breaks?”

We always keep three safety nets:

Two-way switch (router/flag): return traffic to the old path in under a minute.
Duplicate journal (outbox/integration log): every operation is replayable.
Minimum-damage principle: money paths stay synchronous + idempotent; everything else is async where failure means degraded UX, not CFO-level incidents.

Practical Recommendations If You’re Starting Now

Contract first. Don’t argue tables—agree on JSON/gRPC.
Assign an ACL owner. It’s “dirty work” that saves months.
Enable CDC. Even if you’re not ready for a full event bus, start streaming and storing changes. You’ll thank yourself later.
Pilot narrowly. One scenario. One traffic slice. One clear SLO.
Invest in observability. Without it, debates devolve into “legacy vs. new” blame games.
Version discipline. Version contracts; set retirement dates; communicate them early.
Business communication. Package each iteration as a business win: “form time −40%,” “integration errors −60%,” “report at 9:00 instead of 12:00.”

Closing

Legacy integration isn’t a generational war; it’s an architecture of compromise. You don’t defeat the old system—you teach it a new language with grammar (the contract), a dictionary (the ACL), and mail (events). In a few months, the business will operate on the new perimeter while the monolith quietly powers down piece by piece.

If you remember only one thing, let it be this: Strangler + ACL + CDC. Start small. Keep a canary and a rollback flag. Measure SLOs. You’ll be surprised how willing “the old” is to work with “the new” when you speak to it correctly.

About me

Here are a few of my recent features in major outlets:

https://www.cio.com/article/4064316/31-of-it-leaders-waste-half-their-cloud-spend.html

https://www.cio.com/article/4059042/it-leaders-see-18-reduction-in-it-workforces-within-2-years.html

Best regards,
Roman Rylko
CTO at Pynest (Dallas, TX) - https://pynest.io
LinkedIn - https://www.linkedin.com/in/roman-rylko/

The Post-SaaS Era: How AI Agents Are Redefining Software Delivery

Rylko Roman — Wed, 29 Oct 2025 14:52:19 +0000

When McKinsey introduced the term post-SaaS this year, they captured a quiet revolution already underway in software. The SaaS era centered on humans using cloud apps. The next phase centers on AI agents using software on our behalf — interacting with APIs, automating decisions, and redefining value creation.

From Users to Agents

McKinsey predicts that the number of human software users will plateau, while the number of autonomous AI users will grow exponentially. These agentic systems will log in, query, analyze, and transact just as employees do today — only faster and at scale.

At Pynest, we already see this shift in how enterprise clients deploy our automation frameworks. A single AI agent can now replace multiple routine workflows — from CI/CD triggers to customer onboarding sequences — previously requiring human coordination.

“We’re moving from software that serves people to software that collaborates with software. The interface is no longer the dashboard — it’s the protocol.”

— Roman Rylko, CTO at Pynest

Three Architectures of Post-SaaS

McKinsey’s framework identifies three archetypes shaping this transition:

Agents as Users – augmenting human workers by operating within existing SaaS environments.
Agent-Centric Architecture – where a single front-end agent orchestrates tasks across multiple systems.
Agents as Experts – trained on domain-specific data to execute specialized reasoning, such as legal or medical interpretation.

These models are not hypothetical. Startups like Anysphere (Cursor) and Gamma are already proving that lean teams can reach massive ARR by building AI-native architectures from day one. In traditional organizations, this same architecture is emerging within DevOps, support, and analytics teams.

“The agent layer is becoming the new middleware,” says Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute. “It connects tools, decisions, and outcomes — not through code integration, but through reasoning.”

The End of Per-Seat Pricing

Perhaps the most disruptive consequence of the post-SaaS era is economic. The familiar per-user or per-seat subscription model no longer maps to a world where agents — not humans — perform most actions.

Instead, usage-based and outcome-based models are emerging.

In practical terms, this means billing by data processed, tasks completed, or measurable business results rather than headcount.

At Pynest, for example, some of our clients have transitioned to event-driven billing — they pay per automated workflow execution rather than per logged-in engineer. This approach aligns software value directly with operational output.

As Gartner noted in its CIO Agenda 2025, more than 45% of B2B software providers expect to adopt mixed monetization models within three years. For customers, that means unpredictable invoices may become predictable again — but only if usage transparency and cost explainability mature alongside it.

Why the Shift Is Accelerating Now

Three forces make this transformation unavoidable:

Economics of automation – As gen-AI productivity gains plateau, enterprises turn to agentic AI for deeper process automation and reduced headcount.
Data centralization – Modern data architectures (like Lakehouse and VectorDB) allow agents to reason over unified datasets instead of siloed applications.
Trust infrastructure – With auditability frameworks such as AI provenance chains and model governance APIs, companies can now deploy agents securely within regulated environments.

The result is what McKinsey calls “AI-centric software”: systems built around continuous learning loops, explainable decision paths, and measurable business outcomes.

Implications for Customers

For end users, this shift brings both empowerment and opacity.

AI agents will deliver outcomes faster — but understanding how those outcomes were generated will require visibility into model reasoning, not just software logs.

Customers should expect:

Dynamic pricing models that reflect computation or decision complexity.
Autonomous orchestration — where apps negotiate data exchange via APIs without user input.
Service-level guarantees for AI reasoning — explaining decisions becomes part of the SLA.
Continuous delivery of intelligence — updates will target agents’ reasoning capabilities rather than app features.

“AI won’t replace people, but people using AI will replace people not using AI.”

— Andrew Ng

In the post-SaaS world, that logic extends to software itself: tools that don’t interoperate with agents will be replaced by those that do.

Risks: Vendor Lock-In and Data Entanglement

With great autonomy comes great dependency.

When AI agents learn from proprietary customer data, the line between vendor service and business intelligence blurs. Enterprises risk data entanglement — where leaving a platform means losing the trained behavior of their agents.

“The new lock-in won’t be your data — it will be your agent’s memory. Whoever controls how that memory evolves controls the customer relationship.”

— Roman Rylko, Pynest

To mitigate this, industry leaders advocate for open agent standards similar to what REST and OAuth achieved for web interoperability. Initiatives such as OpenAI’s A2A protocol and Anthropic’s Claude Context Sharing hint at this direction.

Navigating New Licensing Models

CIOs and procurement teams will need to rethink software evaluation frameworks:

Audit for explainability – Vendors should provide traceable logs of agent decisions.
Evaluate total cost of outcomes – Measure not the license cost, but the efficiency gain.
Demand interoperability clauses – Ensure agents can export knowledge or connect to external orchestrators.
Plan for adaptive budgeting – With variable usage, financial planning becomes continuous rather than annual.

According to a 2025 survey by IDC, 58% of enterprise buyers say pricing complexity is their top barrier to adopting AI-centric platforms. Vendors that simplify billing transparency — showing exactly which actions drive cost — will earn long-term trust.

Case in Point: AI Orchestration at Pynest

At Pynest, we piloted agentic delivery within our internal DevOps systems.

Previously, human engineers triaged incidents, coordinated deployments, and tracked metrics. Our AI orchestration layer now automatically classifies incidents, deploys corrective scripts, and updates dashboards — all without direct human input.

Over six months, this automation:

Reduced average incident resolution time by 37%.
Cut repetitive manual interventions by over 60%.
Enabled engineers to focus on architecture rather than firefighting.

For clients, we’ve extended similar principles to customer support automation, where AI agents integrate directly into ticketing and CRM systems to resolve cases autonomously.

This agent-to-agent interaction represents the post-SaaS delivery model in action — not a dashboard for a human operator, but a self-improving workflow mesh between intelligent systems.

What the Next Five Years Look Like

McKinsey projects that AI-centric software could account for $600–800 billion in global value creation by 2030.

We’re entering a period where the differentiation between “software product” and “AI service” fades.

Expect three major trends:

Consolidation around AI platforms – just as Salesforce unified CRM, a few ecosystems will dominate agent orchestration.
Data-driven defensibility – access to proprietary datasets will outweigh UI/UX as a differentiator.
AI-native ecosystems – marketplaces where agents trade capabilities and data securely.

In this environment, success won’t come from the breadth of features but from the depth of reasoning — how well a product’s agents can understand business context and act autonomously.

The Strategic Imperative

Becoming post-SaaS isn’t about adding AI features — it’s about rethinking the entire business architecture around autonomy and intelligence.

Software companies that embrace this shift will need to evolve across four dimensions:

Product: Embed reasoning agents at the core, not as add-ons.
Pricing: Align monetization with measurable outcomes.
Operations: Automate internal workflows with the same intelligence offered to clients.
Governance: Establish ethical and transparent oversight for agentic behavior.

The post-SaaS landscape will reward adaptability over scale.

“In the last decade, SaaS democratized software. In the next one, AI will democratize capability. The companies that survive will be those that learn to sell not subscriptions — but intelligence itself.”

— Roman Rylko, Pynest

How Intuitive AI Is Enhancing the Information Management Process

Rylko Roman — Tue, 28 Oct 2025 12:27:22 +0000

Artificial intelligence has already reshaped how we store, organize, and analyze information — but the next leap comes from what’s often called Intuitive AI, also known as Cognitive AI or Neuro-Symbolic AI. Unlike traditional machine learning models that rely purely on statistical pattern recognition, Intuitive AI can reason, infer, and make context-aware decisions.

At Pynest, we’ve seen first-hand how this class of systems transforms information management. Beyond automation, it helps enterprises understand their own data in more human-like ways — bridging structured and unstructured sources, recognizing intent, and drawing logical connections that older systems missed.

In this article, I’ll share three real-world challenges we faced and solved with Intuitive AI, along with lessons learned and perspectives from industry research and experts.

1. Challenge One: Fragmented Knowledge Across Teams

One of our first challenges was a classic enterprise problem — knowledge fragmentation.

Our teams were drowning in documentation, project briefs, and archived chat logs. Traditional search tools returned hundreds of irrelevant results because they were based on keyword matching rather than semantic understanding.

To solve this, we developed an intuitive knowledge retrieval layer powered by a neuro-symbolic model. It combined vector embeddings for semantic similarity with symbolic logic rules representing business hierarchies and project relations.

When a developer searched for “API throttling limits in production,” the system understood not just the words, but the intent — surfacing relevant configuration docs, Jira tickets, and meeting summaries that described the same concept using different phrasing.

Within a month, the time spent locating critical information dropped by 42%. More importantly, employees reported feeling “less blindfolded” when onboarding into new projects.

“Neuro-symbolic systems combine the pattern-matching ability of neural networks with the reasoning capabilities of logic, making AI systems less brittle and more aligned with human thinking.”

— Dr. Yejin Choi, University of Washington

That observation proved true — by adding structure to semantics, our AI didn’t just find documents; it understood context.

2. Challenge Two: Managing Information Lifecycle and Compliance

Our second challenge came from regulatory complexity.

As Pynest scaled its data infrastructure for multiple clients, we had to manage retention policies across thousands of records, each governed by different compliance frameworks (GDPR, HIPAA, ISO 27001).

Manual tagging and classification were too slow. We needed a system that could intuitively understand data sensitivity — differentiating between innocuous metadata and regulated personal identifiers even in loosely formatted text.

We implemented an Intuitive AI engine trained on anonymized compliance scenarios. It could reason over text structure and infer likely sensitivity using contextual cues, such as “medical diagnosis” versus “technical incident.”

The model didn’t simply flag keywords — it reasoned about relationships. For example, “Patient A’s ECG was reviewed” was classified as sensitive even though it lacked explicit identifiers, while “Server A’s logs were reviewed” wasn’t.

This cognitive understanding significantly reduced false positives, improving our data classification accuracy by 36%.

A similar approach was presented at the NeurIPS 2024 Workshop on Neuro-Symbolic Learning by researchers from IBM and MIT, emphasizing how hybrid reasoning models improve auditability and compliance traceability in enterprise AI systems.

In our experience, Intuitive AI made governance feel less like a burden and more like a living, adaptive process — one that learns continuously as regulations and data types evolve.

3. Challenge Three: Connecting Insights Across Modalities

The third challenge was insight integration — connecting what different departments were learning from text, numbers, and visuals.

Our marketing, data science, and operations teams each produced valuable insights, but they lived in silos: marketing reports in PDFs, analytics in dashboards, incident reviews in Confluence.

We applied a multi-modal Intuitive AI pipeline capable of linking heterogeneous data. It understood that a graph of declining conversion rates could be connected to customer sentiment trends extracted from feedback emails.

This allowed us to build what we now call “cross-modal knowledge maps.” Executives could ask, “What customer behavior changes preceded last quarter’s churn increase?” — and the system would combine numerical and linguistic signals to generate a hypothesis.

The result wasn’t just better visibility; it encouraged collaborative reasoning. Teams could validate insights across functions, instead of operating in parallel data universes.

“AI that understands cause and consequence, not just correlation, is what will make it truly useful in business contexts.”

— Gary Marcus, author of Rebooting AI

That’s exactly what we observed. The system didn’t replace analytics teams — it amplified their intuition with data-driven reasoning.

Pros and Cons of Intuitive AI in Information Management

Pros

Contextual understanding – Unlike pure LLMs, Intuitive AI captures the why behind information, not just the what.
Explainability – Because it relies partly on symbolic reasoning, decisions are traceable — a huge plus for compliance and audit.
Cross-domain adaptability – It integrates structured and unstructured data seamlessly, from documents to logs to images.
Reduced cognitive load – Employees spend less time interpreting fragmented data and more time acting on it.

Cons

Integration complexity – Deploying neuro-symbolic systems requires specialized infrastructure and ontologies.
Training cost – Building reasoning frameworks still demands domain experts and iterative refinement.
Limited off-the-shelf tools – Commercial solutions are emerging, but most enterprise use cases still require customization.

Surprising Lessons Learned

One of the biggest surprises was how quickly non-technical users adapted to working with intuitive systems.

Initially, we assumed it would be too abstract. Instead, employees found it natural — “it thinks like I do” was common feedback. The AI’s ability to infer context made it feel collaborative rather than mechanical.

Another revelation was bias correction through reasoning. Purely neural systems sometimes reinforced data biases. By adding logical constraints — for instance, flagging inconsistencies between data sources — we reduced bias propagation without retraining models.

We also discovered that the explainability inherent in Intuitive AI changed how people trusted AI outputs. When the system could explain why it linked two documents or classified a record as sensitive, adoption accelerated across departments.

Advice for Leaders Considering Intuitive AI

Start with a knowledge problem, not an AI goal.

Ask, “Where do people lose context?” rather than “Where can we use AI?”

Intuitive systems shine when applied to reasoning gaps, not repetitive automation.
Build hybrid data pipelines early.

The strength of Intuitive AI lies in combining structured and unstructured data. Invest in clean metadata and interoperable formats first.
Pilot explainability features.

Treat explainability as part of user experience. It’s what converts AI skepticism into trust.
Involve domain experts in ontology design.

Logical rules work only when they reflect real business language. Collaboration between engineers and subject matter experts is non-negotiable.
Measure ROI beyond automation.

Track improvements in decision accuracy, knowledge reuse, and compliance resilience — not just time saved.

The Future of Intuitive AI

Industry researchers predict that within five years, Intuitive AI will underpin enterprise knowledge systems much like relational databases did in the 1990s.

At the 2024 AAAI Conference on Artificial Intelligence, multiple panels emphasized that neuro-symbolic reasoning could bridge the gap between human logic and neural computation — enabling systems that understand rather than approximate.

For companies like Pynest, the promise is enormous:

AI that not only answers but reasons, connects, and explains.

It’s the difference between a search engine and a colleague.

Final Thought

Intuitive AI isn’t a replacement for human intuition — it’s a mirror of it.

When designed responsibly, it brings the organization’s collective knowledge to the surface, making decisions faster, smarter, and more explainable.

The key is not to chase artificial intelligence, but to cultivate augmented understanding.

That’s where the real value of Intuitive AI lies — in helping humans think more clearly in a world overflowing with information.

Connected Care, Simpler Workflows: How to Improve Patient Outcomes and Avoid Extra Burden for Clinicians

Rylko Roman — Thu, 23 Oct 2025 08:08:45 +0000

As hospitals wire up wearables, EHRs, mobile apps, and AI decision support, tech leaders face a dual mandate: deliver measurable gains in patient outcomes while keeping clinical workflows simple. Evidence shows digital health can boost adherence and safety, but it can also add friction if it forces clinicians into new tools or extra clicks. That tension is the real design problem to solve. See, for example, analyses on how digital rollouts can both help and hinder frontline teams and why embedded, point‑of‑care support matters for adoption (Wolters Kluwer, McKinsey).

What “patient outcomes without clinician complexity” looks like

For patients: earlier detection, tailored care pathways, and continuity between home and clinic. For clinicians: fewer manual steps, in‑context insights inside the EHR, and automated documentation where safe. Studies suggest digital tools can raise engagement and support faster decisions when they are designed around existing clinical routines, not parallel to them (BMC Health Services Research, Wolters Kluwer).

“The future isn’t about more tech, but smarter tech embedded right where decisions are made,” notes Peter Bonis, MD, Chief Medical Officer at Wolters Kluwer Health.

How we build it in practice at Pynest

In our recent project — the Health Monitoring Service — we stream real‑time metrics from wearables and mobile apps, analyze risk, and surface alerts in the clinical workflow. The architecture is deliberately boring and proven:

Data ingestion: REST and WebSocket endpoints accept heart‑rate variability, SpO₂, movement, sleep patterns.
Stream & storage: Apache Kafka for event pipelines; TimescaleDB for time‑series biometrics; PostgreSQL for transactional records.
Microservices: Python 3.11 with FastAPI, containerized via Docker, orchestrated on Kubernetes (EKS/GKE).
ML/AI layer: scikit‑learn and TensorFlow for training; inference served with Seldon Core; feature stores synchronized to streaming topics.
Clinician UI: React dashboard with Grafana/Superset visualizations embedded as widgets; alerts appear inside the EHR via HL7 FHIR Subscriptions and SMART‑on‑FHIR launch. For context on the standard, see the HL7 FHIR overview.
Patient app: Flutter app provides insights, trends, and nudges that tie to the care plan rather than generic notifications.

Why this matters for outcomes: Patients get earlier outreach when risk elevates, not after deterioration. Why this avoids clinician complexity: the physician never leaves the EHR; the alert is short, explainable, and linked to the evidence in the chart.

Guardrails that keep the experience simple

1) Embed, don’t bolt on. If a nurse must hop to a separate portal, adoption craters. We use SMART‑on‑FHIR and in‑frame widgets so decision support travels with the chart. Background sync keeps dashboards current; clinicians see one source of truth. Reference guides agree: embed evidence at the point of care to help rather than hinder (Wolters Kluwer).

2) Automate the unseen. Signal cleaning, deduplication, unit normalization, and thresholding all run server‑side. Clinicians see a curated signal, not raw noise.

3) Explain the “why.” Every alert carries a short rationale: “HRV dropped 15% from this patient’s baseline for 5 consecutive nights and correlates with risk pattern X.” Trust rises when the model’s logic is visible.

4) Govern the model lifecycle. We log prompts and outputs for generative components, version models, and route all AI calls through a policy proxy that redacts PII on outbound requests. That keeps governance tight without slowing teams.

Outcome impact, quantified

When you instrument the journey, you can measure impact as reduced readmissions, fewer escalations, shorter time‑to‑intervention, and improved PROMs. Systematic reviews indicate digital health can improve provider efficiency, continuity, and decision speed when well‑implemented (BMC Health Services Research PDF). At the same time, surveys of health leaders show the gap between AI prioritization and scaled results, underscoring the need to redesign workflows and governance, not just buy tools (McKinsey survey of health system executives).

Technical blueprint: a reference stack

Below is a blueprint we’ve found reliable across providers:

Ingestion and streams: Kafka or AWS Kinesis; idempotent producers; schema registry for event contracts.
Storage: TimescaleDB for high‑frequency vitals; PostgreSQL for clinical metadata; object storage for raw device payloads.
Processing: Python/FastAPI services; batch jobs on Airflow; real‑time workers on Flink or Kafka Streams.
AI/ML: scikit‑learn, TensorFlow, PyTorch; MLOps with MLflow; drift monitoring with Evidently.
Interoperability: HL7 FHIR R4/R5 resources; SMART‑on‑FHIR launch; terminology mapping via SNOMED/LOINC. See the HL7 FHIR overview and the evolving spec at build.fhir.org.
Security: OAuth2/OIDC, mTLS for service‑to‑service, encryption in transit and at rest, DLP on ingestion, PHI tokenization for analytics sandboxes.
Observability: Prometheus/Grafana, distributed tracing with OpenTelemetry, lineage with OpenLineage to support audits.

This is intentionally modular: you can swap components without re‑architecting the whole system, which makes upgrades safer for clinical operations.

Patient vs clinician value, side by side

Patients get:

Earlier intervention thanks to continuous monitoring and risk scoring.
Personalized plans tuned to their data and cohort, not one‑size‑fits‑all recommendations.
Continuity via telehealth triggers and proactive check‑ins rather than reactive visits.

Clinicians get:

Fewer clicks because alerts and summaries show up inside the EHR note or task list.
Explainable insights that tie to the chart and evidence links.
Lighter documentation with safe automation for summaries and discharge instructions.

Peer‑reviewed studies and policy analyses echo that aligning digital investment with clinical workflow and patient goals is what drives real value — not just rolling out more tech (BMC studies on digital transformation and outcomes, McKinsey perspectives).

Integration details that often make or break adoption

FHIR Subscriptions stream event notifications into the EHR inbox or worklist so teams see changes naturally.
Clinical decision support hooks call external services during order entry, reducing errors without adding steps.
Task routing sends follow‑ups to the right role at the right time, avoiding blanket alerts.
Patient‑facing nudges are tied to the care plan in the chart, so messages are relevant and traceable.

When organizations treat these as first‑class design elements, setup time falls and satisfaction rises. Health systems also need to plan for change management and training, as multiple analyses stress that investment must include workforce enablement to realize benefits (Financial Times report on NHS digital investment and change management).

Expert viewpoints, in context

“Connected care works when data moves silently and clinicians receive one clear recommendation in the moment,” says Peter Bonis, MD.

“Leaders are pivoting from pilots to operating‑model change because that’s where the outcomes show up,” note analysts in McKinsey’s digital priorities survey.

Research in BMC Health Services Research links effective digital transformation with better health status when spending aligns with change management, not just tools.

Where hype still outpaces value

Fully autonomous clinical agents. Useful assistants, yes. Replacement for clinical judgment, no.
Chat‑only workflows for complex decisions. Natural language is a good control surface; clinicians still need structured views, trends, and safeguards.
Unembedded point solutions. If it lives outside the EHR and doesn’t write back, expect drop‑off.

More promising are AI systems for resilience and continuity: early‑warning signals, guided triage, and automated preparation of evidence for the clinician’s next step. That is where we see the most reliable patient benefit with the least cognitive overhead on staff.

Closing: a practical path forward

If your goal is better patient outcomes with no extra burden on clinicians, design your stack around a few rules:

Put insights where care happens. Use SMART‑on‑FHIR and write‑back to the chart.
Automate the plumbing. Normalize signals, manage identity, and codify policies centrally so clinicians see only the end product.
Measure outcomes, not logins. Track readmissions, time‑to‑intervention, PROMs, and clinician time saved.
Invest in change, not only tech. Training and workflow redesign turn tools into results.

At Pynest, we build these systems with modern data streams, ML, and FHIR‑based integration so patients get earlier, more personalized care and clinicians get clearer, lighter workflows. For a concrete example, see our case study: Health Monitoring Service. When healthcare gets more connected, the winning design makes the patient safer and the clinician’s day simpler.

The State of AI Adoption in Project Management

Rylko Roman — Wed, 22 Oct 2025 16:28:14 +0000

2023 was discovery and 2024 was early use, 2025 is the year AI enters the day-to-day fabric of project management. Enterprise surveys show regular generative-AI usage has surged, and leadership teams are redesigning workflows to capture measurable value, not just run pilots.

Below is how my teams at Pynest actually use AI in PM work today, what still gets in the way, where the role of the project manager is heading over the next 12–24 months, and a few “hot takes” on what’s overhyped.

How we use AI across the PM lifecycle

Planning. We start with a structured backlog written in plain language. A planning agent translates it into epics, stories, acceptance criteria, risk flags, and rough-order-of-magnitude estimates by pattern-matching against our historical projects. It proposes dependency charts and suggests critical-path alternatives. PMs review everything; nothing goes straight to execution without human approval. The agent’s value is speed and recall: it surfaces similar work we shipped two years ago and asks, “Do you want to reuse this runbook?”

Reporting and comms. We have an “executive brief” generator that turns Jira and Git data into narrative weekly updates with highlights, risks, and deltas against OKRs. It drafts stakeholder-specific versions (finance vs. product vs. security) and auto-links evidence (PRs, tickets, deployments). PMs still edit the tone, but the baseline is produced in minutes, not hours.

Risk & issue management. We run anomaly detection on lead times, review latency, and defect arrival patterns. If cycle time slips two standard deviations, the agent opens a risk with root-cause hypotheses: scope creep, flaky tests, dependency on a single reviewer, or cross-team bottlenecks. It also flags “silent failures” where work moves but value doesn’t—useful after platform incidents reminded everyone that resilience is about distribution and independent recovery paths.
McKinsey & Company

Resourcing. A skills-graph (kept current from commits, PR reviews, and certifications) helps allocate work. The agent proposes staffing swaps when a specialist becomes a bottleneck and highlights “key person risk.”

Documentation. Every significant change produces a doc snapshot: context, decision, alternatives, trade-offs, and links. The agent maintains a living index and generates diffs across versions so new joiners see how decisions evolved. This reduces the PM’s manual “historian” burden and improves onboarding.

Tools are converging fast: even mainstream platforms now bundle AI assistants that summarize work, draft tickets, and surface blockers inside the PM system of record. Atlassian’s recent announcements are a good example of how native assistants are moving from novelty to “default UI.”

Barriers we’ve encountered

1) Trust and data readiness. The sharpest constraint isn’t model quality; it’s data quality and access. We’ve seen initiatives stall when backlogs are messy, statuses are inconsistent, or telemetry is missing. Analysts keep repeating it because it’s true: great AI needs great data.
hbr.org

2) ROI pressure. Boards want real outcomes, not demos. McKinsey notes companies are shifting from experimentation to org-level changes (workflow redesign, governance) to capture bottom-line impact. That’s where PM leaders must be hands-on.
McKinsey & Company

3) Policy and security. Shadow-AI usage creates risk. We route all model calls through an internal proxy with data redaction, DLP checks, audit logs, and role-based access. That satisfies governance while preserving speed.

4) Skill gaps and tool overload. PMs don’t need to be ML engineers, but they do need prompt literacy, data sense, and comfort with structured experimentation. We standardized a small toolset and wrote “AI runbooks” so teams stop reinventing the wheel.

5) Hype vs. reality. Gartner has warned that many agentic-AI projects will be canceled due to unclear value and complexity. We’ve seen the PM flavor of this: “autonomous planning” that ignores constraints or “self-driving” standups that spam stakeholders. A disciplined pilot beats a flashy demo.

How AI will reshape the PM role in the next 12–24 months

From status collector to decision facilitator. Routine reporting, meeting notes, action extraction, and risk surfacing will be largely automated. The PM’s leverage shifts to framing decisions, aligning trade-offs, and negotiating scope across teams. PMI’s thought leadership has been clear: business acumen and adaptability are the differentiators as project work evolves.

From artifacts to systems. The center of gravity moves from static plans to live systems: observability dashboards, dependency maps, and feedback loops. PMs who can read these systems—and ask the right questions—will unlock faster, safer delivery.

Agent-orchestrated workflows become normal UI. Expect more assistants embedded in PM tools that suggest staffing, reorder backlogs, and pre-populate risk registers. But beware “agent washing.” Set clear criteria for what “autonomous” means in your context, then test it with real workloads.

Skills that matter. Data hygiene, prompt design, metrics literacy, and the ability to run hypothesis-driven experiments. We train PMs to treat AI features like any other capability: define success metrics, run A/Bs, and deprecate what doesn’t work.

“Hot takes” on overhyped PM use cases

Overhyped: Fully automated project planning. Plans encode politics and trade-offs as much as tasks. AI can propose structures and dependencies, but it won’t replace stakeholder alignment. Treat “auto-plan” as a first draft, not a source of truth.

Overhyped: Magic risk prediction without instrumentation. You can’t predict what you don’t measure. Without reliable telemetry—lead time, WIP limits, defect age—risk models are theater. Gartner’s caution on AI projects without “AI-ready data” applies directly here.
gartner.com

Overhyped: Chatbot-only PM. Natural language is a great control surface, but critical decisions need context, visuals, and governance. We pair chat with structured dashboards and audit trails.

Under-hyped: AI for resilience and continuity. We’ve gained outsized value from agents that simulate failure scenarios (supplier slips, platform incidents) and propose fallback plans. This connects PM with reliability engineering, a theme you’ll hear across PMI and leading conferences this year.

Practical adoption playbook

Pick workflows with measurable pain. Weekly reports, risk reviews, and resource allocation deliver visible wins without upending governance.
Harden your data layer first. Clean backlogs, consistent status fields, and standardized tags outperform a bigger model.
Wrap AI in guardrails. Use an internal proxy, redact PII, log prompts, and define approved tools and data types.
Run time-boxed pilots. Define success metrics, use control groups, and stop what doesn’t work.
Invest in people. Train PMs in prompt practices, metrics, and experiment design. The “learning organization” outpaces the “tool-buying organization.”

Expert perspectives worth watching

Antonio Nieto-Rodriguez & Ricardo Vargas argue AI will transform PM by shifting effort from administration to strategic leadership—an early but still relevant view framing today’s changes.

McKinsey’s State of AI reports orgs are redesigning workflows and governance to capture value beyond pilots—a signal that PM leaders must engage at the operating-model level.

Gartner highlights both potential (agentic decision-making) and risk (high cancellation rates when value and data foundations are weak). Use that as a sober benchmark for your AI roadmap.

PMI Pulse emphasizes adaptability and business acumen as core to future project work—exactly the skills amplified, not replaced, by AI.

Closing

AI won’t replace project managers; it will replace the parts of the job that kept PMs away from stakeholders, strategy, and outcomes. The winners will be teams that pair trustworthy data and clear guardrails with PMs who know how to frame decisions and run disciplined experiments. That’s not hype—that’s craft.

If your leadership team is moving from pilots to operating-model change, Pynest helps enterprises build AI-ready PM workflows: clean data layers, embedded assistants, and governance that enables safe innovation. Learn more at Pynest.