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The Tech Weekend Read from Zeren

The Tech Weekend Read from Zeren

 

The Work Issue: automation, outsourcing, and where tech actually gets done

Five Pieces Worth Your Coffee This Weekend

What the smartest research desks published this week on automation, outsourcing, and the future of tech work – curated by the Zeren Software team, with our take on why each one matters if you build software for a living.

This week the theme chose itself. A BBC investigation, a peer-reviewed economics paper, a staffing giant’s data set, and two industry analyses all circled the same question from different angles: as AI automates routine work and reshuffles where it happens, what actually becomes of the jobs – and the people – in the global tech economy? Here are the five pieces that earned a spot, each with the core insight in two minutes and a link if you want the full read.

1.”I dug my own grave.” The workers who trained the AI that replaced them.

BBC — “‘I feel like I dug my own grave’: The workers caught in the AI transition” (August 2026)

The BBC spoke to Philippine outsourcing employees who describe being asked to edit AI-generated copy and teach the system their employer’s house style – and then being let go, in one case a month before the role was due to become permanent. One of them summed it up: “I feel like I dug my own grave.” They had trained the very model that displaced them. Several signed confidentiality agreements in exchange for severance. The context is enormous: the International Labour Organization estimates around 12.7 million workers in the Philippines – more than one in four – are in occupations that could be affected by generative AI. Two sober footnotes from the reporting keep it honest. One former writer said her workload actually *increased*, because AI output needed constant editing and fact-checking. And an analyst noted that evidence these tools deliver their promised productivity is still limited, and that some layoffs blamed on AI are really responses to weak demand wearing a better story.  Read the full article.

The Zeren lens: The headline risk isn’t “AI replaces your vendor’s staff.” It’s that nobody owns the transition. When AI integration is quietly dumped on the same people being measured for redundancy, you get fear, attrition, and worse output. The alternative is a partner who treats AI adoption as an engineering programme – with named owners, review gates, and honest metrics –  not a cost-cutting rumour.

2. Automation doesn’t delete jobs. It disassembles them.

UPI Study — “How Are Automation and Outsourcing Changing Jobs Worldwide?” (July 2026)

The clearest mental model we read all week. A job is twenty or thirty tasks; firms automate the easiest handful, outsource the repetitive ones, and keep the rest for people who handle exceptions, judgment, and the messy cases. “That is task displacement, not disappearance – and the work often reappears in a new shape, the way a self-checkout still generates demand for maintenance, loss prevention, and floor support.” Demand shifts toward data, oversight, security, and vendor management, while pay polarizes: the workers who complement machines pull ahead of those who compete with them. Read the full article.

The Zeren lens:  The right angle îs “re-slice the work, then put your best people on the 40% that needs judgment.” A good engineering partner isn’t cheaper hands for the routine tasks — it’s the crew that owns the integration and exception-handling the model can’t.

3. Will AI reverse offshoring – or supercharge it? An economist’s answer: it depends on autonomy.

IESE Insight – “Will AI upend the offshoring of global knowledge work?” (May 2026), drawing on research in the Journal of Political Economy and the Journal of Monetary Economics

For forty years, advanced economies kept the complex problem-solving, emerging economies took the routine knowledge work — the call center, the QA queue. IESE economists – Enrique Ide and Eduard Talamas – argue generative AI could either flip that split or entrench it, and the hinge is how autonomous the technology becomes. If AI agents stay at roughly the level of a capable pre-AI worker, emerging-economy teams can absorb them as extra hands, move their best people up into problem-solving, and could even begin exporting higher-end services — reversing the old pattern. The most knowledgeable workers pair their expertise with ever-more-capable AI and pull ahead, while the least knowledgeable are the ones most likely to be substituted. Read the full article.

The Zeren lens: The defensible position, for a nearshore partner and for your own team alike, is to be the people who wield the AI rather than the people it wields. That’s a deliberate climb up the value chain, not a hope.

4. Remote work is retreating. Flexibility is still what moves talent.

Robert Half — “Remote work statistics and trends for 2026” (updated August 2026)

Fully in-office job postings jumped from 65% in Q4 2025 to 87% in Q2 2026, leaving just 10% hybrid and 3% fully remote, and 36% of employers added on-site days over the past year. Flexibility now skews senior, too — access to hybrid and remote arrangements rises with experience. Read the full article.

The Zeren lens: The return-to-office wave quietly strengthens the case for a dedicated nearshore team. You get a cohesive, time-zone-aligned group that works together in one place — without forcing your own people back to a commute they’ll leave over. “Where the work happens” is being renegotiated everywhere; the win is designing that on purpose instead of defaulting to a mandate.

5. The big picture: outsourcing grew up. It’s a partnership now, not a handoff.

**EvokeHub — “The Evolution of IT Outsourcing: Trends to Watch” (July 2026)**

The market has moved from “send us the cheap tasks” to outcome-based, modular engagements judged on uptime, customer experience, and risk reduction rather than headcount saved. The emerging default is hybrid: governance and customer-facing work stays close or onshore, scalable engineering distributes globally, and service-level agreements get more granular and business-focused. The line that stuck with us: “effective outsourcing is no longer about handing off problems, but about co-designing a secure, scalable, adaptable technology ecosystem”. Set that against the macro backdrop — “the WEF’s Future of Jobs Report 2025 projects 170 million new roles created and 92 million displaced by 2030, a net gain of 78 million”, with around 39% of core skills transformed within five year — and the firms that treat sourcing as an ongoing discipline rather than a one-off purchase are the ones that stay resilient. Read the full article.

The Zeren lens: This is the model we already run – co-designing, sharing responsibility for outcomes, keeping governance close while scaling delivery across borders. In a region living under the EU AI Act, “compliance-ready by design” isn’t a tax; it’s a feature.

Read side by side, these five pieces make one argument from five directions. Automation îs disassembling and redistributing  the work (UPI Study). Where it lands depends on who climbs the value chain fastest (IESE), and the human cost of getting that transition wrong is already visible on the ground (BBC). Meanwhile the ground rules for where and how people work are being rewritten in real time (Robert Half), and the outsourcing model itself has matured into something that looks far more like partnership (EvokeHub).

The WEF’s headline number is the whole story in miniature: a net 78 million new jobs by 2030 — but 39% of skills transformed along the way. The jobs change. And the winners are whoever builds the capacity to change with them – resilient, AI-fluent, well-governed, and distributed on purpose.

That’s the work. It’s also, not coincidentally, the work we do.

*Enjoyed this? The Weekend Read lands every two weeks on Friday. Meanwhile, if any of these hits close to home — from AI-era engineering capacity to building nearshore teams that absorb new technology instead of piloting it forever — talk to us.

*Sources: BBC News (August 2026), UPI Study (July 2026), IESE Insight / Journal of Political Economy & Journal of Monetary Economics (May 2026), Robert Half (August 2026), EvokeHub (July 2026), and the World Economic Forum Future of Jobs Report 2025. All insights paraphrased; follow the links for the original research.*

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Artificial Intelligence Case study News Uncategorized

Velocity Is the New Strategy in the AI Era

Velocity Is the New Strategy in the AI Era

 

Two McKinsey studies published this year – the Global Tech Agenda 2026 and a new survey on resource allocation in the age of AI  – arrive at the same conclusion from two different directions: in the AI era, the companies pulling ahead are the ones that move. Not the ones with the best predictions or the biggest budgets.

Read together, the two reports form a playbook. One tells you who should lead the change (technology leaders, now sitting at the strategy table). The other tells you how the change actually happens (fast, disciplined reallocation of money, people, and attention).

Here is what we took away from both – and what it means in practice for companies building software in 2026.

The CIO is no longer running IT. They’re running strategy.

McKinsey’s survey of more than 600 technology and business leaders found a widening split between two kinds of companies. In the first group, the CIO is still primarily modernizing infrastructure and defending a cost center. In the second – the top performers, defined as companies growing revenue and EBIT at 10%+ over three years – technology has become the growth engine itself, and the CIO is helping design the business, not just support it.

The numbers behind that shift are striking. Nearly two-thirds of top-performing companies say their technology leaders are deeply involved in shaping enterprise strategy. Almost half of top performers now co-create strategy continuously between business and technology teams throughout the year – roughly double the rate McKinsey measured in its previous survey. Annual planning cycles are quietly dying; iterative, quarterly business–tech alignment is replacing them.

Structurally, the winners are converging on product and platform operating models: cross-functional teams organized around customer outcomes rather than departmental silos, with fewer handoffs and decisions made in days instead of months. McKinsey points to DBS Bank, which reorganized into 30+ business-and-tech-led platforms and turned itself into one of the world’s leading digital banks as a result.

Our take: most mid-sized European companies don’t need to copy DBS. But they do need to stop treating software delivery as a procurement exercise. When your development partner sits inside your product teams, you get the same effect at a smaller scale: fewer handoffs, faster decisions, and technology work that maps directly to business outcomes.

AI is now the #1 investment – but budgets alone don’t move the needle

For the first time in McKinsey’s survey, AI has overtaken both cybersecurity and infrastructure modernization as the top technology investment priority. Half of all companies put it first; among top performers, that rises to 54%. And the money is following: 28% of top performers plan to grow tech budgets by more than 10% in 2026, versus just 3% of everyone else.

The catch is in the obstacles. A quarter of even the top performers admit they lack the data foundations to scale agentic AI securely. Nearly 33% of companies report talent and capability gaps and difficulty integrating AI into existing systems. And revealingly, top performers are far more likely than others to name change management – not technology – as their core scaling challenge.

McKinsey’s example here is instructive: Aviva, the UK insurer, deployed more than 80 AI models across its claims journey and paired that with a full operating-model and cultural transformation. The results: liability assessments 23 days faster, complaints down 65%, customer satisfaction up sevenfold.

There’s also a talent finding worth pausing on. Top performers are insourcing strategic technology capability and reskilling their own people, while laggards keep outsourcing commodity work and hoping vendors deliver transformation. At the same time, 40% of all companies are opening or expanding global delivery centers to reach international talent pools.

Our take: Choose partners that build your capability instead of renting you capacity. This is exactly why modern staff augmentation and nearshore models have evolved: embedded engineers who transfer knowledge, work in your workflows, and leave your team stronger – rather than a black-box outsourcing contract that keeps expertise on the vendor’s side of the wall. Romania’s engineering talent pool has become one of Europe’s most effective answers to precisely this equation.

The second study: being first matters less than being able to move

McKinsey’s July 2026 survey of 1,200+ executives adds the uncomfortable half of the picture. 40% of respondents believe their business model will need significant change within 3 years just to stay economically viable. Nearly the same share expect to be AI first movers – yet fewer than half of those aspirants have ever been first movers at anything.

What separates companies with a genuine track record of moving first? The research is blunt about this: first movers are better at committing resources despite uncertainty. They are more than three times as likely as late movers to reallocate at least 20% of their resources year over year. Ideas don’t create advantage; the capital, talent, and management attention behind them do. Apple didn’t invent the MP3 player – it reorganized around digital music before the market matured.

How do they pull it off? Four patterns from the data: They align on trade-offs, not just strategy – everyone knows what will be defunded, not only what will be funded. They decide on performance, not politics – killing yesterday’s priorities is the discipline most organizations lack. They use hard metrics (discounted cash flow, IRR) to compare unlike initiatives. And they correct course in frequent, small moves rather than rare, giant transformations. McKinsey’s geology metaphor is apt: many small earthquakes release less destructive energy than one big one.

What this means if you’re building software in 2026

Pulling the two studies together, four practical implications stand out for technology and business leaders:

1.Shorten your planning cycle before you grow your AI budget. Continuous business – tech cocreation predicts performance better than spend does. Quarterly reviews are a realistic first step.

2. Fund fewer things, harder. Reallocation is the muscle to build. If nothing in your portfolio got defunded this year, that’s a warning sign, not stability.

3. Buy capability, not just capacity. Whether you insource, reskill, or augment your teams, the test is the same: is your organization more capable after the engagement than before it?

4. Treat agentic AI as an operating-model change. Data foundations, integration, and change management are where scaling efforts stall – plan for them from day one.

The through-line of both reports is the same idea McKinsey closes with: the goal isn’t to be right the first time. It’s to shorten the cycle between action, feedback, and adjustment. Velocity is the new competitive advantage.

*Zeren Software helps European companies build that velocity: embedded nearshore engineering teams, AI integration, and custom software delivered inside your product organization, not outside it. If 2026 is the year your technology roadmap becomes your business strategy, let’s talk .

**Sources:** McKinsey & Company, “McKinsey Global Tech Agenda 2026” (February 2026) and “Why accelerated resource allocation matters in the age of AI” (July 2026).

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The Future of Tech Talent and Four Scenarios for the World of 2050

The Future of Tech Talent and Four Scenarios for the World of 2050

AI Abundance. Battling Blocs. Climate Coalition. Digital Darwinism. The BCG Henderson Institute gave the next 25 years four names – and every one of them rewrites how companies will hire, skill, and access the people who build their tech environments.

Most strategy still runs on a single, unspoken assumption: that tomorrow will look roughly like today, only more so. The BCG Henderson Institute’s report, Beyond Tomorrow: Four Scenarios for the World of 2050, makes the case that this is the one assumption no leader can afford.  The only unacceptable strategy is planning for just one future.

BCG built the four scenarios on a quantitative analysis of more than a hundred megatrends, a century of historical data, and dozens of expert interviews, then stress-tested each across twenty economic, geopolitical, societal, and environmental metrics. These scenarios are a map of the plausible – and the spread between them is staggering.

At Zeren, we read futures work like this through one lens: talent. Because whichever of these four worlds we drift toward, each one reshapes the most important question our industry answers – how do companies get the right capabilities, in the right place, at the right moment? Here are the four scenarios in full, followed by what they mean for hiring and IT staff augmentation specifically.

1. AI Abundance – the regulated boom

The world. AI explodes, nearly breaks society, and is then reined in by global cooperation. In BCG’s telling, a wave of AI-enhanced cyberattacks in the 2030s – the “Compute Wars” – cripples hospitals, grids, and transport, affecting more than a billion people.

The result by 2050 is a genuine productivity miracle. Global GDP more than triples, driven not by population or globalization but by soaring productivity – high-income labor productivity grows at roughly 5.7% a year. Clean energy becomes cheap and plentiful, a robotics and “physical AI” revolution transforms manufacturing and services, and the average person works about 25% fewer hours than today – roughly 1,600 a year, down from 2,100, with four-day weeks common in many regions. Healthy life expectancy climbs from 63 to 70. Most nations build expanded safety nets or basic-income programs funded by automation taxes.

The catch is freedom. To combat misinformation, guardrails on digital platforms constrain civil society; governments quietly trade some individual liberty for stability. And the climate is hot – around 2.2°C above pre-industrial levels — though emissions are finally falling fast.

The tech talent earthquake. AI and robots displace much of what people used to do, and the wage premium for expertise erodes across many professions. New opportunity concentrates in three places: caring professions, AI oversight and judgment roles, and skilled manual trades. BCG’s sharpest warning is the rise of AI-only firms – networks of specialized AI agents that run with little or no human involvement, and that appear first in digital-native sectors with minimal physical interface: software development, digital marketing, algorithmic trading. In other words, Zeren’s industry’s heartland.

2. Battling Blocs – the fractured world

The world. Globalization goes into reverse. After a tariff war, a wave of nationalist leaders, splintering of the internet, the collapse of the WTO, and the hollowing-out of the UN, the world hardens into rigid, mutually distrustful blocs that prize security and self-sufficiency over collaboration. Trade falls back to Cold War levels — from 57% of global GDP to 35%. Defense spending nearly triples, from 2.4% to 7% of GDP.

The line between government and business blurs into state capitalism. Traditional multinationals all but disappear, forced to pick a bloc or juggle a fragile web of regional joint ventures. Innovation narrows to defense, dual-use technology, and bloc self-reliance, while consumer and health domains starve for investment. Growth stalls at 1.8% a year, productivity at just 1.0%. Democracies fall from 49% of countries to 25%. Worldwide happiness drops 10%, extreme poverty rises from 8% to 10%, and with multilateral climate action dead, warming still reaches 2.1°C.

The tech talent earthquake. This is the scenario where BCG states it most directly: talent becomes a scarce strategic asset and a dimension of great-power competition. Aging populations and restricted migration tighten labor markets; immigration policy shifts from a growth lever to a geostrategic weapon. The race for talent plays out across three fronts – capturing scientific and technical expertise, sustaining entrepreneurial clusters, and protecting the academic centers that train the next generation. Meanwhile, a non-aligned Global South – India projected to be the world’s third-largest economy by 2029, with Brazil, Indonesia, and others climbing fast – becomes a coveted source of young, expanding workforces.

3. Climate Coalition — resilience over growth

The world. A run of extreme weather events in the late 2020s – catastrophic flooding, deadly heat waves – triggers a global wave of citizen pressure for coordinated action. A “climate club” of industrial nations forms, requiring members to price carbon domestically and apply carbon border adjustments. By 2040 most major economies have joined; by 2050 carbon sells at $300 a ton. It works: warming stabilizes at 1.8°C, the share of unabated fossil fuels in the energy mix collapses from 81% to 35%, and low-carbon sources generate 92% of electricity.

But it’s a delicate balance. Taxes are high and spending is lean. Growth is slow but steady at 2.5% a year, dragged by aging societies and the fading dividends of globalization. The upside is broadly shared – extreme poverty is halved, from 8% to 4%. The friction is generational: with carbon revenues earmarked for restoration and pension liabilities heavy, working-age adults in advanced economies end up with less disposable income than retirees, and politics turns on intergenerational fairness.

The tech talent earthquake. Crucially, in this world AI is a support for humans, not a substitute – job losses happen, but they’re temporary because nations and companies invest continuously in upskilling and reskilling. Innovation pours into low-carbon energy, new materials, biotech, and agriculture, creating demand for entirely new skill profiles. And aging hits hard: labor shortages spread across the Global North, making aging-workforce strategy – late-career pathways, multigenerational teams, knowledge transfer between older and younger workers – a frontline competitive issue rather than an HR footnote.

4. Digital Darwinism — survival of the fittest

The world. The opposite of AI Abundance’s bargain. A race to the bottom on regulation unleashes tech companies, governments retreat, and a survival-of-the-fittest ethos takes hold. Growth is strong – global GDP grows 4% a year, near-tripling – and trade stays open out of commercial self-interest (61% of GDP). But the spoils are brutally concentrated: the richest 1% come to hold nearly half of global wealth, a level not seen since the early 1900s, while the middle class shrinks and extreme poverty climbs from 8% to 12%.

Work fractures into two tiers. Those with creative or high-skill expertise thrive; everyone else faces stagnant prospects, gig-style and short-term contracts mediated by algorithmic platforms, AI “cobots” that double as surveillance, and an epidemic of digital overload, burnout, and addiction. Knowledge gets locked inside megacorporations, eventually dampening the pace of innovation. Democracies fall to 30% of countries. With decarbonization sidelined for adaptation that mostly protects wealthy enclaves, warming hits 2.5°C.

The tech talent earthquake. This is the staff-augmentation model taken to a dystopian extreme: contingent, algorithmically-brokered, commoditized labor at civilizational scale, stripped of security and stability. In a low-trust, cutthroat environment, BCG argues that trust itself — auditable governance, provenance, cyber resilience, genuine investment in people — becomes one of the few durable differentiators. Multitier offerings emerge everywhere: premium for the elite, bare-bones for the mass market.

What the four scenarios mean for hiring and IT staff augmentation

Read together, the four worlds deliver a striking verdict for our industry: the demand for flexible, on-demand access to specialized talent doesn’t just survive in every scenario – it intensifies.

In AI Abundance, the commodity layer evaporates — and the judgment layer becomes gold. If AI-only firms can spin up in software development and digital marketing first, then supplying generic “three backend developers for six months” is the part of our business most exposed to automation. But the same scenario tells us exactly where human value migrates: agenda-setting, taste, assessment, oversight, empathy, and the orchestration of agentic workflows. The staff augmentation that wins here doesn’t sell seats; it sells AI-fluent architects, human-in-the-loop judgment, and the embedded leadership that helps a client become AI-first before an AI-only rival makes the choice for them. Reskilling stops being a perk and becomes the core product.

In Battling Blocs, location becomes destiny — and within-bloc nearshore talent becomes a strategic asset. When mobility tightens and data localizes, a client can no longer freely tap a global talent pool. They need capability inside their own bloc and jurisdiction. For an EU-anchored, Romania-based partner, this is structurally favorable: deep engineering talent, nearshore proximity to Western European clients, and shared regulatory ground at exactly the moment those things become scarce and valuable. The flip side is real — fragmentation makes cross-border sourcing harder and turns talent access into a geopolitical question — but in a bloc-based world, being inside the right bloc with the right people is a moat, not a footnote.

In Climate Coalition, the mandate is reskilling and demographics. Continuous upskilling is explicitly what keeps job losses temporary in this world, and chronic labor shortages across an aging Global North create durable, structural demand for flexible and specialized talent. Add the green-skills gap — climate-tech, energy software, MRV and carbon-accounting systems, new-materials engineering — and you have a market that needs partners who can both close skill gaps fast and design multigenerational, late-career-inclusive workforce models. This is the scenario most aligned with staff augmentation as a strategic capability rather than a stopgap.

In Digital Darwinism, trust is the only defensible margin. This world commoditizes contingent labor and pushes the whole industry toward a price-driven, platform-brokered race to the bottom – with worker wellbeing as collateral damage. The firms that don’t get commoditized are the ones that invest in the opposite: rigorous vetting, embedded delivery leadership, auditable quality, and a genuine duty of care to the people they place. The “pod and squad” model – cross-functional teams with embedded tech leads and delivery managers who own outcomes – is precisely the antidote to anonymous gig brokering. In a low-trust world, being the trusted name is the premium.

The through-line: BCG’s five low-regret moves

Across all four scenarios, BCG identifies five “low-regret” moves that make sense no matter which future arrives. One of them reads almost like a job description for the next era of our industry:

Reimagine talent for aging populations and AI – build models for intergenerational word, more flexible roles, and talent mobility; extend your talent footprint into emerging labor markets; and design new human-machine operating models that combine agentic AI workflows with human oversight, judgment, and creativity.

The other four reinforce the same direction of travel. Enhance structural resilience (diversify, build regional optionality). Build digital flexibility and trust (modular stacks, cybersecurity, verifiable systems). Sharpen sensing and influencing (foresight, faster decision loops). And embrace a broader societal role — because companies that look after workers’ wellbeing will, in BCG’s words, earn a premium in talent markets.

That last point matters most for an industry built on people. In a world where skills expire faster than ever and adaptability beats permanence in every scenario, the organizations that treat talent as a strategic system — not a cost line — are the ones positioned to win.

Where Zeren stands

Strip the four scenarios down to their common core and two truths hold in every one:

First, the half-life of skills keeps shrinking. Whether AI augments work, fragments it, greens it, or commoditizes it, no one builds a 2050-proof workforce by hiring once and standing still. Reskilling, redeployment, and flexible access to specialized tech capability move from “nice to have” to the center of workforce strategy.

Second, the value of getting the right tech capability, exactly when you need it rises in all four futures. That has always been the premise of staff augmentation – and these scenarios suggest the premise only gets stronger. What they also make clear is where the work has to move: up the value chain. Away from filling seats and toward outcome-aligned pods, embedded leadership, AI-fluent talent, and a trust standard that a platform can’t replicate.

That’s the bet we’re already making. We build tech talent models backwards from outcomes rather than forwards from job titles. We deploy cross-functional pods rather than scattered individuals. We treat embedded tech leads and delivery managers as the multiplier, not the overhead. And we work at the intersection of tech talent and human potential – because in every one of BCG’s four worlds, that intersection is exactly where durable advantage lives.

You can’t plan for a single version of 2050. But you can build the one capability that pays off in all of them: the ability to access, shape, and continually renew the tech talent your strategy depends on. That’s the future we’re preparing our clients — and ourselves — to thrive in.


Source: BCG Henderson Institute, “Beyond Tomorrow: Four Scenarios for the World of 2050” (April 2026). All scenario data and projections are BCG’s; the talent and staff-augmentation analysis is Zeren Software’s own.

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Technology – Shifting from a Cost Center to a Value Creator

Technology – Shifting from a Cost Center to a Value Creator

Some companies still treat technology as a cost center,  others treat it as a growth engine.

McKinsey released their Global Tech Agenda 2026 back in February, surveying 632 C-level executives across 69 countries.

Key Findings

Here’s what separates the top performers:

Nearly 2/3 of top-performing companies have their technology leaders “very involved” in crafting enterprise strategy – vs. just 52% of other organizations.

Half of top performers now co-create strategy between business and tech teams continuously throughout the year – nearly double the rate from last year.

28% of top performers were planning to increase their tech budgets by more than 10% in 2026.

More than half of top performers have already transformed their IT function using AI in the past two years.

Forward-thinking CIOs are investing in agentic automation to change how business gets done and in data productization to generate entirely new revenues.

They are replacing annual budget planning with practices that fuel innovation – i.e. product and platform models, continuous decision-making, engineering excellence, and capability-led talent models.

The #1 investment priority is obviously Artificial Intelligence. And this has now surpassed cybersecurity and infrastructure as the top technology investment area.

We already see leaders building in-house capabilities, reskilling their own people, and weaving AI into decision-taking.

Vision for the future

We can find thousands of IT stories out there. But very few still are business transformation stories like, for instance, Aviva. They deployed 80+ AI models across their claims journey. This is how they reduced liability-assessment time by 23 days, cut customer complaints by 65%, and increased their customer satisfaction score sevenfold.

At top-performing companies, technology’s center of gravity has shifted from a cost center to a value creator.

What about you? Are you writing the AI story as we speak? Is your technology leader shaping your company’s future – or just keeping the lights on?

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Artificial Intelligence Case study Uncategorized

Why your AI agents keep failing – and it’s not AI

Why your AI agents keep failing – and it’s not AI

Most companies experimenting now with AI agents never manage to scale them. Fewer than one in ten do. The problem, almost always, is what’s underneath: the data.

The gap everyone is trying not to talk about

So, there’s a version of the AI story that sounds like this: companies deploy AI agents, the agents automate complex tasks, productivity soars, and everyone wins. That version exists. It’s just rarer than the headlines suggest.

In other words, according to a McKinsey study published in April 2026, roughly two thirds of enterprises worldwide have run experiments with AI agents. Fewer than ten percent have managed to scale them into something that delivers real, measurable value. And the failure isn’t usually the AI itself – it’s what the AI is running on.

“Eight in ten companies say fragmented, siloed data is what stops them from scaling AI agents.”

What good foundations actually look like

Actually, McKinsey’s research identifies four steps that separate organisations managing to scale agentic AI from those who get stuck in pilot purgatory. They’re worth understanding as a sequence – each one builds on the last.

  1. Find the right workflows to automate. Not everything benefits from an AI agent. The organisations getting results start by identifying a small number of end-to-end processes where autonomous decision-making could genuinely change outcomes – and map exactly what data those processes would need.
  2. Clean up the data architecture, layer by layer. This doesn’t mean rebuilding everything from scratch, it actually means modernising how data flows, connects, and becomes usable – progressively. Thus, data from different systems (CRM, supply chain, finance) needs to speak the same language.
  3. Move from cleanup sprints to continuous quality management. One of the most common failure modes is treating data quality as a periodic project. In an agentic environment, we should be able to monitor data quality in real time, with automated checks.
  4. Build governance for what agents are allowed to do. As agents gain autonomy, the rules governing their behaviour become the primary mechanism of control. Clear policies – defining what data an agent can access need to be automated and embedded. Human roles shift from doing the work to supervising and orchestrating agent-driven workflows.

Not to mention that the thread running through all four steps is the same: we need to treat data as infrastructure.

Where Zeren fits into this picture

In fact, this is the layer of work Zeren’s consultants operate in. We have consultants like Sânziana for whom the data architecture makes the AI models reliable.

Sânziana, one of Zeren’s senior data architects, is currently leading a Business Intelligence engagement for a major manufacturing company in the Nordics. Her framing of the problem captures it well:

“Without properly modelled data, AI cannot produce good results. We are among the fortunate ones for whom the rise of AI brings more work, not less.”

What Zeren Software does

Nevertheless, Zeren connects specialist data professionals – data architects, data engineers, BI consultants, AI engineers – with complex international projects. Our consultants work across industries building the data foundations that make AI actually usable in production environments.

The question worth asking now

So, if your organisation is planning an AI initiative, the most useful diagnostic relates to the data underneath the AI model. Is it connected or consistent? Is it governed? Do your agents have access to what they need, and only what they need? All these are questions to consider.

“In the agentic age, data foundations are becoming the primary source of competitive differentiation.”

Undoubtedly, for the companies already operating at scale, this prediction is already a sheet fact.

Curious about how your data infrastructure stacks up? Get in touch with Zeren.

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Artificial Intelligence Case study

Romania and Bulgaria are EU’s AI Readiness Laggards

Romania and Bulgaria are EU’s AI Readiness Laggards

A new peer-reviewed study maps AI readiness across all EU member states. The findings are blunt: Southern and Eastern Europe – and Romania in particular – face systemic barriers that go far deeper than infrastructure or investment. Here is what the research says, and what needs to change.

This should not be surprising. For years, analyses of the EU’s digital landscape have pointed to the same fault line – a persistent gap between Northern and Western European member states on one side, and Southern and Eastern ones on the other. Lower levels of digital and AI literacy, slower e-government adoption, educational disparities, weaker institutional capacity: the diagnosis is familiar.

What is new — and what should prompt genuine concern — is the specific nature of the deficit now being quantified. A 2026 peer-reviewed study published in Telematics and Informatics on ScienceDirect (“Unequal AI readiness: institutional and digital disparities in e-government across the European Union”) does not merely describe a digital divide. It maps, clusters, and names the countries that are structurally unprepared for artificial intelligence in public governance.

Romania and Bulgaria appear in the worst-performing cluster. The study calls them AI Readiness Laggards.

“Extremely low levels of digital skills, suggesting systemic barriers to AI readiness.”

What the study actually measures

Unlike broader digital economy indices, this study focuses specifically on the prerequisites for AI adoption in e-government — the machinery of public services and state institutions. The researchers identify two independent and underlying dimensions that determine whether a country is ready for AI-driven governance:

  1. Digital Skills & E-Government Engagement – Measures citizen-side readiness: the prevalence of digital competencies in the general population and the degree to which citizens actually interact with government digitally.
  2. Transparency & E-Government Service Availability – Measures institution-side readiness: how openly and completely public services are delivered digitally, and the transparency of government operations and data.
How the EU clusters

The study identifies six distinct clusters of member states based on their combined scores across the two dimensions. The distribution is uneven, and the gap between the best and worst performers is stark.

On one side, the AI – Ready Leaders: High digital skills. High transparency. A self-reinforcing cycle: skilled citizens demand better services; better services built more skilled citizens. Institutions here are the closest to being operationally ready for AI in governance. Here you can find countries like Denmark, Finland, The Netherlands.

On the other side, the AI Readiness Laggards: Low digital skills. Low transparency. Systemic, not surface-level, barriers. Extremely low digital skills in the general population combined with limited institutional transparency creates a compound readiness deficit. Here you can find countries like Romania or Bulgaria.

What Romania must do – and where to start

The study does not prescribe solutions, but the diagnosis makes the priorities clear. Closing the AI readiness gap requires action on multiple levels simultaneously, because the two dimensions interact: you cannot build institutional AI capacity without a population that can engage with digital public services, and you cannot drive citizen digital literacy without better, more transparent digital services to engage with.

Our view

At Zeren Software, we work at the intersection of digital transformation, software development, and institutional capacity. The study’s findings confirm what we observe in our client work across the region: the barriers to AI readiness are organisational and human before they are technical. Procuring AI tools is not the same as being ready to use them. Writing a national AI strategy is not the same as having the institutional infrastructure to implement it.

Romania can close this gap. But it requires treating digital literacy and institutional transparency not as outputs of AI adoption, but as the prerequisites for it. The study makes clear that the countries which are genuinely ready started building those prerequisites long before AI became the policy priority it is today.

The readiness gap is a shared problem. Closing it requires pressure and example also from the private sector, not just government initiative.

 

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Agile Development Artificial Intelligence Case study Cloud Custom Software News Staff Augmentation

Partnership Extension Announcement | Delivering Core Tech Foundations in the Insurance Sector

Partnership Extension Announcement | Delivering Core Tech Foundations in the Insurance Sector

We’re excited to announce that our collaboration with one of our trusted technology partners is growing, as we continue to support a long-term project for a leading insurance company based in Scotland.

Since July last year, Zeren consultants have been part of a foundational engineering group that plays a critical role in enabling three distinct product teams across the organization. We’re proud to see this collaboration expand, with the project’s scope broadened as a result of positive client feedback.

Our team contributes to the delivery and evolution of core internal services, including:

✅ Authentication and authorization systems
✅ Cloud-based data storage using Microsoft Azure
✅ Development of internal APIs that abstract and streamline access to underlying services

This recent extension stands as a testament to the value of close collaboration, technical ownership, and the lasting impact of a strong engineering foundation at scale.
We’re grateful for the ongoing trust and excited to keep building together.

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Case study Data Engineering

New Partnership Announcement: Strengthening Our BI & Data Engineering Team

New Partnership Announcement: Strengthening Our BI & Data Engineering Team

We are excited to announce the expansion of our team in the Business Intelligence (BI) and Data Engineering sector, bringing on board exceptional expertise in Migration from On-Premise to Cloud solutions. This expansion comes as we embark on a new collaboration with one of our esteemed Nordic clients — a global leader in vehicle manufacturing, for which we have been working for the last three years.

The project will focus on delivering advanced development in Snowflake, dbt, and Power BI, with an emphasis on data engineering and database management. Our consultant, S.I.T., has demonstrated exceptional analytical skills throughout the selection process, impressing our client with her technical acumen, leadership capabilities, and strong communication skills. Her ability to think critically and solve complex problems, paired with a deep passion for creating innovative solutions, ensures the delivery of top-notch results in data analysis and reporting.

This successful partnership marks an important milestone for Zeren Software, as we continue to grow our team to meet the needs of our clients. We are thrilled to see our proposal be so well received and look forward to starting the project in April. Our commitment to ensuring our customer’s success remains steadfast, and we are excited to contribute to their continued growth and innovation.

Stay tuned for more updates from Zeren Software!

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Case study Cyber Security Data Engineering IT Project Management Staff Augmentation

Tech Skills for the Future: Preparing for 2025 and Beyond

Tech Skills for the Future: Preparing for 2025 and Beyond

The technology landscape is evolving at an unprecedented pace, reshaping industries and creating demand for new skills. As businesses adopt cutting-edge solutions like AI, quantum computing, and cloud services, IT professionals must adapt to stay relevant. Zeren Software goes beyond building teams for today’s challenges—we’re equipping them for the demands of tomorrow. Here’s a deep dive into the tech skills that will dominate the IT world in 2025 and beyond—and why these skills are critical for businesses aiming to thrive in the future.

1. Cloud Engineering and Architecture

The global shift to cloud-first strategies is accelerating, with businesses increasingly moving their operations to platforms like AWS, Microsoft Azure, and Google Cloud. The demand for cloud engineers is surging as companies rely on experts to design, deploy, and manage their cloud infrastructure.

Cloud engineers aren’t just responsible for keeping systems running—they optimize resources, ensure scalability, and maintain robust security protocols. As hybrid and multi-cloud environments become more prevalent, proficiency in cloud orchestration, containerization (Kubernetes, Docker), and cost management tools will be indispensable.

Zeren’s teams are built to help clients unlock the full potential of the cloud. From migrations to custom cloud-native solutions, our experts enable businesses to innovate faster and scale smarter.

2. AI Ethics and Responsible AI

Artificial intelligence is no longer a futuristic concept—it’s embedded in everyday processes, from customer service chatbots to financial decision-making systems. However, the rapid adoption of AI has raised critical questions about ethics. How can organizations ensure their AI systems are unbiased, transparent, and fair?

Skills in AI ethics are becoming essential for IT professionals, particularly in industries where regulations demand accountability. Expertise in responsible AI development, bias mitigation, and algorithmic transparency will be in high demand.

Our approach to AI ensures businesses can leverage this technology responsibly. Zeren equips teams to design and implement AI systems that not only meet technical requirements but also adhere to ethical and regulatory standards.

3. Quantum Computing Expertise

Quantum computing, a field once confined to research labs, is nearing practical application. The ability to solve complex problems exponentially faster than traditional computers has enormous implications for industries like healthcare, finance, and logistics.

Professionals skilled in quantum algorithms, quantum machine learning, and post-quantum cryptography will be at the forefront of this revolution. While still an emerging technology, the groundwork for quantum computing’s widespread adoption is being laid today.

Zeren prepares its teams to monitor and adapt to quantum advancements, ready to integrate quantum-ready solutions as this transformative technology becomes accessible.

4. Cybersecurity and Zero-Trust Models

As cyber threats become more sophisticated, cybersecurity has evolved from being a technical necessity to a strategic priority. The traditional perimeter-based security model is being replaced by zero-trust architectures, which assume that no user or device can be trusted without verification.

IT professionals skilled in implementing zero-trust frameworks, cloud security, and AI-driven threat detection will be indispensable. Additionally, as IoT devices proliferate, securing these endpoints will become a critical focus area.

Our cybersecurity teams at Zeren work to help businesses adopt the latest security measures. Whether it’s securing cloud infrastructure or safeguarding IoT ecosystems, we ensure clients stay protected in an increasingly interconnected world.

5. Data Analytics and Machine Learning (ML)

The explosion of data in recent years has made advanced analytics and machine learning pivotal to business success. Companies need professionals who can turn vast amounts of raw data into actionable insights, using tools like TensorFlow, PyTorch, and advanced visualization platforms.

Machine learning engineers and data scientists who can design predictive models, automate processes, and provide data-driven recommendations will continue to be highly sought after.

We prioritize building teams capable of creating robust data strategies that allow businesses to uncover trends, optimize operations, and gain a competitive edge.

6. DevOps and Automation

Automation has become a cornerstone of IT operations, and DevOps practices are essential for achieving seamless collaboration between development and operations teams. Professionals who understand continuous integration/continuous deployment (CI/CD) pipelines, infrastructure as code (IaC), and automated testing frameworks will remain in high demand.

Through streamlined workflows and reliable tools, Zeren’s specialists ensure businesses can deliver faster, more scalable, and cost-efficient solutions while minimizing operational burdens.

How Forward-Thinking Teams Prepare for the Future

Staying competitive in the future of IT requires more than keeping pace—it demands foresight and adaptability. Zeren Software is committed to equipping teams with the skills and knowledge to thrive in an ever-changing industry. By fostering continuous learning, upskilling, and real-world application of emerging technologies, we ensure our teams are always prepared to deliver innovation.

From navigating quantum computing to implementing robust security frameworks, we enable businesses to capitalize on opportunities while overcoming technical challenges.

Conclusion

The IT skills of the future reflect the rapidly evolving demands of a digital-first world. As businesses seek to stay competitive, investing in these high-demand skills—cloud engineering, AI ethics, quantum computing, cybersecurity, and more—will be crucial.

We aren’t just responding to trends; we’re leading them. At Zeren Software, our mission is to unlock the full potential of every business by building teams ready for tomorrow.

Ready to future-proof your IT capabilities? Let’s build the next generation of IT solutions together.

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Artificial Intelligence Case study IT Project Management News

How Zeren Ensures Success in an Era Where 80% of AI Projects Fail

How Zeren Ensures Success in an Era Where 80% of AI Projects Fail

Artificial Intelligence (AI) is rapidly transforming industries, promising unprecedented efficiencies, insights, and innovation. However, according to a recent article in the Harvard Business Review, up to 80% of AI projects fail to reach their intended goals. This sobering statistic raises a critical question: how can organizations ensure their AI initiatives succeed?

At Zeren, we understand the challenges inherent in AI project implementation. From the complexities of data management to the need for specialized skills, launching a successful AI initiative requires more than just cutting-edge technology. It demands a holistic approach combining best practices, expert project management, and tailored consultancy. Here’s how Zeren ensures that our clients don’t just participate in the AI revolution—they lead it.

Best Practices: The Foundation of Success

Successful AI projects are built on a foundation of best practices, which are integral to every stage of development at Zeren. Our team follows a proven framework that begins with a deep understanding of the client’s business objectives. We know that AI solutions must align with broader strategic goals to deliver tangible results.

Moreover, we emphasize the importance of data quality and governance. AI models are only as good as the data they are trained on. At Zeren, we employ rigorous data validation processes to ensure the accuracy, consistency, and reliability of the data that powers AI models. This attention to detail minimizes the risk of biases and errors, leading to more accurate predictions and insights.

Project Management: Steering AI to Success

Even with the best technology and data, AI projects can still falter without effective project management. Zeren’s project management approach is designed to navigate the complexities of AI initiatives, ensuring they are delivered on time, within budget, and with the desired outcomes.

Our project managers are not just coordinators—they are AI-savvy professionals who understand the technical intricacies of AI. They work closely with clients to set realistic timelines, manage expectations, and adjust strategies as needed. By maintaining clear communication and a focus on objectives, our project managers keep AI projects on track, reducing the risk of failure.

Tailored Consultancy: Expertise That Drives Value

Zeren’s consultancy services are tailored to each client’s unique needs, ensuring that AI initiatives are not just technically sound but also strategically aligned. We begin by conducting a thorough assessment of the client’s current capabilities and future goals. This allows us to design AI solutions that are scalable, flexible, and, most importantly, impactful.

Our consultants bring deep industry knowledge, allowing them to foresee challenges and mitigate risks before they become roadblocks. They also provide ongoing support, helping clients adapt to new developments and continuously improve their AI systems. This level of personalized consultancy is what sets Zeren apart in a crowded AI landscape.

Conclusion: Partnering with Zeren for AI Success

In a world where the majority of AI projects fail, Zeren stands out as a beacon of success. By combining best practices, robust project management, and bespoke consultancy, we ensure that our clients’ AI initiatives deliver real, measurable value. Partnering with Zeren means not just adopting AI but thriving with it.

Are you ready to unlock the full potential of AI for your business? Let’s make your AI journey a success story.