MindTech

Has AI changed who pushes changes to production at your company? 🤔 Nicolás, our CEO, opened the conversation. We'd love to hear how it's going on your team. Cast your vote below 👇

How much would we actually save with nearshore? It’s one of the first questions that comes up when engineering leaders start discussing headcount with finance. And the answer is rarely as simple as comparing hourly rates. Role, seniority, location, hiring timelines, and the cost of leaving critical positions open all change the equation. That’s why we created the 2026 LATAM Nearshore Tech Talent Benchmark: LATAM rates by role and seniority, side-by-side U.S. market comparisons, potential savings, and the impact of delaying hiring on the roadmap. A practical reference for your next headcount conversation. Get the full benchmark here → https://lnkd.in/dumgt52Y

Your staffing partner passed the first few months. That's not the real test. 4 questions to find out if they're still delivering, and what it's really costing you. Read the full scored diagnostic here 👉 https://lnkd.in/dq4fdXGb #EngineeringLeadership #StaffAugmentation #Nearshore

The hardest part of adding engineering capacity usually isn't knowing you need it. It's getting the investment approved. CTOs see the delivery risk: overloaded teams, delayed projects, and a roadmap that keeps slipping. Finance sees a different question: what will this really cost, how fast will the investment pay off, and what happens if we wait? That's where many engineering decisions get stuck. We created the CFO One Pager to help technology leaders translate the need for more capacity into a financial case, looking beyond salary to ramp time, delivery risk, flexibility, and the cost of delay. If your next headcount conversation is going to end with "what's the ROI?", go into it with the numbers already in hand. Get the CFO One Pager. Link here 👇 https://lnkd.in/dj4_tmip

The project was already 4 months behind. And the first outsourcing vendor hadn’t fixed it. By the time Mindtech stepped in, the ServiceNow implementation was dealing with multiple active incidents across billing, notifications, supplier logistics, a backend portal, and a mobile app. This wasn’t a project that needed more generic capacity. It needed people who could join mid-configuration, understand what was already in place, and start solving problems immediately. We brought in 3 senior ServiceNow developers + 1 technical lead. They joined the project in progress and started tackling critical issues from week one, while continuing to absorb new requirements from the end client. About 2 months later, the 4-month delay had been recovered and the project was back on track. That’s the part that often gets missed when a project is already under pressure: adding people isn’t enough. You need the right expertise to become useful before the project falls even further behind. Read the full story 👉 https://lnkd.in/dbp9kvEp

This isn't really a story about vibe coding going wrong. It's a story about what happens when something built incredibly fast suddenly has to behave like a real product. We recently worked on a digital education platform that had already made it into active user testing. From the outside, everything looked fine. Underneath, the frontend had grown into a single 6,000+ line file that was becoming harder to change without breaking something else. Starting over wasn't an option, so the team took a different route: refactor the architecture incrementally while testing continued. The migration took 4 weeks, versus an internal estimate of roughly 4 months without that approach. AI wasn't the problem here. In fact, it was part of the solution too. The difference was having the technical judgment to know what to keep, what to change, and how to do it without stopping the product. We broke down the full case in the carousel 👇 Full technical breakdown in the comments 👇

Vibe coding isn’t the problem. Skipping architectural judgment is. AI can get an MVP up and running incredibly fast. But what works for validation doesn’t always hold up once real users, new features, and scale enter the picture. We recently worked on a product with a 6,000+ line frontend built through vibe coding. It worked, but it had become increasingly unstable and difficult to maintain. Rather than rewriting everything from scratch, we refactored it incrementally into a modular architecture without interrupting active testing. The result? 4 weeks instead of an estimated 4 months. In our latest resource, we break down what went wrong, how we approached the refactor, and what teams should watch for when building products with AI. Read the full breakdown 👉 https://lnkd.in/dfug5m6t

A 90-day hiring delay doesn't just cost you time. It can create 8–12 weeks of delivery debt and make recruiting up to 3x more expensive when you eventually restart the search. Delaying a hire can feel like the safer, more cost-controlled decision, especially when budgets are tight or headcount is under pressure. The problem is that the cost doesn't really go away; it simply shows up somewhere else in the business. Roadmap priorities start getting pushed back, existing engineers absorb the extra workload, and critical initiatives stay on hold longer than expected. Over time, that capacity gap becomes delivery debt that is increasingly difficult to recover. And when the company is finally ready to hire again, the search itself can be more expensive. In fact, 40% of teams that pause hiring don't restart the process in time. That's why the question shouldn't only be whether you can afford to hire right now. It's also worth asking what another 90 days of waiting could cost your roadmap. We built the Cost of Delay Framework to help engineering leaders put numbers behind that decision and compare the cost of waiting with the cost of adding capacity now. Full framework in the comments 👇

AI is changing more than processes. It’s changing how organizations make decisions. At Argentina’s HR Congress, Mariano Obligado , Head of HR at Mindtech, joined the conversation around a challenge that will become increasingly important: how to scale AI while keeping people at the center of critical decisions. The opportunity is not simply to automate more. It’s to build smarter systems that combine the speed of AI with human judgment. Thanks to WorkTec Argentina for creating the space for this conversation. Where should we draw the line between AI and human decision-making? #Mindtech #AI #HumanResources #DigitalTransformation

The most interesting AI story in software right now might not be code generation. It might be the org chart. That’s what I see behind the renewed attention around the Forward Deployed Engineer. The role itself isn’t new. But seeing it move closer to the center of the AI conversation feels significant. OpenAI launching a Deployment Company built around embedding FDEs inside organizations is one example. To me, the signal is bigger than the title. For years, one of the biggest constraints in software was simply producing enough of it. More roadmap usually meant more engineers, more teams, more specialization,  and eventually, more coordination between all of them. AI changes that equation. If engineers can build, test, and iterate much faster, you’d think complexity would shrink. It doesn’t. It moves somewhere else. It moves into understanding the problem, making the right tradeoffs, integrating with real systems, and making sure what gets built actually works in the business. That’s why the FDE model catches my attention. It brings technical ownership much closer to the actual problem, and it challenges the idea that engineering capacity should be measured by headcount or by how much software you can produce. A smaller team isn’t automatically better. But a senior team with context, autonomy, good judgment, and fewer handoffs can become remarkably capable when each person has more leverage. Sometimes I wonder how much of today’s technology organization exists because software used to be much harder and slower to produce. More people meant more capacity. Specialization was necessary. Coordination was the price we paid for scale. I’m not convinced those assumptions survive unchanged when engineers suddenly have a lot more leverage. To me, that’s the interesting part of the FDE conversation. It’s not really about a new role. It’s about what happens to the rest of the organization when the old bottleneck starts to move.