How to Extend a Software Development Team (Without Losing Speed or Quality) 

How to Extend a Software Development Team (Without Losing Speed or Quality) 

Nearshore, Nearshore Software Development, Software Development

Written by: Monserrat Raya 
Team extension model for software development in Austin and Dallas

Introduction

If you’re leading an engineering team today, chances are you’ve lived this story before. The roadmap is ambitious, the backlog keeps growing, and your leadership team is asking for faster releases. Yet your recruiting pipeline looks like a slow drip: qualified candidates are scarce, the interview process takes months, and some of your best offers are rejected because bigger competitors can simply pay more. Meanwhile, your developers are stretched thin. Deadlines slip, morale dips, and the pressure builds. You’ve probably thought: “We don’t need more ideas, we need more hands on the keyboard.” This is the reality in tech hubs like Austin, Dallas, New York, and the Bay Area. Demand for engineering talent keeps outpacing supply, and internal hiring alone isn’t enough. That’s why more companies are exploring extended development teams as a practical way to grow capacity without the headaches of traditional recruitment. But what exactly is a team extension model, and how is it different from outsourcing or staff augmentation? Let’s break it down.

What Is an Extended Development Team?

There’s often confusion around terms like outsourcing, staff augmentation, and team extension. So let’s start by clearing that up. An extended development team is a group of engineers provided by a trusted partner who work as a seamless extension of your in-house squad. They don’t sit on the sidelines, and they don’t deliver work in isolation. They:
  • Join your daily standups and agile ceremonies.
  • Commit to your product roadmap.
  • Share accountability for outcomes, not just tasks.
This is very different from outsourcing, where you hand off an entire project to a vendor and wait for deliverables. Outsourcing can work for side projects, but it disconnects engineering from product strategy. Extended teams, by contrast, are built for integration, not delegation. It’s also not the same as freelancing. Freelancers are great for one-off tasks, but they rarely provide the stability and knowledge retention needed for multi-year products. As Forbes Tech Council highlights, distributed engineering teams succeed when they are fully integrated into the company’s culture, processes, and communication practices—exactly the foundation extended teams are built on (Forbes).
Assessing skill gaps in software development teams for agile delivery in Austin and Dallas
Tech leaders in Dallas, Austin, and New York use nearshore partners to cover skill gaps.

How to Extend a Software Development Team

Extending a software development team isn’t just about “adding more developers.” If it were that simple, every CTO with a LinkedIn account could solve their backlog tomorrow. The real challenge is doing it in a way that maintains speed, protects quality, and preserves the culture you’ve worked hard to build internally. Over the years, engineering leaders in Austin, Dallas, and New York have learned that successful team extension follows a few essential steps:

1. Assess Skill Gaps and Project Needs

Start with an honest look at your backlog and roadmap. Are your sprint demos constantly delayed because the frontend team can’t keep up? Do you have ambitious DevOps goals, but only one engineer maintaining CI/CD pipelines? Or is QA debt slowing down every release? Mapping these pain points tells you where extension will have the biggest impact first. Some companies extend by specialty roles (e.g., cloud engineers), while others extend by complete agile squads that handle entire features.

2. Choose the Right Extension Model

Not all team extension models are created equal, and this is where many leaders make their first mistake.
  • Nearshore (Mexico, Colombia, Brazil): Best option for agile delivery. Time zones overlap, cultural alignment is high, and communication flows naturally. For companies in Dallas or Austin, working with Mexico often feels like having colleagues one state away.
  • Offshore (Asia, Eastern Europe): Often marketed for cost savings. While rates can look attractive, agile delivery struggles when your standup happens at 10 p.m. local time. Feedback loops get delayed, and velocity suffers.
  • Local Contractors (U.S.): Integration is simple, but the cost is highest, and availability is limited in today’s competitive market.
A smart approach many leaders use is to pilot a nearshore squad, measure sprint velocity against current benchmarks, and expand once they see consistent improvement.

3. Ensure Cultural and Time Zone Alignment

Agile is built on speed and interaction. It’s not just about writing code—it’s about feedback, iteration, and accountability. If your extended engineers are 10–12 hours away, by the time you receive feedback, a sprint is already slipping. This is why nearshore extended teams in Latin America often outperform offshore. They can join your sprint planning at 10 a.m. CST, just like your in-house developers. They’re also more likely to share communication norms—direct feedback, accountability in retros, and proactive collaboration. Related: Cultural alignment in extended teams

4. Establish Collaboration Tools and Practices

This step is where many extensions succeed—or fail. Adding engineers isn’t enough; they need to feel like part of the team, not “the external devs.” Practical ways to do this include:
  • Shared Jira boards where tasks are distributed equally.
  • The same GitHub repos with pull request reviews across in-house and extended engineers.
  • A Slack or Teams channel where conversation flows naturally across borders.
Companies that treat extended engineers as “outsiders” usually end up with silos and inconsistent quality. Those that fully integrate them into agile practices see extended teams become indistinguishable from internal squads.

5. Work With a Partner That Supports Retention

This is often overlooked but crucial. Adding engineers is only half the battle—keeping them engaged and stable is where long-term velocity is protected. This is where Scio’s nearshore team extension model stands out. Beyond providing engineers, Scio supports them through Scio Elevate:
  • Growth paths so engineers stay motivated.
  • Coaching frameworks to keep delivery aligned.
  • Retention programs that reduce turnover and protect your product knowledge.
The result? Teams that don’t just add capacity, but build momentum.
Benefits of extended development teams for agile software delivery in Mexico and U.S. tech hubs
Nearshore extended teams add speed, alignment, and stability for agile delivery.

Benefits of Extended Development Teams

When tech leaders first hear about extended development teams, it’s easy to assume they’re just “more developers.” But the real advantage is not about numbers—it’s about solving strategic bottlenecks that hiring or outsourcing rarely address. Think about the challenges most engineering leaders face:
  • Hiring cycles drag on for months, while the roadmap can’t wait.
  • Outsourcing vendors deliver outputs, but often miss the product’s bigger picture.
  • Internal teams burn out when asked to cover more ground than they can reasonably handle.

Why extended teams are more than “just more developers”

Built to remove strategic bottlenecks without breaking your roadmap or culture.

Speed

    • Senior capacity in weeks, not quarters.
    • Sprints keep moving—onboarding happens alongside delivery.
    • Perfect for time-sensitive launches in Austin/Dallas/NYC.

Alignment

    • Full participation in standups, reviews, and retros.
    • Same tools (Jira, GitHub, Slack), same rituals.
    • Workday overlap with nearshore teams (Mexico/Colombia).

Stability

    • Low turnover; product knowledge compounds over time.
    • Stable velocity across sprints.
    • Less rework; no “restarting” onboarding every quarter.

Cost & Control

    • Predictable opex/capex without inflating payroll.
    • Scale up/down by release, not by fiscal year.
    • Focus on outcomes, not billable hours.

Tip: For leaders in Austin and Dallas, nearshore squads in Mexico and Colombia enable real-time agile ceremonies and faster ramp-up.

Faster Scaling Without Long Hiring Cycles

Recruiting senior engineers in the U.S. is notoriously slow and expensive. By contrast, extended teams can integrate in a matter of weeks, letting you react to customer demand or competitor moves in real time.
  • Recruiting senior engineers is not just costly—it’s slow. McKinsey reports that 60% of companies identify tech talent shortages as a major barrier to digital transformation (McKinsey & Company). In practice, this often translates into hiring cycles that can stretch over six months in competitive U.S. markets.
  • For leaders in Austin or New York, nearshore extended teams offer a faster path—allowing companies to spin up capacity within weeks, not quarters.

Access to Specialized Skills On-Demand

Today’s products often require niche capabilities—like Kubernetes orchestration, AI/ML integration, or cybersecurity architecture—that aren’t needed full-time but are critical to stay competitive. Extended development teams let you tap into those skills on demand, without bloating your payroll or entering slow recruitment cycles.
  • A Bain & Company study shows that 60% of engineering leaders plan to increase outsourcing of R&D and engineering over the next few years to fill skill gaps and accelerate innovation (Bain).

Flexibility in Project Length and Size

Your roadmap isn’t static. Some quarters are heavy with feature builds, others focus on stabilization. Extended teams give you the ability to scale up or down without layoffs, severance, or HR headaches. For U.S. companies, this flexibility is especially valuable in uncertain markets, where budgets tighten but delivery expectations remain high.

Retention and Knowledge Continuity

One of the biggest hidden costs in software delivery isn’t tools—it’s attrition. When engineers leave, you lose product knowledge, disrupt velocity, and restart onboarding cycles.
  • According to SHRM, the cost to replace a skilled employee can reach up to 60% of their annual salary (SHRM).
  • Work Institute further estimates that total turnover costs—when factoring in both hiring and productivity impacts—can range from 33% to 200% of salary, depending on role and organization (Work Institute).

Extended Teams Benefit Matrix

Comparison of Nearshore Extended Teams (LATAM), Traditional Hiring (U.S.), and Offshore Outsourcing
Benefit
Nearshore Extended Teams (LATAM: Mexico, Colombia, Brazil)
Traditional Hiring (U.S.)
Offshore Outsourcing
Scaling Speed Weeks, aligned with U.S. time zones 6–9 months per hire Weeks, but time zone/cultural delays
Specialized Skills On-demand across modern stacks Limited by local talent availability Available, but harder to integrate
Flexibility Scale up/down without HR overhead Tied to payroll & benefits Limited to contract scope
Knowledge Retention High — teams stay long-term, knowledge compounds High, but slow to build Low — frequent rotation
Cultural Fit Strong, aligned with U.S. work culture Perfect fit Often mismatched, delays agile
Cost Efficiency 30–40% lower than onshore hiring with stable delivery Highest Lower rates, hidden inefficiencies

Extended Development Teams vs. Staff Augmentation

It’s easy to confuse team extension with staff augmentation. Both add capacity, but the philosophy is different.
Extended Development Teams vs. Staff Augmentation
Factor
Extended Development Teams
Staff Augmentation
Integration Fully embedded in agile squads Temporary contractors with limited integration
Commitment Long-term partnership, shared accountability Task-based, accountable only for hours worked
Knowledge Retention Retains product knowledge over years High churn, knowledge often lost
Hiring Effort Weeks to onboard via partner Constant recruiting and onboarding
Cost Predictability Transparent, long-term contracts Hourly rates, less predictable
Compare scenarios with Scio’s TCE Calculator to see the real cost of team extension vs augmentation.

Why Nearshore Extended Teams Are Ideal for U.S. Companies

For U.S. tech leaders, nearshore extension hits the sweet spot between onshore and offshore:
  • Real-time collaboration: Engineers in Mexico, Colombia, or Brazil share your workday, so agile ceremonies stay real.
  • Cultural alignment: Communication, accountability, and work ethic align naturally with U.S. teams.
  • Legal/IP alignment: Nearshore vendors operate under frameworks closer to U.S. standards, reducing compliance risks.

How Scio Builds and Supports Extended Teams

At Scio, we’ve learned that success isn’t just about finding good engineers—it’s about helping them stay engaged and aligned for the long run. That’s why we created Scio Elevate, our framework for growth, coaching, and retention.
  • Growth: Engineers have clear career paths and access to continuous learning.
  • Coaching: Agile coaches and mentors ensure delivery remains aligned with product goals.
  • Retention: Engagement programs keep turnover low, preserving product knowledge and team stability.
This is why we’ve maintained:
  • 98% client retention.
  • 5+ years average engagement per client.
  • Teams that don’t just deliver code—they become part of your company’s story.
When to choose nearshore team extension for software development in Austin, Dallas, and New York
U.S. tech leaders rely on nearshore models for real-time collaboration and scalable growth.

When to Choose the Team Extension Model

The team extension model isn’t a silver bullet for every situation. But it’s the right fit when:
  • You need to scale rapidly without expanding payroll.
  • Your roadmap demands stable engineers, not constant contractor rotation.
  • You want cost-efficient but culturally aligned talent.
  • You’re in a U.S. hub like Austin, Dallas, or New York, and need real-time collaboration.
If you see your backlog growing faster than your capacity, team extension is worth serious consideration.

Conclusion

Extended development teams represent a middle ground between hiring and outsourcing—but with advantages that neither model can deliver on its own. They give you the ability to scale quickly, retain critical knowledge, and align culturally, all while controlling costs. For U.S. tech leaders facing overloaded teams, missed deadlines, and hiring bottlenecks, the question isn’t whether you can afford an extended team—it’s whether you can afford to keep moving without one. Let’s talk about how an extended team can support your roadmap—partner with Scio and build capacity with confidence.

FAQs About Extended Development Teams

  • A long-term group of engineers that integrates with your in-house squad, sharing accountability for product outcomes.

  • By identifying gaps, choosing a nearshore model, ensuring cultural/time-zone alignment, and embedding teams into agile practices.

  • No. Outsourcing hands off entire projects. Team extension integrates engineers directly into your squads.

  • Because they provide real-time collaboration, cultural alignment, and legal/IP frameworks closer to U.S. standards.

  • Team extension offers stability and knowledge retention, while augmentation is short-term and prone to churn.

Enhancing Developer Experience with AI Tools in Multidisciplinary Software Development Teams 

Enhancing Developer Experience with AI Tools in Multidisciplinary Software Development Teams 

Data Management Software and Services, High-Performance Collaboration, Outsourced Engineering Team, Software Development

Written by: Rod Aburto – 

Multidisciplinary software development team using AI tools to improve developer experience.
The Developer Experience (DX) is at the forefront of innovation in software development companies. As the demand for high-quality software increases, so does the complexity of development environments. Multidisciplinary teams—bringing together developers, designers, testers, and project managers—require seamless collaboration, streamlined workflows, and access to tools that enhance efficiency and creativity.

Enter Artificial Intelligence (AI), a transformative force reshaping the way software development companies approach DX. AI tools are enabling teams to work smarter, solve problems faster, and focus on what they do best: creating exceptional software.

Here’s how software development companies are leveraging AI tools to enhance DX among multidisciplinary teams.

For teams looking beyond AI to strengthen collaboration, building high-performing engineering teams is just as critical to long-term success.

1. Streamlining Coding with AI-Powered Assistant

AI-driven coding assistants, such as GitHub Copilot and Tabnine, are revolutionizing the way developers write code. These tools use machine learning to analyze context and generate suggestions, completing code snippets and recommending improvements.

  • How it helps DX: Developers save time on repetitive coding tasks and reduce errors, allowing them to focus on solving complex problems and building innovative features.
  • Multidisciplinary impact: With faster and cleaner code, other team members—like testers and designers—experience fewer delays and smoother integration into the development cycle.

According to McKinsey’s State of AI 2023 report, more than two-thirds of organizations already use AI in at least one business function, underscoring its growing impact on software development workflows.

2. Automating Quality Assurance

AI tools are transforming Quality Assurance (QA) by automating tasks such as test case generation, regression testing, and defect detection. Tools like Testim and Applitools leverage machine learning to identify and resolve issues before they escalate.

  • How it helps DX: Developers spend less time debugging and more time coding, while testers gain powerful tools to streamline their workflows.
  • Multidisciplinary impact: QA teams can collaborate more effectively with developers and designers, ensuring a higher standard of quality across the board.

3. Enhancing Collaboration with AI-Driven Project Management

Project management platforms like Jira and Monday.com are integrating AI capabilities to improve task assignment, predict project bottlenecks, and analyze team performance.

  • How it helps DX: Developers and other team members can rely on intelligent task prioritization and automated status updates, reducing the burden of manual reporting.
  • Multidisciplinary impact: Project managers can make data-driven decisions, ensuring that all disciplines are aligned and working efficiently.

4. Improving Communication and Documentation

AI tools like Grammarly and Notion AI are transforming how teams communicate and document their work. These tools can draft meeting notes, summarize lengthy discussions, and even translate technical jargon for non-technical stakeholders.

  • How it helps DX: Developers and designers can quickly create clear documentation, reducing misunderstandings and improving team collaboration.
  • Multidisciplinary impact: Non-technical team members, such as project managers or clients, can easily stay informed and contribute meaningfully to discussions.

5. Supporting Design with AI

AI tools such as Figma AI and Canva Magic Design are empowering designers to create interfaces more efficiently. These tools can suggest layouts, auto-generate assets, and provide user behavior insights.

  • How it helps DX: Developers receive designs faster, with detailed insights that help them implement features accurately and efficiently.
  • Multidisciplinary impact: Designers and developers collaborate more seamlessly, ensuring a smoother transition from concept to implementation.
Artificial Intelligence hologram showing AI-driven DevOps and software automation
AI transforms DevOps by enabling faster deployments and reliable systems.

6. Enhancing DevOps with AI

AI tools like Jenkins and Harness are optimizing DevOps practices by automating build pipelines, monitoring system performance, and predicting failures.

  • How it helps DX: Developers experience faster deployment cycles and more reliable environments, reducing frustration and downtime.
  • Multidisciplinary impact: Operations teams gain better visibility into system health, allowing them to proactively address issues before they impact the development process.

7. Personalized Learning and Growth

AI-driven learning platforms, such as Pluralsight Flow and Degreed, offer personalized learning paths tailored to each developer’s strengths and areas for improvement.

  • How it helps DX: Developers can upskill efficiently, staying ahead in their field without sacrificing productivity.
  • Multidisciplinary impact: Teams benefit from increased expertise across disciplines, fostering a culture of continuous learning and collaboration.

8. Predicting and Mitigating Risks

AI-powered analytics tools can predict potential risks in projects, from missed deadlines to resource conflicts. Tools like ClickUp and Asana AI analyze data to provide actionable insights.

  • How it helps DX: Developers face fewer last-minute crises, while project managers can proactively adjust plans.
  • Multidisciplinary impact: Teams can align better, avoid burnout, and maintain steady progress toward project goals.

9. Boosting Creativity with AI

AI tools like OpenAI’s DALL·E or ChatGPT are being used to boost creativity across teams. Whether it’s generating ideas for new features, brainstorming UX concepts, or drafting initial code, AI is a creative partner.

  • How it helps DX: Developers and designers gain inspiration and starting points for innovative projects.
  • Multidisciplinary impact: Collaboration thrives as teams use AI-generated ideas to spark discussions and refine concepts.
Traditional Workflow vs. AI-Enabled Workflow in Multidisciplinary Teams
Area Traditional Workflow With AI Tools
Coding Manual code writing, frequent bugs Assisted coding, faster delivery, fewer errors
QA Manual test cases, reactive debugging Automated tests, proactive issue detection
Project Management Manual task updates, unclear bottlenecks AI-driven prioritization & risk prediction
Communication Long emails, manual notes AI-generated summaries, real-time clarity
Design Manual prototyping AI-suggested layouts, faster asset generation
DevOps Manual monitoring, reactive fixes Predictive analytics, automated pipelines

Conclusion

AI tools are redefining what it means to create a great Developer Experience. By streamlining workflows, automating repetitive tasks, and fostering collaboration across disciplines, these tools empower teams to focus on innovation and impact.

As software development companies continue to integrate AI into their workflows, DX will become more seamless, productive, and enjoyable. For teams working together across multiple disciplines, the future of work has never looked brighter. The companies that embrace these AI-driven advancements will not only retain top talent but also set the standard for excellence in the software development industry.

FAQs About AI Tools in Developer Experience

  • AI tools automate repetitive tasks, provide intelligent code suggestions, and free developers to focus on solving complex problems—enhancing developer experience across multidisciplinary teams.

  • Because AI enhances collaboration across roles—developers, designers, testers, and project managers benefit from faster workflows, reduced bottlenecks, and more agile delivery.

  • Top AI tools for U.S. tech hubs like Dallas and Austin include coding assistants such as GitHub Copilot, QA platforms like Testim, and project management tools with AI features such as Jira or Asana AI.

  • AI supports developers by handling repetitive or routine tasks. It enhances, rather than replaces, human creativity and technical expertise—keeping innovation at the center of software delivery.

  • By combining AI-driven workflows with culturally aligned, real-time collaboration from nearshore teams, companies reduce risks, accelerate delivery, and increase speed to market in U.S. hubs like Dallas and Austin.

Rod Aburto - Senior Partner

Rod Aburto

Senior Partner

What is a growth mindset truly about? 4 myths that you should avoid

What is a growth mindset truly about? 4 myths that you should avoid

Scio, Software Development, Successful Outsourcing

Written by: Scio Team 
Business professional reviewing Agile methodology dashboard while choosing a Lean Product Development partner

Introduction

In software development, the difference between a team that stagnates and one that scales often comes down to mindset. CTOs and VPs of Engineering in hubs like Austin, Dallas, and Silicon Valley know this well: technologies evolve, markets shift, and the pressure to deliver innovation never slows down. This is where the growth mindset comes in. Popularized in education and psychology, it’s now a critical concept for software teams. But despite its popularity, the term is often misunderstood. Let’s clarify what a growth mindset really means for software leaders and explore the myths that can derail your teams if left unchecked.

Why Growth Mindset Matters for U.S. Software Teams

For U.S.-based technology companies, having developers with a growth mindset means more than just a positive attitude—it translates into resilience, adaptability, and faster adoption of new tools and practices. Take, for example, distributed or nearshore teams. Leaders in Austin working with developers in Mexico often highlight how a growth mindset culture reduces friction, accelerates onboarding, and creates an environment where challenges become stepping stones rather than roadblocks. In today’s market—whether you’re scaling SaaS products, integrating AI-driven features, or managing compliance-heavy systems—a growth mindset in your development team is not a “nice to have.” It’s strategic.
Growth mindset in software engineering — continuous learning, feedback and collaboration.
A growth mindset helps developers expand skills, collaborate better, and adapt to new technologies.
And a lot has changed in the software development field over the years. New languages, frameworks, and development practices mean that it’s more important than ever to develop a well-rounded skill set. To become a truly effective software developer, you need to be able to work in a variety of environments and be comfortable with a range of technologies. You also need to have a strong foundation in the basics, including principles of software design, data structures, and algorithms. And finally, it’s important to be able to communicate effectively with other team members, whether it’s working with architects to design a system or collaborating on code reviews. A growth mindset is the best strategy to do so, helping you stretch into other important areas (like teamwork, communication, or leadership) outside of your normal interests. However, getting into a growth mindset is not an easy task. And it isn’t because accomplishing this is singularly hard or demanding, but because there are a lot of myths and misconceptions about what a growth mindset is, or how to effectively harness this way of thinking to become a better developer. So, what are some of the myths about developing a growth mindset, and how to avoid falling into them?

Myth 1: It’s an intrinsic quality to have

We see this kind of thinking all the time, from the “there are two kinds of people in the world” type of mentality, to the idea that natural talent or ability is the most important quality to have (and bad luck to anyone born without it). However, when it comes to a growth mindset, this idea is harmful and simply not true.  After all, a person with a true growth mindset believes that intelligence and talent are not fixed traits; everyone can grow and improve with the necessary effort, and that every challenge is an opportunity to grow. So why isn’t everyone running around with a growth mindset? Well, because a fixed mindset, or the belief that intelligence and talent are fixed traits that cannot be changed, is still very prevalent, and even the default in our current society. This mentality leads people to give up easily, believing that they cannot improve, simply because they are afraid of failing. However, with the right tools and environment, anyone can learn to grow, stop fearing the failures that are necessary to evolve, and better themselves in areas of skill that they thought impossible before.

Myth 2: It’s all about being positive

Being «positive» is often touted as the key to success in life, an antidote of sorts for all kinds of problems, from personal relationships to financial success. Generally, the thinking goes that if you stay positive, good things will happen to you. Although starting with a positive attitude certainly helps, this is not the most important element of a true growth mindset. A growth mindset is about taking risks, learning from failure, and always striving to improve.  In fact, «positive thinking» can be a form of self-deception that can prevent people from achieving their full potential; being successful in any area requires the willingness to face your limitations, recognize them, and make an effort to improve. By pretending that everything is always rosy, people with an uncritically positive outlook may avoid taking risks and miss out on growth opportunities. So, if you want to achieve real growth, you need to have a positive attitude toward failure and a willingness to take risks. Only then will you be able to reach your full potential.
Chess piece symbolizing strategy and growth mindset in software development challenges
A growth mindset in software development helps teams face challenges and improve performance.

Myth 3: A growth mindset guarantees positive results

One of the key elements of a growth mindset is the willingness to take on risks and challenges. Learning and improving on areas we never considered before requires effort, the willingness to hear criticisms and feedback, and committing time and resources to achieve it. But most importantly, anyone who wishes to get into a growth mindset needs to understand that failure is always an option and that a growth mindset does not guarantee positive outcomes all of the time. Instead, it is simply one tool that can help achieve goals.  What matters is how we deal with these challenges and setbacks. If we allow them to defeat us, then our growth mindset won’t matter. But if we use them as opportunities to learn and grow, then we can overcome anything. So yes, a growth mindset is important, but it’s not a silver bullet. It won’t magically make everything better. But it will give us the strength to keep going when times are tough, helping us see failure as a normal part of the learning process, and letting us get ready for the next challenge. As one might say, “you are either learning or winning”.

Myth 4: Absolutely everything is possible

As the saying goes, a “jack-of-all-trades is a master of none”, and the notion that anyone can be an expert at everything is misguided and can set unrealistic expectations when it comes to getting a growth mindset. The core tenet here is that you can develop any skill you want if you put effort into it, and that people in general don’t exist in a static state that is impossible to change. If, as a developer, you want to have skills that go beyond pure technical know-how, like leadership, teamwork, negotiation, or public speaking because you want to become more well-rounded. It could open up opportunities for you and there are techniques and strategies you can try to be more proficient at.  But don’t develop unrealistic expectations about it. If we believe that we should be able to do everything expertly, we’re bound to feel like failures when we inevitably fall short. An average person has affinities and weak spots in different areas, which is fine and normal. This should neither stop you from trying new things nor make you believe that you need to be the best at everything you attempt. What’s more, this belief devalues expertise. If everyone is supposedly an expert, then what’s the point of learning from those who have spent their lives honing a particular skill? Instead of trying to be good at everything, we would be better off accepting that we have our limits and that there are some things we’re simply not cut out for and focusing on becoming the best at what we’re interested in. Only then can we truly excel.

Growth Mindset vs Fixed Mindset in Software Teams

Growth Mindset vs Fixed Mindset — Key Dimensions for Software Teams
Dimension
Growth Mindset
Fixed Mindset
Learning Sees mistakes as feedback for improvement Avoids challenges for fear of failure
Collaboration Values feedback and peer reviews Sees feedback as criticism
Innovation Experiments with new tech stacks Sticks only to what already knows
Adaptability Thrives in nearshore and hybrid models Struggles outside comfort zone

How Leaders in Austin and Dallas Apply Growth Mindset

Local tech leaders know that a growth mindset is not just theory—it’s a competitive advantage.

  • Austin startups: invest in continuous learning, sponsoring certifications and training in emerging frameworks.
  • Dallas enterprises: strengthen collaboration by pairing senior engineers with nearshore juniors, creating mentorship loops that benefit both sides.
  • Silicon Valley companies: normalize failure as part of innovation, rewarding teams not only for wins but also for documenting lessons that improve delivery speed.

This approach demonstrates that adopting a growth mindset is not only about individual improvement—it’s about how entire teams adapt, collaborate, and sustain growth across distributed models.

Hand placing wooden blocks with lightbulb icons, symbolizing innovation and growth mindset in software development
Visual representation of growth mindset and continuous learning in software development.

Key Takeaways

  • Growth mindset ≠ positivity only — it’s about resilience, risk-taking, and learning from feedback.
  • Failure is feedback, not the end — the best U.S. tech teams see mistakes as data to improve.
  • Not everything is possible — realistic expectations prevent burnout and value real expertise.
  • Leaders in Austin & Dallas apply it daily — through mentorship, certifications, and cultural alignment with nearshore teams.
  • For U.S. companies, mindset is strategic — it impacts delivery speed, team morale, and long-term innovation.

Final Thoughts: Why It Matters Now

At its core, acquiring a growth mindset should benefit you personally. It’s about believing in your ability to learn, improve, and become a better developer—and a better leader. The payoff? Increased motivation, resilience, and a stronger capacity to see challenges as opportunities instead of setbacks.

But for U.S. tech leaders in Austin, Dallas, and beyond, the stakes are even higher. In today’s competitive market, a growth mindset directly impacts delivery speed, team morale, and innovation. When combined with the right cultural alignment—like what nearshore teams in Mexico can offer—it becomes a driver for real business outcomes.

Let’s talk about nearshoring. At Scio, we’ve been building and mentoring software teams since 2003, helping CTOs and VPs of Engineering create high-performing squads that don’t just code—they adapt, grow, and scale alongside your business.

FAQs About Growth Mindset in Software Teams

Q1: Does a growth mindset really improve developer performance?

Yes. Studies show growth mindset teams adapt faster, handle feedback better, and innovate more effectively.

Q2: How can U.S. companies foster growth mindset in nearshore teams?

By encouraging mentorship, continuous learning, and cross-border collaboration in distributed teams.

Q3: Is growth mindset the same as optimism?

Not quite. It’s about resilience and adaptability, not blind positivity.

Q4: Can developers shift from fixed to growth mindset?

Absolutely — with the right leadership and culture, developers can change how they approach feedback and challenges.

Q5: Why is growth mindset critical for Austin or Dallas tech leaders?

Because adaptability and cultural alignment directly impact delivery speed, product quality, and innovation.

Suggested Resources for Further Reading

To explore more about how mindset and methodology shape software success, here are some recommended resources:

Internal Links

Discover how Latin American nearshore teams align culturally with U.S. companies and why this cultural fit drives stronger outcomes. Read more.

Compare Traditional vs Agile software development methods and see which approach best supports your product strategy. Learn more.

External Links

Harvard Business Review – What Having a Growth Mindset Actually Means: A must-read analysis of how this concept is often misunderstood inside organizations.

McKinsey – Achieving Growth: Putting Leadership Mindsets into Action: Practical insights on how leaders turn growth mindset into behaviors that accelerate business outcomes.

McKinsey – How Top Performers Drive Innovation and Growth: Research on how leading companies foster innovative mindsets to expand within and beyond their core business.

Top Priorities for Software Teams in 2025 

Top Priorities for Software Teams in 2025 

Software Development, Strategic Digital Nearshoring

Written by: Luis Aburto – 

Software team priorities 2025 – digital strategy and performance goals.

As we head into 2025, the landscape for software engineering teams is evolving rapidly. Economic challenges, the rise of generative AI, and shifts in team dynamics are shaping the decisions engineering leaders make daily.

In this blog post, we’ll look at what engineering teams are focusing on for the year ahead, and why understanding these priorities can help guide your own team’s strategy. This information is drawn from many in-depth conversations with our clients complemented with research published in industry publications. The goal is to use awareness of current trends to align our plans with the strategies driving success across the industry.

The Current Landscape for Engineering Teams

Engineering leaders are dealing with multiple external pressures—economic uncertainty, hype around artificial intelligence, and the constant need to maintain momentum in a competitive market. These pressures have led engineering leaders to prioritize optimization, adaptability, and strategic clarity as the key themes for 2025. In response, many teams are reevaluating their processes, leveraging new technologies, and reassessing how best to structure their operations.

Top Priorities for Engineering Teams in 2025

To provide more clarity, we’ve grouped the priorities into four main categories: Product Expansion and Innovation, Operational Efficiency and Developer Enablement, Ensuring Customer Satisfaction, and Leveraging AI & Data.

Top Priorities for Software Teams in 2025
Category
Key Focus Areas
I. Product Expansion and Innovation
  • New features & capabilities (differentiation, growth, alignment with customer needs)
  • New products or services (market expansion, revenue diversification)
  • Performance improvements (architecture, scalability, monitoring)
  • R&D and experimentation (new tech, AI integration, user‑centered design, security testing)
II. Operational Efficiency and Developer Enablement
  • Managing technical debt (code reviews, refactoring, automated testing, incremental improvements)
  • Cost optimization & productivity (lean processes, automation, cloud optimization, nearshore developers)
  • Developer experience (better tools, CI/CD, testing environments, peer reviews)
III. Ensuring Customer Satisfaction
  • Reliability & performance (resilient architecture, monitoring, incident management, chaos engineering)
  • Quality assurance & testing (automation, continuous integration, proactive QA, comprehensive frameworks)
IV. Leveraging AI & Data
  • AI for internal use (automation, predictive maintenance, generative AI, ethical adoption)
  • AI for customer use (personalization, intelligent features, AI‑driven support)
  • Internal data management (data quality, access, utilization, BI alignment)
Software innovation and product expansion for engineering teams in 2025.
Driving growth with new features and capabilities.

Product Expansion and Innovation 

Building New Features and Capabilities

In a market where differentiation is key, new feature development helps companies maintain relevance and offer new value to customers. So, the need to drive growth and meet customer expectations is pushing engineering leaders to prioritize innovation while balancing it with the stability and reliability of their platforms. In this context, well-planned product roadmaps are becoming increasingly important, as leaders aim to keep new features aligned with customer needs, market trends, and technical constraints.

Key focus areas for building new features and capabilities include:

  • Differentiation in the Market: Teams are developing unique features to maintain relevance and stand out among competitors.
  • Driving Growth: Ongoing feature development is directly tied to customer acquisition and retention, leading to revenue growth.
  • Customer Needs Alignment: Ensuring that product roadmaps are in sync with customer expectations (which in part are driven by competing solutions) and evolving market trends.

Adding New Products or Services 

Another major focus area for engineering teams is expanding product offerings. By adding new products or services, teams can target additional market segments and improve the overall value proposition of their companies. This expansion is critical in gaining a competitive edge and diversifying revenue streams.

Performance Improvements 

Optimizing the performance of existing products is a priority to ensure that systems operate effectively and provide a high-quality user experience. Improving performance not only enhances customer satisfaction but also sets the foundation for future scalability.

Key areas of focus for performance improvements include:

  • Architecture, Database, and Code Optimization: Focusing on refining software architecture, optimizing data architecture and database queries, and enhancing code efficiency to improve overall system performance.
  • Performance Testing: carried out under various conditions and scenarios, ensures that the software can handle different types of user behavior and system loads effectively.
  • Scalability Planning: Making sure that systems are ready to scale as demand increases (gradually, cyclically, or event-driven), ensuring a seamless user experience.
  • Real-time Monitoring: Implementing effective monitoring to quickly identify and resolve performance issues.
  • Infrastructure Optimization: Investing in infrastructure enhancements that support consistent performance and reliability.

R&D and Experimentation

This involves experimenting with new ideas and technologies to enhance both the product and the development process. Teams focus on improving product functionality, ease of use, performance, and other user-facing features. Additionally, efforts are made to boost development efficiency by introducing advanced coding tools, leveraging Generative AI, exploring new programming languages, enhancing CI/CD pipelines, and adopting innovative practices that improve efficiency and/or the developer experience.

Key areas of focus for R&D and Experimentation include:

  • New/Different Technologies: Experimenting with technologies outside the current stack to explore opportunities for enhancing functionality, user experience, or performance.
  • Performance Optimization: Testing new approaches to improve system speed and efficiency.
  • Developer Tools: Introducing advanced tools that make the development process more seamless.
  • Generative AI Integration: Leveraging AI to enhance both product functionality and development workflows.
  • User-Centered Design Experiments: Incorporating user feedback during the experimentation phase to iteratively enhance the product’s usability and user experience.
  • Security Testing Innovations: Experimenting with advanced security tools and methods to proactively identify vulnerabilities and enhance product security.
Developers managing technical debt and improving system stability.
Operational efficiency and developer enablement.

Operational Efficiency and Developer Enablement 

Managing Technical Debt and Maintenance 

Technical debt, often neglected during high-growth periods, is now receiving the attention it needs to ensure long-term stability. The priority on managing technical debt is about maintaining a stable and sustainable codebase. Leaders are increasingly aware that maintaining system stability is crucial for long-term success and that ignoring it today only amplifies future risks. Effective technical debt management also frees up resources that would otherwise be tied up in fixing issues, allowing teams to focus on more strategic goals. 

Key areas of focus for managing technical debt include:

  • Code Review Best Practices: Ensuring that code is regularly reviewed to maintain quality and prevent accumulation of technical debt.
  • Refactoring Legacy Systems & Code: Modernizing older systems and codebases to make them more maintainable and efficient.
  • Automated Testing: Investing in automated testing tools to catch defects faster and reduce technical debt.
  • Incremental Improvements: Addressing technical debt in small, manageable increments to avoid overwhelming engineering teams.
  • Long-term Stability: Prioritizing actions that contribute to the long-term stability and sustainability of the codebase.

Cost Optimization, Productivity, and Efficiency 

Economic uncertainty has prompted engineering teams to reassess operations for efficiency and productivity. Many teams are adopting leaner processes, automating repetitive tasks, and aiming to get more output from the same or fewer resources. For engineering leaders, the challenge is creating environments where cost efficiency is achieved without compromising culture and innovation. 

Key strategies for cost optimization include:

  • Improving Productivity: Teams are focusing on maximizing output by streamlining their operations and removing inefficiencies. Examples include fine-tuning agile methodologies to enhance team collaboration, implementing continuous integration and deployment (CI/CD) to speed up releases, and using data-driven metrics to identify bottlenecks and areas for improvement.
  • Automation Initiatives: Leveraging automation tools to handle repetitive tasks and remove the potential for human error can free up engineers for more strategic work and improve overall quality.
  • Leveraging Cost-Effective Engineering Teams: Augment in-house engineering teams with engineers from cost-effective regions, particularly nearshore developers, to maintain cost advantages while minimizing collaboration challenges.
  • Cloud Resource Optimization: Reviewing and optimizing cloud infrastructure to control spending and improve cost efficiency.

Developer Experience (DX) 

Enhancing developer experience—by reducing unnecessary friction and improving internal tools—has become a significant focus to ensure effectiveness. This effort is closely related to improving productivity, as they are two sides of the same coin. Many engineering teams are investing in better development tools, streamlined CI/CD pipelines, and robust testing environments to create a seamless workflow. 

Key strategies for improving developer experience include:

  • Reducing Friction: Minimizing obstacles in workflows to ensure developers can focus on coding without unnecessary interruptions.
  • Better Development Tools: Investing in tools that make coding easier and enhance developer productivity.
  • Streamlined CI/CD Pipelines: Ensuring continuous integration and deployment processes are smooth and efficient.
  • Robust Testing Environments: Creating reliable testing frameworks that provide developers with confidence in their changes.
  • Peer Reviews and Pair Programming: Encouraging collaboration to enhance code quality and foster a culture of learning.

Developer experience is now being treated as an essential part of productivity; leaders recognize that developers empowered with intuitive tools and smooth workflows are less prone to burnout and more likely to deliver high-quality code.

Ensuring customer satisfaction with reliable software performance.
Reliability and performance as engineering team priorities.

Ensuring Customer Satisfaction

Reliability and Performance Improvements 

The need for operational resilience has made reliability and uptime key priorities for engineering teams. Ensuring system reliability directly impacts customer satisfaction and remains a key focus. For engineering leaders, it means making investments in infrastructure and system architectures that help minimize downtime and prevent issues before they affect users. This includes improving monitoring capabilities and adopting a proactive approach to incident management. 

Key strategies for reliability and performance improvements include:

  • Operational Resilience: Investing in infrastructure that enhances reliability and minimizes downtime.
  • Resilient System Architecture Design: Designing system architectures with resilience in mind, incorporating redundancy, failover mechanisms, and modular components to minimize the impact of failures.
  • Proactive Monitoring: Improving monitoring capabilities to detect and address issues before they escalate.
  • Incident Management: Adopting a proactive approach to managing incidents to minimize customer impact.
  • Chaos Engineering and Stress Testing: Utilizing these practices to build resilient systems.
  • Team Upskilling: Training teams to respond effectively to incidents and recover gracefully when issues arise.

Quality Assurance and Testing

As customer expectations for software remain high in terms of availability, performance, functional accuracy, and usability, Quality Assurance (QA) continues to be a key priority for 2025. Teams are focusing on building automated testing frameworks to ensure stability and reduce the chances of defects in production. Investing in comprehensive QA practices ensures that systems are reliable and helps in maintaining customer trust.

Key strategies for quality assurance and testing include:

  • Automated Testing Frameworks: Building and implementing automated testing to ensure stability and catch defects early.
  • Continuous Integration: Utilizing continuous integration to maintain code quality and quickly identify issues.
  • Proactive Quality Measures: Adopting proactive QA practices to enhance reliability and robustness.
  • Comprehensive QA Practices: Investing in extensive quality assurance to improve system reliability.
  • Customer Satisfaction and Trust: Prioritizing bugs and improvements that directly enhance the user experience while ensuring quality to maintain and build customer trust by minimizing production issues. This combined focus leads to greater customer loyalty.
Leveraging AI and data to improve engineering team productivity.
Using AI for internal productivity and decision-making.

Leveraging AI & Data 

AI for Internal Use 

Engineering leaders are exploring how AI can improve team productivity and assist in decision-making, bug detection, and predictive maintenance. AI-driven insights enhance decision-making speed and accuracy, providing valuable data-backed support. However, effective adoption requires deliberate prioritization and investment in upskilling teams to understand and work effectively with these tools, while also navigating the risks and ethical implications associated with AI in engineering processes. 

Key strategies for using AI internally include:

  • Automation of Repetitive Tasks: Leveraging AI to handle mundane, repetitive tasks, freeing up team members for more complex work.
  • Decision-making Support: Utilizing AI-driven insights to assist in making faster, data-backed decisions.
    Bug Detection and Predictive
  • Maintenance: Implementing AI to identify bugs and predict potential system failures before they happen.
  • Generative AI for Code Generation: Using Generative AI tools to assist in code generation can significantly enhance developer productivity by automating boilerplate code and suggesting code solutions. However, it is important that generated code is thoroughly reviewed to mitigate risks such as vulnerabilities and technical debt.
  • Team Upskilling: Investing in training to ensure teams understand and work effectively with AI tools.
  • Ethical AI Use: Addressing ethical concerns and ensuring AI is used responsibly within engineering processes.

AI for Customer Use 

AI for customer use targets enhancing products and services. This could involve personalizing user experiences, building intelligent product features, or creating AI-driven support solutions. The value of AI in customer-facing products is increasingly becoming apparent, especially in terms of providing better and more efficient service, though integration hurdles remain significant. 

Key strategies for using AI for customer purposes include:

  • Personalizing User Experiences: Leveraging AI to create tailored experiences that better meet individual customer needs.
  • Intelligent Product Features: Building smart features powered by AI that enhance product functionality and user engagement.
  • AI-driven Customer Service/Support Solutions: Creating automated support systems, such as chatbots, that provide immediate assistance to users.
  • Addressing Integration Challenges: Focusing on overcoming the technical and operational hurdles of integrating AI into customer-facing systems.

Internal Data Management 

Internal data management focuses on leveraging data effectively within the organization to drive better decisions, streamline processes, and enhance operational efficiency.

Key strategies for internal data management include:

  • Improving Data Quality: Investing in solutions that ensure high-quality data, reducing errors and improving the reliability of insights.
  • Enhancing Data Access: Implementing architectures and solutions that allow easier and more secure access to data for teams that need it.
  • Optimizing Data Utilization: Ensuring that data is used effectively across the organization to support AI initiatives and business intelligence.
  • Supporting AI Initiatives: Providing a strong data foundation to enable more effective AI applications.
  • Business Intelligence Alignment: Using data to drive strategic decisions that align with broader organizational goals.
Engineering teams aligning technical goals with business needs.
Balancing delivery speed with long-term sustainability.

Key Insights for Engineering Leaders

Aligning Team Goals with Business Needs

The key to prioritization this year lies in aligning technical work with business outcomes. Engineering teams must not only understand what they are building but also why it matters for the broader organization. This means that leaders must ensure there is transparency about how engineering initiatives tie into company objectives, allowing teams to remain motivated and purpose driven.

Balancing Immediate Delivery with Long-term Sustainability

Engineering leaders are tasked with balancing rapid feature delivery with the need for sustainable codebase health. Investing in the long-term stability of the codebase and reducing technical debt means fewer emergencies, fewer last-minute firefights, and smoother long-term development. A sustainable codebase leads to higher productivity over time, as teams spend less effort on bug fixing and more time on innovation.

Key strategies for balancing immediate delivery with long-term sustainability include:

  • Incremental Technical Debt Reduction: Addressing technical debt in small, manageable increments helps maintain stability without overwhelming the team or stalling new feature development.
  • High-impact Refactoring: Identifying and executing refactoring efforts that provide substantial improvements in system maintainability and scalability.
  • Maintaining Strong QA During Fast Delivery: Ensuring quality assurance processes are not bypassed during rapid feature releases, to prevent accumulating issues that could compromise long-term code health.
  • Stakeholder Communication: Clearly communicating the importance of technical debt reduction and long-term sustainability to stakeholders helps gain their support for initiatives that may not provide immediate visible results but are critical for future growth.
  • Dedicated Maintenance Sprints: Allocating specific sprints for addressing technical debt, system optimization, and maintenance tasks can help strike a balance between adding new features and ensuring stability.
  • Adopting a Sustainable Culture: Promoting a culture that values both speed and long-term sustainability encourages teams to make decisions that support a healthy codebase, reducing rework and boosting efficiency over time.

Conclusion

As we move into 2025, software engineering teams face a mix of opportunities and challenges shaped by economic pressures, advancements in AI, and the continuous demand for customer satisfaction. The ability to balance rapid innovation with long-term stability is more crucial than ever. Teams that prioritize aligning their goals with business outcomes, leveraging new technologies responsibly, and enhancing operational efficiency are best positioned to thrive.

The key insights provided in this blog are intended to guide engineering leaders in making thoughtful, strategic decisions that improve both productivity and resilience. Whether it’s managing technical debt, empowering developers with the right tools, or incorporating AI into both internal processes and customer experiences, every decision should be made with the goal of delivering enduring value to both the organization and its users.

How about you?

What priorities is your engineering team focusing on for 2025? Are your strategies aligned with broader business goals, and are you adopting a balanced approach to innovation and stability?

We’d love to hear your thoughts and insights! Share your experiences and challenges with us by reaching out on LinkedIn or sending us a message through our contact us page to discuss how we can help your team achieve its goals in the upcoming year.

Luis Aburto CEO Scio

Luis Aburto

CEO

Beyond Salary & Rate Cards: The Real Total Cost of Software Engineering 

Beyond Salary & Rate Cards: The Real Total Cost of Software Engineering 

Nearshore, Nearshore Software Development, Outsourced Engineering Team, Product Development, Software Development

Written by: Luis Aburto 
Scio TCE Calculator showing real total cost of software engineering beyond salary and rate cards.

A CFO & CTO guide to comparing in-house, offshore, and nearshore

If you’ve ever compared a $120k salary to a $55/hour vendor rate and felt like the decision was obvious, this post is for you. Salary and rate cards are the sticker price. What Finance actually pays – and what Engineering actually lives with – includes ramp time, coordination, security, inefficiencies in collaboration, and a handful of small costs that quietly add up. My aim here isn’t to scare you; it’s to make the math honest so you can choose the right mix with fewer surprises.

I built a Total Cost of Engagement (TCE) Calculator to make these trade-offs concrete. Plug in your assumptions to compare the actual costs of in-house hiring with offshore and nearshore outsourcing side by side. You’ll find the download link at the bottom of the page.

Why total cost comparison beats sticker price

The fastest way to derail an engineering budget is to compare costs on the wrong basis. A salary alone ignores benefits, PTO, tools, recruiting, and management time. A vendor’s rate card hides ramp time, internal oversight, security, travel, and more. Once I normalize these, the option with the apparent lower cost is often just the least complete.

Breakdown of Total Cost of Engagement (TCE) including benefits, bonuses, and hidden costs of software development.
Scio’s TCE framework showing the real cost of software engineering beyond salary — including payroll taxes, benefits, PTO, bonuses, tools, and recruiting.

What I mean by Total Cost of Engagement (TCE)

Total Cost of Engagement (TCE) is an annualized, apples-to-apples number that captures everything you pay to turn ideas into shipped software. The sections below outline the cost elements that belong in a true comparison.

In-house hiring: what sits on top of gross salary

Let’s make this concrete. A Senior Developer doesn’t just cost their base. On top you’ll typically see:

  • Employer payroll taxes & insurance (Social Security/Medicare, unemployment, workers’ comp).
  • Benefits & retirement (health, dental/vision, 401(k) match).
  • PTO cost (holidays, vacation, sick days).
  • Performance/annual bonus (annualized) and stock options/RSUs (annualized).
  • IT equipment & tools (laptop, monitors, peripherals) and software licenses (Office 365, IDEs, Slack/Jira/GitHub, security scanners).
  • Cloud/test environments for realistic integration.
  • Training & development, beyond onboarding.
  • HR & recruiting costs, amortized over expected tenure.
  • Management overhead, because leads and managers spend time coaching and reviewing.
  • Facilities or remote stipend (office, coworking, home setup).
  • Attrition & backfill buffer, if you model churn explicitly.
  • Ad-hoc tooling costs for project-specific devices, services, or environments.
  • In many U.S. contexts, the fully loaded number lands ~35 – 60% above base salary, depending on benefits and your toolset. The TCE Calculator can show this as a waterfall from base → fully loaded so Finance and Engineering can see exactly what drives the delta.
  • CFO takeaway: this is where forecast variance hides – especially bonuses, benefits, recruiting, and training.
  • CTO takeaway: lead times and retention matter as much as cost; continuity reduces rework.

Outsourcing: what sits on top of the rate card

Most proposals show a clean rate. Delivery reality adds layers:

  • Knowledge transfer costs. Expect a few weeks of overlap or slower velocity while context is built. Over time, the KT overhead % depends on the effort required for knowledge transfer and any pilot work. Greater real-time overlap (time-zone alignment) speeds shadowing and code walkthroughs and reduces this overhead.
  • Productivity losses costs. A velocity buffer and rework allowance during early sprints and major scope changes. The delta % here depends on the extra capacity you carry to absorb slower velocity and re-work due to collaboration friction and cultural differences.
  • Team management costs. Product owner, project manager, and architect/tech lead time plus Scrum ceremonies – the coordination tax you pay to keep everyone aligned. The overhead % here depends on time invested by these roles, communication latency across time zones, and the number of asynchronous hand-offs.
  • Tooling & environments. Extra seats, VPN/SSO, CI/CD, scanners, and non-prod data – plus ad-hoc tooling costs that are project-specific.
  • Security & compliance. SOC 2/ISO controls, background checks, DPAs, and data residency constraints.
  • Legal & IP / Administration. Assignment of inventions, privacy addenda, contracting cadence, and local counsel where relevant.
  • Travel & on-site. Kickoff and periodic planning often repay themselves in fewer misunderstandings.
  • FX & payment. If the vendor is not a U.S. company, account for currency spreads, wire/processing fees, and invoice terms.
  • Attrition & backfill. A modest overlap budget keeps continuity when someone turns over. Consider the average voluntary attrition rates in your industry and the typical time it takes to recruit and onboard replacements.
  • Inflation/escalation clauses. Annual adjustments should be explicit, capped where possible, and tied to a known index or collar.

When you account for these, outsourced TCE commonly adds ~20 – 40% on top of the vendor’s published rate over a year. The point isn’t to inflate costs; it’s to avoid being surprised later.

Comparison of offshore vs nearshore software development costs, including time-zone overlap, cultural alignment, and travel expenses.
Offshore vs. Nearshore cost comparison highlighting key TCE drivers such as time-zone alignment, cultural fit, FX invoicing, and travel overhead.

Offshore vs. nearshore: the same categories, different weights

Although both models are common, they differ in TCE drivers – not only the rate card, but also the overhead created by time zones and the collaboration friction they introduce:

  • Time-zone & language overlap. Nearshore teams work the same or adjacent hours, which reduces coordination friction and shortens ramp-up.
  • Travel. A quarterly on-site from Dallas to Guadalajara is simpler and cheaper than a long-haul to APAC.
  • Cultural differences. Communication norms, decision-making, and feedback styles can influence productivity and quality; align working agreements early and use real-time overlap to reduce rework.
  • FX & invoicing. Nearshore engagements are more likely to invoice in USD with smaller FX spreads; offshore corridors may carry higher friction.
  • Attrition & backfill. Patterns vary by market; your buffer should match reality, not generic averages.

The TCE Calculator can generate side-by-side stacks that show how the same project’s TCE shifts between offshore and nearshore with identical assumptions.

  • When nearshore wins: fast feedback loops (agile ceremonies), all-day collaboration in real time, incident response during your business day, and predictable, lighter travel.
  • When offshore still fits: large, well-bounded workstreams where overnight cycles are acceptable and travel is infrequent.

A simple decision guide

Map your situation on two axes: urgency/throughput and compliance/variance tolerance.

  • In-house core + nearshore delivery (Scio). Strong overlap and fast iteration, with travel you can actually budget.
  • Nearshore core + offshore scale. Elastic capacity for well-bounded streams.
  • All in-house. When IP proximity and domain depth outweigh flexibility.

My point of view (Scio): I’ll recommend the mix that fits your throughput, risk, and budget certainty – even when that means not engaging Scio for every role. The calculator helps ground that conversation in numbers, not vibes.

Download the TCE Calculator to run your own numbers, or contact us and I’ll walk through the trade-offs with you.

Luis Aburto_ CEO_Scio

Luis Aburto

CEO

“They have programmers in Mexico?”: The story of remote work at Scio with CEO and Founder Luis Aburto (Part 1)

“They have programmers in Mexico?”: The story of remote work at Scio with CEO and Founder Luis Aburto (Part 1)

Interculturality, Latin America Nearshoring, Nearshore Software Development, Outsourced Engineering Team, Scio, Software Development, Strategic Digital Nearshoring, Success Stories

By Scio Team 
Luis Aburto, CEO and Founder of Scio, a nearshore software development company in Mexico, specializing in remote teams for U.S. tech companies.
When it comes to working remotely and managing a hybrid working model, nothing is better than hearing it from someone doing it since 2003. So we sat down with Luis Aburto, CEO and Founder of Scio to find out what worked, what didn’t, what is Nearshore development, and the long road from emails to agile methodologies. Enjoy!
As a potential client, if I wanted to work with Nearshore developers, I would like to know how they can maintain cohesion in the team. Anyone can say “I’ll find you a developer” and then open LinkedIn, but that doesn’t make you a recruiter.

It’s not about just finding resources, it’s about building high-performing teams of people who integrate well, and I’d like to see how they achieve that and motivate their collaborators to strive for a well-done job. That’s what I would look for in a Nearshore company.

Scio started all the way back in 2003, and in the years since, it refined a unique perspective on software development, remote hybrid work, and what’s next for a programmer interested in joining an industry at the forefront of innovation and adaptability. But how did it all begin?

Luis Aburto, CEO and Founder of Scio, a nearshore software development company in Mexico, specializing in remote teams for U.S. tech companies.
Luis Aburto, CEO & Founder of Scio, on building nearshore software teams for U.S. companies—especially in Texas.

Nearshore: A new way to develop software

Well, at the end of the 90s, very few organizations in the US realized that software development could be done in Mexico. Clients had the idea that “IT outsourcing” was something you did in India, and nowhere else you could get these kinds of services.

One of the first companies to talk about “Nearshore development” was Softtek, which started to promote this model around 1998 or so. At the time, the attitude was something like “Seriously? They have programmers in Mexico?”, and certain friction existed towards the idea of outsourcing development here.

Now, since Scio began, our focus has been working with North American clients so, by definition, we have been doing remote work since day one. Sure, we occasionally visited clients to discuss the stages of a project, collect requirements, and present advances, but collaboration has mainly been remote, through conference calls and the like.

Technology wasn’t what it is now. Skype was the most advanced thing then, but Internet speeds gave us barely enough quality to do videoconferences, so we used phone landlines and conference speakers to make calls. It sounds quaint nowadays, I think, but it helped us start developing efficient ways to collaborate remotely.

It all happened exclusively at the office, too. Today it is very common to have a good broadband connection with optical fiber at home, but in ’03, dedicated Internet connections for businesses were barely enough, so if you worked from home, sending your code to a remote server somewhere and trying to integrate it with the code written by the office team was a very slow process, and not efficient at all.

Vintage office desk with a typewriter, invoices, and coins—illustrating the pre-Cloud era of software development and Scio’s early remote-work context serving U.S. clients from Mexico.
Early nearshore realities: collaborating with U.S. clients from Mexico before Cloud DevOps—foundations that shaped Scio’s modern remote delivery.
Also, we didn’t have stuff like GitHub or Azure DevOps, where everybody can send their code to the Cloud and run tests from there, so even if your clients were remote, you needed to be at the office to access your Source Code Repository with reasonable speed.

Internet speeds eventually started to get better and the possibility of working from home became more feasible. Around 2012 we started by implementing a policy where you could choose one day to work remotely per week, so by the time this pandemic got here, everyone already had a computer and good Internet plans, so it wasn’t a very radical change for us. We just leaped from doing it a single day of the week to doing it daily.

And yes, I do mean “this” pandemic because it isn’t the first one Scio has gone through. Back in 2009, we had the Swine Flu (AH1N1) in Mexico, and we had to completely shut down because going home and working from there couldn’t be done by everyone. The infrastructure necessary wasn’t there yet, so you couldn’t ask the team to work remotely overnight, even for a short while.

Other things changed once we could implement this “Home Office Day” policy, mainly realizing this was not a “lost” day of work. The response to it was great, as you could keep in contact with the team without getting lost in a “black hole” of not knowing what was going on, and do other stuff if your tasks allowed it.

Eventually, we had a couple of team members that, for personal reasons, left the office to work remotely full-time. The spouse of one of them got a job in Guadalajara and he didn’t want to leave us, so asked if we would be okay with this arrangement. After some time seeing how well this worked out, we fully opened to the idea of hiring more people remotely, to the point we had four full-time collaborators in Guadalajara on a co-working space we rented so they wouldn’t feel alone.

Computer screens with programming code reflected on eyeglasses, symbolizing Scio’s transition from email-based workflows to agile methodologies for U.S. clients.
Scio’s shift from email-heavy workflows to agile practices transformed collaboration with U.S. tech companies.

A technology leap

For our clients, things worked a little differently too. Back in the early 2000’s, collaboration happened a lot through email, where you had these long chains of messages that contained whole project proposals and development plans.

You can still do that of course, but it’s more common nowadays to just say “hey, let’s have a quick call, I’ll explain this and you can give me your feedback” to arrive at a decision, than having to compose an email, read it, discuss it with every relevant person, take note of all the stuff that wasn’t clear, and respond back and forth during the whole dev cycle.

This was our very early collaboration flow until agile methodologies became the norm. Soon our teams had daily scrum meetings with clients, with the key difference that, instead of a call of 10 or 15 participants joining from home, you had a meeting between two boardrooms: on one side of the call was the team at Scio, and on the other, our counterparts at the client’s office.

Everyone gave their status and comments, and once we finished, further exchanges were done by email or phone calls. We canceled several phone lines last year, by the way, when we realized they hadn’t been used in years. In the beginning, we needed lots of lines for every team to keep in touch with their respective clients, but now Zoom, Hangouts, Microsoft Teams, and Slack offer plenty of more convenient options to do so. Shortly before the COVID-19 pandemic, this was still our collaboration dynamic, with two meeting rooms giving their respective status, and anyone working from home for the day joining the call.

Developer working remotely on a laptop during a video call, showing Scio’s bilingual nearshore collaboration with U.S. tech teams.
Scio’s remote-ready developers in Mexico work seamlessly with U.S. teams thanks to strong English skills and cultural alignment.
But now that everyone is working remotely, barriers have started to diminish, both in culture and in attitude. In the US you are probably already working with people in California, Texas, or New York, so working with someone in Mexico doesn’t feel different, as long as the language skills of the person are good.

The newer generations of developers and engineers have a better level of English now than just a few years ago. Maybe because there are more opportunities to get acquainted with the language; earlier you had to go to very specific stores to get books and other materials in English, which wasn’t cheap, and without stuff like YouTube and Netflix, the type of content you could get to practice was very limited.

This evolution of the software developers, when you are not limited to local options as long as you have the necessary skills to collaborate with a remote team, is very notable. The people we used to hire outside of Morelia were the ones willing to move here, and the process of seeking out people to explicitly be remote collaborators was gradual until we developed a whole process to assess which ones fit Scio’s culture the best.

Team meeting in a bright office, illustrating the importance of soft skills in Scio’s nearshore software development teams for U.S. companies.
At Scio, strong communication and collaboration skills are as valuable as technical expertise when working with U.S. clients.

Soft skills: The key to a good team

In that sense, I think soft skills will have more weight in the long run than purely technical skills. Someone with an average technical level, but who is proactive, knows how to communicate, and can identify priorities is someone who brings more value to a team than a technology wizard that doesn’t play along and keeps themself isolated, or assumes stuff instead of validating it.

You would think social skills are irrelevant for someone working remotely when they are actually critical to collaborate effectively. Some people prefer to not interact with others and would rather just get instructions on what to do, but this only works for well-defined tasks in which it is very clear what you are trying to accomplish.

I know this is the optimal way to collaborate for those developers who are less interested in social aspects, but it doesn’t work for projects that require innovation, creativity, and problem solving, with complex workflows involving tons of people whose input is important at every step.

This is why, I think the “introvert programmer” stereotype is something of a myth, at least nowadays. This profession is moving towards a place where the most valuable persons are the ones with a well-rounded profile, capable of communicating with the business sponsors, his or her coworkers, and final users, and not only those who are super-gifted in their programming skills.

People in software, as a whole, are becoming more versatile, and the ones capable of connecting are going to be more visible and be considered more valuable, getting more opportunities in their careers. This is what I can say about the path that the people at Scio have followed so far. From now on, collaboration is a priority because remote work makes it more important than ever, and motivating and stimulating this collaboration, indeed this cohesion, is what will differentiate good Nearshore companies from the best ones.