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.
Why Traditional Software Development Still Works for Regulated Industries 

Why Traditional Software Development Still Works for Regulated Industries 

Agile Methodology, Compliance & Risk, Software Development

Written by: Monserrat Raya 

A group of wooden figures gathered around a diagram illustrating a structured software development process.
In a world obsessed with speed and flexibility, traditional software development methods like Waterfall may seem like a relic. But for regulated industries in the U.S.—such as healthcare, finance, and government—these methodologies offer unmatched strengths in compliance, documentation, and traceability.

For healthcare providers in Austin or fintech startups in Dallas, predictability isn’t optional—it’s a requirement.

While Agile dominates the tech conversation, traditional approaches are quietly powering mission-critical systems behind the scenes. This blog explores why these methods still matter and how nearshore partners like Scio can help you implement them strategically.

Why Regulated Industries Can’t Always “Go Agile”

Agile prioritizes flexibility and rapid iteration. But in regulated sectors, that flexibility can conflict with strict legal and operational requirements. Companies must often comply with standards and laws such as:

  • HIPAA – Health Insurance Portability and Accountability Act (U.S. healthcare)
  • FDA 21 CFR Part 11 – Electronic records and signatures (pharmaceuticals and medical devices)
  • SOX – Sarbanes-Oxley Act (U.S. financial sector)
  • ISO/IEC 27001 & 62304 – Security and software lifecycle requirements

Regulatory agencies continue to evolve their software lifecycle expectations.
For example, AAMI and the FDA are working toward new guidance for software in healthcare environments.
Explore the AAMI/FDA workshop summary

These frameworks mandate:

  • Detailed documentation
  • Formal validation procedures
  • End-to-end traceability
  • Version-controlled audit logs

Agile frameworks like Scrum or SAFe can be adapted, but doing so often introduces overhead that cancels out their benefits. For example, continuous delivery pipelines must be paused to meet regulatory sign-off requirements, or backlogs must be retrofitted into compliance reports.

Puzzle pieces illustrating a linear software development process from question to solution.

The Benefits of Traditional Approaches in Compliance-Driven Contexts

Unlike Agile’s iterative uncertainty, traditional development follows a structured path: requirements → design → implementation → verification → maintenance. In regulated environments, that linearity becomes a strength.

Key Advantages

Benefit
Relevance to Regulated Sectors
Predictable Development Cycles Projects proceed through defined gates with approvals at every stage.
Heavy Documentation All decisions, validations, and test cases are captured—ideal for FDA or ISO audits.
Audit Readiness Each step creates records that support legal, compliance, and security reviews.
Clear QA and Validation Paths Defects are easier to trace back to source requirements or design decisions.
Version Control & Risk Management Reduces ambiguity when regulators require historic data or justification.

In fact, the FDA explicitly endorses structured lifecycle models (like Waterfall or V-Model) for medical device software to ensure reproducibility and risk management.
Learn more: FDA General Principles of Software Validation

Traditional ≠ Obsolete: Debunking the Myths

Let’s break a few common myths:

Myth
Reality
“It’s outdated.” Waterfall is still required or preferred in many federal and state contracts.
“It’s slow.” It’s deliberate. Stability and validation are prioritized over iteration.
“Nobody uses it anymore.” NASA, the DoD, and global banks continue using traditional models in key systems.

Traditional software development is not about resisting change—it’s about preserving integrity when the stakes are high.

Learn more in our related blog: Traditional Agile Software Development Method

Agile vs. Traditional: A Sector-Based Comparison

Here’s how traditional development stacks up against Agile in regulated sectors:

Dimension
Agile
Traditional
Documentation Minimal by design Comprehensive
Change Management Frequent and flexible Controlled and traceable
Stakeholder Approval Ongoing Gate-based
Audit Preparation Manual effort required Built-in artifacts
Best Fit For Startups, SaaS, rapid prototypes Compliance-driven systems, enterprise-level software

In finance, for instance, systems managing transaction records or audit logs benefit from traditional traceability. In healthcare, where software might interact with patient health data or diagnostics, validation is not negotiable.

Curious about how vendor location affects legal and IP exposure? Here’s how nearshore can reduce your risk.

How Nearshore Teams Like Scio Adapt to Regulated Environments

Scio is more than a vendor—we act as a nearshore extension of your team, aligning with your governance, documentation, and compliance workflows without introducing

Capability
How It Supports Regulated Teams
Adaptable SDLC Integration We map our development workflows to your QMS and compliance structures.
English-First Communication & Artifacts All technical documentation, tickets, and deliverables are prepared in English for easy integration with your internal audits.
Change & Release Governance Our teams can work under gated workflows, maintaining detailed change logs, version histories, and approval trails.
Collaboration in Real Time Operating in the U.S. Central Time Zone ensures constant alignment between your stakeholders and our engineering leads.

How We Collaborate With Regulated Clients

  • Initial Alignment: We start every engagement by mapping out documentation, validation, and compliance needs together.
  • Project Gating: Development flows are organized around sign-off points and deliverables aligned with your internal processes.
  • Continuous Visibility: You’ll have direct access to our team, progress dashboards, and full transparency into what’s being built and validated.

Want to learn more about how we handle communication, governance, and delivery across borders?
Check out this guide on seamless nearshore collaboration.

Hybrid Models: Where Flexibility Meets Control

In some cases, our clients want both worlds. That’s where hybrid development models come in. These combine traditional checkpoints with Agile workflows to maintain both speed and compliance.

Example Hybrid Flow

  • Discovery & Requirements Gathering →

    • Fully documented and client-approved.

  • Design & Prototyping →

    • Agile sprints within defined scope.

  • Development →

    • Controlled iteration, traceable stories, and validation prep.

  • Testing →

    • Manual and automated validation aligned with compliance needs.

  • Deployment →

    • Gated releases with rollback mechanisms and compliance sign-offs.

This model works well in financial and healthcare settings where innovation is needed—but without sacrificing control or risking noncompliance.

Why Nearshore Development Is Ideal for Regulated U.S. Companies

Traditional development requires high-touch communication, detailed documentation, and tight feedback loops. That’s where nearshore beats offshore—especially when your development partner:

  • Works in the same time zone (CST)
  • Has bilingual engineers experienced in English documentation and client-side tools
  • Offers fast onboarding with minimal cultural or workflow friction
  • Understands U.S. regulations and works in full alignment with compliance teams

Scio is located in Mexico, providing a talent base with strong STEM backgrounds, English proficiency, and cross-border work culture alignment—ideal for companies that need performance and regulatory assurance.

Final Thoughts: The Strategic Role of Traditional Development

Not every project needs to move fast. Sometimes, what you need most is:

  • Stability
  • Audit-readiness
  • Risk mitigation
  • Documentation-rich delivery

For companies in regulated sectors, traditional software development is not a relic—it’s a strategic necessity.

“Choosing the right methodology isn’t about trends. It’s about risk, regulation, and reliability.”

Two developers working side-by-side on compliance-ready software with code and documentation on screen.
Nearshore engineering in action: Scio helps U.S. companies build secure, compliant, and high-performing software.

Ready to Build Compliance-Ready Software?

If your software touches sensitive data, regulated workflows, or audit requirements—Scio is ready to help.

Let’s talk about building compliance-ready software without sacrificing momentum.
Contact our team today

FAQ: Traditional Software Development in Regulated Sectors

What is traditional software development?

Traditional software development refers to structured, sequential models like Waterfall or V-Model where each phase—requirements, design, development, testing, deployment—is completed before moving to the next. These models emphasize documentation, predictability, and control.

Why is traditional development used in regulated industries?

Because regulated industries (healthcare, finance, government) require documentation, traceability, and validation, traditional models provide the audit-ready structure and control necessary to meet compliance standards like HIPAA, FDA 21 CFR, and SOX.

Is Agile software development suitable for regulated sectors?

Agile can work in regulated sectors, but often needs to be adapted or combined with traditional practices. Many companies use hybrid models that mix Agile delivery with traditional validation to ensure compliance without sacrificing flexibility.

What are the benefits of Waterfall for healthcare or finance?

Waterfall allows for:

  • Full documentation of each step
  • Clear approval gates
  • Validation planning upfront
  • Strong alignment with ISO, FDA, or SOX requirements
    This makes it ideal for sectors where predictability and audit-readiness are critical.
Can nearshore teams like Scio support traditional development in regulated environments?

Yes. Nearshore partners like Scio can align with your existing development processes, including traditional models such as Waterfall or gated workflows. Our teams integrate with your project governance, provide English-first documentation, and maintain traceability from requirements to release—making collaboration in regulated contexts both practical and effective.

What regulations impact software development in the U.S.?

Key regulations include:

  • HIPAA for healthcare privacy and security
  • FDA 21 CFR Part 11 for electronic records in pharma/medical devices
  • SOX for financial reporting integrity
  • ISO 27001 for information security
  • ISO 62304 for medical device software lifecycle processes

What Agile Really Means When It Comes to Software Quality

What Agile Really Means When It Comes to Software Quality

Agile Methodology, High-Performance Collaboration, Software Development, Strategic Digital Nearshoring

Written by: Monserrat Raya 

Team reviewing Agile workflows and technical diagrams, illustrating the connection between Agile delivery practices and software quality outcomes.

What Agile Really Means When It Comes to Software Quality

Agile has become the go-to framework for software development in many tech organizations. But despite its widespread adoption, many teams still misunderstand one of its most critical aspects: quality. Too often, “working software” is equated with “quality software”—a misconception that can erode long-term product value and customer satisfaction.

At Scio, we work with engineering leaders across the U.S. to build high-performing nearshore Agile teams. And one pattern we’ve seen time and again is this: Agile isn’t just about delivering fast—it’s about delivering value. And that’s where the real conversation around quality begins.

The Problem With “Done” in Agile Projects

Why Features That Work Aren’t Always Valuable

Many Agile teams celebrate shipping new features as a sign of progress. But just because a feature functions doesn’t mean it’s valuable. In fact, one of the most common Agile software quality issues is mistaking «done» for «done right.»

When teams are under pressure to deliver, it’s easy to check boxes and move on—ignoring whether what was delivered actually improved the product. In our blog on The Benefits of Agile Development, we explore how this disconnect can waste resources and lead to bloated software that’s technically functional but strategically weak.

“Working software is not enough. If it doesn’t solve a user’s problem, it’s just noise.”

The Risks of Equating ‘Done’ With ‘Delivered’

In Agile, the definition of done should go beyond just passing QA. It should reflect actual value delivered to the end-user—a concept often lost in the rush to push code to production.

When “done” equals “delivered,” but not validated, teams risk accumulating technical and functional debt that undermines quality over time. Without a feedback loop, there’s no guarantee that what you ship matters to your users.

What Agile Actually Says About Quality

Working Software as a Principle

The Agile Manifesto famously states: “Working software over comprehensive documentation.” But this doesn’t mean software that merely compiles or runs. It refers to software that delivers consistent value.

In practice, working software must be:

  • Maintainable
  • Usable
  • Valuable
  • Secure

The World Intellectual Property Organization (WIPO) adds that modern development—especially in distributed teams—should also ensure IP protection, sustainability, and legal clarity across jurisdictions.

The Role of User Feedback and Continuous Delivery

Continuous delivery best practices help close the gap between development and feedback. Agile isn’t just iterative—it’s adaptive. By incorporating user input regularly, you can ensure the product evolves in the right direction.

At Scio, our nearshore teams embed feedback loops at every stage of the sprint—through internal demos, usability tests, and stakeholder reviews—ensuring quality is validated in real-world scenarios, not just test environments.

Redefining Quality in Agile Teams

Person evaluating software quality metrics on a laptop, with visual icons for performance, rating, and continuous improvement in an Agile environment.

Functional vs. Strategic Quality

Functional quality means a feature does what it’s supposed to. But strategic quality means it serves the product’s broader goals. For example, a “notifications” module may function perfectly—but if users find it annoying or irrelevant, its quality is questionable.

This is why our teams work closely with Product Owners to ensure that user stories align with product vision—not just technical requirements.

Code That Works vs. Code That Solves

A major pitfall in Agile teams is shipping code that meets the “definition of done,” but fails to solve the real problem. In our article Why “If It Ain’t Broke, Don’t Fix It” Can Be a Costly Mistake in 2025, we explore how legacy decisions can erode innovation and, ultimately, software quality.

Business Value as a Quality Metric

Agile quality metrics should focus on value delivered, not just velocity or code coverage. Metrics like:

  • Feature adoption
  • Customer satisfaction (e.g., NPS)
  • Time-to-value

…are more useful than story points alone. This concept aligns with agile quality metrics frameworks promoted by Scaled Agile Framework (SAFe) for modern software teams.

Practical Guidelines for Delivering Value Over Features

Collaborative Definition of Done

A truly effective definition of done involves more than QA sign-off. It should include user feedback, documentation, and business validation. At Scio, this is a collaborative process between engineers, QA analysts, and stakeholders—built into sprint planning from day one.

Integrating QA in Every Sprint

A common myth is that QA happens after development. In Agile, QA and testing should begin in the planning phase. According to TestRail’s QA in Agile guide, this integrated approach helps catch issues early and raises the overall standard of code delivery.

Our QA engineers participate in backlog refinement, standups, and retrospectives—ensuring quality isn’t a task, it’s a shared responsibility.

Building Feedback Loops Into Your Dev Process

Agile thrives on feedback-driven iteration. Our nearshore teams build automated testing, capture usage analytics, and host biweekly demos to ensure continuous improvement.

The ability to quickly adapt is one of the reasons our nearshore model excels—shared time zones, cultural alignment, and high English proficiency eliminate the friction often experienced in offshore setups. We discuss this further in 10 Risks of Offshore Outsourcing.

How Scio Ensures Agile Quality Standards

At Scio, quality isn’t optional—it’s embedded in how we work. Here’s how we uphold Agile software quality across all our engagements:

  • QA engineers embedded in every sprint
  • Collaborative sprint planning with Product Owners
  • Use of Scio Elevate, our proprietary quality and performance framework
  • Continuous refactoring, code review, and user-centered design
  • Bi-weekly audits on testing, UX consistency, and stakeholder feedback

Combined with our nearshore engineering teams based in Mexico, Scio provides the transparency, speed, and expertise required for teams that want to build software that lasts.
Hand stacking wooden blocks with an upward arrow, symbolizing continuous value delivery and incremental improvement in Agile software development.

Final Thoughts: Agile Quality Is About Continuous Value

Agile isn’t a process—it’s a philosophy. When you shift your mindset from “finishing tickets” to delivering continuous value, quality becomes a natural byproduct.

If your current Agile practice feels like a checklist with little strategic impact, maybe it’s time to revisit what “done” really means—for your users, your business, and your product.

At Scio, we’ve seen firsthand how teams transform when they start thinking in terms of outcomes instead of outputs. It’s not just about how many features you ship—it’s about how each one contributes to a better, smarter, more resilient product. Agile quality isn’t measured at the end of a sprint; it’s measured when your software makes a difference for real users.

When you embed that mindset into your Agile culture—with collaborative planning, built-in QA, and clear communication across teams—you not only improve the product, you improve the way your team works. And that’s where true software quality begins.

In a world where speed is a given, value is the differentiator. Agile done right helps you deliver both.

FAQs

What does Agile really mean by “working software”?

In Agile, “working software” refers to more than code that compiles without errors. It means the software is usable, valuable, tested, and ready for deployment. It’s a product that delivers functional outcomes and solves real user problems—not just a feature completed on a Jira board. This is why many Agile teams define working software based on how it performs in the hands of users, not just in QA environments.

How do Agile teams measure software quality?

Agile teams measure quality through a combination of automated testing, functional acceptance criteria, user satisfaction metrics (like NPS or CSAT), and business KPIs such as feature adoption and retention. Some teams also track agile quality metrics like escaped defects, cycle time, and time-to-feedback. The key is to align your definition of “quality” with both technical performance and business value.

How is QA integrated into Agile development sprints?

In high-performing Agile teams, QA is not a separate phase—it’s embedded in every sprint. QA engineers participate in planning, refinement, and standups, and write tests before or alongside development. Practices like test-driven development (TDD), pair testing, and continuous integration help Agile teams maintain high quality without slowing down delivery. At Scio, QA is part of our cross-functional teams from day one, not brought in at the end.

Is nearshoring better than offshore for Agile teams?

Yes. For Agile teams, nearshoring—especially to regions like Mexico under USMCA—offers faster feedback cycles, real-time communication, and greater cultural alignment, which are all crucial for Agile practices like sprint planning, retrospectives, and backlog refinement. Unlike traditional offshore models, nearshoring allows for daily collaboration without time zone delays, which is key when your team is focused on continuous delivery and iteration.

What’s the difference between “done” and “delivered” in Agile?

This is one of the most common Agile misunderstandings. “Done” often means a task has passed internal QA, but “delivered” means the value has reached the user and been validated. Teams that confuse the two can end up with features that technically work but deliver no real value. A clear, collaborative Definition of Done should include user feedback, business validation, and documentation—not just functional testing.