AI adoption hinges on trust, governance, and data. Strategies to scale smartly—without the usual risk or complexity.
Luis, as CPTO of OutSystems, you’re deeply involved in driving AI-powered innovation. From your perspective, what are the biggest barriers organizations face when adopting AI, and how can they overcome them?
Trust is one of the most significant barriers to AI adoption for IT teams. According to the OutSystems 2025 State of Application Development (SoAD) report, generative AI (gen AI) tools don’t provide accurate code 100% of the time. This lack of reliability introduces risks such as technical debt and unstable applications. Furthermore, only 40% of developers surveyed say they “mostly” trust gen AI to write code autonomously without human oversight. This data highlights the critical need for robust validation processes to mitigate risks, such as digital vulnerabilities or non-compliance, and to build confidence in AI-powered tools.
Another major barrier to AI adoption is the lack of proper governance. The SoAD report found that 62% of IT professionals have concerns about the security and governance of gen AI technology. At OutSystems, we believe governance is the cornerstone of successful AI adoption. Effective governance frameworks address compliance, risk management, and accountability, ensuring that AI implementations are both safe and effective.
Building trust requires implementing validation processes to ensure that AI-generated outputs are reliable and align with enterprise standards. Simultaneously, prioritizing governance ensures transparency, auditability, and adherence to organizational policies. Together, these efforts set the stage for confident and responsible AI adoption.
Data quality is often the make-or-break factor in AI success. What steps should companies take to ensure their data is accurate, clean, and ready for AI-driven decision-making?
Data quality is critical for AI success because poor data can lead to biased models, incorrect predictions, and flawed decision-making. To ensure high-quality data for AI-driven decision-making, I recommend the following:
- Define clear data standards and governance: Establish company-wide data quality standards, including accuracy, completeness, consistency, and timeliness. Implement governance policies to maintain these standards.
- Implement robust data cleaning and pre-processing: Use automated tools and processes to handle missing values, remove duplicates, correct inconsistencies, and standardize formats. Regular data audits can help catch errors early.
- Ensure data integration and consistency: Data often comes from multiple sources, such as CRM, IoT devices, or customer interactions. Ensuring consistency and avoiding conflicting records is crucial for AI models to work effectively.
- Leverage automated data validation and monitoring: Use rule-based validation techniques to check for anomalies, outliers, or incorrect entries in real time. Continuous monitoring ensures data quality remains high over time.
- Manage bias and fairness: AI models are only as good as the data they are trained on. Companies should proactively detect and mitigate bias by using diverse datasets and fairness-aware algorithms.
- Optimize data labeling and annotation: High-quality labeled data is essential for supervised learning models. Investing in human-in-the-loop review processes and automated labeling techniques improves data accuracy.
- Implement scalable data pipelines: AI requires continuous data updates. Scalable ETL (Extract, Transform, Load) pipelines ensure that data remains fresh, structured, and readily available for modeling.
While most of these items are necessary for data to be reliable in any situation, items 5 through 7 are particularly important in an AI environment.
Many enterprises struggle with AI integration due to legacy infrastructure. What are some key strategies for businesses looking to seamlessly connect AI with their existing systems?
Many enterprises struggle to integrate AI into their legacy systems, a challenge compounded by the increasing pressure on IT teams to deliver more projects, drive productivity, and meet growing expectations. At the same time, CIOs and business leaders are under immense pressure to demonstrate how these initiatives translate into tangible business value. We often advise businesses to adopt a “think big, start small, scale fast” methodology to navigate these challenges. By starting with iterative pilot projects, teams have time to identify integration challenges and adapt before scaling to larger, mission-critical applications.
In addition to “think big, start small, scale fast”, you need to leverage APIs and middleware as much as possible (so you connect your AI smarts to real data), you should leverage the cloud if possible (keep your legacy wherever it is, do the AI in the cloud), modernize your data flows (hint: data lakes, data pipelines help) and, finally, invest in AI-ready platforms with built-in AI capability.
AI-powered low-code platforms, like OutSystems, play a pivotal role in bridging the gap between AI and legacy systems. They reduce the complexity of integration by offering pre-built connectors, automated workflows, and a more agile development environment. This approach enables businesses to modernize at their own pace without creating additional technical debt.
AI adoption isn’t just about technology—it’s about fostering an innovation-first mindset. How can organizations create a culture that embraces AI-driven transformation while staying agile?
Creating an organizational culture that embraces AI starts with empowering teams and prioritizing collaboration. Teams should be granted the autonomy they need to succeed, which means ongoing encouragement of experimentation and learning. However, this doesn’t mean creating a “Wild West” environment where individuals and teams can choose to use any AI tools they’d like: clear frameworks must be in place to guide decision-making and avoid duplicative purchases or technical debt.
Leadership becomes essential to navigating the balance between autonomy and structure. Business leaders who are open to doing things differently can set the tone for innovation, breaking through growth plateaus and inspiring their teams. Organizations that deliberately cultivate a culture of collaboration and accountability put themselves in the strongest position to adapt and thrive in the rapidly changing AI landscape.
Scalability is a major concern for AI initiatives. What advice would you give to businesses looking to scale AI solutions without creating technical debt or operational bottlenecks?
Scaling AI isn’t just about adding more models—it’s about doing it strategically to avoid technical debt and bottlenecks. A few strategies businesses can use to scale AI efficiently and sustainably include:
- Build a strong data foundation: AI is only as good as the data it’s trained on. Ensure clean, well-structured, and accessible data.
- Leverage cloud and hybrid architectures: Scale compute power on demand without overhauling legacy systems.
- Adopt modular, API-first AI solutions: Keep AI flexible and interoperable to avoid vendor lock-in.
- Implement robust governance and validation: Establish clear policies for data security, compliance, and AI ethics while continuously validating model performance.
- Automate model monitoring and re-training: AI degrades over time; continuous tuning ensures accuracy and relevance.
- Strategically integrate complementary technologies: AI works best alongside automation, analytics, and existing enterprise tools.
- Empower teams with low-code AI tools: Reduce dependency on scarce AI/ML experts and enable broader adoption across the business.
That’s where platforms like OutSystems make a difference. With AI-powered low-code, businesses can scale AI responsibly—ensuring governance, seamless integration, and efficiency without the usual complexity.
OutSystems is known for accelerating digital transformation. How does your platform help businesses streamline AI implementation and reduce complexity in enterprise applications?
OutSystems simplifies AI adoption by combining low-code capabilities with generative AI to create what we’ve deemed the Generative Software Era. With AI-driven tools like Mentor, our AI digital worker, we help businesses accelerate application development by automating tasks like parsing user stories and auto-generating application components. This reduces development time by up to 50%—as seen by OutSystems customers like KPMG—while freeing up IT teams to focus on more strategic initiatives.
By abstracting complexity and reducing the amount of code required, our platform helps businesses avoid common pitfalls like technical debt or governance issues. Enterprises can build applications faster, more securely, and with better cost predictability—turning digital transformation from a daunting challenge into an exciting opportunity for innovation and productivity.
What are some common misconceptions about AI in enterprise software development, and how should organizations rethink their approach to AI-driven solutions?
There are several common misconceptions about AI in enterprise software development that can lead to unrealistic expectations or misaligned strategies. Here are examples of the most prevalent misconceptions:
- Misconception: “AI will replace developers.”
- Reality: AI is a powerful assistant, not a replacement. It automates repetitive coding, suggests optimizations, and enhances productivity, but human expertise is still essential for designing, testing, and maintaining enterprise applications.
- Misconception: “More data = better AI.”
- Reality: Quality matters more than quantity. Enterprises need clean, well-structured, and relevant data, not just vast amounts of uncurated information. Governance, bias reduction, and validation are key.
- Misconception: “AI works out of the box.”
- Reality: AI models need customization and continuous improvement to fit business needs. Fine-tuning, retraining, and integrating AI into existing workflows are essential for long-term success.
- Misconception: “AI adoption requires a massive IT overhaul.”
- Reality: Organizations can gradually integrate AI using low-code platforms, APIs, and cloud-based solutions without overhauling their entire infrastructure.
- Misconception: “AI is just about automation.”
- Reality: AI goes beyond automating tasks—it enhances decision-making, personalization, predictive analytics, and intelligent workflows, driving real business value.
With this in mind, a few ways organizations should rethink their approach include:
- Focusing on AI augmenting human capabilities rather than replacing them.
- Investing in governance, security, and compliance to ensure responsible AI adoption.
- Starting with high-impact, practical use cases before scaling across the enterprise.
- Using AI-powered low-code platforms to streamline AI integration, reduce complexity, and accelerate development.
Security and compliance are top priorities when integrating AI. What best practices should businesses follow to ensure AI systems remain secure, ethical, and aligned with regulatory requirements?
Security, ethics, and compliance should be foundational considerations when integrating AI into enterprise software development, especially in regulated industries. Strong governance frameworks are critical to managing risks associated with generative AI. Transparency, auditability, and traceability are essential to avoid “black-box” solutions and ensure AI-generated outputs are accurate, ethical, and compliant with regulations.
Robust security measures are also vital, as AI systems often handle sensitive data, making them vulnerable to cyberattacks. Businesses should use secure infrastructures with tools for continuous monitoring and updates to safeguard AI solutions. Staying up-to-date with regulations like GDPR and HIPAA supports the responsible use of AI while protecting user privacy.
Equally important are ethical considerations, such as fairness and accountability. Organizations must avoid biases in AI models and adopt industry standards like the EU AI Pact for guidance. Regular monitoring ensures AI systems are aligned with ethical principles and avoid discriminatory outcomes.
Finally, cross-departmental collaboration and training on technical and ethical AI practices empower teams to make responsible decisions that align with organizational values and regulatory obligations.
What role do you see low-code and no-code platforms playing in making AI more accessible and efficient for businesses of all sizes?
Low-code and no-code platforms are game changers for AI accessibility. They remove many of the traditional barriers to entry, such as the need for highly specialized coding skills, and allow organizations to focus on leveraging AI for tangible outcomes. For example, OutSystems research shows that 32% of organizations are using low-code to improve the performance of less-experienced developers, reducing the skills gap and enabling broader adoption.
One of the key advantages of low-code platforms is their ability to structure the work AI does. Generative AI models can be prone to errors or hallucinations, but when paired with the constraints and logic provided by low-code, these risks are minimized. Low-code also accelerates the time it takes to get applications into production, which is well aligned with the pressing need for speed and agility in today’s business environment.
AI is evolving at an unprecedented pace. What trends or breakthroughs do you see shaping the future of AI in enterprise technology over the next five years?
Over the next five years, organizations adopting AI will face significant challenges in areas such as data integration, privacy, scalability, talent shortages, and cost management, all while working to ensure ethical standards are upheld. Addressing data silos will require unified platforms while safeguarding privacy and mitigating bias will be essential for maintaining trust and regulatory compliance. Scaling AI effectively will demand investments in cloud and edge infrastructure, while closing the talent gap will rely on upskilling initiatives and fostering partnerships with academic institutions. As AI models grow in complexity, explainability tools will become critical for transparency, and integrating AI with legacy systems alongside MLOps frameworks will be vital for sustainable growth. AI systems capable of autonomous decision-making are also becoming more prevalent, enhancing operational efficiency and enabling a virtual workforce that augments human capabilities. Overcoming these challenges will be crucial for unlocking AI’s full potential and ensuring long-term value.
In this same timeframe, a defining trend in software development will be the integration of AI-driven tools to automate processes such as coding, testing, deployment, and design. These advancements will streamline workflows, improve efficiency, and enable greater customization, allowing development teams to shift their focus from repetitive tasks to strategic and innovative initiatives. Furthermore, enterprises are increasingly leveraging AI to automate complex business processes, moving beyond simple task automation to more sophisticated decision-making workflows. Meanwhile, AI systems are also becoming more and more capable of processing and integrating multiple data types—such as text, images, and audio—leading to more intuitive and human-like interactions. This multimodal capability enhances user engagement and broadens the applicability of AI across various sectors.
These shifts will drive progress and transformation across industries, leveraging AI to accelerate development and deliver greater business impact.
A quote or advice from the author: AI has gone through cycles of hype and disillusionment before. While I am an AI enthusiast (and have been since the 90s), I would encourage critical thinking, caution, and pragmatism to navigate this “AI revolution”.
Luis Blando
CPTO, OutSystems
Luis Blando is Chief Product and Technology Officer at OutSystems. He is a proven leader who is using his digital transformation expertise to help OutSystems stay ahead of the competition by continuing to deliver a world-class AI-powered low-code platform. His career spans over three decades and includes senior roles at industry leaders like Intel, McAfee, and Proofpoint, where he led global teams, drove significant revenue growth, and spearheaded groundbreaking product initiatives.