Integrating artificial intelligence (AI) onto legacy software is like trying to get an old flip phone to run the latest augmented reality (AR) apps. Yet, enterprises continue to take this approach, in the process encountering compatibility issues, sluggish performance and AI behavior which deviates from expectations.
We live in an AI-centric age, where organizations have AI integrated into at least one business function. Statista reports that global AI adoption soared to 72% in 2024 from 55% in 2023. Hence, it is no secret that AI is quickly becoming a mainstream business tool instead of remaining an emerging trend. However, Boston Consulting Group’s report reveals that 74% of companies are yet to gain a tangible return on investment from AI.
So, how do we reap the obvious benefits of AI, which the 4% of the companies that have built cutting-edge AI capabilities across functions are reaping? The answer is simple — AI-native software.
Understanding AI-Native Software
Building AI-native software entails leveraging AI and machine learning (ML) as core components in applications and systems from the start, rather than considering it as an afterthought in the architecture. Such software can learn and improve its performance over time.
What sets AI-native software apart is its ability to combine the trifecta of deep research, reasoning and Agentic AI. This combination enables systems to analyze vast datasets and reason through complex scenarios to determine optimal and personalized outcomes. It then collaborates dynamically with AI agents to enhance research and decision-making.
The key difference lies in the structure. Older systems are about retaining stability and depend on fixed, predefined workflows that produce expected results. AI-native apps refine and continuously improve to adapt based on learned information. This shift in design philosophy demands an entirely new DevOps approach, one that aligns with the changing nature of AI models.
Challenging Traditional DevOps
DevOps pipelines were automated to improve the integration, testing and deployment of code in the software development lifecycle. This set of workflows meets the needs of deterministic applications where updates follow a set and predictable plan. Nevertheless, AI-native applications need more than a simple code change. They require continual retraining of models, handling of complex data evolution, ensuring real-time performance and validating outcomes across dynamic environments. Without a holistic approach, these challenges can disrupt established pipelines and lead to inconsistent results.
The complexity of AI-native applications requires seamless cross-functional collaboration. Quality engineering ensures model validation and addressing issues like bias and accuracy, while delivery excellence focuses on smooth integration, testing and deployment. Data science optimizes model performance, and security teams safeguard models from threats while ensuring compliance. Business analysts translate AI insights into actionable strategies, and product managers ensure alignment with business goals. Together, these teams work to maintain model performance, scalability and reliability throughout the lifecycle.
At Persistent, we have witnessed how organizations struggle when retrofitting AI into legacy systems — dealing with outdated infrastructure that isn’t AI-ready, fragmented data silos and the difficulty of adapting existing DevOps pipelines to handle AI models. By rethinking software architecture with AI as a foundational element, we can help them navigate these hurdles seamlessly. This approach ensures that data is clean, integrated and accessible, enabling AI to perform its best. Incorporating feedback loops, data validation and continuous model evolution into the development lifecycle allows AI systems to remain reliable and adaptive as the application evolves. This shift has empowered clients to move beyond one-off implementations, enabling smarter, more agile decision-making while keeping systems flexible, scalable and future-proof.
These complexities of AI-native applications demand an advanced DevOps strategy — one that incorporates an agile approach to AI for operations (AIOps) and ML for operations (MLOps) to ensure that applications are built and released in an optimized, dependable state at all times.
Leveraging AIOps and MLOps
AIOps introduces real-time monitoring, anomaly detection and automated issue resolution. It ensures that AI-powered applications evolve correctly. For example, if an AI chatbot begins generating biased responses due to skewed training data, AIOps steps in to flag and correct the anomaly before it disrupts customer interactions.
Meanwhile, MLOps manages a model’s life cycle, including version control, automated model retraining and performance audits. Without MLOps, AI models risk stagnation, leading to degraded performance and ethical concerns such as data drift, bias or compliance violations.
Together, AIOps and MLOps form a strategic framework that oversees the entire lifecycle and integrity of AI models, ensuring that every decision and action are accurate, unbiased and continuously improving.
Rethinking CI/CD for AI
Traditional CI/CD pipelines need to be redesigned to focus on data and model performance rather than just the code. Unlike AIOps and MLOps, which maintain the ongoing health and evolution of the models themselves, the modern CI/CD evolution centers on the deployment pipeline, which ensures that as models are updated, the process remains agile and responsive to new data.
Automating retraining and robust validation within the deployment pipeline can enable businesses to keep their models as agile as their environments. The approach goes further by shifting the focus of continuous monitoring from simplistic uptime checks to real-time evaluation of accuracy, bias and other performance factors to ensure swift, well-informed action can be taken when needed.
At Persistent, we encourage this shift — moving from code-centric to data-centric pipelines. We guide organizations to integrate model training and validation into CI/CD, implement continuous monitoring and feedback loops to eliminate biases and embed explainability and interpretability into every layer of the stack.
This evolution in CI/CD is less about software deployment and more about intelligence deployment, where AI models are refined and optimized just as frequently as the underlying application code. This evolved approach safeguards the integrity of AI models and reinforces their ability to drive impactful, reliable decisions in a world that waits for no one.
Securing AI-Native Applications
Adopting AI-native software is not just a technological change; it is a redefinition of how companies tackle software engineering, security and organizational workflows. To have value-based AI-native systems, businesses need to adopt cloud-first, containerized environments for scalable AI workloads, migrate from traditional databases to deep learning and predictive data lakes and balance regulatory compliance with ethical AI development to ensure unbiased and transparent decision-making.
Security is another essential aspect to consider in AI-native applications. The presence of adversarial attacks, model manipulation and data poisoning necessitates an integrated effort from security, DevOps and data science teams. The responsibility of making sure AI models function well while ensuring fairness, security and legal compliance is swiftly becoming an issue that companies must deal with.
At Persistent, we are committed to developing AI-native software that not only meets the highest standards of quality and performance but also addresses the unique security, compliance and ethical concerns associated with these applications. Our delivery excellence and quality engineering center of excellence (COE) play a critical role in ensuring that our AI-native software is developed with security, compliance and ethics in mind. Toward this, they conduct threat modeling & risk assessments, outline & implement secure coding standards & practices, develop & implement compliance frameworks, collaborate with regulatory bodies and finally conduct regular audits and assessments.
Paving the Road to AI-Native Systems
Software development is rapidly evolving — from integrating AI to being fundamentally shaped by it. The next wave of innovation requires companies to invest in AI architects who can code and embed intelligence deep within software.
At the leadership level, fostering an agile and adaptive mindset is essential. We focus on strategic guidance — setting ethical and business boundaries while empowering AI systems to self-optimize. Success indicators will evolve from counting detected flaws or delivered functions to measuring autonomously enhanced outcomes, such as AI-driven engagement models reducing customer churn.
The future belongs to those who lead, not just adapt. AI-native development isn’t merely an advantage — it is the foundation of the next era of technology. Companies that move decisively today won’t just optimize efficiency; they’ll create new business models, revolutionize customer experiences and set the benchmark for intelligent software. As AI evolves, the true differentiators will be those who innovate boldly, embed AI as a core capability and shape a future where software doesn’t just enable businesses — it drives entirely new possibilities.