As of 2025, the integration of Artificial Intelligence (AI) has transitioned from a futuristic ambition to a core business function. However, many organizations face a significant challenge: attempting to incorporate AI projects into a traditional Software Development Life Cycle (SDLC) is often inefficient and can act as a barrier to innovation. To successfully industrialize AI, organizations need to develop a tailored AI SDLC.
The traditional SDLC, which includes models like Waterfall and Agile, operates on a deterministic framework. In these structured environments, developers write explicit code based on specific requirements, yielding predictable outcomes. If the same input is provided, the output remains constant. This code-centric approach is governed by logic that, although intricate, is ultimately understandable and testable in binary terms—an application either functions correctly or it does not.
In contrast, AI development disrupts this conventional approach. It is inherently probabilistic and relies on data, whereby the “logic” within an AI model is derived from data rather than being explicitly coded. This introduces three critical variables that traditional SDLCs struggle to manage: data, model code, and hyperparameters. A modification in any of these elements can significantly impact the system’s performance, resulting not in a simple pass or fail but in a prediction accompanied by a confidence score.
Managing AI projects through traditional SDLCs often results in chaos. Issues like lost data versions, irreproducible successful experiments, and a blurred line between research and production create a “shadow IT” landscape where data scientists operate in isolation from engineering and operations teams.
A bespoke AI SDLC can formalize the distinct, non-linear stages inherent to the creation and maintenance of AI systems. It transforms AI development from a craft into an industrialized process.
Data Scaffolding is one foundational aspect, frequently consuming the majority of a project’s duration. An AI SDLC stipulates formal processes for data acquisition, cleaning, labeling, and notably, versioning. Similar to how Git manages code versions, tools like DVC (Data Version Control) should be employed to version datasets, ensuring traceability of every model back to the exact data it was trained on—a requirement critical for reproducibility and troubleshooting.
Experimental Prototyping is another unique phase. Unlike traditional development, which focuses on building features, AI development requires running numerous experiments to identify viable models. An AI SDLC incorporates this experimental component, offering a structured framework for recording hypotheses, model architectures, hyperparameters, and results through platforms like MLflow or Weights & Biases. This approach reduces wasted effort and safeguards institutional knowledge against staff turnover.
Holistic Model Validation expands testing beyond traditional unit tests. A strong AI SDLC outlines a multifaceted validation gate, incorporating not only statistical performance metrics (e.g., accuracy, F1-score) but also assessments for fairness and bias across different demographic groups. It additionally encompasses checks for robustness against adversarial attacks and evaluates the model’s explainability using techniques like SHAP or LIME.
Continuous Training & Monitoring (CT/CM) recognizes that, unlike traditional software applications that may only require occasional updates, AI models are never truly complete. The ever-changing real-world conditions can lead to model drift (changes in input-output relationships) and data drift (shifts in the statistical characteristics of input data). An effective AI SDLC includes a comprehensive MLOps (Machine Learning Operations) pipeline to continuously monitor live model performance, triggering alerts or retraining cycles as needed.
Developing a custom AI SDLC is critical for managing risks, ethics, and compliance as organizations navigate AI implementation. Without a structured process, companies may navigate through a landscape filled with significant risks. An established method serves as a tool for governance and risk mitigation.
An AI model is considered a corporate asset that carries inherent risks. Managing it with makeshift processes is akin to running a data center without backups or security measures.
Ethical and Bias Mitigation aspects are embedded directly into the AI SDLC, prompting teams to evaluate critical factors at every stage: Is the training data representative? Does the model show equitable performance across diverse user groups? By instituting bias audits prior to deployment, the SDLC elevates fairness from a secondary concern to a core requirement, thereby shielding organizations from reputational harm and legal risks.
With regulations like the EU AI Act becoming enforceable, demonstrating compliance is essential. Regulators will require evidence of how high-risk AI systems (e.g., in hiring or credit scoring) were developed, tested, and monitored. A comprehensive AI SDLC, tracking experiments and versioning data, creates an auditable trail that is challenging to reconstruct retroactively, thus ensuring compliance and preventing significant penalties.
AI systems also introduce novel security vulnerabilities overlooked by conventional AppSec practices, including data poisoning, adversarial attacks, and model inversion. An effective AI SDLC must integrate tailored security measures designed to test these vulnerabilities, ensuring the overall AI system bolsters the organization’s security profile.
Building an AI SDLC represents not just a protective strategy but a strategic investment that can unlock considerable business value and foster a sustainable competitive edge.
Accelerated Time-to-Value is one immediate benefit. Many organizations struggle with the “last mile” dilemma—transitioning a model from a data scientist’s workstation to a production environment. A standardized AI SDLC, driven by MLOps automation, optimizes this transition, decreasing deployment timelines from months to days. This agility empowers organizations to leverage AI-driven insights expeditiously.
Reproducibility and Scalability are also enhanced. In the absence of a formal process, AI projects may become singular “science experiments,” reliant on specific individuals, and challenging to reproduce or scale. An effective AI SDLC ensures that each successful model constitutes a reusable and scalable asset, enabling new projects to build on prior achievements with versioned data and models, greatly enhancing efficiency and return on investment.
Enhanced Collaboration is another pivotal aspect, as AI development necessitates a variety of expertise, from data scientists and ML engineers to DevOps specialists and legal advisors. A unified SDLC fosters effective collaboration by establishing a common framework and workflow, clarifying roles and responsibilities at every stage of the lifecycle.
In current conditions, treating AI development as an extension of traditional software engineering is likely to result in failure. The distinct data-centric and experimental nature of AI necessitates its own structured framework. An organization-specific AI SDLC constitutes the blueprint for evolving AI from isolated, high-risk projects into a scalable, reliable engine for driving enterprise value.
By investing in a customized AI SDLC, organizations can transition beyond haphazard experimentation and establish what can be described as an “AI factory,” a systematic approach that consistently generates high-quality AI solutions. This shift is not merely beneficial; it has become a fundamental necessity for organizations intent on competing in a future powered by AI.
The author, Srikanth, is the CTO of Bajaj Credit.
Disclaimer: The views expressed herein are solely those of the author, and ETCIO does not necessarily hold these views. ETCIO is not accountable for any damages incurred by individuals or organizations as a result of this analysis.
Note: The narrative surrounding AI adoption mirrors the principles represented by the Making AI Work Summit & Awards, a leading conference designed to facilitate dialogue, collaboration, and innovation in AI. The integral recognition component, the ET AI Awards India, honors individuals and organizations for their significant contributions to the field of AI. One platform, one purpose: to harness AI for business and societal advancements.
