Across industries, leaders are making bold moves with AI – investing in models, mapping out use cases, and launching pilot projects with high expectations. But too often, I’ve seen a familiar pattern: ambition without alignment. The strategy is sound, the excitement is real but the foundations aren’t built to carry the weight. Not because the technology isn’t ready. But because the infrastructure isn’t. The data isn’t. The Application architecture isn’t. Of course, AI tools can be used as a patch work on top but if you really need the benefits, you need to have all these layers ready for AI.

AI may look like plug-and-play. But it’s compute-heavy, data-hungry, and operationally demanding. Without the right foundation beneath it AI stalls. AI readiness is a multi-faceted concept, encompassing infrastructure, data, application architecture, and strategy, governance and security.

Infrastructure

Ask yourself: Is your current infrastructure designed to support compute-heavy, data-hungry AI models at scale? Is on premise or Hybrid or Cloud best for your data size and AI use cases?

Without robust infrastructure, AI models can’t be trained or deployed efficiently. You need scalable and secure infra muscle to power AI and make it cost effective.

The increasing adoption of specialized hardware like NVIDIA GPUs and Google’s TPUs, designed for high performance and efficiency at scale, alongside cloud solutions that offer cost optimization features, highlights the need for this intricate balance. Achieving this balance is not a one-time capital expenditure but an ongoing strategic decision regarding cloud versus on-premise deployments, hardware choices, and the effective use of MLOps to manage resource consumption, all of which directly impact the long-term return on AI investments.

While on-premises capabilities are considered, cloud computing, offered by major providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), provides the scalable and on-demand power required for AI workloads.

Key considerations for future success:

• • Cloud-native or hybrid infrastructure: Migrate to or integrate with scalable cloud platforms (e.g., AWS, Azure, GCP) that offer GPU/TPU support and managed AI services.
  • High-performance compute (HPC): Dedicate resources for specialized GPUs, TPUs, or other AI accelerators, especially vital for training complex deep learning models.
  • Containerization & orchestration: Leverage Kubernetes or Docker to efficiently manage ML workloads, ensuring scalability and portability.
  • Edge readiness: For AI deployments at the edge (e.g., IoT devices), ensure devices and gateways support local inference.
  • Optimized network performance: Recognize AI’s demand for fast and reliable internal data transfer across systems. Poor network architecture will introduce latency and impede AI insights.
  • Cybersecurity readiness: As AI adoption increases system complexity, it also introduces vulnerability to sophisticated cyber attacks. Your AI-ready infrastructure must incorporate enhanced cybersecurity and data protection measures.
  • Power-hungry AI workloads: Be mindful that AI consumes significant energy. Invest in power-efficient infrastructure and data centers capable of handling the load.

At Neurealm, we helped a large manufacturing customer replatform their applications, previously hosted across diverse data centers, onto a highly secure and scalable cloud environment, significantly enhancing performance and storage capabilities.

Data

Ask yourself : How confident are you in the accuracy and completeness of your data when making business-critical decisions?

Think of AI-ready data as the fuel for your AI engine – it needs to be clean, structured, and accessible to power meaningful results. It’s paramount to have high-quality data, characterized by accuracy, completeness, and consistency, as poor data can erode trust in AI’s outputs. Equally important is ensuring data accessibility and integration by breaking down silos and unifying data from various sources. Robust data governance, encompassing ownership, access controls, privacy, and regulatory compliance, is also essential.

Key considerations for future success:

• • 1. Understand AI use case & data fitness: Not all AI applications require the same type of data. Understand the specific AI need (e.g., generative, predictive) to define data requirements. Tailor data to the needs of specific models, whether it’s for generative AI (e.g., LLMs) or predictive ML pipelines. Assess data quality, relevance, and completeness based on the intended AI application.
    2. Governance & compliance
       Establish a robust data governance framework that includes:
       • Clear ownership and stewardship roles
       • Policies for ethical data use, privacy, and security
       • Ongoing compliance with regulatory standards (e.g., GDPR, HIPAA)
    3. Metadata & lineaget management
       Maintain comprehensive metadata to track:
       • Data sources and their transformations
       • Meaning and usage across AI applications
       • Evolution over time to ensure traceability and trust
    4. Observability & monitoring
       Implement data observability mechanisms to:
       • Monitor the health of data pipelines
       • Detect and resolve anomalies or data drift before they impact model performance
       • Support explainability and auditability for AI decisions
    5. Centralized & Scalable Infrastructure
       Adopt centralized data management platforms that streamline ingestion, processing, and analysis across diverse sources. Ensure:
       • Unified access and integration, breaking down silos across systems
       • Real-time data availability for AI and analytics consumption
       • Scalability for future needs, with flexible architectures that can evolve alongside AI use cases and business goals

As new AI use cases emerge, your data systems must continuously adapt and improve to support changing requirements. Think of it as a long-term discipline, not a one-off project. Unified data platforms, which integrate both data management and AI capabilities, are increasingly becoming the cornerstone of modern data infrastructure, enabling widespread AI adoption across the enterprise.

CASE STUDY

Scalable AI Data Platform for Improved Accuracy and Growth

A leading health tech company specializing in vocal biomarkers partnered with a technology provider to build a high-performance, scalable data platform using Databricks. The platform unified batch and streaming data through Delta Lake, ensuring ACID compliance and enabling seamless ingestion of audio data.

With features like Unity Catalog for HIPAA-compliant governance, and MLflow for efficient model deployment, the platform enabled better handling of voice-derived metadata using PySpark and SparkSQL. Real-time dashboards powered by Databricks SQL supported both internal and client reporting.

As a result, the company achieved a 60% improvement in data ingestion and processing, 30% boost in model accuracy, and 50% reduction in infrastructure overhead. The faster performance also led to a 25–30% increase in Voice API adoption and 100% growth in B2B partnerships through scalable analytics and reporting.

Our data expert, Pragadeesh J. says, “AI-ready data goes far beyond just collecting information — it’s about ensuring the right structure, context, and governance from day one. Transforming raw voice into clinically meaningful insights requires precise feature engineering, ethical data sourcing, and robust pipelines, reminding us that the success of AI often depends less on the model and more on the readiness of the data behind it.”

Application architecture

Ask yourself: Can your current application architecture support the pace and complexity of AI deployment?

AI-ready application architecture is needed to ensure AI technologies can be effectively integrated, scaled, and managed within the application environment to deliver real business value and insights.

The integration of AI necessitates a fundamental design approach from inception, rather than a supplementary addition. It’s critical to ensure the harmonization of application development processes with AI tools and use case development methodologies. Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) represent the application of DevOps principles to the distinct complexities inherent in AI and machine learning.

These methodologies facilitate the automation of diverse phases within the Machine Learning (ML) and Large Language Model (LLM) lifecycles, encompassing data preprocessing, model training, validation, and deployment. Such automation serves to mitigate human error and enable continuous integration and continuous delivery (CI/CD) of AI models.

Many enterprises still rely on legacy systems that were not originally designed to support AI-powered functionalities, often lacking modern APIs, which can make integration complex and costly. To overcome these challenges, adopting an API-first approach, utilizing middleware, or implementing API gateways becomes crucial. Modular, API-driven architectures significantly simplify the process of plugging AI capabilities into existing business processes and applications. Here are key aspects of modern application architecture that make enterprise AI-ready.

Integrating AI into enterprises with legacy systems is challenging due to the lack of modern APIs. An API-first approach, middleware, or API gateways are essential to overcome these integration complexities. Modular, API-driven architectures simplify incorporating AI capabilities into existing applications and processes, making the enterprise AI-ready.

Here’re key aspects of modern application architecture that facilitate this readiness:

• • • Modular design (aka Microservices): Breaking big software into smaller, independent parts.
      Why does it matter for AI? You can plug in AI for specific tasks (e.g., fraud detection) without rebuilding everything.
    • APIs (like plug points): Ways systems talk to each other.
      Why does it matter for AI? AI tools can easily connect to apps (e.g., chatbot AI talks to your website).
    • MLOps pipelines: Process to train, test, and deploy AI models.
      Why does it matter for AI? Helps launch AI features faster and update them without risk.
    • Monitoring & control: Keeping track of how the system performs.
      Why does it matter for AI? Ensures AI is doing what it’s supposed to without bias or mistakes.

Our Digital Platform Engineering practice head, Manisha Deshpande adds:

“To truly harness AI, application architecture must be conceived through two critical lenses:

• • 1. Leveraging AI for Efficiency and productivity: This would require modularization, robust data pipelines, cloud native architecture with containerization, and centralized logging.
    2. Leveraging AI for Product innovation: This would need API-first design approach for AI services, low latency data processing, user data privacy by design, and customer-centric monitoring and feedback.

In essence, a well-thought-out application architecture provides the necessary flexibility, scalability, data access, and operational robustness for AI integration, whether the goal is to optimize flows or to delight customers with new features. Keep humans in the loop, prioritize ethical AI, and enable the teams to experiment and adapt constantly. “

Stay tuned for Part 2 of this article, where I’ll explore the critical elements that ensure sustained, responsible, and secure AI adoption for any organization.