In my nearly two decades of navigating the product landscape – from the complex regulatory environments of healthcare at Genentech to the high-scale trust and safety challenges at Walmart and the cloud infrastructure demands of AWS – I’ve seen "data-driven" evolve from a competitive advantage to a basic price of admission.

Today, as we stand at the precipice of the agentic era, the stakes have shifted again. We’re no longer just building dashboards for human analysts; we’re architecting the "nervous systems" for autonomous agents and generative interfaces. 

After 13+ years of leading cross-functional execution in supply chain, compliance, and cloud products, I’ve learned that AI-readiness is not a technical problem – it's a product strategy problem.

If your data is fragmented, your AI will be confidently, expensively, and dangerously wrong. To move from experimental pilots to production-grade intelligence, we must treat data not as a byproduct of our software, but as the primary product itself.

"If your data is fragmented, your AI will be confidently, expensively, and dangerously wrong." – Shivakumaran Venkataraman Kattemalawadi Sr. Business Product Manager, Genentech

1. Shifting the mental model: From data lakes to data products

Historically, organizations adopted a "save everything" mentality. We dumped raw events into data lakes and hoped a data scientist could eventually find the golden nuggets. In an AI-first world, this approach leads to model hallucination and high latency.

As a senior PM, your first mission is to define data products. A data product is a high-quality, discoverable, and secure dataset that has an owner, a clear schema, and an SLA (service level agreement).

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The experience gap: During my time in compliance and trust systems, I often saw models fail because "user activity" meant something different in the CRM than it did in the backend logs. AI cannot negotiate these differences. It requires a semantic layer.
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The fix: Implement a single source of truth where business logic – like the definition of an active customer – is defined once and inherited by every model. This eliminates the "garbage in, garbage out" cycle at the root.

2. High-fidelity labels: The new code of the AI era

In traditional software, we write code to define logic. In AI, our data is the logic. If you are building a generative AI for pharmaceutical supply chains or healthcare diagnostics, your data isn't just the raw documentation; it’s the documentation paired with verified ground truth.

Example: The healthcare compliance bot

Imagine building a tool to help clinicians navigate complex regulatory compliance.

  • Legacy data: 100,000 PDF policy documents stored in a SharePoint drive.
  • AI-ready data: These same documents, chunked into logical sections, indexed with metadata (region, effective date, product type), and paired with a "golden set" of 1,000 Q&A pairs verified by legal experts.

Your value as a PM is no longer just in the UI; it’s in the quality and diversity of the training set you curate.

"Your value as a PM is no longer just in the UI; it’s in the quality and diversity of the training set you curate." – Shivakumaran Venkataraman Kattemalawadi Sr. Business Product Manager, Genentech

3. Solving the "unstructured" problem and dealing with dark data

Throughout my experience at KPMG and Accenture, I saw billions of dollars in enterprise value trapped in "dark data" – unstructured PDFs, emails, Slack messages, and recorded calls. AI-readiness requires building automated ingestion pipelines that turn this noise into signal.

  • Metadata-first architecture: When you ingest a document, the AI needs to know more than the text. It needs the context: Who wrote it? When was it last updated? What is the sensitivity level?
  • Intelligent chunking: As a PM, you must decide how information is consumed by an LLM. Does the agent need the whole contract to answer a question, or just the "Indemnification" clause? Designing the information architecture for retrieval-augmented generation (RAG) is now a core PM competency.

4. Governance as an accelerator, not a brake

In the highly regulated sectors I’ve worked in – healthcare and compliance – governance is often viewed as a hurdle. However, in the AI era, governance is your safety net. Without clear data lineage and provenance, you cannot safely feed data into a Large Language Model. If you can’t prove where a piece of information came from, you cannot trust the AI's output.

Feature

Legacy data mindset

AI-ready data mindset

Data quality

"Clean it before the quarterly report."

Real-time validation at the point of ingestion.

Privacy

"Protect the database perimeter."

Granular PII masking integrated into the data pipeline.

Bias

"Check for errors after shipping."

Proactive auditing of training sets for demographic and edge-case balance.

5. The "data flywheel": Building feedback loops into the UI

One of the most critical lessons I’ve learned across Walmart and AWS is that the best data isn't found – it’s generated through user interaction. AI-ready products must have "implicit" and "explicit" feedback loops built into the core UX.

Example: An AI-powered sales intelligence tool

  1. Ingestion: The tool analyzes customer emails and suggests a response.
  2. Interaction: The sales rep edits the suggestion before sending.
  3. The loop: The difference between the AI’s suggestion and the rep’s final version is the most valuable data point you own. It’s a direct signal of how to improve the model for that specific user persona.

As a PM, you should be tracking the correction rate of your AI features as a primary KPI for data health.

6. Scaling through infrastructure: The cloud perspective

Drawing from my time at AWS and Slalom, making data AI-ready at scale requires a shift toward serverless data architectures and vector databases.

You cannot expect a legacy SQL database to handle the high-dimensional similarity searches required for modern AI agents. Making your organization AI-ready often involves a cloud-first migration where data is stored in a format that is natively accessible to machine learning services (like Amazon SageMaker or Vertex AI).

7. The ethical imperative: Trust and safety

In my tenure focusing on trust and safety at Walmart, I realized that AI-readiness isn't just about accuracy; it's about alignment.

AI-ready data must be scrubbed of historical biases. If your historical hiring data shows a preference for a certain demographic, feeding that raw data into an AI will only automate discrimination at scale. We must be intentional about synthetic data augmentation – creating artificial data points to fill gaps in our real-world datasets to ensure fairness and robustness.

"AI-readiness isn't just about accuracy; it's about alignment." – Shivakumaran Venkataraman Kattemalawadi Sr. Business Product Manager, Genentech

Final thought: The PM’s new north star

The transition to AI-readiness is the most significant architectural shift of our careers. For my fellow product leaders, my advice is this: Stop asking what the AI can do, and start asking what your data can teach.

Your success will not be measured by the coolness of the LLM you plug in. It will be measured by the integrity, context, and freshness of the data pipelines you build. In the race to AI, the company with the best model might lead for a month, but the company with the best AI-ready data ecosystem will lead for a decade.

Let's stop building silos and start building the foundations of the future.

About the author

Shivakumaran Venkataraman is a Senior Product Leader with 13+ years of experience across Genentech, Walmart, AWS, and KPMG. He specializes in scaling enterprise platforms, data governance, and leading cross-functional teams to solve complex problems in cloud, healthcare, and compliance.