1. Clear business use cases (not “AI for AI’s sake”)

AI only works when:

• The decision or workflow to augment or automate is clearly defined
• Success metrics are explicit (cycle time, accuracy, cost, revenue impact)

If the use case is vague, AI becomes experimentation, not production value.

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2. Trusted, high-quality data

Before AI, the platform must have:

• Consistent definitions for key metrics and entities
• Data quality checks (freshness, completeness, accuracy)
• Clear ownership and accountability

AI amplifies data problems—it does not fix them.

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3. Governed access to data

The platform must support:

• Role-based access controls
• Data classification and masking
• Auditability and lineage

Without governance, AI introduces unacceptable security, privacy, and compliance risk.

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4. Availability of relevant data (especially unstructured)

AI needs:

• Access to documents, logs, tickets, emails, transcripts, not just tables
• Metadata, embeddings, and searchability

If unstructured data is inaccessible, GenAI value is limited.

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5. Scalable and flexible architecture

The platform must support:

• Separation of storage and compute
• Batch + streaming workloads
• Cost control and elasticity

AI workloads are spiky and expensive without architectural flexibility.

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6. MLOps / AI lifecycle readiness

AI becomes realistic only when:

• Models can be versioned, monitored, and retrained
• Drift, bias, and performance are tracked
• Human-in-the-loop workflows exist

Without this, AI remains a demo, not a product.

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7. Organizational readiness

This is often the real blocker:

• Teams understand how to use AI outputs
• Clear ownership across data, ML, security, and business
• Leadership accepts probabilistic systems, not deterministic ones

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“AI becomes realistic when the data is trusted, governed, accessible, and tied to a real business decision—otherwise it stays a science experiment.”

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Truth you can say confidently

“If a customer hasn’t operationalized data quality, governance, and ownership, the AI conversation should start with fixing the data platform—not deploying models.”