
Developer Productivity
When AI Writes the Tests: Check the Business Rule
Use a hypothetical discount boundary to review whether generated tests establish the requirement or merely repeat the implementation.
Parveen KRPublished 4 min read
Insights
Guidance, technical explainers and practical approaches to help your organisation adopt technology and build the skills to use it effectively.

Developer Productivity
Use a hypothetical discount boundary to review whether generated tests establish the requirement or merely repeat the implementation.
Parveen KRPublished 4 min read

Developer Productivity
Buying GitHub Copilot seats is easy. Scaling them across an engineering organization without leaking secrets, skipping review, or creating un-auditable agent output is not. This note covers the governance work we run before license counts grow.
Parveen KRPublished 3 min read

Skills & Enablement
Check meeting evidence, owners and conditions before turning a generated summary into a record colleagues rely on.
Parveen KRPublished 4 min read

AI Adoption & Agents
Compare candidate workflows using real inputs, review effort and accountable ownership before committing to a pilot.
Parveen KRPublished 4 min read

AI Adoption & Agents
Organisations that struggle to get value from enterprise AI investments almost never fail because of model capability. They fail because the data is not ready, the teams are not aligned, and the governance layer does not exist. This post outlines the readiness work that precedes meaningful AI adoption.
Parveen KRPublished 3 min read

Data & Analytics
Microsoft Fabric unifies lakehouses, data warehouses, real-time intelligence, and Power BI into a single SaaS platform. Yet many enterprise teams struggle to move from an approved pilot to production workloads. This post unpacks the patterns we see in teams that succeed — and the organisational friction that trips up the rest.
Parveen KRPublished 3 min read

AI Adoption & Agents
Retrieval-Augmented Generation (RAG) has become the dominant pattern for grounding enterprise LLM applications in proprietary data. Yet most organisations underestimate the architecture decisions required to move from a working demo to a production system that is accurate, cost-controlled, and auditable.
Parveen KRPublished 3 min read
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