Insights on enterprise AI
Practical guidance, case studies, and analysis on AI strategy, RAG, agents, automation, and integration — written by IDS engineers and consultants.
Time to value: when enterprise AI actually pays back
Median time-to-value on agent deployments in 2026 is around 5.1 months, but the spread by function is wide — from 3.4 months for sales development to 8.9 for finance and operations.
What enterprise RAG actually has to get right
Retrieval is now described as the foundation tier that grounds agents in real enterprise data. Getting it right is mostly about retrieval quality, permission-aware filtering, and knowing when to refuse.
Integration is the real blocker: 95% of IT leaders report AI integration problems
The dominant gap between AI adoption and value capture is not model capability. It is the unglamorous work of authentication, rate limits, partial failure and observability across the systems an enterprise already runs.
Vietnam’s new digital and high-tech laws took effect on 1 July 2026 — a compliance read
The Law on Digital Transformation and the Law on High Technology came into force on 1 July 2026, following the Law on Artificial Intelligence in March. What changes operationally for enterprises deploying AI in Vietnam.
The agent governance gap: what to put in place before agents touch production
AI agents moved into production during 2026; governance did not follow. Agent governance is now being described as the new cybersecurity concern — here are the controls that actually matter.
Vietnam’s National Digital Transformation Strategy 2026–2030: what it means for enterprises
Vietnam approved its National Digital Transformation Strategy on 14 July 2026, targeting a digital economy worth 30% of GDP by 2030 and committing support for 500,000 SMEs. Here is what enterprises should actually do about it.
Why most enterprise AI pilots never reach production
Analyst data puts enterprise AI pilot failure near 88%, yet the cause is rarely the model. It is scope chosen without a baseline, no owner for the workflow, and no plan for the cases that break the happy path.
From RAG to GraphRAG: when vector search isn’t enough for legal, finance, and engineering docs
Vector search finds chunks similar to your query — that’s the whole mechanism. For legal contracts, financial filings, and engineering BoMs where relationships between entities matter, similarity isn’t structure. A practical guide to GraphRAG and the hybrid retrieval pattern that fits most enterprises.
Why your RAG system gets worse over time — and how to fix retrieval drift before users complain
The first 90 days, your RAG system feels accurate. By month five it’s firefighting. Four drift drivers, four detection signals, three embedding refresh strategies, and the operational practices that catch the regression in dashboards instead of customer complaints.
Knowledge graphs + LLMs for Vietnamese enterprises: handling language nuance at scale
Vietnamese tone marks. Compound-noun word boundaries. Company-name conventions (Công ty Cổ phần / TNHH / JSC). Administrative restructuring of districts and wards. Code-switching with English. Regional vocabulary. Six realities that break off-the-shelf retrieval — and how a knowledge-graph layer handles them.
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