By Vikas Singh, Chief Growth Officer at Turinton AI.
A line goes down in a plant in Germany at 9 AM. By 9:02 you shift production to two lines to the United States, keep priority POs on track, and avoid a write off. No records cross a border. That is weightless AI.
For years enterprises tried to pull everything into one place. That made sense when pipelines were fragile and inference was batch based. The pattern has changed. Move questions to where data already lives, assemble only the context a decision needs, and prove every step is governed. Leaders do not win by moving petabytes. They win by moving decisions.
Weightless does not mean careless. It means Zero ETL and in place access so models fetch small slices of context without bulk copies. Keep the loop simple. Discover connects to sources like SAP PP, Snowflake finance marts, and document stores. Correlate builds a knowledge map with a semantic layer and knowledge graphs. Explore answers questions with retrieval and reasoning. Observe tracks audits, data quality, and cost. The result is faster answers, lower risk, and less duplication.
Across the industry, three themes stand out.
Governance by design. Put policy, lineage, and oversight in the core stack. Reuse your identity and data controls, whether that is Okta, Purview, or custom rules. Treat policy as code, apply least privilege at retrieval, and keep answers explainable and auditable. Clear lineage reduces approval friction and shortens timelines.
Retrieval first and residency aware. Borders matter. Keep embeddings and indexes near the source and run serving and retrieval inside each region. When sharing is limited, pass features or scores rather than rows. That supports global decisions with local control and avoids new data migrations.
From prompts to workflows and operations. Move beyond one off prompts to simple governed workflows that plan steps, call tools, and coordinate tasks. Monitor relevance, latency, and cost so teams tune with evidence. People stay in control and operations scale without big rewrites.
Bringing these ideas together is not as difficult as it may sound. A practical playbook can be:
1. Keep data in place and bring the model to the question. Use connectors and a semantic layer to translate business language into source queries. Build embeddings or indexes close to each domain. Use short lived caches that expire by design so sources remain the truth.
2. Run retrieval and inference where the rules live. Many countries require that data created in a country is stored and processed there. Run local retrieval and, when needed, local model execution inside each region. Share only small policy approved signals into a global control plane. A predicted disruption in Germany can trigger a shift to the United States while source records stay within borders.
3. Prove governance at every hop. Apply least privilege, policy as code, and full lineage from source to answer. Mask or exclude sensitive fields at retrieval. Keep end to end logs. Use explainability and human in the loop review where risk is higher. This builds trust and speeds adoption.
4. Measure outcomes, then tune. Define success as time to answer, case resolution, unit cost per decision, and customer satisfaction. Track retrieval quality and cache hit rates. Tune prompts, tools, and indexes to raise these measures. The proof is not how much data you move but how quickly you turn questions into action.
Data gravity was a response to older limits. The future is weightless. Keep data where it belongs, let context travel with strong governance, and run simple governed workflows. That is how leaders ship AI fast and how IT stands out.