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Enterprise AI is more likely to deliver value when leaders make 12 connected decisions before and during deployment: define measurable outcomes, establish business ownership of data, choose infrastructure and models for the workload, maintain retrieval and governance, and earn employee adoption. Anupam Arora, Bharat Forge’s Head of Data, AI and BI, outlined this practitioner framework in an ETCIO article published and updated September 17, 2026. It reflects his manufacturing experience, not a universally validated standard.
1. Is the organization ready, and who owns the outcome?
Before approving a project, assess readiness across four areas: data maturity, talent, infrastructure, and leadership commitment. Identify the weakest area and decide whether to address it before proceeding. A technically capable team cannot compensate for missing data or absent business ownership.
Assign one accountable owner and set a specific, measurable target before committing budget. Cost savings, headcount efficiency, and cycle-time reduction are examples of outcomes to measure. The target should connect the AI work to a business objective rather than treating deployment itself as success.
2. Who is accountable for data quality and meaning?
The teams that generate or enter data should help own its quality and interpretation. Business units and operational teams understand what fields, records, and documents mean in practice; that context is essential when the information is used to support decisions.
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Plan for both structured data, such as ERP records, and unstructured material, such as drawings, PDFs, and engineering documents. A governed data layer should account for information from both IT and operational technology, not just the systems easiest to connect.
Arora summarized the importance of this foundation with the principle, “Without data, your AI will fail.”
3. Should the workload run in the cloud or on premises?
There is no single deployment choice for every enterprise workload. Compare the workload’s characteristics, sensitivity of its data, cost, and risk of vendor lock-in. Preserve portability where practical so that an initial platform choice does not unnecessarily constrain later options.
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Arora notes that regulated, high-compliance environments such as defense may need on-premises systems. That is a context-specific consideration, not a rule that all sensitive workloads must use the same architecture.
4. When should inference use APIs, managed cloud, or owned GPUs?
For many inference workloads, pay-as-you-go APIs can be a sensible starting point while usage volume, latency requirements, and query patterns are still uncertain. Once those patterns become predictable, compare the ongoing workload with the investment and operating burden of owned GPU capacity.
Managed cloud services may reduce operational burden. Open-source options can be useful during early research and development. The choice should follow evidence about the workload rather than assumptions about which infrastructure sounds most advanced.
5. Which data platform fits, and does every use case need real-time processing?
Choose a warehouse or lakehouse according to the data and business use, and make sure business analysts retain ownership of domain logic and calculations. A data platform is not only an engineering project: analytical definitions have to reflect how the business measures its work.
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6. How should embeddings and vector search be designed?
Meaning-based retrieval depends on more than selecting a vector database. Decide which embedding model to use, how to split long documents into chunks, and how to preserve relationships among the source material. Those choices affect whether retrieval returns useful context or fragments that have lost their meaning.
Vectors can support retrieval by meaning rather than exact keyword matching. That is useful when a person’s query and the relevant document use different wording, but the system still needs a sound representation of the underlying content.
7. How will retrieval-augmented generation stay current?
Retrieval-augmented generation (RAG) is an operating pipeline, not a one-time installation. Corporate documents change; ingestion and retrieval need to reflect those changes if answers are to remain grounded in current material.
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Organize documents by department or domain before ingestion. Arora says this can improve retrieval speed and answer quality. The structure should preserve the context needed to find and interpret a relevant document, rather than treating the entire organization’s content as an undifferentiated collection.
8. Which model fits the task, rather than simply being the newest?
Select models according to the use case’s performance requirements and cost. The newest model is not automatically the best fit. Different tasks may call for general-purpose, domain-specific, or routing models, so avoid designing the enterprise around dependence on a single vendor when requirements vary.
Make the choice against the task the system must perform and the cost of doing so. This keeps model selection connected to the business outcome established for the project, rather than to novelty alone.
9. Is this a case for an agent or ordinary automation?
Ask whether the workflow can be described as a series of defined decision points. If it can, ordinary automation may be sufficient. Agents are more appropriate when outcomes are genuinely unpredictable and the task requires judgment that cannot be captured in a fixed set of steps.
For high-stakes or safety-critical decisions, retain human oversight. The use of an agent does not remove the need to decide who is responsible when its output affects people or operations.
10. Should productive predictive machine learning continue?
Do not defund predictive machine-learning systems simply because generative AI has become more prominent. Predictive and generative or agentic approaches serve different kinds of work; keep them in parallel and match each to suitable use cases.
The relevant question is which approach fits the problem and its outcome, not which category is receiving more attention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.11. What does operational AI governance require?
Classify projects by risk, monitor for hallucinations, and apply controls in proportion to potential impact. A low-risk productivity aid and a system involved in a consequential or safety-critical decision do not call for identical oversight.
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12. How can employees trust and adopt the system?
Involve the people who do the work, address concerns about trust and job loss, and invest in training and change management. A tool that employees do not use has not delivered its intended operational value, regardless of whether it was technically deployed.
ETCIO reports that Bharat Forge scrapped 10–12 AI projects over the prior year because plant employees did not use them, citing indifference or fear of job loss. The article also cites Bosch as having trained 30,000 employees on AI, and names HUL as achieving full workforce AI literacy and Dr. Reddy’s as using dedicated “AI translators” between technical and business teams. These are examples as reported in the article, not independently verified measures of outcomes.
How should the rollout change as experience grows?
Arora suggests a gradual sequence rather than beginning with the most complex systems. It is a proposed progression, not a proven universal timeline.
| Stage | Suggested focus |
|---|---|
| Year one | Small, low-risk productivity wins |
| Year two | Individual, well-scoped agentic use cases |
| Year three onward | Multi-agent, complex, cross-functional systems |
Arora also cites GE’s Predix as a cautionary example of scaling before proving value at smaller scale; the ETCIO article provides no further detail for drawing conclusions beyond that caution.
His closing advice is to “blend your business strategy with your AI strategy first, and bring your business owners along for the journey.”
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