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Enterprise AI succeeds when leaders make a connected set of decisions about business outcomes, data, infrastructure, models, risk and adoption—not when they simply pick a promising model. Anupam Arora, Head of Data, AI and BI at Bharat Forge, outlined 12 such decisions in an ETCIO article published and updated September 17, 2026. His framework is practitioner advice rooted in manufacturing experience, not a universally validated standard; the choices below should be adapted to each organization’s workloads and constraints.
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1. Is the organization ready, and who is accountable?
Before approving a substantial AI budget, assess four foundations: data maturity, available talent, infrastructure and leadership commitment. Identify the weakest area rather than letting a strength in one category conceal a blocker in another. Then assign one accountable owner and define a measurable outcome—such as lower cost, improved headcount efficiency or shorter cycle time—before committing funds.
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A useful readiness decision is specific: which process is expected to change, who owns that change, how the baseline will be measured and what result would justify continuing? Without those answers, a technically successful pilot can still be an organizational failure.
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The teams that generate or enter data should be accountable for its quality and interpretation. Central data teams can provide standards and platforms, but they cannot reliably infer every operational definition on behalf of the business units that use the information.
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Plan for both structured sources, such as ERP records, and unstructured material, including drawings, PDFs and engineering documents. In manufacturing, include operational technology as well as IT data. Arora’s principle, as quoted by ETCIO, is direct: “Without data, your AI will fail.” The practical implication is not merely to collect more data, but to establish who can correct it, explain its context and maintain it.
3. Is AI the right intervention for the problem?
Match the method to the task rather than defaulting to generative AI. Predictive machine learning remains useful where historical data can support forecasts or classifications. Generative systems and agents address different needs, such as working with language or handling less predictable tasks. Keep productive predictive ML in operation while exploring generative or agentic work; replacing a useful system simply because a newer category is prominent is not a strategy.
Choose the technical approach around actual workloads
4. Cloud or on-premises?
Compare the workload’s requirements for data sensitivity, compliance, cost and portability. Cloud can reduce the burden of operating infrastructure, while on-premises systems may be appropriate where strict regulatory or confidentiality requirements apply. Arora cites defense as an example of a high-compliance setting that may need on-premises systems. Do not treat that example as a rule for every regulated organization: make the choice against the applicable requirements for the particular workload.
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Portability matters whichever route is chosen. Consider how easily data, models and application logic could move if costs, vendors or compliance needs change. A deployment that works today but makes future movement prohibitively difficult creates a separate business risk.
5. How should inference compute be funded?
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. The choice becomes more consequential once those patterns are stable: owned GPU capacity may merit consideration when measured demand justifies the investment and the organization can operate it. Managed cloud services may reduce operational burden; open-source options may be useful during early research and development.
Compare options using evidence from the intended workload, not broad assumptions about which infrastructure is inherently cheaper. The key inputs are volume, latency and predictability of queries, alongside operating costs and the skills needed to maintain the system.
6. Warehouse, lakehouse or another data-platform design?
Choose a platform based on the organization’s data and the business work it must support. Keep domain logic and calculations under the stewardship of business analysts and domain experts; a platform should not become an engineering-only project whose users cannot explain its rules.
Do not assume that every use case requires real-time processing. Arora estimated that “roughly 90% of use cases are perfectly served by batch data,” as reported by ETCIO. That is his estimate, not an independently established industry statistic. Use the required freshness of each decision to determine whether batch processing is sufficient.
7. Which embedding model, chunking method and retrieval structure?
For systems that search long documents by meaning, decide how to represent the material as embeddings, how to split documents into chunks and how to preserve relationships among the resulting pieces of information. Vector search can retrieve semantically related content even when a query does not repeat the exact keywords in a document. That capability does not remove the need to make good choices about context and document structure.
Chunking that is too broad can bring in irrelevant material; chunking that is too narrow can strip facts of the context needed to answer correctly. The suitable approach depends on the documents and questions the system must handle.
8. How will retrieval-augmented generation stay current?
Retrieval-augmented generation (RAG) is an operating pipeline, not a one-time setup. Corporate documents change, so plan how revised, superseded and newly added material will be organized and ingested. Organizing content by department or domain before ingestion can improve retrieval speed and answer quality, according to Arora’s account.
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Decide who maintains that pipeline and how updates reach it. If the underlying material changes but the retrieval index does not, answers can reflect outdated documentation even when the model itself has not changed.
9. Which model or combination of models fits the task?
Select models against the use case’s performance and cost requirements, not recency alone. Different tasks may call for a general-purpose model, a domain-specific model or a routing approach that directs requests to appropriate systems. Avoid making one vendor or model the default for every workload when needs differ.
Model selection should be evaluated alongside infrastructure and data choices: a model that performs well in isolation may not be the right operational choice once cost, latency, data sensitivity and portability are considered.
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10. Does this workflow need an agent, or ordinary automation?
First ask whether the workflow can be described as defined decision points. If the steps and conditions are known, conventional automation may be enough. An agent is more appropriate when outcomes are genuinely unpredictable and the task requires judgment rather than simply following an established sequence.
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11. What operational governance matches the risk?
Classify projects by risk and potential impact, then apply controls proportionate to that classification. Monitoring for hallucinations is one operational concern; governance also needs to shape where outputs can be used and what happens when a system behaves incorrectly. A written ethics charter alone does not provide those operating controls.
Governance should be part of deployment and ongoing operation, rather than a document produced separately from the system’s actual use. The more consequential the decision, the more important it is to define review, escalation and human responsibility in advance.
12. Will employees use the system, and are they prepared?
Involve the people who do the work while the system is being designed and introduced. Address concerns about trust and job impact, and provide training and change management suited to the tasks employees are expected to perform. Adoption is not a final communications step; it is one of the conditions that determines whether a tool creates value.
ETCIO reported Arora’s account 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. That is a reported company example, not a general failure rate, but it makes a practical point: a deployed system that its intended users avoid has not transformed the process.
Arora also cited Bosch’s training of 30,000 employees on AI and described HUL as having achieved full workforce AI literacy. ETCIO’s account names Dr. Reddy’s use of dedicated “AI translators” between technical and business teams. These are examples reported by Arora, not independently verified benchmarks for what every organization should replicate.
Sequence deployment by risk and learning
Arora suggests a staged rollout: small, low-risk productivity gains in year one; individual, well-scoped agentic use cases in year two; and multi-agent, complex, cross-functional systems from year three onward. Treat this as a proposed sequence, not a proven timetable. An organization’s readiness, risk and results should determine whether it advances, pauses or changes course.
| Stage | Suggested focus | Decision gate |
|---|---|---|
| Year one | Small, low-risk productivity wins | Can the organization demonstrate a measurable benefit and support the system in use? |
| Year two | Individual, well-scoped agentic use cases | Is the task genuinely unpredictable, with suitable boundaries and human oversight? |
| Year three onward | Multi-agent, complex, cross-functional systems | Are the organization’s data, ownership, governance and adoption foundations strong enough for broader coordination? |
Arora also points to GE’s Predix as a cautionary example of scaling before proving value at smaller scale; the ETCIO account does not support adding further detail to that example. The broader decision is to establish value and operational fit at a manageable scope before expanding complexity.
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