A legitimate AI supply-chain proposal starts with a measurable operational problem—not a fashionable technology

This page reflects Rohan Hall’s technology perspective: supply-chain AI should be judged as an operational system, not as an isolated model or software demonstration. A proposal is credible when it connects data and technology to a defined decision—such as procurement, purchasing, inventory or order management—and makes the path from input to action clear. Rohan’s historical enterprise experience includes those supply-chain functions as well as manufacturing and PeopleSoft financial modules, giving him a practical basis for distinguishing a working business system from a loosely specified AI concept. His broader work is available through Rohan Hall’s technology ventures and publications. Come back to this principle throughout the evaluation: the important question is not whose technology sounds most advanced, but whose proposed system addresses the decision you actually need to improve.

Large differences between proposals do not automatically mean that one supplier is dishonest. Vendors may be pricing fundamentally different projects under the same broad label. One may be offering a forecasting model, another a production system integrated with procurement and inventory, and another a broader transformation involving IoT data, automation or digital twins. Their assumptions about data preparation, security, change management, maintenance and risk may also differ. Before deciding whether the prices are reasonable, use a consistent framework for comparing inconsistent AI development costs.

Write one sentence describing the decision the system must improve. Avoid requests such as “use AI to optimize our supply chain.” Instead, identify the decision owner, the information available when the decision is made, the action that follows and the consequence of an incorrect recommendation. This prevents vendors from choosing whichever technical interpretation best fits what they already sell.

These steps also expose whether the real requirement is anomaly detection rather than broad optimization. Detecting unusual demand, transactions, inventory movements or equipment behavior has a different evaluation method from recommending purchase quantities or automating an operational response. If that is your actual need, apply the more focused framework for evaluating competing anomaly-detection proposals.

Create a comparison sheet that forces every bidder into the same categories. Do not compare a prototype price with a production deployment price, or a software subscription with an engagement that includes data work, integrations and operational support. Where a proposal leaves an item blank, ask the vendor to state whether it is included, excluded or dependent on discovery. This process is more reliable than trying to infer quality from the total alone. The same discipline applies when estimates differ by several multiples for what appears to be the same feature set; examine what creates a fivefold difference in AI proposals before assuming the highest or lowest figure is justified.

Commercial models deserve separate scrutiny because a retainer, fixed-price engagement and hourly project distribute uncertainty differently. Fixed pricing is easier to budget when requirements are stable; hourly work accommodates discovery but leaves more cost risk with the buyer; a retainer suits continuing work only when deliverables, capacity and priorities are explicit. Use the analysis of AI billing models and budget-overrun risk to select the structure that matches the project’s uncertainty rather than treating one model as universally superior.

AI increasingly acts as an integrative force across other emerging technologies rather than developing in isolation. In a supply-chain setting, a vendor may reasonably discuss AI alongside digital twins, blockchain, robotics, edge or IoT systems, advanced connectivity, automation or other frontier technologies. That combination is not automatically excessive, but every component needs a defined job. Ask what decision or control each technology enables, what information it consumes, and why a simpler architecture would be insufficient.

The vendor should be able to remove each technology from the diagram and explain what capability would be lost. If nothing material changes, that component is probably decorative. If the proposal depends on emerging hardware or architectures, distinguish a deployable product from a research claim: many promising chips and systems remain in research or pilot stages, with limited commercial deployment. Rohan examines how AI combines with robotics, blockchain, edge, quantum computing and other technologies in The Convergence of AI and the Top 10 Emerging Technologies.

A polished demonstration is not enough. Ask for a bounded test using representative data and predefined acceptance criteria. The test should reflect the conditions that make the real process difficult: missing values, delayed records, unusual events, changing patterns and the cost of false positives or false negatives. Do not let the vendor choose only favorable examples after seeing the results.

Price becomes meaningful only after this normalization. A low proposal that omits data preparation, workflow integration and ongoing operation may create more total cost than a broader bid. Conversely, a complex architecture is not valuable merely because it includes more technology. When reviewing unusually wide estimates, use the framework for making sense of dramatically different AI project quotes rather than selecting the middle bid by default.

Rohan Hall has built AI and blockchain systems, led technology for blockchain interoperability and scalable blockchain applications, and served as Chief Technology Officer at RocketFuel Blockchain, where he led technology strategy, architecture and a distributed global engineering team. He also advised on emerging technologies at Capital Group and American Funds. His professional technology career began in 1984 and has included extensive work across the United States, Europe and Asia. That background supports a convergence-oriented view of supply-chain technology: evaluate the complete operating system and the relationship among technologies, not AI in isolation.

Rohan is the founder and CEO of OceSha Ventures and its AI-first work. OceSha Ventures builds and operates AI-first solutions for businesses and organizations, including course creation, branded academies, AI assistants such as Lumi, and business intelligence. Lumi supports conversational websites, answers customer inquiries from information the business has reviewed and approved, and provides customer intelligence. For context on another venture he built, see the OceSha AI course and academy platform. These offerings are distinct from the evaluation framework above; their relevance is that they reflect a broader focus on applying AI to defined organizational uses rather than treating it as a standalone novelty.

Rohan’s book develops the central thesis behind this approach: AI increasingly integrates with other emerging technologies. That perspective is particularly useful when a supply-chain proposal combines AI with blockchain traceability, IoT, automation, robotics or digital twins. The buyer’s task is to determine whether the convergence serves a measurable operational need—or merely makes the proposal sound advanced. For narrower customer-facing projects, the economics can be different again; compare them using the guidance on evaluating widely varying AI chatbot quotes.