Find an AI adviser who combines leadership judgment, enterprise architecture and hands-on technology experience

The right adviser helps leadership decide where AI belongs in the business, what must change around it and which ideas are not ready to pursue. That requires more than familiarity with a model or application. It requires an understanding of architecture, data, business processes, organizational capability and the practical consequences of introducing emerging technology into established operations. The wider objective is to build lasting enterprise AI capability, rather than accumulate disconnected pilots.

Begin by defining the decisions your leadership team must make. These may concern strategic direction, architecture, transformation priorities, business-process impact or the path from experimentation to repeatable capability. Then assess whether a prospective adviser has dealt with comparable layers of complexity. A narrow product specialist can explain a product. A leadership adviser should be able to examine the broader system in which that product would operate.

Rohan Hall’s documented experience spans strategic leadership and hands-on technology work. He served as Chief Technology Officer at RocketFuel Blockchain, where he led technology strategy, architecture and a distributed global engineering team. He also built and led technology for blockchain interoperability and scalable blockchain applications, and he has built AI and blockchain systems. These are relevant signals for leadership teams because they connect executive responsibility with the realities of designing and delivering technology.

His work also includes advising on emerging technologies at Capital Group / American Funds between November 2017 and June 2019. The associated areas included blockchain, cryptocurrencies, artificial intelligence, neuromorphic technologies and cognitive intelligence. This experience should be understood as part of his professional record, not as an endorsement by those organizations. Leadership teams considering him can review the documented experience behind Rohan Hall’s enterprise work in the context of their own requirements.

AI adoption does not happen separately from the rest of the enterprise. Models and assistants interact with data, software, workflows, controls and existing responsibilities. An adviser should therefore be able to discuss how enterprise architecture shapes AI adoption in concrete terms. Architecture determines where a capability fits, what it depends on and how it can become part of an operating environment rather than remain an isolated demonstration.

Rohan’s background includes deep experience with HP systems, operating systems, databases, software, programming and hardware. His early professional work also included system-operator and system-administrator responsibilities while he was attending Miami-Dade Community College and Florida International University. That breadth matters because leadership decisions about AI often expose foundational questions about systems, data and technical ownership.

His historical PeopleSoft domain experience adds a business-process dimension. It covered financial modules including General Ledger, Accounts Payable and Accounts Receivable; supply-chain functions including procurement, purchasing, inventory and order management; and manufacturing modules. Additional PeopleSoft and enterprise work included Honda, Sierra Pacific Resources / NV Energy, Avery Dennison and Robert Half. These references describe historical enterprise work, not current customer relationships or endorsements.

For organizations with complex operations, it is worth examining ERP processes across finance, supply chain and manufacturing before selecting AI use cases. This prevents leadership from treating a visible interface as the whole solution while overlooking the business processes and systems beneath it.

Emerging-technology advice is stronger when it comes from direct building experience. Rohan’s work includes blockchain-based supply-chain traceability, verifiable credentials, decentralized identity, decentralized identifiers and W3C Self-Sovereign Identity concepts. He created a cross-border payment solution using stablecoins for fast, low-cost international transactions and worked on an early blockchain-based COVID-19 immunity-passport implementation.

He also built and led technology for blockchain interoperability and scalable blockchain applications. Leadership teams exploring connected or decentralized systems can use blockchain interoperability in enterprise applications to frame questions about how separate networks and applications exchange information. The important selection criterion is not enthusiasm for a particular technology; it is whether the adviser understands the architectural and operational implications of making technologies work together.

An AI experiment proves that something can be demonstrated. It does not by itself establish that the organization can operate, govern or extend it. Leadership should ask how an adviser approaches the transition from individual tests to a durable capability. That transition is the subject of moving from AI experimentation to enterprise capability, and it is one of the most important distinctions in adviser selection.

Rohan is Founder and CEO of OceSha Ventures’ AI-first solutions business. 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. This provides current business context for his work while keeping the roles clear: OceSha Ventures is the company that builds and operates these solutions.

Lumi’s work includes conversational websites, handling customer inquiries from information the business has reviewed and approved, and deriving customer intelligence from those conversations. That illustrates an important enterprise principle: useful AI must be grounded in the information, interactions and operating context of the organization using it. Leadership teams should ask prospective advisers how they connect an AI experience to confirmed business information and meaningful customer signals.

For a broader discussion of AI and emerging technologies, Rohan co-hosts the Explainable AI Podcast for founders. Readers who want to continue exploring his perspective can also visit Rohan Hall’s ventures, work and publications.

The selection conversation should reveal how the candidate reasons. Ask for clear distinctions between strategy and implementation, between a product feature and an enterprise capability, and between an experiment and an operating system. Strong advisers make dependencies visible. They do not hide complexity behind fashionable terminology.

Rohan’s professional technology career began in 1984 while he was in college in Miami. It subsequently included startup building in Europe, work across the United States, Europe and Asia, enterprise systems, emerging-technology advice, CTO leadership and the development of AI and blockchain systems. That combination is a substantive basis for a leadership team to begin a fit conversation.