Jim Rowan, US Head of AI at Deloitte – Interview Series

Jim Rowan is US Head of AI at Deloitte and a principal in Deloitte Consulting LLP, helping organizations translate AI ambitions into business outcomes. His career spans enterprise technology, analytics, data, and digital transformation, with experience across life sciences, health care, and telecommunications. He has held leadership roles in Deloitte’s Strategy & Analytics and AI & Data practices, and his work includes developing the talent and capabilities organizations need to adopt AI.

Deloitte is a professional services organization providing consulting, audit and assurance, tax, and advisory services. Its AI work brings together strategy, data, technology, talent, and governance to help businesses adopt and scale AI across their operations. In digital commerce, that work includes preparing product information and enterprise systems for AI agents, connecting customer experiences across channels, and establishing the identity, authorization, and trust controls needed for automated interactions and transactions.

You’ve spent more than two decades at Deloitte working across enterprise technology, analytics, data, and now AI. From that vantage point, when did it become clear to you that AI agents would not only transform how companies operate internally, but also become an intermediary between businesses and their customers?

Over the past few years, I’ve seen AI advance firsthand while helping organizations translate business goals into measurable outcomes that once seemed unimaginable. My background in data, processes and technology has been foundational to my understanding of AI. I started building Deloitte’s capabilities focused on the talent needed to be successful with AI, such as our AI Academy that creates curriculum for both our employees and clients.

In the current landscape, organizations are using AI agents to transform their digital commerce strategies in multiple ways: increasing brand visibility, using personalization engines to enhance conversation, and orchestrating consistent omnichannel experiences across physical, digital and social touchpoints. This inflection point comes at a time when brands are navigating a volatile market, creating an opportunity for agentic commerce to help them respond with greater speed, relevance and consistency for their customers.

You’ve discussed the need for companies to prepare for “Agentic Search Optimization.” How do you define that concept, and how is optimizing for an AI agent fundamentally different from traditional SEO or the newer approaches to optimizing content for AI answer engines?

Traditionally, online competition for consumer attention has been defined by Search Engine Optimization (SEO) and assumes that at the other end of a query, a human is reading, evaluating and deciding their purchase. As consumers shift towards using AI-powered tools to conduct research, Generative Engine Optimization (GEO) has been introduced. Agentic Search Optimization is a progressive iteration of GEO that considers how AI no longer just recommends products or services but acts on those purchasing decisions as well. That shift means brands must optimize not only for generative AI, which primarily provides recommendations to the end user, but also for agentic AI, which is built for execution and can coordinate systems, execute workflows and trigger transactions.

When an AI agent is asked to research, compare, and shortlist products or services, what signals do you expect it to value most? Will structured product data, pricing, reviews, third-party coverage, brand authority, or some entirely new set of signals become more important?

An AI agent’s consideration set is developed based on visibility and relevancy and will show preference to brands whose products, services, pricing and data are accessible, machine-readable and instantly verifiable. Consistency across platforms, ratings/reviews and brand authority still matter, but these signals must also translate into data an agent can parse through. Personal context will matter too as agents working on customers’ behalf will need reliable access to that person’s preferences, permissions and prior interactions to generate the most relevant recommendations. Aside from the signals themselves, the speed at which organizations can act on making their data readable, understandable, trustworthy and quantifiable in terms an agent can compare, will be a differentiator for the brand.

One challenge is that there may eventually be thousands of consumer and enterprise agents built on different models and connected to different data sources. How can a company remain accurately represented and discoverable across that fragmented ecosystem without trying to optimize for every individual agent?

Organizations focus should be on creating a trusted, machine-readable foundation that any agent can access and interpret rather than optimizing for individual agents. This means publishing verifiable, reliable, current information about products, pricing and policies through authoritative sources. Discoverability alone likely won’t be enough, as agents will begin acting on consumers’ behalf, furthering the need for systems that allow identity verification, eligibility confirmation and secure transactions. Across a now fragmented agent ecosystem, the brand that makes their information the easiest to understand and obtain will rise to the top of search. However, companies should avoid building around a single protocol because these are still emerging and it’s not yet clear which will become dominant thus the importance of designing for interoperability and the ability to adopt or switch protocols quickly as the ecosystem evolves.

If commerce evolves from business-to-consumer toward business-to-agent-to-business, what changes at the technical level? Will companies need to rethink websites, APIs, product catalogs, identity systems, or other infrastructure so that agents can interact with them directly?

Yes, organizations will need to rethink and reimagine their current systems to build the right technical architecture capable of achieving B2B agentic commerce. It won’t happen overnight; it will take time due to the level of work and degree of change involved. Today, discovery and payment capabilities are advancing faster than the decision-making stage where businesses are still developing the confidence to let agents make choices within established guardrails. The focus for any CIO, CTO or CISO should be delivering scalable, low-latency integration platforms supported by strong technology and integrator partnerships. Rather than a complete system overhaul, leaders should spend time determining which systems can connect to agents, identifying ones that are blocked and testing simple tasks in contained environments. Preservation of humans owning the transaction is important too so companies should create flexible paths in which the customer can move easily between agent-led and person-led interactions rather than weakening the human experience in the pursuit of automation. Over time, organizations can progress to building a platform that manages multiple agents, upgrading or connecting legacy systems for agent access, and deploying advanced security with real-time monitoring.

Traditional digital marketing has been heavily optimized around rankings, impressions, clicks, and website traffic. If agents increasingly conduct the research before a person ever visits a website, what new metrics should companies use to understand whether they are actually visible and competitive in the agentic buyer journey?

Historically, measurement of success focused on capturing consumers’ attention and ultimately their trust. As agents assume more of the buying journey, organizations will need new ways to measure whether their brands are visible, relevant and ultimately selected. This includes linking branded agent development to performance measurement, scoring whether an agent surfaces the brand, recognizing eligible offers and successfully completing a transaction. Brands should focus on their buyers vs. their competitors to determine investments or experience changes, tracking how customers discover products, where they leave or where they stay. These metrics can not only help brands differentiate their value propositions but can also prove ROI with quantifiable outcomes that reinforce customer loyalty and operational excellence.

AI agents can summarize a company, compare it with competitors, and potentially make recommendations without the company having direct control over how it is represented. How should brands monitor for inaccurate or outdated information and establish an authoritative source of truth that agents can reliably use?

Like any other industry, auditing product data is critical to maintaining agentic discoverability across channels. Brands should also consider Model Context Protocol (MCP) as a key enabler, which creates a controlled environment where data, tools, and context are shared securely with external shopping agents and platforms. Combining structured data and MCP can enable agent-to-agent interactions, improve interoperability and help represent brand information accurately and ensure it is prioritized appropriately. Again, because protocols are still emerging and it is not yet clear which will become most popular, companies should build for flexibility so they can connect to, switch between and adopt new standards quickly as the ecosystem evolves.

As agents move from simply recommending products toward taking actions such as requesting quotes, negotiating terms, or making purchases, how important will identity, authorization, and trust infrastructure become? How does a business distinguish a legitimate customer agent from an automated system it should not trust?

Agent-to-agent commerce presents new risks because transactions can happen quickly without human involvement; therefore, making human intervention and migration throughout the process critical. Agentic commerce transactions require new types of fraud prevention measures, strategic financial controls and targeted reporting frameworks built specifically for machine-initiated activity.

In the near term, organizations should treat their AI agents like employees, with defined responsibilities, managed access, and clear accountability. They will also need protocols to verify legitimate agents, authorize payments and enforce transaction thresholds, API permissions and other operational guardrails.

There is also the possibility that advertising and paid placement will follow consumers into the agentic ecosystem. How do you see commercial incentives evolving when an AI agent is recommending products on someone’s behalf, and what transparency will be necessary for people to trust those recommendations?

We already see capital being reallocated to agentic commerce, with U.S. advertisers projected to spend about $26 billion on AI search ads by 2029. It’s inevitable that commercial incentives will need to evolve as enterprises decide whether promotions and pricing offered through owned channels are honored in agentic environments. Part of this evolution requires explicit rules for how agents interact with pricing and promotions. Without guardrails in place, agents could execute purchases based on pricing available at the moment of transaction, potentially triggering unintended promotion and pricing tiers and creating margin leakage. Enterprises should implement policies that align with global privacy and data standards, and align with human-controlled, preapproved parameters for each customer.

Despite increasing automation, you’ve emphasized that human judgment will remain central to purchasing decisions. Where do you see the boundary forming between decisions consumers will comfortably delegate to agents and decisions where people will still want to research, compare, and make the final call themselves?

The boundary will depend on the stakes of the decision and the consumer’s confidence in the agent. People may be comfortable delegating routine, low-cost purchases when their preferences are clear and the consequences of a poor choice are small. For decisions involving significant spending, sensitive information or contractual commitments, they will likely expect greater visibility and retain final approval. Consumers will grow more comfortable with a brand once friction is reduced, so making it easy to understand what’s happening and trust that the transaction will be completed as intended, and safely. Brands should therefore give consumers clear control over what an agent can do independently, when it must ask for permission and how easily its actions can be reviewed or reversed.

Thank you for the great interview, readers who wish to explore agentic commerce, AI-driven discoverability, and the trust infrastructure behind automated purchasing should visit Deloitte.