AI Agents and the Economics of Growth

The next stage of AI adoption gives software a more active role in how businesses operate and compete. Agents can work across tasks and systems to pursue a goal, bringing them into decisions that shape costs, customer relationships, and revenue.

This month we look at the State of AI Agents: where agents are producing measurable results, how a company stays in consideration when a customer’s shortlist is drawn up by an agent, and what it takes to establish who an agent represents once they work across organizations.

Join us on Thursday, 19 November at 3:00 PM CT / 4:00 PM ET.

For business operators, this creates an opportunity to handle more work with existing teams while adapting to a buying process increasingly influenced by agents. Capturing that opportunity requires company knowledge agents can use, an offer they can find and understand, and clear authority to act.

We see growing value in the systems that make this possible, from digital maps of a company’s operations to infrastructure that verifies permissions and executes agreements across organizations. Our view is that the companies connecting these foundations to specific business needs will be best positioned to turn agents into greater capacity, stronger margins, and growth.

In McKinsey’s August 2026 survey, 40% of respondents at companies with more than $1 billion in annual revenue reported scaling AI agents in at least one business function. Among smaller companies, the figure was 22%.

The business case comes down to leverage: increasing what a company’s people can get done with the time and resources they have.

Businesses can already buy agents for specific jobs. Fin handles customer service questions. Salesforce’s Agent force offers agents for sales, service, and other business functions. GitHub Copilot can take on coding tasks and prepare changes for a developer to review.

Custom agents can be built around a company’s own processes. A distributor could build an agent that checks inventory, applies customer pricing, confirms delivery dates, and prepares a quote for approval.

To do that well, the agent needs to understand how those pieces connect. A customer’s contract may set their price. An inventory shortage may change the delivery date. A substitution may require approval. Experienced employees understand these relationships, often through years on the job.

A digital map of the business, called an ontology, connects company knowledge, systems, and rules. Our product, Orbis, builds this shared context so agents can make better decisions and handle more complex work while employees can more easily find information and understand how it connects across the business.

That shared knowledge can support work across the company. Sales can prepare quotes faster. Service can investigate problems with a fuller account history. Fewer tasks have to wait for someone to gather information or explain how a process works.

Done well, agents help a business handle more volume and make its people more productive. The return depends on how much work agents complete correctly and the cost of running and supervising them. An agent creates leverage when it frees up more capacity than it consumes.

Consumer agents such as Meta’s Muse help people research products, compare options, and make purchases with their approval. As customers delegate more of that work, businesses need to consider how agents find and evaluate what they sell.

This already carries significant weight in B2B. 6sense’s 2025 Buyer Experience Study found that the winning vendor was on the buyer’s day-one shortlist 95% of the time. As agents support more buying research, we expect their recommendations to help shape those shortlists.

HUMAN Security’s August 2026 data shows where that activity is concentrated: 80% of observed agentic traffic went to product and search pages, compared with 2.1% to checkout and payment pages. These figures measure traffic shares, rather than purchase conversion rates. They suggest agents’ current role is largely in gathering information that can inform buying decisions.

Businesses need to make answer engine optimization (AEO) and GTM engineering part of how they compete for customers. AEO helps AI systems find and understand their offers through clear answers to buyer questions, accurate product information, and credible evidence across websites and trusted sources. GTM engineering combines target account research, buying signals, outreach, landing pages, and follow-up to reach relevant buyers and turn interest into sales conversations.

Together, these efforts help businesses earn consideration earlier and generate qualified pipeline. Companies should track how they appear in relevant AI answers alongside the sales opportunities and pipeline their efforts produce.

As agents take on more responsibility, businesses need ways to verify who they represent, what they are allowed to do, and whether an agreement has been fulfilled. Blockchain can support this through cryptographic verification, shared records, and smart contracts. As agents act at the speed of compute, permissions need to be checked and agreements executed automatically.

In September 2025, I wrote about it in Blockchain’s Role in the AI Era: Programmable Trust for Agentic Systems. We continue to see an opportunity for infrastructure that allows agents to work across companies and borders with rules each party can verify.

That role extends beyond payments. Smart contracts can check permissions, require approvals, and execute transactions when defined conditions are met. Cryptographic signatures help verify who authorized an action, while shared records give participants a history they can audit. Efforts such as ERC-8004 explore how blockchain can support agent identity, reputation, and independent validation of work.

For a business, this could mean allowing an agent to purchase data access within an approved budget, transfer a digital asset after receiving authorization, or release funds when a verified milestone is reached. The value comes from coordinating those actions across organizations with fewer manual checks and less reconciliation.

Established payment networks are also building infrastructure for agents. On October 20, 2026, Visa, BILL, and Billtrust announced a live pilot allowing agents to complete approved invoice payments through supplier portals. It combines agent identification, protected payment credentials, and controls tied to the buyer’s instructions.

We believe AI and blockchain will become increasingly connected as agents work across organizational boundaries. Companies will need infrastructure that lets them delegate useful work, enforce agreed rules, and verify the outcome. Building that foundation will help determine how much responsibility businesses can confidently give their agents.

For operators already using AI, the next decisions are where to expand its role, what needs improvement, and which investments are producing results.

Inside the business, assess where agents complete useful work and where employees still spend time correcting errors, finding information, or managing handoffs. Better access to company knowledge and systems may improve performance. Expand responsibility where accuracy, cost, and oversight support it.

On the customer side, assess whether your growth strategy reflects how buyers now research and compare providers. AEO and GTM engineering should help your company earn consideration, reach relevant accounts, and generate qualified pipeline.

As agents take on more work across systems and companies, permissions, approvals, and records need to keep pace. Operators need to know what agents can do, when people must intervene, and how results are verified.

We see the next advantage coming from how well companies connect these efforts. Turning AI use into greater capacity, stronger margins, and consistent growth takes company knowledge, coordinated execution, and clear accountability. Those are the foundations we would strengthen as adoption expands.

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