- AI agents are becoming the front door to B2B purchasing, influencing vendor shortlists, negotiations, autonomous decisions, and payment authorization, which changes traditional sales models, commercial terms, pricing strategies, and buyer-seller interactions.
- As AI agents mediate discovery, procurement, and access to buyers, bargaining power shifts toward agent operators through ranking, approved vendor policies, fees, and preferred terms, creating new pressures on seller economics and margins.
- Lessons from the dot-com era point to AI agents emerging as powerful intermediaries, driving reintermediation, shaping market access, accumulating influence over participation rules, and making trust a competitive differentiator.
- B2B organizational improvements include adapting by building botcentric capabilities, rethinking pricing and contracts, updating sales channels, strengthening auditability and explainability, and preparing for transactions involving both human buyers and AI agents.
- Emerging use cases in procurement, legal research, financial services, and botassisted sourcing indicate that AI agents are already influencing vendor evaluation, purchasing workflows, product discovery, and commercial decision-making across multiple industries.
In more and more B2B transactions, sellers are learning that their buyer may be sitting behind an AI agent. Human buyers are turning to and empowering AI agents to create seller shortlists, make autonomous decisions, and authorize automatic payments. The shift from human to autonomous agent rewrites traditional commercial terms, pricing, and negotiation tactics that can affect a seller’s bottom line. As executives figure out an internal AI strategy, they also need to address how external AI agents will impact their sales model.
How can B2B sellers operate in an agent-led buying process?
For a B2C look at how this shift translates into a phased crawl–walk–run operating model, see A Retailer’s Guide to Leading in the New Agent Economy.
Selling to bots is often framed as a shift from search engine optimization (SEO) to generative engine optimization (GEO). In B2B markets, GEO may help a seller get noticed but not necessarily chosen. B2B deals rarely come down to a single query or buyer; they run through many buying groups, negotiated contracts, approvals, and system integrations. When discovered by a bot, a seller’s product must still meet procurement requirements, reconcile against approved vendor lists, and plug into the buyer’s existing environment. Winning the sale takes more than just being machine-readable, but it does start with being visible.
Four Forces Reshaping B2B Seller Economics
Agent influence is changing the power dynamics within buyer leverage, payment authority, brand control, and partner channels. Leaders should watch for these four pressure points as bots mediate more transactions:
Buyer bargaining power increases, pressuring seller economics
Leverage shifts to buyer-side policy owners as AI agents take on more purchasing decisions based on how buyer constraints (approved vendors, thresholds, preferred terms). The negotiation moves upstream into how the buyer configures the agent before the seller is in the room.
WATCH METRIC: Rising share of POs/RFQs initiated by conversational or agent workflows vs. buyer portal clicks
Payment authorization and liability blur when agents transact autonomously
As commerce shifts to agent-led transactions, payment rails and agent identity systems gain power. This change may see traditional payment authorization and buyer consent start to break down. Sellers will need clear answers on agent scope and dispute handling when an agent acts outside its authority.
WATCH METRIC: Chargeback/dispute rates for agent-originated transactions vs. human-originated transactions
The loss of channel ownership and margin compression when the bot controls access
As AI agents mediate buyer relationships, sellers lose direct ownership of the channel that once carried their brand and pricing power. The agent decides how the product is represented, and it may require fees or preferential terms for buyer access. The risk here is margin: whoever controls the path to the buyer captures a share of the economics.
WATCH METRIC: Increase in transactions reaching buyers only through paid or intermediated bots
Traditional partner channels may erode
Intermediary agents gain power because they will control the path to conversion. Partner programs built around referrals and assisted conversions break down when the agent does the summarizing, comparing, and buying. Sellers will need to rethink which partners still earn their margin in an agent-led flow.
WATCH METRIC: Reduction in referrals from partner(s)
Dot-Com Lessons for the Agent Economy
This is not the first time a new intermediary revolutionized the way business gets done. The closest parallel can be seen in the rise of enterprise B2B platforms during the late 1990s dot-com-era. For example, Salesforce was able to experience success convincing business leaders that enterprise software could be delivered inside a browser, while emphasizing access control, data ownership, and security. By sitting between businesses and their customers, digital platforms increased their influence and started setting terms for access, charging for visibility, and owning data that flowed through them.
AI agents are heading down the same path, and several lessons can still apply:
The story 20 years ago was that e-commerce would cut out the middleman. It did lower many transaction costs and opened up direct sales that would not have happened otherwise, but it also created new middlemen – the enterprise software providers. These digital middlemen handle search, payments, trust, and logistics. In turn, AI agents will likely disintermediate (cut out) one layer of today’s market and add a new version of middlemen.
Even if it starts as “just a channel”, once an intermediary reduces buyer effort at scale, it accumulates bargaining power over fees, ranking influence, and policy control. Agents stepping into this same role are expected to set the rules of participation as their market share grows.
Trust in new technologies was essential to early e-commerce and cybermediation. Bot intermediaries can jeopardize buyer trust through a single agent mistake or unauthorized action. Sellers need to stay on the shortlist and get recommended once an agent, not a person, is doing the screening. To earn trust, sellers should be reliable in the ways an agent can verify: accurate machine-readable data, clean transaction history, transparent terms, and a track record of honoring transactions without dispute. Winning means closing the sale when agents are considering multiple competitors. The sellers who win will be the ones the agent and the buyer can trust by default, leading to a successful close.
How to Compete in a Bot-Led Ecosystem
Bots are more than a distribution channel. They are emerging as a platform layer between buyers and sellers. As bots handle more discovery and access for buyers, sellers should operate less like standalone vendors and more like informed participants in an ecosystem where the intermediary sets the rules, ranks competitive options and likely makes a recommendation. By ranking the options, bot agents shift the bargaining power from human negotiators to algorithmic generated recommendations.
Whichever Agent the Buyer Picks Holds the Leverage
How leverage is shaped depends on how the buyer selects the intermediary which could be a single platform (single-homing) or many (multi-homing). If buyers settle on one internal agent while sellers integrate with multiple bots, the agent can become a bottleneck for reaching the buyer. Bargaining power moves to the agent and sellers may need to continually interface with multiple agents to ensure their solutions are represented in the most up-to-date way possible.
Ranking Concentrates Bargaining Power
Bot platforms shape demand through ranking — position materially affects choice. The bot decides who gets seen, and sellers compete for placement rather than for the buyer, making ranking the strongest point of leverage.
Openness vs. Control Tradeoff
Platform owners decide how open or controlled participation, interfaces, and extensions should be. The right level depends on growth goals, innovation needs, and value capture. Too open lets quality slip and makes value harder to hold onto; too closed slows supplier integration and limits coverage.
Know Your Agent (Kya) Extends Existing Governance Frameworks
As bots act as both customers and channel partners, verifying their legitimacy becomes a governance requirement. KYA confirms that an agent is authentic and clear of sanctions or fraud. It is a natural extension of the third-party risk and Know Your Customer (KYC) frameworks companies already run. Integrating KYA into existing risk and compliance governance, rather than treating it as a separate exercise, makes the verification process more scalable.
These dynamics are already reshaping product discovery, pricing, and the customer experience in retail, explore How Bot-to-Bot Commerce is Rewriting the Rules of Retail.
Early Signals From the Market
B2B industries are already adjusting. Music streaming offers the curious early case study with discovery-driven platforms reducing royalty rates in exchange for algorithmic promotion, and catalog owners increasing licensing activity (e.g., movies, commercials, shows, influencers) to boost exposure. Other use cases are showing up across financial, legal, and operational sectors: boutique investment bankers using LLMs for fundamental data analysis without needing to utilize paid data providers, corporate lawyers using deep research for recent 10-K filing examples to help them document new risk factors inside their disclosures, and procurement teams using agents to source supplies from approved vendor lists. Aggregators, paid financial data providers, and legal information vendors are all feeling the competitive pressure of these free alternatives.
Explore five changes drawn from past disruptions, observed buyer behavior, and early experiments for executives to explore:
1) Build a Dedicated Bot-Centric Enterprise Unit
Stand up a dedicated unit with its own product, sales, technology, and operating model built around serving bots as key stakeholders.
When algorithmic trading reshaped equity markets in the early 2000s, financial information vendors faced the same choice: start investing more for machines or keep the focus on human traders. The internal debate over allocating capital to data feeds for algorithms or terminals for people played out for years. Many vendors eventually chose to build dedicated enterprise units with their own product, R&D, sales, technology, and operations model that focused on selling to machines.
Retail is one example. Companies selling directly to consumers who are using LLMs to conduct product research face the greatest immediate pressure to engage with bot capabilities. BDO’s A Retailer’s Guide to Leading in the Agent Economy describes cross-functional teams formed to test bot-to-bot transactions, but the same restructuring logic applies wherever an agent starts sitting between the seller and the buyer.
Product management gets more difficult when the machine is the customer. Before AI and bots, when a person was conducting product research, a product manager could use data exhaust from website logs (e.g., search terms, filtering) and/or feedback from salespeople to observe the behavior. As human buyers conduct product research via a third-party bot, the product manager may have limited-to-no-visibility into buyer behavior. Product managers will need to work with bot operators for access to prompt history and other signals of buyer intent. Expect buyer behavior to keep shifting as buyers, sellers, and bots learn new ways to interact.
2) Rethink Pricing and Contracts for a Mixed Customer Base
Reevaluate pricing and contracts as bots take a seat in the middle of the transaction.
Companies need to prepare for self-cannibalization when end buyers rely on bots instead of buying directly. By tracking enterprise and direct sales separately at the Profit and Loss (P&L) level, organizations can purposely cannibalize themselves to prioritize total customer value rather than protect a single product line. This strategy takes a disciplined approach to commercial terms at the customer level, not the product level.
Sellers will also need to take a clear strategic negotiation stance:
- A fixed-rule approach (same terms for every agent).
- A win-win approach (aim for mutual gain).
- A trade-off approach (balance competing priorities).
- A value-maximizing approach (optimize each deal on its own).
The choice is strategic, not tactical.
Buyers may shift between direct and bot-delivered experiences for years. Customer segmentation needs to account for varying adoption speeds, demand for full bot delivery, occasional vs. heavy use, and hybrid preferences. This customer- segmentation work, typically owned by product and commercial strategy teams, is what keeps commercial models resilient across a mixed customer base.
Bot-delivered product pricing is an ongoing challenge, and the bots are still figuring it out themselves. Enterprise AI assistants are being tested across tiered price points, and major platforms are experimenting with bundling AI services into existing offerings. Sellers should prepare to negotiate around the types of incentives that agents use to evaluate and recommend products or services.
3) Update the Sales Channel
Sales teams will need to sell to both humans and agents, structure bot-enabled deals, negotiate shorter agreements, and operate on new incentive plans.
As salespeople see their audience expand to include AI agents, sellers can acquire new sales skills to effectively negotiate master agreements with end-buyer organizations and structure deals with intermediary bots.
As customer expectations shift, salespeople can combine creativity and financial fluency to build new master agreements and negotiate new commercial models in real time. Agreements should offer shorter coverage terms than the traditional multi-year norm to stay flexible. Salespeople will also need strong relationships with product managers and business unit leaders to consider total customer profitability, because bot-delivered products will likely cannibalize legacy products built for direct buyers. Although it may disrupt sales motion, companies can manage the changing demands by rewriting compensation plans.
A new kind of seller for the intermediary bot is also emerging as part coach and part prompt engineer. Picture a user writing research prompts inside a bot that returns free-tier outputs, such as a stock chart built using free public data. After the initial chart is shared, the bot itself can sell a paid version with more granularity by offering a free trial and routing the lead back to sales. This type of innovation requires a seller who can collaborate with a bot operator to shape how leads are identified and converted.
4) Rebuild Trust Through the AI Agent
Trust and the customer relationship will be rebuilt through the agent.
In B2B relationships, trust is not just personal — it is institutional. As AI agents take on a larger role in evaluating vendors, making recommendations, and facilitating transactions, sellers will need to demonstrate that agent-driven decisions can be understood, explained, and trusted. The principles that matter most are specific and testable:
- Is the agent’s decision auditable (steps are traceable)?
- Is the agent’s decision explainable (is the “why” understood)?
- Does the agent’s decision follow/comply with agreed terms?
Trust follows from a seller’s ability to demonstrate reliability on demand. Buyers and their risk, compliance, and procurement teams will increasingly expect transparency into how recommendations are made, what data was used, and how decisions align with established business rules and contractual terms. In response, organizations may need to strengthen the auditability and explainability of their customer-facing processes, so they can clearly demonstrate how agent-supported decisions are reached. Companies that build confidence in these systems with both human buyers and agents acting on their behalf will be better positioned to maintain trust as buying processes continue to evolve.
5) Choose a Technical Connection Point, and Keep it Reversible
Choose how to technically connect with external bots, whether through an agent, an API, or raw data feeds, and keep the choice reversible while the rules are still being written.
Executives will need to collaborate with IT and sales leaders on how the sellers connect with external buyer bots. The choices include:
- Building a narrowly focused agent that talks to third-party bots.
- Exposing an API.
- Providing raw data feeds.
Using a data maturity assessment can be a good starting point to help identify legacy system limits that could show whether product, pricing, and inventory data are ready for AI systems to interpret or could negatively affect automation.
As the bot landscape continues to evolve, the strengths and limitations of different approaches are still becoming clear. While caution is warranted, customers are already experimenting with agents, so taking a wait-and-see approach carries its own cost. The decision hinges on whether to commit to one approach or test several options in parallel for a defined period before deciding on a long-term strategy.
To keep options open, organizations can consider using contractual language that limits any single integration approach to a fixed period. Pilot programs may be beneficial in exposing higher-than-expected support costs, operating expense irregularities, or cannibalization issues. These findings can prompt companies to switch to a more effective approach, such as moving from raw data feeds to agent-to-agent interactions.
What This Means, and Where to Start
Companies Need to Redesign Their Commercial Model Now.
First adopters will not have the luxury of certainty. However, their advantage is in the opportunity to shape their own commercial model as the rules and expectations are still being defined. BDO is helping leaders work through these decisions in real time. If you are thinking through how agents will change your channel, pricing, or customer relationships, contact us to start a conversation.
Looking to go deeper into how AI agents are reshaping buying, discovery, and commerce models?