Of late, businesses operating in B2B ecommerce have started to embrace another wave of digital transformation, driven by what has been termed “Agentic AI”. These are, essentially, AI systems endowed with autonomy, i.e. that don’t simply respond to fixed prompts or follow rigid rules, but can set goals, make decisions, adapt to changing contexts, and basically get things done with minimal human intervention.
The rise of Agentic AI is starting to reshape how B2B e-commerce operates: from procurement, catalogue management and pricing to customer service, supply chains, and even marketing. We’re going to take a closer look at how and why it’s growing, what it’s being used for, what the risks and barriers are (and there are currently a fair few of these), and what we might realistically expect in the near future.
What is Agentic AI?
Hitherto, AI in commerce (and elsewhere) has often involved fairly deterministic automation, such as rule-based systems, chatbots with ‘canned’ scripts, or analytics that potentially flag issues but require human intervention to resolve. Agentic AI goes further to include:
- Establishing goals and planning: Agentic AI can take a high-level objective (for instance, “optimise inventory turnover”) and break it down into subtasks
- Autonomy: making decisions without needing explicit instructions for every step, monitoring, adjusting, and adapting
- Learning and memory: repeating over time, utilising feedback, retaining knowledge of past interactions, and improving performance
- Integration with third-party systems: interacting with APIs, enterprise systems, data sources (ERP, CRM, back-office systems) to affect human-like behaviour
This all allow Agentic AI to handle tasks that are an ever-moving feast, have a number of stages, or where conditions change (e.g. an interruption to supplies, a change in demand). In short, areas where simpler automation may well fail or require frequent tinkering. And adjustments from human beings.
Why Now? What’s Pushing Agentic AI in B2B
Several strands coming together are driving B2B ecommerce towards adopting Agentic AI:
- Complexity and scale: B2B commerce often involves large catalogues, has multiple stakeholders (such as buyers, suppliers, 3PLs, and so on), custom pricing (which is sometimes extremely complicated and even esoteric), contracts, and complex dependent tasks. Manual or semi-automated systems really can struggle to keep up. Agentic AI appears to unlock a way to reduce manual burden.
- The availability of enabling technologies: Recent strides in Large Language Models (LLMs), reinforcement learning, multi-agent orchestration, robust API ecosystems, real-time data streaming, and cloud computing – all make Agentic AI more feasible and practical.
- The demand for responsiveness and agility: Whether it’s disruptions in supply chains, sharp shifts in demand, or the need to personalise offers across a range of buyer groups, the ability to act quickly confers some real competitive advantage. Agentic AI systems help detect anomalies (for example, inventory shortages), adjust pricing or provide delivery options, or re-route orders with minimal lag.
- Cost pressures and efficiency demands: staff costs, logistics costs, inventory carrying costs are all more than ever. Automating the decision-making process (where possible and safe to do so) helps reduce overheads and derisks error, while freeing human teams to focus on strategy, relationship building, and negotiation.
- Customer expectations: Buyers (particularly in the world of B2B) increasingly expect digital experiences that are akin to B2C ecommerce. They want the site they’re using to be quick, transparent, customisable, and with up-to-date information. Agentic AI offers a way to help with personalised offers, management of contract compliance, and anticipation of renewal needs.
Current Examples in B2B eCommerce
Whist still in their infancy there are multiple promising use-cases where agentic AI is already being piloted or deployed in B2B ecommerce.
- Procurement agents / purchasing bots
Some companies are exploring agents that automatically reorder supplies based on stock levels, demand forecasts, or usage trends. These agents negotiate with preferred suppliers, compare pricing, and even schedule delivery, sometimes initiating procurement without human intervention for standard or routine orders. This cuts time and reduces errors. Use of such agents depends on good integration with inventory and/or ERP systems - Dynamic pricing & contract compliance
For those companies with complex pricing structures, volume discounts, and contracts, Agentic AI will monitor competitor pricing, supply and demand, and adjust offers in real-time (albeit within contract rules). Additionally, helping to ensure compliance with trade agreements, margin thresholds etc., can be enforced via AI agents - Personalised customer experience and sales
Agentic AI is powerful. Insights it gains from buyer behaviour, interpreting usage data, or account history may be used by agents to suggest cross-sells, upsells, or renewal prompts. For example, an agent might monitor key account details and detect that usage is dropping off or that a competitor product is muscling in, and prompt intervention or an adjustment in pricing. Agents might also streamline multi-touchpoint buyer journeys - Supply chain and logistics optimisation
Agentic systems are being used (or forecast to be used) in supply chain management (SCM) to adapt to disruptions, optimise routes, manage inventory across multiple nodes, and trigger responses (e.g. reorder, reroute) when conditions change. Gartner predicts that by 2030, 50% of cross-functional SCM solutions will incorporate agentic AI capabilities - Customer support & self-service
Agents that autonomously respond to enquiries, resolve routine issues (such as returns, order tracking), escalate only when necessary, and even anticipate issues (e.g. stock delays) to proactively communicate with buyers. This reduces support burden, improves consistency, and may improve buyer satisfaction - Operational decision-making and insights
Agentic AI is always on. As it is continuously monitoring key performance indicators (KPIs), it can forecast demand, detect anomalies, and recommend or implement changes. For example, adjusting inventory safety stock levels, flagging supplier risk, or recommending alternative shipping routes
Risks, Challenges and Barriers of Agentic AI
There’s a lot of promise surrounding agentic AI but there are a bunch of challenges, especially in B2B environments, which are typically higher stakes. Some of the main barriers:
- Data quality, integration and silos
Like humans, Agentic AI depends heavily on accurate, up-to-date data. But for AI it has to be machine-readable, too. In many B2B companies, relevant data is housed across a lot of disparate systems (ERP, CRM, legacy platforms, etc.), is inconsistent, incomplete, or badly maintained. And like humans, without reliable data, autonomous agents will make poor decisions. - Trust, transparency and accountability
Businesses, both buyers and sellers, are inevitably and quite rightly cautious. If an agent makes a wrong decision (such as getting the wrong price or supplier delivery date), the consequences may be significant and potentially disastrous. Trust in being able to audit decisions, understand the rationale behind exactly why something was chosen, and set guardrails is crucial. - Liability, legal, and ethical concerns
Most businesses don’t tend to operate a blame culture, but who is responsible when an automated decision causes loss or breach of contract? If an agent buys outside the agreed guardrails or misrepresents product information, contracts may not yet clearly assign responsibility. Also, issues of bias, fairness (e.g. favouring certain suppliers over others), regulatory compliance (data protection, trade, export controls, etc.) are pretty serious! - Security and fraud risk
Agents, particularly ones that are empowered to affect finances, contracting, or external interfaces, are potential back doors for malicious parties. Identity theft, adversarial inputs, compromised credentials, or badly implemented access permissions could let agents be manipulated in cybercrime, a very real and present threat. - Technological maturity and cost
Whilst the AI technology improves at a rate of knots, in many cases, agentic systems still struggle with the weird edge case scenarios, the unpredictable, or long-term planning. Maintaining, training, tuning, and monitoring these systems requires will require substantial investment both in financial and in skilled personnel time to manage them. - Change management and organisational readiness
Using Agentic AI isn’t going to be only a technical project. It’s going to involve some widespread process reengineering, people skills, and profound shifts in company culture. Employees need to trust the agents, know when a human override is necessary, and understand how decisions are made. There is going to be resistance, especially where human decision-making has been central. People are people, let’s face it. There will also need to be governance, oversight, and audit trails built in organisationally.
How to Use Agentic AI Successfully
For B2B businesses wanting to make the most of the potential opportunity, there are some key things that need to be defined first:
- Do the groundwork
Ensuring clean, standardised, accessible data across your product catalogues, contracts, inventory, and pricing. Without this, autonomy is unstable and probably unfeasible. - Define your scope and guardrails clearly
Decisions will need to be made as to which tasks will agents perform autonomously, under what constraints, and when a human needs to get involved. This is particularly true for high-value, high-risk decisions so you’re going to need to do this gradually. - Make sure there’s an audit trail
It’s important that you’re logging and auditing agent decisions, with the ability to play back how a pricing decision was made, or why a supplier was chosen. Establishing exactly when to override will be an important element. - Security and regulatory compliance
You’re going to need strong authentication, along with role-based access, encryption, secure APIs to make sure your security protocols are as robust as they can be. And compliance with relevant laws (data protection, commercial law, export controls, anti-corruption, etc.) will need to be watertight. On the legal side, it’s important to contractually define liability so you know where you stand. - Invest in talent, change management, and governance
Moving to an Agentic AI model is going to be a huge jolt to the system. You’ll need to make sure you have the right team in place. You’ll need to make sure you involve legal, compliance, operations, supply chain experts, sales and marketing. And you will also need to train staff to work with, supervise, and trust agentic systems. - Test and iterate
Don’t go in all guns blazing. Start with lower-risk scenarios (like routine procurement or routine customer support) to test performance, build confidence, monitor impact (such as error rates, cost savings or customer satisfaction), and then refine.
The Near and Medium-Term Outlook
If we look at what could be in place over the next few years, several forecasts suggest Agentic AI will be deeply embedded in B2B ecommerce.
- Gartner predicts that 50% of supply chain management solutions will include agentic AI capabilities by 2030
- Many of the larger software firms are incorporating agentic features (autonomous agents, agents that reason, adapt, etc.) into their roadmaps
- More B2B companies will be using autonomous agents in procurement, supplier management, finance (especially in laborious tasks such as invoice matching, reconciliation), but more sophisticated ones too, such as contract renewal and pricing
- Agentic commerce (where purchasing decisions, transactions or parts of them are handled by AI agents) might well begin to reduce friction in buyer-seller interactions—e.g. automated contract fulfilment, compliance checks, and payments.
But let’s be honest, it’s not going to be seamless. Those barriers we explored earlier will mean widespread adoption will be uneven, and many companies will certainly be at the back of the queue. ROI will be most impressive where:
- Data is already relatively clean and accessible
- Systems are modern or can be modernised (e.g. good ERP/CRM/APIs, etc.)
- Where management will take a risk and be willing to try pilot programmes
- The business area they operate in has complexity that lends itself to automation (e.g. logistics and supply chains, and with large or recurring orders)
Final Word
Agentic AI really does offer an exciting evolution in B2B ecommerce. Using systems that act, learn and adapt independently, businesses will get improved efficiency, better decision-making, faster responses to change, and more personalised buyer journeys. That said, the risks are very real—as are the organisational, technical and ethical challenges. For B2B ecommerce players who approach Agentic AI with the right combination of a sense of realism, strong data foundations, clear guardrails, human oversight and governance (from a team that has bought into what you’re setting out to achieve), the rewards may well be transformational.
The rise of agentic AI isn’t just another white elephant. It’s fast becoming a strategic necessity. Those that succeed will, in all likelihood, redefine what competitive advantage looks like in ecommerce. And that means not just having the best products or lowest margins, but those who can conduct autonomous intelligence throughout their operations, while keeping trust, transparency, and control. A prize worth striving for.