Most ‘agentic AI’ isn’t agentic. Enterprise buyers are starting to notice.

Enterprise buyer intelligence for technology vendors

Agentic AI has become one of the fastest-moving claims in enterprise technology. Vendors are adding agents to roadmaps, product pages and sales decks because autonomy sounds like the next logical step beyond generative AI. But senior enterprise buyers are becoming more careful about what the term actually means.

Across recent conversations with technology, security and data leaders, a clear tension emerged. Many systems described as agentic still rely on tightly supervised workflows, pre-defined actions and frequent human approval. That does not make them weak products. It does mean vendors need to be precise about the level of autonomy they are selling.

For vendors, this is becoming a credibility issue. Buyers are starting to look beyond the label and ask what the agent is actually allowed to decide, which actions it can take, what data it can reach, who owns the outcome and what happens when it behaves unexpectedly.

The opportunity is significant. Enterprise leaders are interested in agentic AI. But the vendors most likely to earn serious consideration will be those that can explain the difference between automation and autonomy, show why additional agency creates business value, and prove that the organisation can retain control when the system starts acting for itself.

Our buyer intelligence is informed by ongoing conversations with senior enterprise leaders through roundtables and leadership communities.

The agentic label has moved faster than buyer confidence

The enterprise market is still working out what agentic AI should mean in practice.

Senior leaders discussed autonomy as a spectrum rather than a binary state. At one end sits conventional automation: a system follows defined rules and executes a known workflow. Further along the spectrum are AI-assisted workflows that can interpret context, generate recommendations and trigger approved actions. True agency implies more discretion. The system can decide what to do next, select between available actions and act without waiting for an explicit human instruction at every step.

That distinction matters commercially.

If a vendor describes a supervised workflow as fully agentic, the buyer may initially be impressed by the language but disappointed by the substance. If the vendor describes genuine autonomy without being able to explain the controls around it, the buyer may see unnecessary risk.

The strongest position is therefore not to claim the highest possible level of autonomy. It is to explain exactly where autonomy begins and ends, and why that design is appropriate for the use case.

This is a subtle but important change in the sales conversation. The question is no longer simply, “Do you have agents?” It is becoming, “What decisions does your agent actually make, and why should we let it make them?

Enterprise buyers are testing the boundary between automation and agency

For vendors, one of the biggest risks is assuming that buyers will accept the category definition presented in the sales deck.

Increasingly, they will not.

Enterprise technology teams are becoming more familiar with agent architecture, orchestrated workflows, human-in-the-loop patterns, retrieval systems, approval gates and delegated permissions. Security and risk teams are also paying closer attention because autonomous systems introduce a different failure pattern from conventional software.

A traditional application may do the wrong thing when a user triggers it. An agent may decide to take an action because the system believes that action is the next appropriate step. That difference changes the buyer’s assessment of control.

Vendors therefore need to be able to describe the operating model in plain language. Where does the agent interpret intent? What decisions can it make independently? Which decisions require approval? What happens when confidence is low? Which actions are reversible? Which actions are prohibited completely?

A vague answer makes the product sound less mature, not more sophisticated.

Buyers do not necessarily want maximum autonomy. In many enterprise environments, they want the right amount of autonomy for the level of risk involved.

Why overclaiming agentic capability can weaken the deal

Agentic AI is attractive marketing language because it suggests a step-change in productivity. But overclaiming creates three problems for vendors.

First, it raises the proof threshold. If the product is positioned as autonomous, buyers will expect evidence that it can operate reliably without constant intervention. A demo that still depends on hidden manual steps or tightly scripted scenarios may undermine confidence.

Second, it raises the governance threshold. The more independently a system can act, the more seriously buyers need to consider identity, permissions, auditability, escalation and accountability.

Third, it raises the value threshold. If the buyer can achieve the same outcome through lower-risk automation or a conventional AI assistant, the vendor must explain why agentic capability is worth the additional complexity.

This is why the strongest vendors will avoid treating autonomy as a feature count. Agency should be tied to a specific business outcome. The buyer needs to see that the system can remove a meaningful bottleneck, accelerate a decision, reduce operational effort or coordinate work that would otherwise require multiple manual hand-offs.

If the additional autonomy does not create additional value, the buyer may reasonably ask why it is there at all.

The buying committee is asking different questions

Agentic AI moves the sales conversation beyond the technical buyer.

Security leaders want to understand identity and access. Data leaders want to know what information the agent can reach and under which permissions. Risk and legal teams care about accountability. Business leaders want to know whether the system will improve an outcome rather than simply create a new layer of technology. Procurement needs to understand what is provided directly by the vendor and what depends on underlying models, services and subcontractors.

The result is a wider set of buying questions than many AI vendors are currently prepared to answer.

The real issue is decision rights

The most important agentic AI question may not be how intelligent the model is. It may be what authority the organisation is willing to delegate.

Senior leaders repeatedly returned to the idea that certain decisions should remain human, particularly where the outcome is sensitive, difficult to reverse or carries legal, safety, financial or reputational consequences.

For vendors, this creates a useful way to frame the proposition.

Do not force the buyer into a binary choice between full autonomy and no autonomy. Show how decision rights can be designed around risk.

Low-risk, high-volume actions may be suitable for automated execution. Medium-risk decisions may require review or approval. High-risk actions may remain advisory only. The right architecture allows the buyer to change those thresholds as confidence increases.

This matters because buyers are unlikely to trust an agent simply because it performs well in a controlled environment. They want to understand what happens when the context changes, when data is incomplete, when the model is uncertain or when the agent encounters a situation the designer did not anticipate.

A vendor that can show how authority is constrained, escalated and withdrawn gives the buyer something more valuable than a capability claim. It gives them an operating model they can defend internally.

Accountability does not disappear when the agent acts

One of the strongest themes in the roundtable was accountability.

Enterprise leaders are not assuming that responsibility transfers to the AI system when an autonomous action goes wrong. The organisation still needs a human or business owner who is accountable for what the agent is allowed to do and how it is governed.

This has a direct implication for product design and sales enablement.

Vendors should be able to show how an agent is assigned an owner, how permissions are approved, how actions are logged, how exceptions are handled and how the system is stopped or changed when required.

Several enterprise discussions used an employee-like analogy for agent governance. An employee has an identity, a role, defined responsibilities, access rights, supervision and a process for changing or removing access. Buyers are beginning to expect similar discipline around non-human identities.

The analogy is not perfect, but it is commercially useful. If your product creates agents that can act on behalf of the enterprise, the buyer will increasingly want to know who those agents are, what they are allowed to do and who remains responsible for them.

What vendors should be ready to prove

The agentic AI sales conversation will increasingly reward evidence over vocabulary.

A buyer evaluating an agentic solution is likely to respond better to a clear demonstration of control than to another claim about autonomous intelligence.

Vendors should be ready to show five things early in the buying process.

First, an autonomy map. This should explain which parts of the workflow are deterministic, which use AI judgement, which actions are autonomous and which require human approval.

Second, a permissions model. Buyers need to see how data access and action rights are scoped. Least privilege, named identities and role-based controls are easier to defend than broad access granted for convenience.

Third, an intervention model. The buyer should understand how the system escalates uncertainty, how a human can override it, and how the agent can be paused or disabled.

Fourth, an evidence trail. Important actions should be traceable. Buyers need enough logging and context to reconstruct what happened when an output or action is challenged.

Fifth, a business-value case. The vendor should connect autonomy to an outcome that matters. Reduced handling time, faster resolution, lower manual workload, improved service responsiveness or another measurable business objective is more persuasive than the number of agents deployed.

What can slow the deal

Agentic AI introduces several predictable confidence gaps. Vendors that identify them early can reduce friction later.

Unclear autonomy is one. If the buyer cannot work out whether the product is an assistant, an automated workflow or a genuinely autonomous agent, the sales story becomes harder to trust.

Unclear ownership is another. If the vendor cannot explain who is accountable for the agent’s actions, the buyer inherits an internal governance problem.

Broad or opaque permissions create a third problem. An agent that needs access to large parts of the enterprise environment may trigger deeper security review, particularly if it can write, approve, modify or trigger downstream actions.

Weak failure containment also matters. Buyers need to know whether one incorrect decision remains local or can cascade across connected systems.

Finally, generic ROI claims can weaken the case. Agentic AI is still new enough that many organisations are working through how to measure value. Vendors that rely on broad productivity language may struggle when finance or procurement asks what the additional autonomy actually changes.

None of these issues automatically kills a deal. But each can create another review, another stakeholder and another reason for the internal champion to wait.

The strongest vendors will make the buyer’s internal case easier

Enterprise technology purchases are rarely won by convincing one enthusiastic buyer.

The internal champion still needs to bring security, architecture, data, legal, procurement, risk, finance and business stakeholders with them. Agentic AI makes that internal alignment more demanding because it changes not only what the software can produce, but what the software can do.

This is where vendors can create disproportionate value. Give the buyer the artefacts they need before they ask: a clear autonomy model, documented identities and permissions, model dependencies, review and override processes, and understandable audit evidence.

A vendor that helps the champion answer internal objections becomes easier to buy because the customer does not have to translate the product into governance language themselves.

How vendors should position agentic AI now

The temptation is to make the product sound as autonomous as possible. The better approach is to make the autonomy sound intentional.

Instead of saying, “Our agents can operate independently across your enterprise,” a stronger enterprise message may be, “You decide which decisions the agent can make, which actions require approval and where human ownership remains mandatory.”

Instead of leading with the number of agents, lead with the business process they improve.

Instead of treating guardrails as a technical appendix, make control part of the value proposition.

Instead of saying the system replaces people, show where it removes repetitive coordination while preserving expert judgement where the organisation still needs it.

Vendors do not need to settle the debate over whether agents are replacements, assistants or a new class of operational actor. They need to show that the product can operate safely within the buyer’s chosen governance model.

Questions your sales team should ask before the buyer asks you

Agentic AI discovery should begin with the buyer’s operating reality, not the product architecture.

Ask which decision or workflow the organisation is trying to improve. Ask what currently slows that process down. Ask which actions could be automated safely and which still require human judgement. Ask what data the agent would need and who owns it. Ask which stakeholders must approve the use case. Ask what happens if the agent is wrong. Ask what evidence the buyer would need before increasing autonomy.

Those questions help the vendor determine whether agentic AI is actually the right solution while signalling that the supplier understands enterprise implementation rather than simply chasing the category.

Senior decision-makers do not need another vendor telling them that agents are the future. They need suppliers who can show where agency creates value without creating a control problem the buyer cannot defend.

What this means for vendors trying to win enterprise AI deals

Agentic AI will create real enterprise opportunities, but the market is moving beyond the novelty of the label.

Buyers are becoming more precise about what autonomy means. They are thinking about decision rights, ownership, identity, permissions, failure containment, human review and measurable value. They are also learning that some workflows described as agentic are still highly supervised.

That is not a reason for vendors to become less ambitious. It is a reason to become more exact.

The strongest proposition is not, “Our AI can act on its own.”

It is, “We can show you where it acts, why that autonomy creates value, how you control it and how you remain accountable.”

That is a much more enterprise-ready conversation.

For vendors trying to meet senior decision-makers, the lesson is straightforward: do not sell the label. Sell the operating confidence behind it.

The Leadership Board helps technology vendors understand what senior enterprise buyers are prioritising before those priorities become obvious in the wider market. We are at the tables where leaders are working through AI, security, data and technology decisions, and we can introduce vendors whose propositions align with those priorities.

Meet senior enterprise decision-makers working through agentic AI, security, data and governance priorities.

Frequently asked questions

What do enterprise buyers mean by agentic AI?

Enterprise buyers increasingly distinguish between supervised AI workflows and systems that can make decisions or take actions with a degree of autonomy. For vendors, the important point is to explain exactly where the system has discretion, where approval is required and what controls apply.

Why are enterprise buyers sceptical of agentic AI claims?

The category is moving quickly and some products described as agentic still operate through tightly controlled workflows. Buyers are therefore testing whether the autonomy is real, whether it creates additional business value and whether the organisation can control the associated risk.

What should an agentic AI vendor prove during the sales process?

Vendors should be ready to prove the boundaries of autonomy, identity and permissions, human oversight, intervention and shutdown mechanisms, auditability, underlying model dependencies and the measurable business outcome created by the agent.

What can cause an agentic AI deal to stall?

Deals can slow when autonomy is unclear, permissions are too broad, accountability is vague, the vendor cannot explain failure containment, or the business case does not justify the additional risk and complexity of autonomous action.