Who Owns AI Agent Strategy Now? Meet the New Buying Committee

• AI agents are changing how organizations evaluate marketing technology. As agents move from recommendations to execution, purchasing decisions require greater scrutiny.
• Agentic AI requires a broader buying committee. Marketing, IT, procurement, legal, finance, and agency leadership each oversee different risks and controls.
• Oversight should reflect an agent’s authority. Permissions, approval thresholds, auditability, and intervention controls are essential as autonomy increases.
• Agencies need consistent governance standards. A shared framework must accommodate individual client requirements for data, spending, and approvals.

AI agents are changing what it means to buy marketing technology. As these systems move beyond generating insights and recommendations to executing campaigns, adjusting budgets, and accessing business data, organizations must rethink how they evaluate and approve new tools.

A marketing platform that recommends shifting spend is one thing. An AI agent that can independently make that change introduces new questions about permissions, oversight, financial exposure, and accountability.

These decisions now extend beyond marketing and into IT, procurement, legal, finance, and agency leadership. Each function has a role in determining what an agent can access, which actions it can take, and how those actions are governed.

The level of review should reflect the authority an AI agent is given.

A read-only assistant does not require the same scrutiny as a system authorized to commit media dollars or change campaign settings.

The shift is already underway. Gartner forecasts that up to 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.

Meanwhile, Forrester reports that three-quarters of enterprise leaders are adopting agentic AI, although relatively few have reached meaningful production-scale deployment. As organizations introduce agents with greater autonomy, the purchasing process must evolve alongside them.

Why does agentic AI require a broader buying committee?

The AI buying committee is expanding because different business functions are responsible for different aspects of agent governance. Evaluating an agent requires agreement on business objectives, access permissions, contractual protections, financial controls, and accountability. Each stakeholder brings a specific set of questions to the evaluation process.

Marketing and media: Is the agent optimizing for our business objectives and brand priorities? Can it account for factors beyond immediate performance metrics?

IT and data: What systems and information can the agent access? How does it authenticate, and can permissions be revoked immediately?

Procurement: What happens if the vendor changes its underlying models, capabilities, permissions, or third-party AI providers after the contract is signed?

Legal and compliance: Can the organization reconstruct the agent’s decisions and demonstrate compliance with relevant data, disclosure, and audit requirements?

Finance: What spending authority does the agent have? How are financial commitments monitored, approved, and reconciled against existing budgets?

Agency leadership: What governance standards should apply across all clients, and where do individual client policies require additional controls?

AI systems can be categorized into three levels of autonomy. Assistive AI provides analysis and recommendations for people to act on. Constrained execution allows agents to operate within predefined budgets, thresholds, and rules. Delegated authority enables agents to independently take consequential actions, access sensitive systems, or commit spending within an established mandate.

As organizations move toward constrained execution and delegated authority, evaluating an AI agent requires more than assessing its features and performance. It requires a shared understanding of how the technology will operate within existing business controls.

What should each function evaluate before buying an AI agent?

Marketing and media: Define the agent’s objectives and operating boundaries

Marketing teams should establish what the agent is expected to achieve, which business constraints it must respect, and when human approval is required. An instruction to “optimize performance” leaves too much open to interpretation.

An effective mandate should account for brand priorities, campaign pacing, inventory quality, measurement requirements, budget limitations, and escalation procedures. These parameters help ensure the agent’s actions remain aligned with the organization’s broader marketing strategy.

IT and data: Establish identity, access, and security controls

IT and data teams should evaluate how the agent authenticates, which systems it can access, and how its permissions are monitored and managed. These considerations belong in the purchasing process rather than being deferred until implementation.

A 2026 Cloud Security Alliance research report found that 92% of large-enterprise security leaders lacked full visibility into AI-agent identities, while 95% expressed doubts about their ability to detect or isolate a compromised agent. Least-privilege access, time-bound credentials, activity monitoring, audit logs, and immediate access revocation should be baseline requirements for organizations evaluating agentic technology.

Procurement: Account for how agentic products evolve

Procurement teams need to evaluate how vendor relationships and contractual obligations may change as agentic technology develops. Unlike traditional software, AI agents may gain new capabilities through model updates, additional tool integrations, expanded permissions, or changes to third-party AI providers.

Contracts should address material-change notifications, audit rights, liability, data portability, access to activity logs, and transparency into the vendor’s AI supply chain. These protections help organizations maintain oversight as the technology and its underlying dependencies evolve.

Legal and compliance: Ensure decisions can be explained and audited

Legal and compliance teams should determine whether the organization can demonstrate how an agent arrived at a consequential action, including which data, rules, permissions, and thresholds influenced its decisions. Regulatory requirements are also evolving.

The European Commission’s AI Act framework establishes requirements for AI systems based on their risk classification, with different obligations taking effect on different timelines. Although not every marketing AI agent qualifies as a high-risk system, organizations still need clear policies governing data use, documentation, human oversight, and accountability.

Finance: Maintain visibility and control over delegated spending

Finance teams should determine how spending authority is assigned, monitored, and reconciled when AI agents can make financial commitments. According to Deloitte’s Q2 2026 CFO Signals survey, 43% of CFOs expressed confidence in their existing AI-governance frameworks, while 59% identified balancing rapid AI deployment with risk management as their most significant challenge.

Finance should have visibility into both committed and actual spending, clear approval thresholds, and established procedures for reconciling agent activity with existing budget workflows. A designated owner should also be responsible for monitoring financial activity after deployment.

Why do agencies face an additional layer of responsibility?

Agencies must evaluate AI agents for their own operations while ensuring those same systems comply with the requirements of individual clients. That creates additional complexity, particularly when clients have different policies governing data access, campaign approvals, spending authority, reporting, and acceptable levels of automation.

A scalable approach begins with an agency-wide governance baseline covering identity and access management, data use, spending thresholds, auditability, vendor agreements, incident response, and client reporting. Individual client requirements can then introduce additional controls where necessary. Establishing these standards helps agencies maintain consistency across accounts while accommodating different client policies and risk tolerances.

This builds on disciplines agencies already manage, including brand safety, data restrictions, approval workflows, and client-specific media authority. As agents become more autonomous, those existing practices provide a foundation for governing AI-driven execution across accounts.

What should you ask before purchasing an AI agent?

Before signing a contract or granting production access, organizations should be able to answer several important questions about an agent’s authority, operating boundaries, and accountability.

1. Authority: Can the agent spend money, access data, change settings, or take external actions without individual human approval?
2. Scope: What is the agent authorized to do, and which actions are explicitly prohibited?
3. Access: How does the agent authenticate, and can its permissions be revoked immediately?
4. Approvals: Which actions require human intervention, and what thresholds trigger that review?
5. Visibility: Can we track what the agent has done, what it has committed, and the reasoning behind consequential actions?
6. Vendor accountability: Does the contract address changes to models, permissions, capabilities, and third-party AI dependencies?
7. Operational ownership: Who is responsible for monitoring, escalation, and accountability after deployment?
8. Agency governance: What minimum standards apply across clients, and where are additional client-specific controls required?

These questions help organizations establish a governance framework that reflects the actual authority granted to an AI agent. They also provide a starting point for aligning the stakeholders responsible for evaluating, approving, and managing agentic technology throughout its lifecycle.

Ready to bring the right stakeholders into your next agentic AI evaluation?

See how Skai helps marketers and agencies put agentic AI to work with the governance, visibility, and control needed for responsible execution. Contact Skai to learn more.

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