Why Agentic AI Hiring Requires Hands-On Senior Leaders

Agentic AI is moving from experimentation towards enterprise implementation, and that shift is changing the profile of AI talent businesses need.

McKinsey's 2025 global AI survey found that 62% of respondents said their organisations were at least experimenting with AI agents. However, only 23% were scaling an agentic AI system somewhere within their organisation. In any individual business function, no more than 10% reported scaling agents. The gap between experimentation and scaled implementation is significant.

For employers, technology is only part of that challenge. They also need people capable of turning agentic AI concepts into reliable systems and workflows.

According to Hailey Thompson, Principal Consultant at Glocomms, companies increasingly need senior AI leaders who can set the strategic direction while remaining closely involved in the technical work: 

So, what should employers look for, why is this talent difficult to find, and how can they compete for it?

Why has agentic AI changed what companies need from senior AI talent?

Generative AI accelerated demand for people who could develop and work with large language models (LLMs). Agentic AI is adding another layer.

AI agents can be designed to plan, make decisions, and execute multiple steps within a workflow. This creates requirements that extend beyond experimenting with models or fine-tuning existing tools. 

As businesses move further into implementation, senior hires may need to combine technical depth with research, architecture, strategy, and delivery.

Hailey explains:

This is an important distinction for employers. A senior title does not necessarily mean the person should move away from implementation.

In an emerging area such as agentic AI, businesses may need leaders who can:

• Design and build agentic AI systems
• Assess where agents can add value within existing workflows
• Make architectural and infrastructure decisions
• Lead research and technical experimentation
• Set an AI roadmap and translate it into implementation
• Work closely with engineering, data, and infrastructure teams
• Guide technical teams while remaining close to the technology

The demand is also spreading beyond technology companies. Hailey has seen AI requirements within biopharma, for example, where businesses are exploring applications ranging from drug discovery to improving internal processes.

The World Economic Forum reports a similarly broad shift. 86% of surveyed employers expect AI and information-processing technologies to transform their business by 2030, while AI and big data rank as the fastest-growing skills.

Why is hands-on senior agentic AI talent so hard to find?

There is an inherent problem with hiring experienced leaders for a technology that is developing quickly: employers want extensive experience in something relatively new.

Hailey sees particular demand for people who have already worked on high-profile AI models or systems at leading technology companies, and for growing AI businesses that can create a very narrow candidate pool. She says: 

The wider skills market adds further pressure. The World Economic Forum found that 63% of employers already identify skills gaps as a major barrier to business transformation, while almost 40% of skills required for jobs are expected to change by 2030.

The AI and infrastructure gap

Agentic AI does not operate in isolation. Models and agents depend on the data pipelines, compute capacity, cloud architecture and infrastructure required to perform reliably at scale.

Hailey is seeing a growing skills gap at the intersection of these disciplines. Many professionals have deep infrastructure expertise or AI engineering experience, but fewer have worked across both.

This is becoming increasingly important as companies move from AI experimentation to production. AI infrastructure needs to support demanding workloads, growing data volumes and technologies such as advanced GPUs, while providing the scalability and reliability expected of enterprise systems.

For employers, this means looking beyond model development alone. Hiring plans may increasingly need skills across:

• AI and machine learning engineering
• Data engineering and pipelines
• Cloud and AI infrastructure
• GPU and compute environments
• Software and platform engineering
• AI system scalability and reliability

What does senior agentic AI talent cost?

Glocomms’ conversations with candidates and recent searches provide an indication of the premium attached to this expertise in the US market.

Hailey has seen principal-level generative AI engineer positions around $180,000 to $220,000 base salary.

For senior director-level agentic AI positions requiring roughly 10 to 15 years of experience while remaining technically hands-on, she has seen salaries around $250,000 to $312,000 in Philadelphia.

Compensation can rise further in major AI hubs such as New York and the San Francisco Bay Area.

These figures should be treated as market observations rather than universal salary benchmarks. Company stage, location, equity, technical requirements, and candidate experience can all significantly affect compensation.

But they illustrate a wider hiring challenge: businesses targeting experienced AI professionals from leading labs and technology companies need to understand what those candidates can already command.

How can employers attract senior agentic AI candidates? 

Compensation matters, but competing purely on salary may leave startups and mid-sized companies fighting a battle they cannot win. The alternative is to make the work itself part of the proposition.

Hailey says ownership can be particularly compelling for senior candidates coming from large organisations:

Instead of being one member of a large technology function, a candidate could set the vision, influence architecture, and have a direct role in deciding how AI is implemented.

For employers, that means clearly communicating:

• Ownership
  What will this person genuinely control or influence?
• Technical scope
  Will they remain close to research, architecture, and implementation?
• AI roadmap
  What is the organisation trying to build, and why?
• Resources
  Is there sufficient data, infrastructure, budget, and executive support?
• Equity
  Can long-term incentives make the overall package competitive?
• Impact
  How closely will their work connect to products, customers, or business performance?

This is particularly important because AI strategy can affect retention as well as attraction.

Hailey is also seeing AI professionals consider new opportunities when they feel their current employer is not keeping pace with technical developments. 

For employers investing in AI, a clear and credible technical roadmap can therefore be a powerful part of the employer proposition. Giving senior talent the opportunity to influence that roadmap, work with emerging technologies, and see the impact of their decisions can help differentiate an opportunity beyond compensation alone.

Before hiring an AI engineer, define what you actually need

Agentic AI is also creating ambiguity around job titles.

A business might believe it needs an AI engineer when its technical requirements point towards a data scientist, machine learning engineer, software engineer, or infrastructure specialist. 

That distinction matters.

A poorly defined brief narrows the chances of finding the right person and can waste valuable time in a highly competitive market.

Before going to market, employers should answer three questions:

1. What problem will this person solve?
2. What will they actually build, own, or improve?
3. Which technical skills are essential to achieving that outcome?

Only then should the job title and candidate profile be defined.

How Glocomms supports agentic AI hiring

The speed of change in AI makes current market intelligence particularly valuable.

Glocomms' software & analytics specialists work across roles including AI engineering, machine learning, data engineering, data science and senior technology leadership. Our wider expertise across cloud & infrastructure can also support businesses building the technical foundations around AI systems.

For employers developing agentic AI capabilities, Glocomms can provide insight into:

• Talent availability and competitor hiring activity
• Compensation and market benchmarking
• Priority AI and data skill sets
• Candidate expectations and motivations
• Role scoping and job requirements
• Permanent, contract, and multi-hire talent strategies

That intelligence can help businesses answer a fundamental question before a search starts: does the talent profile we want actually exist, and can we realistically attract it?

Agentic AI may be moving quickly, but hiring faster without a clear talent strategy is unlikely to solve the problem.

The companies better positioned to move from AI experimentation to implementation will be those that identify what their systems require, understand the talent market, and find senior leaders prepared to remain close to the technology.

As Hailey puts it:

Building an agentic AI team? Speak to Glocomms about the skills, compensation, and talent available in your market.