AI’s Hottest New Job Reveals the Real Bottleneck for Agencies

There is a new role getting a lot of attention in the AI world: the forward-deployed engineer, or FDE.

The term has been around for years, but it’s taken on new importance in the age of AI. OpenAI, Salesforce, Anthropic, and other major AI and software companies are now investing heavily in people who can go inside organizations and help them turn AI from an impressive technology into practical business value.

The problem is no longer that AI is not powerful enough. The problem is that most organizations do not know how to deploy it. They don’t know where to begin. They don’t know which use cases matter most. They don’t know how to adapt AI to their actual workflows. They don’t know how to move from individual experimentation to repeatable systems.

That is why the FDE model is growing so quickly. Recent coverage in the Financial Times, Reuters, and other major business publications has described forward-deployed engineering as one of the hottest new areas in AI. OpenAI has even launched a dedicated deployment company, bringing in approximately 150 forward-deployed engineers and deployment specialists to help organizations turn AI into operational advantage.

The message is clear: AI adoption does not happen automatically. Even the most advanced companies in the world are recognizing that customers need embedded help to make AI useful.

Agencies are no different.

Agencies Have the Same AI Problem

Most of the FDE conversation is happening at the enterprise level. But small and mid-sized agencies face the same problem at their own scale.

Agency owners know AI is important. Many have experimented with ChatGPT, Claude, Gemini, meeting summarizers, image tools, or automation platforms. Some team members may already be using AI quietly in their own work. But scattered usage is not the same as organizational capability.

The real question is not, “Which AI tool should we use?”

The better questions are:

Where is the work getting stuck?
Where are we losing margin?
Where are we repeating the same tasks over and over?
Where could senior expertise be turned into a reusable system?
Where could AI help us sell, deliver, manage, and scale more effectively?

That is where agencies need help. They don’t simply need another software subscription. They need someone who can understand the agency’s business, identify the best opportunities, build practical workflows, and help the team actually adopt them.

That is the agency version of the forward-deployed model.

The Staffing Question Agencies Cannot Avoid

There is also a bigger question underneath all of this. If AI really does make agencies more productive, what does that mean for the current team?

Does increased productivity automatically lead to layoffs?

It can, if an agency treats AI only as a cost-cutting tool. But that is not the only path. A better path is to use AI to increase capacity, then build the business development and delivery systems needed to fill that capacity with better, more profitable work. In other words, if AI helps the team produce more, the agency also needs to get better at creating opportunity.

That may mean using AI not only inside production workflows, but also inside marketing and sales systems. For example, an agency might build a custom CRM around a very specific outreach strategy, with AI-assisted research, segmentation, message drafting, follow-up reminders, qualification notes, and next-step recommendations.

That kind of system does not replace the agency’s people. It helps create the demand needed to support a more capable team. This is one of the places where the AI conversation needs to mature. Agencies should not only ask, “How can AI help us do the same amount of work with fewer people?” They should also ask, “How can AI help us build a stronger agency with the people we already have?”

That second question is much more interesting.

Why Ironwood Fits This Role

This is exactly where Ironwood is designed to help. We think of Ironwood as a kind of forward-deployed AI partner for agencies.

Not in the enterprise sense. We are not parachuting large implementation teams into Fortune 500 companies. We are bringing the same basic model to small and mid-sized agencies: embedded discovery, practical use-case identification, workflow design, prototyping, technical implementation, and business guidance.

What makes Ironwood especially well suited for this work is the combination of perspectives we bring.

Eric brings the agency management and consulting side. His background includes years of working with agencies and running agency businesses, dealing with positioning, marketing, sales, operations, delivery, staffing, profitability, client management, and growth. That matters because AI adoption is not only a technical issue. It affects how the whole firm works.

Adam brings the technical side. He has the hands-on proficiency to work with the tools, build prototypes, develop workflows, test automations, and turn AI ideas into usable systems.

That combination is important.

Agencies do not need a generic AI consultant who gives a presentation and leaves behind a slide deck. They also do not need a purely technical implementer who builds tools without understanding the business model. They need both.

They need someone who can look at the firm as a business, see where AI can create leverage, and then help build the systems that make that leverage real.

The Future Belongs to AI-Enabled Agencies

The rise of the forward-deployed engineer tells us something important about where AI is going. The companies that win with AI will not simply be the ones that buy the most tools. They will be the ones that adapt their work around the new capabilities those tools make possible.

That’s true for enterprise companies. It’s also true for agencies.

Small and mid-sized agencies do not need enterprise AI programs. But they do need a serious path forward. They need to move beyond casual experimentation and start building practical AI-enabled operations. That requires business judgment. It requires technical capability. It requires a clear understanding of how agencies actually work.

That is the combination Ironwood brings.

AI adoption is not mainly about tools. It is about changing how work gets done.