Most AI programmes get stuck because the organisation is still set up for traditional technology projects, while AI requires a fundamentally different way of working.
The hidden costs of the translation layer
In many organisations, an AI initiative goes something like this:
Business stakeholders formulate a need. A business analyst translates this into requirements. A product owner prioritises. Then data scientists, engineers, and software teams take over before the solution finally reaches the user.
On paper, that seems logical.
In practice, each transfer results in delays, loss of context, misunderstandings, and unclear ownership. What starts as a business problem turns into a technology issue along the way.
The result?
Long lead times. Interesting prototypes that are never scaled up. Insufficient adoption. And above all: limited business impact.
Why the Forward Deployed Engineer emerges
That is precisely why we see the rise of the Forward Deployed Engineer (FDE).
The FDE combines business insight, product thinking, AI engineering, and software development into one role. Instead of acting as the next link in the chain, the FDE works directly with the business to identify problems, build solutions, and realise value.
The success of this role does not lie in technical expertise alone.
The real innovation is that one person becomes responsible for the entire trajectory from problem identification to business result.
This leads to shorter feedback loops, faster learning, more ownership, and ultimately more value creation.
The FDE is not the final destination
Many organisations see the Forward Deployed Engineer as the solution for AI.
That is a misconception.
The FDE is not an end model. It is a transition role.
In the early stages of AI adoption, a small AI Lab with a few FDEs often works excellently. The focus is on experimenting, discovering, and quickly demonstrating business value. Governance remains deliberately light, and the distance between business and technology is small.
But once AI becomes successful, a new problem arises.
More and more departments want AI solutions. The demand grows faster than a small team can deliver.
That is the moment when organisations must evolve.
From AI Lab to enterprise capability
Organisations that successfully scale typically go through four phases.
1. AI Lab
A small central team of AI specialists and Forward Deployed Engineers focuses on experimentation and rapid value creation.
The main question is: “Can we create value with AI?”
2. AI Factory
When demand increases, AI product teams, platform teams, and portfolio management emerge.
The main question becomes: “How can we deliver scalably?”
3. AI Product Organisation
Then the focus shifts from individual use cases to products with clear business objectives.
The main question becomes: “How do we achieve measurable business results?”
4. Federated AI Capability
In the most mature phase, AI is no longer a separate department.
AI becomes an enterprise capability.
Governance, standards, and expertise are centrally organised, while teams in HR, Operations, Finance, and Customer Service independently apply AI solutions within their own domain.
AI transformation revolves around two engines
One of the most important lessons from organisations that successfully scale is that AI solutions and AI capabilities must be developed simultaneously.
Many organisations invest solely in technology.
Others focus primarily on training and awareness.
Both approaches are insufficient.
A brilliant AI solution without adoption creates little value. A well-trained workforce without concrete AI solutions also creates little value.
Therefore, successful organisations simultaneously build:
- AI products and solutions
- AI literacy and adoption
- Governance and standards
- Value measurement and ROI
It is precisely the combination that accelerates.
The most important lesson for leaders
The most successful organisations do not treat AI as a technology programme.
They treat AI as an organisational change.
That means:
- making ownership explicit;
- bringing business and technology closer together;
- taking adoption as seriously as development;
- using governance to accelerate rather than slow down;
- building AI capability as a lasting organisational competence.
The organisations that succeed in this do not just build better AI solutions.
They build an organisation that becomes increasingly better at leveraging AI.
How NXTminds helps organisations
At NXTminds, we see daily that successful AI transformation begins with the right combination of expertise, operating model, and capability building.
We support organisations with interim Forward Deployed Engineers, AI Leads, AI Product Managers, and AI specialists who not only build solutions but also help design the organisation needed to sustainably apply AI at scale.
Because ultimately, AI is not a technology project.
It is a new organisational capability.
