If you’ve been paying attention to enterprise tech conversations, you’ve heard the term Forward Deployed Engineer (FDE) a lot. A16Z called it “the hottest job in tech”. LinkedIn reports FDE positions grew 42-fold between 2023 and 2025. OpenAI built a whole new company around it. Anthropic launched a $1.5 billion joint venture modeled on it. Salesforce, ServiceNow, Google, Microsoft – everyone is either hiring FDEs or talking about them.
So, when Pega took the stage at PegaWorld last week and announced the Pega Solution Designer, a fair question is: why that name and not just ride the FDE wave?
It’s not a branding choice. It’s a fundamentally different philosophy. We believe it’s not an engineering problem. It’s a design problem.
The FDE Model: Real Problem, Incomplete Answer
To be fair, the FDE trend is real, and the problem it’s trying to solve is real.
The model was pioneered by Plantir, who embedded engineers directly inside intelligence agencies and financial institutions – not as consultants with a deck, but as builders who would write code, untangle data pipelines, and adapt workflows from the inside. The underlying insight is correct. The logic was sound: enterprise environments are far more chaotic than product teams ever anticipated, and if your software doesn’t work in that context, well, then it just doesn’t work. The results were hard to argue with.
Fast-forward to 2026, and that model has been adopted across the generative AI industry. OpenAI built a whole new company around it. Anthropic launched a $1.5 billion joint venture modeled on it. Salesforce, ServiceNow, Google, Microsoft…. Everyone is either hiring FDEs or talking about them. The pattern is consistent: deploy a skilled human to fix, post-deployment, what the original design didn’t account for.
A 2024 RAND Corporation study found that AI projects fail at more than twice the rate of non-AI software projects. FDEs exist to close that gap. And they often do…. For a while.
And why is that? Because the problem isn’t downstream. It never was.
The Real Problem is Upstream
Enterprise AI delivery has a structural flaw that has nothing to do with the sophistication of the technology. The gap between a business problem and a production-ready solution isn’t primarily an engineering problem. It’s a design problem and it starts from the very first conversation.
Most enterprise projects enter delivery with requirements that are incomplete, misaligned, or the assumption that a capable-enough AI model will figure out the rest. By the time an engineer (forward deployed or otherwise) encounters the work, the damage is already baked in. Months of rework, misaligned handoffs, and projects that go live late or not at all aren’t engineering failures. They’re design failures.
The FDE model answers this problem by sending more engineers, closer to the client, post-deployment. It’s an understandable instinct. But it’s expensive, it doesn’t scale, and it treats the symptom rather than the cause.
Beyond the cost and scale, there’s a deeper issue: the knowledge leaves with the engineer. When an FDE team spends months embedded inside your operations, they develop deep tacit knowledge of how everything was configured. Then they rotate. The software may stay, but the institutional understanding of how it was built stays with the vendor. That’s not partnership – that’s dependency. The engineer belongs to the vendor, the skills stay with the vendor, the playbook is the vendor’s IP, and also leaves any technical debt behind.
A Different Philosophy – Intentional from the Start
Pega’s approach to this problem isn’t a reaction to the FDE moment. It’s the product of a different core belief about what enterprise AI delivery should actually look like.
We believe the gap between business intent and production-ready execution is a design problem. And that the right solution is to close it upstream, at the first discovery conversation, before a single line of code is written. That belief shaped everything about how the Solution Designer role and skills were built.
Solution Designers can be your people. The model isn’t about embedding vendor engineers who take the knowledge home when the engagement ends. It’s about building your practitioners into Solution Designers who can run projects themselves – with Pega Consulting and our partner network available to do deeper when complexity demands it – all using Pega Blueprint™ as the AI-powered engine that does the heavy lifting.
A Solution Designer leads discovery, captures business intent inside Pega Blueprint (the AI-powered design tool that transforms operational insights into structure, build-ready output) and produces high-fidelity designs from the first discovery conversation. That design flows directly to a Solution Builder who works in Pega Infinity Studio™ to turn it directly into a production application without months of back-and-forth or guesswork. The process is streamlined, clean and collaborative with the knowledge staying in your organization. And the next project is faster because your people have internalized the methodology and the technology possibilities.
Why This Moment
The FDE hype is a signal. It’s telling the industry that the gap between AI promise and enterprise reality is real, and you need a skilled human in the middle to close it. We agree completely. We just think those humans are Solution Designers and should be yours.
And we think this is the right moment to shape the design as the single-source-of-truth anchor that starts at the first conversation and threads through all the way to production and value activation.
This is the time to invest in your people, building the skills with the right tools that are needed to solve the enterprise AI transformation gap so you start to see real value rather than just investing in the hype.
Ready to rethink how enterprise AI gets delivered with Solution Designers?
- Join the Solution Designer hub on Pega Community — connect with practitioners worldwide, earn your credential, and find your place in the Solution Designer continuum
- Explore Solution Designer on pega.com — learn more about the skills framework, credentials, and what it means to design in the age of AI
