Cross Squad Accelerator & Forward Deployed Engineer: New Roles for the AI Era

Artificial intelligence (AI) is advancing at a remarkable pace, fundamentally changing how many companies work. I've experienced this impact firsthand at my workplace.

I started out as a specialist, but as the company transformed to become more adaptive and generalist, I transformed along with it. That shift led me to two interesting new roles: Cross Squad Accelerator and Forward Deployed Engineer (FDE). This isn't just a job title change; it's a major challenge to prepare the entire team for a fast-moving AI era.

In this article, I want to share my experience and perspective on roles that may still be unfamiliar in Indonesia, and how I navigate them day to day.

From Specialist to Generalist: Why This Shift Matters in the AI Era

The tech industry used to push us toward deep specialization, and I genuinely enjoyed the comfort and efficiency of working within my own domain. But the arrival of AI, with all its innovations, is forcing all of us to see the bigger picture.

My company recognized that to stay agile and innovate quickly in the AI era, specialization silos had to be broken down. The need for people with cross-disciplinary understanding, who can integrate AI solutions across different functions, has become critical.

Shifting from a specialist mindset to a generalist one, or at least becoming a generalist who retains a specialty (a T-shaped developer), isn't only about technical skills. It's also about mindset. I had to stop focusing solely on "how do I build X perfectly" and start asking "how can X integrate with Y and Z, and deliver maximum value across the company's ecosystem with the help of AI."

The consequence: I had to open myself up to learning things far outside my comfort zone, from AI model architecture to its impact on entirely different business processes.

Understanding the Cross Squad Accelerator Role: Bridging the AI Adoption Gap

As a Cross Squad Accelerator, my main job is to make sure every team, or "squad," in the company is ready and able to go through this transformation. Picture this: each squad has its own goals and focus, often without a deep understanding of AI's potential or its integration challenges. That's where I come in as the bridge.

• Gap identification. I start by mapping where each squad stands in its AI adoption journey. What do they need? What tools could help them?

• Knowledge facilitation. I'm responsible for transferring knowledge about the latest AI technologies, best practices, and applications relevant to each squad's specific needs. This takes the form of workshops, discussion sessions, or hands-on guidance through pilot projects.

• Innovation acceleration. My goal is to speed up the process of identifying, experimenting with, and implementing AI solutions that improve efficiency and productivity, or even create new products. I often act as a catalyst: pushing squads to think "AI-first" and helping remove technical or procedural roadblocks.

• Strategic collaboration. I don't just hand out directions; I work collaboratively. That means intensive communication with squad leads, engineers, and business teams to ensure the AI solutions we implement align with the company's strategic goals.

One simple example: instead of just sharing documentation about AI tools, I sit down with a squad and work through one of their real cases from start to finish. This "learn by solving your own problem" approach has proven far more effective than theory sessions, because once people feel the results, adoption spreads on its own.

In short, I'm there to make AI adoption smoother and faster across the organization, ensuring no squad gets left behind in this wave of transformation.

Unpacking the Forward Deployed Engineer (FDE) Concept and Its Relevance to AI

The Forward Deployed Engineer (FDE) role is a concept currently being shaped by our management, and I'm among those being prepared for it. It's a relatively new role, especially in Indonesia, and it's often misunderstood.

An FDE isn't just someone who writes code behind a desk. The term was popularized by Palantir: engineers "deployed to the front lines," sitting directly with clients or users, understanding their real problems in the field, then building solutions that actually work in production, not just demos that look good in a presentation.

Why has this role become so relevant in the AI era? Because there's a wide gap between what AI technology can do and its actual adoption in business. An AI model can be incredibly capable, but without someone who understands the client's business context, the data available, and the operational constraints, AI solutions often stall at the experiment stage. The FDE fills that gap.

In practice, the FDE role demands three things at once:

• Technical depth to build and integrate solutions directly, not just hand over recommendations.

• Business sense to identify which problems are truly worth solving, versus which are merely interesting technically.

• Communication skills to translate the needs of non-technical users into solutions, and conversely, to explain technology's limitations in business language.

This combination is what sets FDEs apart from traditional software engineers and consultants. Traditional engineers build to spec; consultants give recommendations and leave. The FDE sits in between: understanding the problem straight from the source, building the solution, and making sure it actually gets used.

The Challenges of Wearing Two Hats

Serving as a Cross Squad Accelerator while being groomed as an FDE hasn't always been smooth. A few challenges I've faced:

Resistance to change. Not everyone is immediately enthusiastic about AI. Some are skeptical; some worry about being replaced. I've learned that the best approach isn't to force it, but to demonstrate real results at a small scale first. One workflow with measurably reduced turnaround time speaks louder than ten presentations.

Constant context switching. Today it's infrastructure, tomorrow another squad's business process, the day after a client's requirements. The switching is exhausting, but it also builds the "generalist muscle" that sits at the heart of this role.

Technology moving faster than documentation. AI tools and models change within weeks. I've had to accept that "done learning" doesn't exist in this role; what exists is a sustainable rhythm of continuous learning.

Lessons Learned

1. An engineer's value in the AI era is shifting from "what can I build" to "what problems can I solve." AI makes building faster and faster; what's scarce is people who know which problems are worth solving.

2. Technology adoption is a people problem, not a technical one. The biggest barriers to AI transformation are rarely technological limitations; they're almost always habits, fears, and old ways of working.

3. Being a generalist doesn't mean being shallow. The specialty I once went deep on has become the very foundation that helps me understand other disciplines faster.

Closing Thoughts

The Cross Squad Accelerator and Forward Deployed Engineer roles may not be common in Indonesia yet, but I believe the need for roles like these will grow as more companies get serious about adopting AI. Not because of the job titles, but because of the function they serve: bridging the gap between technology's potential and real business value.

If you're going through a similar transition, or your company is designing roles like these, I'd be happy to exchange ideas. Feel free to reach out through the contact page on this website.