When new buzzwords emerge in the tech industry, it’s a safe bet that businesses will glom onto them by claiming to be doing whatever the buzzword implies they should. But sometimes, the extent to which they truly achieve meaningful change is limited.

For example, two decades ago, companies that had merely moved some files to remote servers claimed to have gone all-in on cloud computing. The DevOps trend of the 2010s similarly prompted some businesses to claim they were doing CI/CD when some hadn’t really changed their software delivery processes. At present, virtually every company that uses a chatbot claims to be embracing AI transformation, but not all of them are actually being transformed.

I bring up these examples because the same practices are playing out around forward-deployed engineer (FDE) teams. FDE has generated a lot of buzz over the past couple of years as organizations have begun to embrace FDEs to implement and scale AI systems. But whether they’re actually reaping the full value that forward-deployed engineers stand to offer depends on how the FDEs operate and which capabilities they bring to the table.

What Are FDEs, and How Can They Help With AI Transformation?

Forward-deployed engineers are experts who work directly within a client company’s environments to help implement, manage and scale technical solutions. The idea behind FDEs is that they can help businesses leverage highly complex technology that their own, in-house engineering teams might struggle to deploy effectively on their own.

FDEs can theoretically assist in the implementation and management of any technology. But the term has come to be closely associated with AI because many of the large AI vendors offer FDEs to assist their enterprise customers in adopting their technology. A variety of third-party consulting and implementation firms also now provide FDE services tailored toward AI.

Average vs. Excellent FDEs

Virtually every FDE team can offer value by speeding up the process of implementing complex technologies like AI. There is a big difference, however, between FDEs who merely meet basic expectations and those who truly excel.

Here’s a look at key differentiators between FDEs who are “good enough” and those that maximize the value they create for customers in the context of AI system deployment and management.

1. Prompt engineering approach

Tailoring the prompts fed into AI systems plays a major role in shaping the reliability and accuracy of AI outputs. To this end, all FDEs should build controls that do things like prompt filtering. Educating clients in prompting best practices also helps.

Truly excellent FDEs, however, don’t stop there. They apply a software development mindset to prompt engineering that leverages techniques like system-level prompts, sub-prompts and chain-of-thought to optimize interactions with AI systems. The result is much higher rates of AI predictability and reliability, thanks to a more rigorous and well-engineered prompting process.

2. Context engineering capabilities

In a similar vein, great FDEs don’t just connect AI systems to a business’s data and call it a day. They think instead in nuanced ways about context engineering, which is the art and science of ensuring that AI models have access to the right data, in the right form.

To this end, they provide access to relevant structured and unstructured data sources. They clean and transform data where necessary. They help customers improve data governance standards. All of these practices are critical for ongoing AI success.

3. Combining technical knowledge with business knowledge

The most effective FDEs bring more than technical skills to the table. They also provide the “soft” skills necessary to interpret business requirements and translate them into technical solutions.

This ability to operate in a cross-functional manner is essential for building AI systems that don’t merely work well in a technical sense, but that also solve real-world business problems in ways that make most sense to the business.

4. Delivering AI platforms

Any FDE team can go into a client environment and start implementing AI solutions. The most impactful FDEs, however, operate in a systematic way by bringing their own AI platform to bear. An AI platform in this context means tooling that helps automate processes like prompt engineering, context engineering and integration between AI systems and business systems.

AI platforms help to make FDE operations more scalable, repeatable and predictable, as opposed to ad hoc. By extension, they reduce time-to-value for customers while also helping to keep the risks of AI deployments in check.

Conclusion: Thinking Beyond FDE Job Titles

In short, virtually any engineer with AI implementation experience can call himself or herself a forward-deployed engineer. But just because a business has FDEs helping to deploy or scale its AI systems doesn’t mean it’s on the most efficient and cost-effective path to AI adoption. Enterprise must critically assess what their FDEs are actually doing; otherwise, they are at risk of chasing a buzzword without achieving a real value boost.