Increasingly, the secret to AI adoption isn’t choosing the right technology. Today, there are a variety of mature, enterprise-ready AI platforms available, and they’re equally capable of supporting effective AI implementation.
Nor is it adjusting processes. While AI adoption certainly requires some changes to business processes, most organizations are able to handle this adaptation easily enough.
Instead, the move that differentiates successful AI adopters from businesses that remain stuck in the experimentation or pilot phase is a novel practice known as forward-deployed engineering. Going forward, there’s good reason to believe that forward-deployed engineers will be the secret sauce behind effective AI adoption at enterprise scale.
Here’s why, along with practical tips on how to take advantage of forward-deployed engineering to accelerate AI adoption.
What are forward-deployed engineers?
In the generic sense, forward-deployed engineers (FDEs) are developers who embed directly within business environments, as opposed to operating in a silo, with little ongoing engagement with business stakeholders.
But more specifically, “forward-deployed engineer” has become more or less synonymous with “AI engineer.” The reason why is that more and more organizations are leveraging forward-deployed engineers to build AI solutions (especially AI agents) in the same context (i.e., business environments) where those solutions will be deployed.
How forward-deployed engineers solve AI adoption woes
Viewed from the perspective of AI adoption, forward-deployed engineers solve the vexing problem that has caused so many enterprise AI projects to stall to date: The challenge of integrating and customizing AI platforms to meet a specific business’s needs.
This is a common challenge because, although AI platforms are powerful and mature, it’s rarely possible to “drag and drop” them into an enterprise environment and expect results. The platforms must instead connect to enterprise data resources and applications.
AI platforms must also conform with enterprise governance, security and compliance rules. Few AI solutions do this out of the box, since the AI vendors have no way of knowing exactly which governance standards a given organization needs to meet.
Solving this challenge is where forward-deployed engineers come in. As experts who understand both the technical intricacies of AI and (thanks to their close interfacing with business users) what the organization needs to get out of AI, FDEs are in an ideal position to bridge the gap between AI theory and AI reality.
Forward-deployed engineers are all the more important given that AI platforms evolve rapidly. Enterprise AI applications and services that depend on them must evolve, too. Here again, forward-deployed engineers meet this need by keeping up with changing AI technology in alignment with changing business requirements.
Deploying forward-deployed engineers in practice
Making the case for leveraging forward-deployed engineers as a way of accelerating AI adoption and improving governance outcomes is straightforward enough.
The real challenge lies in acquiring the right personnel to serve as forward-deployed engineers. This can be tough for two main reasons:
- Engineers who possess deep expertise in AI (and related domains, like data management and governance) are in short supply. Few businesses have sufficient in-house engineering resources to power large-scale forward-deploying engineering initiatives, and while hiring more engineers may be an option for some organizations, sourcing them in sufficient numbers is likely to be difficult given that everyone is trying to hire FDEs right now. (FDE job postings surged by 800 percent last year.)
- Forward-deployed engineers must understand and be able to work not just with AI applications, but also with the broader toolchains to which AI solutions connect. In other words, they need end-to-end expertise – and this makes them an even rarer breed.
Given the difficulty that the typical enterprise faces in sourcing effective forward-deployed engineers, it’sunsurprising that AI vendors themselves are now investing in their own forward-deployed engineering teams, which they can make available to customers to help the latter adopt AI capabilities.
SAP, for example, has announced an initiative in this vein that it plans to start in July, with the goal of streamlining SAP customers’ implementation of AI features within the company’s ERP platform.
Initiatives like this are promising, although a reasonable person might doubt whether AI vendors alone will be able to meet surging demand for forward-deployed engineers among businesses seeking to accelerate AI adoption. Even companies as large as SAP have only so many engineers on hand that they can deploy to customer environments.
For that reason, I suspect that the forward-deployed engineer trend will also make channel partners (i.e., third-party servicers who can help organizations build and implement AI agents and other solutions using platforms like SAP’s) a central part of effective AI deployment. Partners can help meet the demand for personnel, while also tailoring AI implementations to meet each client’s unique governance needs.
For now, the one thing that is certain is that forward-deployed engineers are poised to become a key resource for translating AI strategy into actual practice. What remains to be seen is exactly how organizations will go about implementing forward-deployed engineering teams, a feat that, like AI adoption, presents a novel set of challenges that the typical organization will likely struggle to meet without third-party assistance.
