By Raghav Gupta, Founder and CEO, Futurense Technologies

Artificial intelligence has moved faster in the last three years than most people expected. Companies across sectors are investing heavily in AI, building internal teams, and launching pilots across use cases.

Yet there is a clear pattern emerging.

A large percentage of these AI initiatives are not delivering sustained value. Research from institutions like MIT and Harvard Business School indicates that more than 70% of AI projects struggle to move beyond initial pilots. The issue is not model performance. In controlled environments, models work well. The breakdown happens after deployment.

Once AI systems are introduced into real workflows, they encounter messy data, legacy infrastructure, compliance requirements, and user behavior that is difficult to predict. Most organizations are not structured to handle this complexity. There is no single owner responsible for ensuring that AI systems are successfully integrated, adopted, and scaled.

This is where the gap lies.

For years, engineering talent has been trained to build models and write code. The focus has been on technical depth within defined boundaries. However, production environments are not defined. They require engineers to navigate ambiguity, make trade-offs, and work across teams.

As a result, companies are now creating and prioritizing a new role that addresses this exact problem, the Forward Deployed Engineer.

A Forward Deployed Engineer takes responsibility for the entire lifecycle of an AI system. This includes deployment, integration, adoption, and continuous improvement. The role requires a combination of engineering depth, product thinking, and the ability to work closely with business stakeholders.

The job is complete only when the system is working reliably in the real world and driving outcomes.

This shift is already visible in hiring patterns.

Over the past two years, there has been a sharp increase in demand for roles that combine engineering with deployment and execution. Job postings related to applied AI, solutions engineering, and deployment-focused roles have grown multiple times across global markets. Companies are actively looking for talent that can bridge the gap between models and real-world systems.

The compensation trends reflect this demand. In India, roles aligned with this profile are already seeing packages in the range of 30 lakh to 1.5 crore per annum, depending on experience and ownership. Globally, similar roles are being hired at USD 150,000 to USD 500,000 annually, particularly in AI-first companies and enterprise platforms.

More importantly, these roles are being treated as critical hires.

Companies like Palantir built this model early by embedding engineers directly within customer environments. More recently, organizations such as OpenAI, Anthropic, and leading AI startups have expanded similar roles focused on deployment and customer-facing execution. Even large enterprises are restructuring teams to ensure that AI initiatives translate into real business impact.

Leaders across the industry have been vocal about this shift. Jensen Huang has consistently highlighted that the value of AI comes from operationalizing it across industries. Sam Altman has pointed out that the challenge is not building models anymore, but getting them to work in real workflows where people depend on them.

These signals point to a clear direction.

AI is no longer just a research or experimentation function. It is becoming core infrastructure. As this transition happens, the most valuable engineers will be those who can ensure systems work outside controlled environments.

This has direct implications for how engineers are trained.

Most traditional programs are still focused on theory, tools, and isolated problem solving. Students learn how to build models, but not how to deploy them into complex systems. They are rarely exposed to real constraints such as incomplete data, changing requirements, or cross-functional dependencies.

The result is a mismatch between what companies need and what talent is prepared for.

There is now a growing realization that training needs to move closer to how work actually happens. Engineers need to learn how to operate in production environments, work across teams, and take ownership of outcomes. This involves working on real systems, handling failures, iterating continuously, and understanding how decisions impact adoption over time.

The emergence of structured pathways focused on deployment and applied AI is a response to this gap. The goal is to build engineers who are comfortable owning systems after launch, not just contributing to them before.

This shift is still early, but the direction is clear.

Forward Deployed Engineers are becoming one of the most in-demand roles in the AI ecosystem. The demand is being driven by necessity. Companies have realized that without this capability, AI investments do not translate into business value.

For engineering education, this is a moment of reset.

The future will favor engineers who can connect technology to outcomes, navigate real-world complexity, and take responsibility for systems at scale. Building models will remain important, but it will no longer be sufficient.

What matters is whether those systems work where it actually counts, i.e. in the real world.