How Forward Deployed Engineering Closes the AI Execution Gap

About the Author:
Vinod Kumar is President and CEO of Akkodis North America. With more than 25 years of experience in IT and product engineering, he helps organizations move beyond AI experimentation to create practical, responsible solutions that improve performance and accelerate innovation.

Most large enterprises already have access to capable AI models, platforms, and infrastructure. Access has become easier, model performance has improved quickly, and experimentation is happening across almost every major business function. 

Organizations are already moving from experimentation to pilots, but can start running into obstacles.

A pilot can demonstrate that AI produces a useful result under controlled conditions, with a defined dataset, a cooperative team, and enough attention to remove obstacles as they appear. Moving that same AI pilot to production means working inside live systems, existing controls, imperfect data, and workflows that were never designed around AI. 

The space between those two environments is the AI execution gap, and closing it increasingly depends on how well engineering is embedded into the business itself.

AI Access No Longer Separates Leaders From the Rest 

Enterprise AI adoption has accelerated rapidly. Stanford’s 2026 AI Index reports that 88% of surveyed organizations were using AI in 2025, while deployment of AI agents remained much earlier across most business functions. 

The pattern may point to access and adoption not automatically creating operational value. Most organizations can procure similar foundation models, cloud services, and development tools, which shifts the competitive question toward what happens after those technologies enter the enterprise.

Production AI has to work with real data sources, security rules, application dependencies, and decision processes. It also needs to keep working when inputs change or business requirements move.

Those dependencies are why broader AI, data, and analytics foundations and application modernization become part of the AI deployment problem. A strong model can still produce weak business outcomes when the surrounding environment can’t support it reliably.

An AI Pilot to Production Journey Must Be Repeatable

A successful pilot proves something important, although its value depends on what happens next. Early use cases are often selected because they offer manageable scope, relatively accessible data, and stakeholders who are motivated to make the experiment work.

In subsequent workflows, data may sit across several systems. Approval paths may be inconsistent. Security requirements may tighten. The people who own the process may have different priorities, while exceptions that rarely appeared in the pilot can suddenly become everyday events.

Research on production machine learning has highlighted these system-level challenges for years. Google researchers have described how dependencies, feedback loops, changing environments, and hidden consumers can create significant technical debt beyond the model itself.

For enterprise AI, the implication is straightforward. A single working use case carries limited value if every new deployment requires the organization to rebuild the approach from the beginning. 

A stronger capability is repeatability, where lessons from one deployment improve the architecture, integration patterns, controls, and delivery methods used for the next.

The AI Execution Gap Lives Inside Business Workflows

The biggest opportunities for AI often sit inside processes that already contain friction.

Claims processing can involve manual review and repeated handoffs.

Supply chain teams may reconcile information across disconnected systems.

Compliance workflows can depend on specialist review at several stages.

Customer service teams often move between multiple tools before resolving a single request.

AI can improve each of these areas, but only when engineering reaches far enough into the workflow to understand how the process behaves.

That means asking questions like: 

• Where does the data required for a decision originate?
• Which handoffs create delays or rework?
• Where do exceptions enter the process?
• Which decisions require human accountability?
• What needs to happen when the AI output cannot be trusted?

These questions connect AI deployment with the wider technology environment, including modern data platforms designed for AI workloads and the infrastructure needed to run enterprise applications reliably at scale.

The goal is to engineer AI around the workflow rather than force the workflow around a prototype.

Forward Deployed Engineering Builds Capability Inside the Business

Closing the execution gap requires engineering work to happen close to the systems, data, teams, and constraints that shape the result.

Forward Deployed Engineering provides a way to create that standing capability.

Engineering is embedded directly into the enterprise environment, where teams can work through architecture, integration, workflow design, and delivery problems as part of the operating context rather than from outside it.

The approach supports the full AI pilot to production journey by allowing patterns to develop across deployments. Each use case can strengthen reusable integrations, governance controls, delivery methods, and technical foundations that make the next implementation easier.

The Forward Deployed Engineering practice is built around that model of embedded delivery, with engineering shaped around the infrastructure, compliance requirements, and maturity of the AI program itself.

This approach also helps reduce a common source of production risk. NIST’s AI Risk Management Framework emphasizes that trustworthy AI depends on risk management across the design, development, deployment, and use of AI systems, rather than at a single checkpoint. Embedded engineering makes those considerations part of the delivery process as systems evolve.

AI Deployment Requires a Standing Operating Capability

Cloud and DevOps followed a similar path of adoption and evolution to each other. Enterprises first gained access to new infrastructure and tooling, while the deeper value arrived later as teams changed how software was designed, released, monitored, and improved.

AI is moving through the same kind of transition, but the cycle is happening much faster.

As more workflows incorporate AI, organizations need engineering capacity that can keep adapting systems after the initial launch. Data changes, processes evolve, models are updated, and new business requirements appear. Production AI has to absorb those changes without forcing teams back into pilot mode every time the environment moves.

Forward Deployed Engineering creates continuity between initial deployment and ongoing improvement, so knowledge stays close to the business and successful patterns can spread across teams.

Closing the AI Execution Gap 

The enterprises that create lasting value from AI will be the ones that change what’s inside the business. Faster approvals, fewer manual errors, shorter cycle times, and workflows that operate with less friction provide a much clearer measure of progress than yet another successful prototype. 

Akkodis Forward Deployed Engineering is designed to help organizations reach that standard by embedding engineering capability directly into their environments and building around the systems, workflows, and constraints that shape real-world execution.

If your organization is ready to move AI from promising pilots into repeatable production outcomes, contact the Akkodis team to discuss how Forward Deployed Engineering can help close your AI execution gap.