There is no shortage of interest in artificial intelligence across the technology industry. Customers are experimenting with Microsoft Copilot, ChatGPT, Claude, AI agents and a rapidly growing ecosystem of AI-enabled applications, while vendors continue to release new capabilities at a pace that can be difficult for businesses to absorb.
The real challenge, however, is no longer gaining access to AI. The challenge is working out how to use it in a way that creates meaningful and measurable business value.
Most organisations do not need another demonstration of what an AI model can do. They need someone who can understand how their business actually operates, identify where inefficiencies or opportunities exist, connect AI to the systems and data they already use, and then build solutions that improve real business processes. This is where the growing interest in the role of the Forward Deployed Engineer, or FDE, becomes particularly relevant.
For MSPs and IT service providers, the rise of the forward-deployed engineer creates an opportunity to move the AI conversation beyond licensing, infrastructure and generic consulting. It provides a model for delivering practical AI solutions that improve productivity, reduce operating costs, enhance customer experience and ultimately increase the strategic value the MSP provides to its customers.
What is a forward-deployed engineer?
A forward-deployed engineer is a highly technical, customer-facing engineer who works closely with an organisation to understand a business problem and then takes responsibility for translating that problem into a working technology solution.
The concept itself is not new. Companies such as Palantir have used Forward Deployed Engineers for many years as a way of placing highly capable engineers closer to the customer, allowing them to understand complex operational problems and develop solutions collaboratively rather than working from a traditional specification created elsewhere.
What has changed is the relevance of the model in the age of generative AI.
As organisations attempt to move from experimenting with AI to deploying it across real business processes, there is a growing need for people who can operate across multiple disciplines. They need to understand business requirements, workflows, software engineering, data, integration, security, cloud platforms and increasingly the capabilities and limitations of large language models.
The defining feature of the FDE is therefore not a particular programming language, certification or technology platform. It is the ability to move between understanding the customer’s business and building the technology required to improve it.
In simple terms, a forward-deployed engineer helps answer the question:
How can we use technology, including AI, to make this business operate better?
That is a much broader question than simply asking where AI can be deployed.
An FDE is more than an AI engineer.
It would be easy for MSPs to see the growing interest in forward-deployed engineers and simply rename an existing automation engineer, developer or solutions architect. Doing so would miss much of what makes the role valuable.
A traditional software engineer is often given a defined requirement and asked to build a solution. A consultant may analyse a business problem and recommend what should change. A solutions architect typically designs the technical architecture, while an AI engineer may focus on models, prompts, agents, retrieval systems or integrations.
A forward-deployed engineer operates across many of these boundaries.
They might begin by sitting with the customer and understanding how a particular business process works today. They will want to understand where employees are spending unnecessary time, why information needs to be entered multiple times, which decisions require human judgement, where errors commonly occur, which systems contain the required data and what the financial impact of the current process is.
From there, they can start designing a solution that may involve AI, traditional automation, APIs, software development or a combination of different technologies. They remain involved as the idea moves from discovery to prototype, then into production and ultimately into measurement of the business outcome.
This is an important distinction because the purpose of the role is not simply to deploy AI. The purpose is to solve a problem, and AI is one of the tools available to achieve that result.
Why AI makes the forward-deployed engineer increasingly important
One of the biggest risks facing organisations adopting AI is that projects begin with the technology rather than the business problem.
A business leader sees an impressive demonstration of an AI agent, chatbot or Copilot capability and immediately begins asking how the organisation can use it. This can quickly result in a collection of interesting pilots that demonstrate what AI is capable of but do very little to improve the underlying economics or performance of the business.
The forward-deployed engineer approaches the problem from the opposite direction.
Rather than beginning with a model or product, the FDE begins with the workflow. They investigate how work is currently performed, what information is required, which systems are involved, where employees spend their time and what the ideal outcome would look like.
Consider a professional services business where employees spend significant amounts of time reviewing incoming documents, extracting information, searching previous correspondence, updating a line-of-business application and preparing a response for a client.
The technology-first approach might immediately suggest deploying an AI assistant.
The FDE would take a different path. They would first map the entire process, understand how often it occurs, identify which elements are repetitive, determine where human judgement is essential, assess the quality of the available data and understand the security and compliance requirements associated with the information.
Only after understanding the process would they determine the best technical approach.
The final solution could involve an AI model interpreting documents, an agent retrieving information from internal systems, APIs updating the organisation’s line-of-business platform and an automation platform orchestrating the workflow. Human approval might remain at critical points to ensure quality and manage risk.
From the customer’s perspective, however, the technology is secondary. What matters is that a process that previously took 30 minutes might now take five minutes, or that employees can process twice as much work without increasing headcount.
That is the difference between demonstrating AI and delivering business value from AI.
Why MSPs are particularly well positioned
MSPs are in an unusually strong position to take advantage of the Forward Deployed Engineer model because they already possess something that many AI consultancies and software vendors spend enormous amounts of time trying to develop: a trusted relationship with the customer and a detailed understanding of the customer’s technology environment.
A mature MSP may already understand the customer’s Microsoft 365 environment, identity platform, devices, security controls, network infrastructure, applications, cloud services and backup environment. It may also manage or support the customer’s CRM, ERP, document management system and other line-of-business applications.
More importantly, years of service desk tickets, projects, account management discussions and strategic reviews often give the MSP an enormous amount of contextual knowledge about how the customer operates.
Historically, most of that knowledge has been used to keep technology running reliably and securely. The Forward Deployed Engineer model creates an opportunity to use the same knowledge to improve how the customer’s business operates.
That represents a significant evolution in the MSP value proposition.
Instead of simply saying, “We manage your Microsoft environment, security and infrastructure,” the MSP can increasingly position itself as the organisation that understands the customer’s technology and business processes, identifies opportunities for improvement and builds solutions that produce measurable outcomes.
That is a far more strategic relationship and one that is considerably harder to commoditise.
Connecting the vCIO and engineering functions
Many mature MSPs already have several of the capabilities required to deliver a forward-deployed engineering service, although those capabilities are often distributed across different people and teams.
The vCIO or strategic account manager understands the customer’s objectives and commercial priorities. A solutions architect understands the customer’s technical environment. Project engineers can implement infrastructure and cloud solutions, and automation specialists can connect applications and streamline processes, while developers and data engineers can build more sophisticated solutions.
The Forward Deployed Engineer connects these disciplines and brings them closer to the customer’s operational problems.
This also has the potential to change the nature of the traditional QBR or Technology Business Review.
Many QBRs continue to be dominated by conversations about infrastructure lifecycle, security risks, licensing, projects and upcoming hardware replacements. These conversations remain important, but they primarily focus on maintaining and improving the technology environment.
An FDE introduces another dimension by asking what processes inside the customer’s business could be materially improved using technology.
That can create an entirely different pipeline of projects and ongoing services because the MSP is no longer looking only for technology that needs replacing. It is looking for business processes that can be improved.
What could a Forward Deployed Engineer deliver?
The opportunities vary enormously depending on the customer’s industry, maturity and existing applications, which is precisely why the role needs to be close to the customer.
A legal practice, for example, might use an FDE to improve the way documents are reviewed, matters are created, correspondence is prepared and internal knowledge is retrieved.
An accounting practice may identify opportunities to automate the collection, classification and validation of client information before work reaches an accountant, reducing the amount of low-value administrative work performed by expensive professional staff.
A construction organisation might use AI to interpret project documentation, summarise site information and extract actions from email, Teams and project management platforms.
A distribution business could combine information from its ERP, CRM, supplier systems and customer communications so that service employees can answer customer enquiries dramatically faster.
The important point is that these do not necessarily need to become enormous software development projects.
Some of the highest-value opportunities may be relatively small processes that are repeated hundreds or thousands of times every month. Saving ten minutes on an activity performed once a month has little commercial value, while saving ten minutes on a process performed 200 times every day can fundamentally change the economics of the business.
Identifying those opportunities is one of the most important responsibilities of the forward-deployed engineer.
Business value needs to be measured.
If MSPs are going to move further into AI and business process improvement, they also need to change the way they measure the success of technology projects.
Traditionally, many IT projects are considered successful when the technology has been successfully implemented. The migration is complete, the system is operational and the application has been deployed.
AI solutions require a stronger connection between the technology and the business outcome.
Before an engagement begins, the MSP should understand the baseline.
If a workflow currently consumes 100 hours of employee time each month, that should be measured. If a customer request takes an average of 18 minutes to process, that should be understood. If a particular process has a high level of rework, or customers routinely wait two days for a response, those metrics provide the foundation for measuring improvement.
Once the solution is deployed, the same metrics can be measured again.
This completely changes the commercial conversation.
The customer is no longer being asked to justify a $30,000 AI project simply because AI is strategically important. They may instead be evaluating an investment that could eliminate $120,000 of annual operating cost, increase employee capacity or materially improve the customer experience.
The conversation moves from the cost of technology to the value of the outcome.
That is exactly where MSPs should want the conversation to be.
A new recurring revenue opportunity for MSPs
Forward Deployed Engineering also creates an opportunity to develop a new category of recurring service around AI, automation and continuous business improvement.
AI solutions are unlikely to be implemented once and then left unchanged for many years. Business processes evolve, applications change, new models become available, employees discover additional use cases and organisations continue to identify opportunities as they become more familiar with what AI can achieve.
Solutions also need to be monitored, governed and improved.
This creates the potential for a recurring engagement where the MSP maintains an ongoing backlog of business processes that can be improved.
An initial engagement could begin with discovery and workflow analysis, followed by a focused proof of value. Once the outcome has been demonstrated, the solution moves into production and the relationship transitions into ongoing optimisation.
One quarter might focus on customer onboarding, while the next addresses finance administration. Subsequent work could improve sales operations, reporting, customer service or internal knowledge management.
Over time, the MSP becomes more than the organisation responsible for maintaining the customer’s technology. It becomes a technology-enabled continuous improvement partner that constantly looks for ways to make the customer’s business more productive, efficient and competitive.
That type of relationship is significantly more strategic and creates far greater customer stickiness than traditional infrastructure management alone.
'Forward deployed' does not necessarily mean 'onsite'.
There is also an important misconception around the word “deployed”.
A forward-deployed engineer does not necessarily need to spend every day physically sitting inside the customer’s office. The more important idea is that they are embedded in the customer’s problem and understand the organisation closely enough to build solutions around its actual workflows.
This creates an attractive operating model for MSPs.
The FDE can remain highly customer-facing and take responsibility for discovery, process mapping, architecture, adoption and business outcomes, while working with a broader delivery team containing specialists in areas such as AI, automation, software development, data engineering, Power Platform, Azure and security.
This is particularly important because a senior forward-deployed engineer is an expensive and difficult capability to recruit, and there is little reason for that person to personally build every integration or component of every solution.
Their highest value comes from understanding the problem, determining the best approach, maintaining alignment with the customer and orchestrating the different capabilities required to deliver the outcome.
For an MSP, this could create a powerful model that combines local customer intimacy and consulting capability with a scalable engineering team supporting delivery.
What makes a good forward-deployed engineer?
Recruiting for this role is unlikely to be straightforward because the ideal candidate is not simply the strongest developer or most senior infrastructure engineer in the organisation.
A successful FDE needs strong technical capability, but they also need curiosity, commercial awareness and excellent communication skills.
They need to be comfortable entering situations where the customer does not yet understand exactly what they need. In many cases, the customer may simply know that a process is frustrating, expensive or inefficient, and it is the FDE’s responsibility to investigate the problem and determine what can realistically be improved.
They need to be able to speak with executives about business outcomes and then sit with operational employees to understand the details of how work is actually performed.
From a technical perspective, the role increasingly requires knowledge across APIs, data, cloud platforms, security, automation and modern software development, together with an understanding of large language models, agents, retrieval techniques, evaluations and AI governance.
Perhaps most importantly, the FDE needs to remain focused on outcomes.
The best solution may use an advanced AI agent, or it may turn out that a simple API integration and workflow automation solves most of the problem at a fraction of the complexity.
A great forward deployed engineer should be comfortable choosing either.
The role is already moving into the services market
The increasing focus on forward-deployed engineering is not limited to software and AI vendors. Large technology service providers are beginning to use the model as a way of taking AI deeper into customer organisations, reflecting a broader recognition that enterprises need people who can bridge the gap between rapidly evolving AI capabilities and existing business environments.
This trend should be particularly interesting to MSP leaders.
Large enterprises may have the resources to build internal teams of AI engineers, data scientists, developers and transformation specialists. Most SMB and mid-market organisations will not.
Yet those organisations face many of the same opportunities to automate work, improve decision-making, increase employee productivity and deliver better customer experiences.
Their MSP is therefore one of the most logical organisations to provide this capability.
The MSP already understands the technology, has a trusted relationship with the customer and is often involved in strategic planning. Adding Forward Deployed Engineering creates a pathway to extend that relationship from managing technology into improving the business itself.
The next evolution of the MSP
Over the past two decades, successful MSPs have consistently moved up the value chain.
The industry moved customers away from reactive break/fix support and towards proactive managed services. Cybersecurity became a core part of the value proposition, cloud changed how infrastructure was delivered, and vCIO services helped MSPs become more involved in strategy and planning.
AI may create the next major shift.
The opportunity for MSPs is not simply to become the organisation that helps customers purchase Copilot licences or deploy the latest AI tools. Those services will undoubtedly be required, but they are unlikely to create long-term differentiation on their own.
The bigger opportunity is to become the organisation that helps customers understand how AI and automation can fundamentally change the way their businesses operate.
Forward-deployed engineers provide a practical model for making that happen because they sit close enough to the customer to understand the problem while retaining enough technical capability to turn that understanding into a working solution.
They can move from a conversation about inefficiency to a mapped workflow, from a workflow to a prototype, from a prototype into production and ultimately from production to measurable business value.
For MSPs looking for the next layer of value beyond traditional managed services, that is an incredibly powerful position to occupy.
The future of the MSP may not simply be about managing more technology for customers. It may increasingly be about using technology, AI and automation to help those customers build more productive, efficient and valuable businesses.
The forward-deployed engineer could become one of the most important roles in enabling that transition.
Exploring what forward-deployed engineering could look like for your MSP? Start a conversation with Dijital Team.