Key Takeaways
- Forward Deployed Engineering (FDE) is a CTERA service in which engineers work inside your environment, with your team, on your organization’s own unstructured data, until a specific AI use case is running in production on the CTERA Intelligent Data Platform.
- Each engagement pairs a Forward Deployed Engineer, who builds the workflow, with a Deployment Strategist, who keeps the work tied to a defined business outcome.
- Engagements follow four phases: discovery and data grounding, a first production use case, scaling across additional workflows, and handover for independent operation.
- Enterprise AI tends to stall on people, data, and the last mile. In MIT’s NANDA research, about 60% of organizations evaluated custom or vendor-supplied enterprise AI tools, but only about 5% reached production.
- An engagement is complete when your team can add the next use case without CTERA. The service is available now for organizations using the CTERA Intelligent Data Platform, and it can be structured by time or as a fixed scope with milestones.
The AI Gap Nobody Budgeted For
I’ve worked in the field for 20 years, from customer data centers to escalation bridges at 2 a.m. I’ve seen plenty of technology waves. None has moved as fast as AI or left such a big gap between what people expect and what actually reaches production.
Most of my conversations with customers this year have started the same way. They have good models. They’ve run a pilot or three. And now someone senior is asking where the results are.
The models are fine. The hard part is getting AI to do reliable work on the company’s own data. MIT’s NANDA research found that only about of organizations evaluating enterprise GenAI tools got one into production.
That’s why, on October 6, we launched CTERA Forward Deployed Engineering.
Where Enterprise AI Projects Get Stuck
Projects don’t fail on the slide. They fail in real environments. When AI projects stall, I usually find the same three problems:
- First, people. The business has the idea and a sponsor, but not the engineers to build it; or the engineers it has are already fully booked.
- Second, data. File shares built up over decades are full of duplicates and stale permissions. I have yet to visit a customer without a folder called “Proposal_FINAL_v7_use-this-one” sitting right next to “Proposal_FINAL_v8.” Humans can tell which one is real. AI can’t. So, you get wrong answers that sound right, or documents shown to people who should never see them.
- Third, the last mile. The demo works, but deployment and adoption drag on until the pilot quietly fades away.
There’s one more thing. Not many organizations really know their data very well: where it lives now, how it’s organized, and who actually owns it. What’s more, they often don’t know what AI could do with it, but they have good ideas and see business opportunities.
Here’s a pattern we see often: Take an IT services firm that lives on public-sector tenders. Often, this process takes a week or more of a bid manager’s time, so they walk away from many tenders they qualify for. They don’t have a talent problem. They have a time problem.
They come to us asking for help writing bids faster. Once we map the workflow, the biggest wins are often things nobody asked for: an early go/no-go call, research on agencies that builds up over time instead of being redone for every bid, and matching past projects and consultants to what the tender actually describes.
The goal is a bid that arrives mostly drafted, so the bid manager edits and decides instead of hunting for reference projects. Underneath, CTERA Content Services enriches the firm’s existing files with the context AI needs while keeping them in place and permission-aware.
What Is Forward Deployed Engineering?
Forward Deployed Engineering (FDE) is a service model in which a vendor’s engineers embed with a customer’s team to get a specific AI use case into production.
The short version: CTERA works inside your environment, with your team, on your real data, until a specific AI use case is live in production and your people can run it.
Classic professional services deliver products as they exist today. Our Forward Deployed Engineering builds something that doesn’t exist yet, for one workflow in your business, tied to an outcome you care about.
We join your project, work at your pace, and talk directly to the people who know how the work really gets done.
The Forward Deployed Engineer is the builder. They deploy the platform, build the agents and integrations, sort out data access and permissions, and check the output against real data.
We also bring a Deployment Strategist who keeps the work tied to the business. They help find the workflows worth doing, agree on how success is measured, and make sure people actually adopt what gets built.
I’ve watched projects fail both because they never work reliably, or because they work and change nothing for the business. You need someone owning each side.
How Does a Forward Deployed Engineering Engagement Work?
There are four phases, and each one ends when the work is actually done, not when the calendar says so.
- Discovery. Metadata only: no file contents are opened or moved. We map the workflow and agree on the first use case and how to measure it.
- First use case. We prepare the data, build the workflow, test it with real users, and go live.
- Scale. We extend what worked to more workflows and sites, as you need.
- Handover. Runbooks, architecture docs, and a formal sign-off.
Within these phases, we have a multi-step process to execute against and flexibly adapt them for your business.
Discovery
Starting with metadata-only discovery makes the first phase easy for security teams to approve. You can buy a block of hours or a fixed scope with milestones, and many customers start with hours and switch to a fixed scope once the plan is clear. At this stage, we commonly find that data flows and business workflows don’t really work the way they look on slides, and we help to resolve that gap with the business and IT teams.
AI Data Readiness and AI Governance Come First
People stop using an AI tool the first time it confidently gets something wrong or shows them a file they shouldn’t have seen. And after they’ve been burned with bad results or spend too much time coaxing the AI tool, they don’t come back easily.
So, before any AI touches your data, we work out exactly which data the workflow needs, then classify and enrich it, and get the permissions right. On the CTERA Intelligent Data Platform, AI only sees what each user is already allowed to see, data stays where it is, everything is logged, and you can run it in your own cloud, on-prem, air-gapped, or as SaaS.
The Goal Is to Hand AI over to Your Team
Early in my career, I learned that the best thing a customer can tell a field engineer is “We’ve got it from here.” We built this service around that.
Your engineers build alongside ours from day one, and we document as we go. The job is done when your team can deliver the next use case without us. My bet is that the companies that do well with AI will be the ones that build the skills in-house.
Why Forward Deployed Engineering Matters to Me
The field is where you learn what customers really need, as opposed to what the brochure says. That’s the whole idea behind this service.
If you have an AI project that depends on your file data and it isn’t moving, whether that’s because you’re short on people, your data isn’t ready, or a pilot never made it out, I’d like to hear about it.
Forward Deployed Engineering is available now for organizations on the CTERA Intelligent Data Platform. Reach out to your CTERA partner and account team or visit ctera.com/support to set up a free consultation.
See you in the field!
If you’ve ever had a file called FINAL_v8, I’m not judging. I have one too.
Frequently Asked Questions
- What is Forward Deployed Engineering?
Forward Deployed Engineering (FDE) is a service model in which CTERA engineers work inside a customer’s environment, with the customer’s team and on its real data, until a specific AI use case is live in production. CTERA’s version pairs a Forward Deployed Engineer with a Deployment Strategist and is production ready when the customer can add the next use case without CTERA.
- How is Forward Deployed Engineering different from traditional professional services?
Classic professional services usually delivers the product as it exists today. Forward Deployed Engineering builds something that does not yet exist, for one workflow in your business, tied to an outcome that is measurable and meaningful.
- What does a Forward Deployed Engineer do?
A Forward Deployed Engineer is the hands-on builder. They deploy the platform, build the agents and integrations, sort out data access and permissions, and check the output against real data. They work with a Deployment Strategist, who helps find workflows worth doing, agrees how success is measured, and makes sure people adopt what gets built.
- What are the phases of a Forward Deployed Engineering engagement?
Engagements follow four phases: discovery and data grounding, a first production use case, scaling across additional workflows, and handover for independent operation. Each phase ends when the work is done, not when the calendar says so. Customer engineers work alongside CTERA from the first phase and document the configuration as they build it.
- How are Forward Deployed Engineering engagements structured?
Engagements can be structured by time, such as a block of hours, or as fixed-scope projects with milestone-based acceptance criteria.
- How does CTERA keep AI from showing people files they should not see?
The team identifies which data sets a workflow needs, then classifies, organizes, and governs them in to meet the objectives. On the CTERA Intelligent Data Platform, AI works only with content each user is already authorized to access, and the data stays where it is.
- Does Forward Deployed Engineering work with the AI tools we already use?
Workflows are built to integrate with the existing systems and AI tools the organization already uses, through open standards such as the Model Context Protocol (MCP) and automation platforms such as n8n.
- Why do enterprise AI projects stall before production?
Common causes are a shortage of specialized skills, file data that is duplicated, stale, and inconsistently permissioned, and a last mile of deployment and adoption that drags on. In MIT’s NANDA research, about 60% of organizations evaluated custom or vendor-supplied enterprise AI tools, but only about 5% reached production.
- Who can get CTERA Forward Deployed Engineering?
CTERA Forward Deployed Engineering is available now for organizations using the CTERA Intelligent Data Platform.
