Strategy consultants
stop at the recommendation. The deck is excellent. Nobody builds anything.
AI EXECUTION · CANADA & UNITED STATES · POWERED BY ROLLING WAVE
Your AI strategy is fourteen months old. Nothing has shipped.
A Forward Deployed Pod is two people — an AI-enabled project manager and an AI engineer — embedded directly in your operation. It is the PM + engineer evolution of Forward Deployed Engineering for enterprise AI implementation: find the highest-value work, build it, put it into production, and measure what it returned. First deployment inside thirty days.
THE PROBLEM
Everyone has AI. Almost nobody has results.
The licenses are bought. Copilot is rolled out. ChatGPT Enterprise is approved by legal. There is a strategy deck, a steering committee, and a slide with three horizons on it.
And the actual work of the business looks exactly the way it did eighteen months ago. The same spreadsheets get rebuilt every Monday. The same approvals sit in the same inboxes. The same three people are the only ones who know how the process really runs.
This is not a technology problem. Every organization we meet already has the tools. What they don't have is anyone whose actual job is to build with them.
THE AI ADOPTION GAP
Everyone stops just before the work begins.
There is a specific place where AI initiatives die, and it is the same place every time.
Software vendors
stop at the license. Adoption is your problem, and it becomes a training budget.
System integrators
start at two million dollars and twelve months, which rules out the ninety percent of opportunities worth thirty thousand.
Your own team
could build it, and would build it well — but they are fully committed to keeping the business running.
The gap between an AI strategy and an AI result is a team that ships.
Forward Deployed Pods exist entirely inside that gap. It is the only thing we do.
THE UNIT
Two people. Embedded. Building from week one.
A Forward Deployed Pod is a two-person AI execution team — one AI-enabled project manager and one AI engineer — embedded into your operation to identify, build, deploy and continuously improve AI solutions inside the systems you already run.
Also called an FDE Pod, Forward Deployed Engineering Pod or embedded AI implementation team, the model builds on the market's understanding of Forward Deployed Engineers while adding product leadership to protect business value and adoption.
A pod is not a project, and it does not end at a handover. It is a standing execution capability that sits next to your team, works at your cadence, and produces working software on a continuous basis.
- They join your stand-ups, your channels, and your planning.
- They work in your environment, under your security and data policies.
- They ship into production, not into a recommendation.
- Everything they build belongs to you — code, documentation, and the reasoning behind it.
- Remote across North America, or onsite where the work requires it.
WHY BOTH
Engineers build. Project managers make it worth building.
Most AI teams are engineers alone. That is why so much AI work is technically impressive and operationally irrelevant. The pod is deliberately two roles, and neither is optional.
Role 01 — AI-Enabled Project Manager
The translation layer between business operations and emerging AI capability.
- Discovery and business analysis
- Process mapping and requirements
- Opportunity identification and prioritization
- Stakeholder management and change management
- Delivery leadership and sprint planning
- AI-assisted documentation and meeting summaries
- Roadmaps and ROI tracking
Role 02 — AI Engineer
The person who turns the highest-value opportunity into working software.
- AI agents and LLM integrations
- Internal tools, copilots and custom software
- APIs, data integrations and governed system connectors
- Workflow and reporting automation
- Code generation and infrastructure
An engineer without a project manager builds the wrong thing quickly, ships it to people who weren't consulted, and watches adoption stall. Wrong priorities, weak alignment, no measured return.
A project manager without an engineer produces an excellent roadmap that joins the queue behind everything else IT is already committed to.
One builds. One makes sure it was worth building.
WHERE POD WORK COMES FROM
The best AI opportunities are usually the work your team never has time to do.
Not the transformation programme. Not the platform decision. The backlog everyone has quietly agreed to stop talking about.
- The report nobody has time to build
- The workflow everyone postpones
- The repetitive task everyone has learned to tolerate
- The integration that has been on the roadmap for three years
- The documentation nobody updates
- The dashboard everyone asks for
- The automation that never quite becomes a priority
None of it is urgent. All of it is expensive. And almost all of it is now buildable in days rather than quarters.
This is where every pod starts.
TECHNOLOGY AGNOSTIC
We work with what you already have.
Pods are not tied to a platform, a vendor, or a reseller agreement. We have no software to sell you, which means the recommendation is never shaped by what we'd earn on the license.
- We don't sell software.
- We don't require migrations.
- We don't force platform decisions.
FITS WHERE YOU ALREADY ARE
We meet your team where they are.
Rather than replacing your systems, Forward Deployed Pods integrate with them. Whether your organization runs modern cloud software, legacy enterprise systems, proprietary platforms, or all three at once, we identify the highest-value AI opportunities and build solutions that fit naturally into the environment your people already use every day.
That includes the parts nobody put in the architecture diagram — the shared drive, the twelve-year-old Access database, the spreadsheet that runs scheduling. Those are usually where the return is.
No new platform. No migration. No disruption to the work already in flight.
INDUSTRY AGNOSTIC
The patterns transfer. The context is yours.
A claims file in insurance, a discharge summary in healthcare, and a change order in construction are the same problem wearing different clothes: unstructured information that a person has to read, interpret, and route under time pressure.
The underlying AI patterns — extraction, summarization, routing, drafting, monitoring, reconciliation — transfer remarkably well between industries. What doesn't transfer is context: your regulations, your terminology, your constraints, your people. That is the first thing the pod learns, and it is why a pod embeds instead of consulting.
WHAT A POD ACTUALLY SHIPS
Not recommendations. Working software.
Representative examples of the work a pod delivers, and the time from kickoff to production.
Example deliverables
ILLUSTRATIVE| Deliverable | What it replaces | To production |
|---|---|---|
| Nightly operations report assembled from four systems | 6.5 hours a week of manual spreadsheet work | 9 days |
| Agent that reads, codes and routes vendor invoices | A nine-day approval cycle and weekly chasing | 3 weeks |
| Copilot over fourteen years of contracts and amendments | 45-minute clause hunts before every renewal | 4 weeks |
| Operations connector syncing approved system data for internal assistants | Ticketed data requests with a five-day queue | 5 weeks |
| Intake triage that classifies and routes inbound requests | A shared inbox worked in arrival order | 2 weeks |
| Automated first-draft SOPs from recordings | Documentation last updated in 2021 | 2 weeks |
| Reconciliation agent flagging exceptions before month-end close | Three days of hunting variances after the fact | 4 weeks |
HOW YOU BUY IT
One pod. Ninety days. Scale by adding pods.
Single pod
One AI-enabled PM, one AI engineer. Ninety-day initial term, then continuous. The default for a first engagement.
Multi-pod program
Two or more pods across business units, sharing patterns, tooling and a common architecture. Coordinated by Rolling Wave delivery leadership.
Pod + enablement
A pod that builds alongside your team with the explicit goal of leaving the capability behind. Ends on a date you choose.
- Fixed monthly fee. No change orders, no time-and-materials surprises.
- Remote across Canada and the United States, onsite where the work requires it.
- You own everything the pod produces, including source code and documentation.
- Ninety-day exit with no penalty. The work stays with you.
THE FIRST NINETY DAYS
From first call to measured return.
DAY 00 — Pod briefing
Thirty minutes. We map where your work actually loses time, and tell you honestly whether a pod is the right answer.
WEEK 01–02 — Embed & map
The pod joins your team, learns the systems and constraints, and produces a ranked opportunity map with sized returns.
DAY 30 — First deployment
The highest-value, lowest-friction opportunity is in production and being used by the people it was built for.
DAY 90 — Measured portfolio
Several solutions live, with a scorecard showing what each one returned. You decide whether to continue, scale, or stop.
After day ninety the cycle repeats. The opportunity map is re-ranked every sprint, because what's most valuable changes as the easy wins get taken.
This is rolling wave planning — commit in detail to the next thirty days, in outline to the next ninety, and re-plan as the picture sharpens. It is the delivery method Rolling Wave is named for, applied to AI execution.
FAQ
Answers for AI execution buyers.
What is a Forward Deployed Pod?
A Forward Deployed Pod is a two-person AI execution team — one AI-enabled project manager and one AI engineer — embedded into your operation to identify, build, deploy and continuously improve AI solutions inside the systems you already run.
Is a Forward-Deployed Pod the same as a Forward Deployed Engineer?
No. A Forward Deployed Engineer is usually one technical builder embedded close to the customer problem. A Forward-Deployed Pod is a small product-and-engineering unit: an AI-enabled project manager plus an AI engineer, accountable for finding the right workflow, building it, deploying it and measuring the result.
How is this different from AI consulting?
AI consultants produce recommendations. A Forward Deployed Pod produces working software in production. The pod does the discovery too, but discovery is the first two weeks of building rather than the deliverable.
Is this staff augmentation?
No. Staff augmentation gives you extra hands to work on your existing backlog under your direction. A pod arrives with its own method for finding the highest-return AI work, and is accountable for the return, not the hours.
Do we need to buy new software?
No. Pods are technology agnostic and integrate with your existing environment — Microsoft, Google Workspace, Salesforce, SAP, Oracle, Epic, ServiceNow, Workday, cloud, on-premise, legacy, or custom. We don't sell software and we don't require migrations.
We already pay for Copilot and ChatGPT Enterprise. Does that help?
Yes — significantly. Those licenses are usually the fastest path to a first deployment, because the security review is already done. Most organizations we meet are using a small fraction of what they've already bought.
How quickly does the first solution reach production?
The target is thirty days from kickoff. Weeks one and two are embedding and opportunity mapping; the first build starts in week two and ships by day thirty.
Who owns what the pod builds?
You do — source code, documentation, prompts, integrations and infrastructure definitions. There is no proprietary runtime you have to keep paying for to run what we built.
Which industries do you work in?
All of them. AI implementation patterns — extraction, summarization, routing, drafting, monitoring, reconciliation — transfer well between industries. Pods have applicability across healthcare, financial services, insurance, manufacturing, government, construction, energy, logistics, retail, education and professional services.
Do pods work onsite or remotely?
Both. Pods are delivered across Canada and the United States, remotely by default, onsite when the work requires being in the room — which it often does in the first two weeks.
How is ROI measured?
Before the first build, the pod records the current-state cost of the process in hours, cycle time or error rate. That baseline is re-measured after deployment and reported on a scorecard every ninety days. If a solution doesn't move the number, we say so and move on.
What is Rolling Wave?
Rolling Wave is the project delivery consultancy behind Forward Deployed Pods. Pods inherit its delivery methodology, governance and quality standards, applied to AI execution.
START HERE
Start with one pod. Judge us on what it ships.
Thirty minutes. Bring the list of things your team keeps meaning to get to. We'll tell you which items a pod could put into production in the first ninety days — and which ones aren't worth it.