Forward Deployed — AI Alignment Training for PMs, BAs, QA Engineers, and Delivery Leads
For PMs · BAs · QA · Engineers · Delivery Leads

Your team is
shipping AI.
Do you know your role?

Most PMs, BAs, QA engineers, and delivery leads are watching AI projects happen around them — without a clear role inside them. This course gives you the language, the process, and the documents to participate. No coding required.

PM / POBusiness AnalystQA EngineerDelivery LeadEngineerOps

Free seminar to start · No coding · No ML background needed

EvalForge ✓ 4/5 evals passed
ADLC Phase 3 ✓
Spec Owner ★ Week 2
AI Spec written ✓
forwarddeployed.app / adlc
ADLC ApplicationPhase 3 / 6
⚡ Grill Skill📜 Specify
AI Development Lifecycle
Discovery
Framing
Architecture
Build
Eval
Production
Specify — Agent-readability
ObligationAgent-ready
GuardrailsAgent-ready
Handoff TriggerNeeds precision
Eval ThresholdAgent-ready
FallbackToo vague
forwarddeployed.app / evalforge
EvalForge6/6 stages
ACTIVE
Eval Pipeline
Golden Set
Pass
LLM Judge
Pass
Red-team
Review
POC Gate
Pass
0
Evals run
0%
Pass rate
0
Red flags
0%
POC gate
ADLC →
EvalForge →
forwarddeployed.app / applied-ai
Applied AI Studio
LIVE
Discover
Process Map
Architect
EvalForge
Observe
Deployment Checklist
Hallucination guardrail
98%Pass
Scope boundary check
94%Pass
Stakeholder alignment
91%Pass
Edge-case coverage
76%Review
Production handoff spec
89%Pass
AI Assistant
What's my role in the ADLC as a PM?
0%
Eval pass rate
0
Mock clarity
0
Evals run
0%
Fit score

What changes for your role

Same job. Different lane
when the product is AI.

You don't change careers. You don't learn to code. You learn what your existing role looks like inside an AI project — and how to be the person who drives it, not just attends it.

PM / PO
Today

Writes user stories based on what users need to do

in AI projects

Defines what the AI is allowed to decide, what it cannot do, and when a human must take over

Your job in AI projects isn't writing user stories — it's defining the rules the AI lives by. Nobody has taught PMs this yet.
BA
Today

Gathers requirements and writes documents that describe what the system must do

in AI projects

Asks the new questions: what does 'correct' mean when the system gives probabilities? What happens when it's wrong?

A traditional requirements document misses 6 things AI teams actually need. We teach you what those 6 things are.
QA
Today

Writes test cases: does the feature work? Pass or fail.

in AI projects

Designs an evaluation plan: how do you know an AI is good enough? What does 'failing' even mean for a model?

You can't unit-test a language model. Your instincts are exactly right — you just need the new playbook.
Delivery
Today

Manages sprint ceremonies and tracks whether features ship on time

in AI projects

Runs the readiness check before an AI system goes live — a very different kind of 'done'

AI projects don't fail in development. They fail at handoff. Delivery leads who know what to check become indispensable.
Engineer
Today

Builds full-stack features: React, Node, Postgres, deployed. Scope is clear. Pass or fail is clear.

in AI projects

Spins up agents, connects them to real systems, maintains them in production, writes the eval plan, and owns the go/no-go before it touches users.

Full stack used to mean you could ship a product alone. Now it means you can deploy an agent, keep it running, and know when to pull it offline.
For software engineers

Full stack is table stakes.
Forward Deployed is what's next.

React + Node + Postgres used to be the differentiator. Now every engineer ships that. The engineers getting pulled into the hardest problems are the ones who can spin up an agent, connect it to real systems, maintain it in production, and know when to shut it down.

Attend the free seminar
Full Stack (2019)
React / Vue
Node / Express
PostgreSQL / Redis
Docker · CI/CD · deployed
Forward Deployed (now)
Spin up + configure agents
Connect to real business systems
Eval plan — test before users see it
Monitor in production · know when to pull it

Sound familiar?

This is the gap
we close.

These are the exact moments our students describe before they enrolled. If any of these feel familiar — you're in the right place.

💬

You sat in an AI planning meeting. Your team debated model accuracy and training data. You nodded along — but you couldn't ask a meaningful question.

After this course: you walk in knowing exactly what to ask, challenge what doesn't make sense, and leave with a decision on record.
📋

Your company launched an AI initiative. The team has engineers, data scientists, and a vendor. Your role — PM, BA, or QA — isn't on the project org chart.

After this course: you have a defined lane in every AI project your company runs. Not observer. Not note-taker. Owner.
📄

A job posting said 'experience with AI products.' You've shipped real products for years. You couldn't map your experience to what they were asking for.

After this course: you know what AI product work looks like in your role — and you have the documents to prove you've done it.

What makes this different

Not a bootcamp.
Not 'prompt engineering.'
A process course for your role.

Every other AI course teaches you to open a laptop and build a model. This course teaches you how AI projects actually work — ADLC, Spec Owner, eval frameworks, production readiness — in the role you already have. No ML background. No IDE. Real documents you bring to work on Monday.

No ML background required — your process skills are the foundation
Write the Specify document that tells your AI team what to build
Run an eval plan and make a defensible go/no-go call
Taught by people who've shipped AI in real enterprise environments

What you'll actually be able to do

01Walk into your next AI planning meeting knowing what to ask — and what to push back on
02Write a brief that tells your AI team what the system must do, and what it must never do
03Know whether an AI feature is actually ready to ship — before it goes live
04Make a go/no-go call on an AI system and defend it to a VP in plain language
05Have a defined role in every AI project your company runs — not just 'the PM on the team'
06Be the person who gets pulled into AI decisions — not the one who finds out after the fact

How it works

Seminar → Course → Real work

01

Come to the free seminar

A 90-minute live session. We show you how AI projects actually work, where your role fits in, and what the job looks like in practice. No jargon. No pitch. Just content — and your questions.

Register — it's free
02

Do the 4-week course

Self-Paced or Live Cohort. Over 4 weeks you'll write your first AI brief, design a testing plan, run a readiness check, and build a real portfolio. These are actual work documents — not slides.

See the course
03

Show up differently

You leave with 4 real documents: a role map for AI projects, an AI brief, a testing plan, and a production checklist. Tools you use in your actual job — not things you put in a portfolio and forget.

See what you'll build
Free live seminar

Not sure yet?
Come to the free session first.

A free 90-minute live session. We show you exactly how AI projects work, where your role fits in, and what the job looks like in practice — using a real healthcare use case you can relate to. No pitch. No pressure. Ask everything you want.

Monthly · 90 minutes · No credit card · 1 month platform access included

Next cohort — July 2026
Applied AI for the Rest of Us
Free seminar · then 4-week cohort
Who it's forPMs, BAs, QA, Delivery, Engineers
Duration4 weeks + free 90-min seminar
FormatSelf-Paced or Live Cohort (max 30)
You leave with4 real work documents for AI projects
30 seats per cohort · once full, next opens next month

The next AI meeting
at your company — be ready for it.

Start with the free seminar. If it clicks, enroll in the 4-week course. Self-Paced or Live Cohort — pricing for India, US, Canada, and UK.

What is the AI Development Lifecycle (ADLC)?

The AI Development Lifecycle (ADLC) is a 6-phase framework — Discovery, Framing, Architecture, Build, Eval, Production — that maps every role's responsibilities as an AI feature moves from idea to deployment. Most AI projects stall because non-engineers don't know which phase they're in, what they own, or what "done" actually means. The ADLC gives PMs, BAs, QA engineers, and delivery leads a shared language and clear accountability at every stage.

Learn more: ADLC guide for product managers · AI alignment for non-engineers

How to Evaluate AI Without Engineering It

QA engineers and BAs can run structured eval frameworks on AI outputs without ML expertise. The key is defining what "good enough" means before the model is built — eval thresholds, guardrail boundaries, and human-handoff criteria. EvalForge walks you through building an eval plan for your specific use case: you define the rubric, the platform runs the assessment, and you make the go/no-go call with evidence, not gut feel.

Related: QA engineer's guide to LLM testing · Eval frameworks for non-ML teams

Pricing for AI Process Training

The free 90-minute live seminar requires no credit card and unlocks 30 days of platform access. The 4-week Applied AI course is $499 self-paced (1 month tools) or $899 for the live cohort with instructor sessions (3 months tools). Both include Applied AI Studio, EvalForge, and the Spec Owner template kit. After the course, continue on-demand or subscribe at $69/month. Teams of 3+ from the same org qualify for group pricing — contact us at support@forwarddeployed.app.

See details: Compare all plans · Register for the free seminar