AI-Native training
ScaledNative combines self-paced role tracks, live cohort labs, and practitioner-led certification. The goal is not to sit through AI content. The goal is to become AI-native in the job you actually do.
Available now
7 web-based tracks
May / summer 2026
live implementation cohorts
Built around
role-specific work artifacts
Web-based catalog
Build the shared AI vocabulary, prompt discipline, responsible-use habits, and personal workflow patterns every professional needs before deeper role-based training.
A comprehensive prompt workflow course for professionals who need repeatable AI output, reusable prompt systems, evaluation criteria, and safer handoffs into production work.
Practical business applications of generative AI covering analysis, writing, presentation support, research, image generation, and workflow integration inside organizational policies.
For product, project, agile, and delivery leads who need to convert AI interest into governed backlogs, work lanes, review rituals, and shipped evidence.
For analysts and operators who need AI-assisted requirements extraction, SOP drafting, process mapping, reporting, and quality review in real work contexts.
For engineers and architects who need implementation patterns for AI-assisted SDLC, RAG, tool use, evaluation harnesses, observability, and secure delivery.
For managers and executives who need a practical AI adoption plan with governance, ownership, use-case triage, and measurable capability growth.
Curriculum model
01
Build the shared AI vocabulary, responsible-use posture, and prompt discipline every role needs before workflow redesign begins.
02
Map the participant's actual job flow and rebuild one recurring task with AI support, review gates, and measurable quality criteria.
03
Produce a real artifact: backlog, operating model, evaluation rubric, policy, automation, code pattern, or 90-day rollout plan.
04
Submit the artifact, reflection, and quality checklist for review so completion is tied to demonstrated capability, not attendance.
Role-based paths
Each path teaches the shared AI-native vocabulary, then turns toward the work products that matter for that role: roadmaps, evaluations, workflows, code, governance, or operating rituals.
6-8 hours · Beginner
A personal AI operating system for daily work.
10-12 hours · Intermediate
An AI-native delivery lane that moves work from idea to shipped evidence.
8-10 hours · Intermediate
A governed analysis workflow that turns messy inputs into reviewable requirements.
12-16 hours · Advanced
A production-minded implementation pattern for AI-assisted software work.
4-8 hours · Executive
A 90-day AI capability plan with owners, governance, and measurable business outcomes.
Track detail
All professionals · 6-8 hours
Modules
AI, GenAI, LLMs, agents, and RAG in plain language
Prompt patterns for research, writing, analysis, and decision support
Workflow redesign with human review points
Responsible use, confidentiality, and output verification
Labs
Redesign one recurring personal workflow
Build a reusable prompt and checklist pack
Create an AI-use decision record
Product, project, and delivery leads · 10-12 hours
Modules
AI-assisted discovery and stakeholder synthesis
Backlog creation, acceptance criteria, and evaluation rubrics
Agent handoffs, review rituals, and delivery governance
Metrics for cycle time, quality, and adoption
Labs
Turn a vague initiative into an executable backlog
Write an evaluation rubric for AI-assisted work
Design a weekly AI-native operating cadence
Business analysts and operators · 8-10 hours
Modules
Process mapping and requirements extraction with AI
Document, meeting, and SOP analysis patterns
Human-in-the-loop quality assurance
Evidence trails for regulated and executive review
Labs
Extract requirements from a mixed-source packet
Draft and validate an SOP with review gates
Build an analysis QA checklist
Engineers and architects · 12-16 hours
Modules
AI-assisted SDLC patterns and context engineering
RAG, tool use, and agentic workflow architecture
Evaluation harnesses, observability, and rollback thinking
Security, prompt injection, data boundaries, and code review
Labs
Build a small AI feature with testable behavior
Add an evaluation harness and risk register
Produce a deployment and rollback note
Managers and executives · 4-8 hours
Modules
Use-case triage and portfolio prioritization
AI operating model, risk ownership, and governance
Team adoption, incentives, and change management
Executive measurement and value realization
Labs
Prioritize a practical AI initiative portfolio
Create a governance decision map
Write a 30/60/90-day rollout plan
Competitive posture
Comparable certification structure to enterprise agile programs, but built around AI-native work products instead of attendance alone.
Every track ends with a role-specific artifact: a workflow, backlog, evaluation rubric, governance charter, or implementation plan.
Web-based training starts the capability build now; live cohorts add coaching, peer review, and implementation labs as the summer 2026 schedule opens.
Live cohorts
New York, NY · 1 day · Intermediate
Chicago, IL · 1 day · Beginner
Virtual · 2 days (4 hrs/day) · Intermediate
Virtual · 2 days (6 hrs/day) · Advanced
Launch plan
The curriculum is staged so teams can start online immediately, then bring higher-stakes work into facilitated labs as the live calendar opens.
Foundations, prompt workflow, business, delivery, engineering, and leadership tracks accept individual and team interest.
Small virtual and city-based cohorts validate the lab format, instructor feedback loop, and capstone rubric.
Live implementation cohorts expand with capped seats, practitioner review, and private team delivery options.
Role tracks extend into deeper specialization for modernization, secure AI delivery, and agent-team operations.
Enrollment
Individuals can request access to the self-paced catalog. Teams can request a private cohort, blended rollout, or summer live-course seat block.