AI Consultant Training
Unlike the engineering course, which focuses on code, this course focuses on context, cost, and compliance. Designed for solution architects, technical product managers, and management consultants who need to bridge business goals and AI capabilities.
Created by Baljeet Dogra
🧠 Course Philosophy: "The Bridge Builder"
An AI Consultant does not need to know how to write a CUDA kernel. They need to know why a CUDA kernel costs money, when to use it, and how to explain the risk to a CEO. This course is about navigating the "Hype Cycle" to find the "Value Cycle".
Perfect For
Solution Architects, Technical Product Managers, and Management Consultants who need to bridge the gap between business goals and AI capabilities.
Management Consultants
Strategy consultants and independent advisors who need to guide clients on AI adoption, build business cases, and navigate enterprise AI decisions.
Technical Product Managers
PMs who need to evaluate AI features, build defensible business cases, prioritize AI initiatives, and make buy vs. build decisions.
Solution Architects
Architects who design AI solutions, assess technical feasibility, make architectural decisions, and ensure solutions meet business needs while managing risk.
What You'll Learn
By the end of this course, you will be able to:
Diagnose AI Opportunities
Identify business problems that are actually solvable by AI (and reject those that aren't). Use impact/effort matrices and process mapping to prioritize use cases.
Calculate True ROI
Build comprehensive TCO models including token costs, infrastructure, and calculate the true Return on Investment of AI projects with 3-year projections.
Navigate Legal Landscape
Understand EU AI Act, GDPR, copyright law, and liability issues. Create compliance checklists and acceptable use policies for enterprise AI.
Design Implementation Roadmaps
Create buy vs. build decisions, change management plans, and phased implementation roadmaps from PoC to production with realistic timelines.
6-Phase Curriculum
From AI literacy to comprehensive strategy engagement.
Phase 1: Foundation & Literacy (Weeks 1-2)
Build executive-level understanding of AI and the vendor landscape.
- Week 1: Executive AI Bootcamp - Generative vs. Predictive AI, Transformer architecture (conceptual), The API Economy
- Week 2: Vendor Landscape - OpenAI vs. Anthropic vs. Google vs. Meta, Closed Source vs. Open Source
- Deliverables: Executive Briefing Note, Vendor Comparison Matrix
Phase 2: Strategy & Selection (Weeks 3-4)
Discover use cases and make architectural decisions.
- Week 3: Use Case Discovery - Impact/Effort Matrix, Process Mapping, Stakeholder Interviewing
- Week 4: Buy vs. Build Architecture - Off-the-shelf (Copilot) vs. Custom (RAG), Technical Debt, Vendor Lock-in
- Deliverables: Top 3 Prioritized Use Cases, Architecture Recommendation Document
Phase 3: The Business Case (Weeks 5-6)
Build defensible financial models and ROI calculations.
- Week 5: Tokenomics & Cost Modeling - Input/output pricing, Vector DB costs, GPU hosting costs
- Week 6: ROI & KPIs - Productivity gains, Deflection rates, Quality metrics
- Deliverables: Excel TCO Calculator (3-Year Projection), Business Case Pitch Deck
Phase 4: Governance & Risk (Weeks 7-8)
Navigate legal compliance and manage enterprise risks.
- Week 7: Legal & Regulatory - EU AI Act, GDPR, Copyright law, Liability
- Week 8: Ethics, Bias & Security - Prompt Injection, Data Leakage, Hallucination management, Fairness
- Deliverables: AI Compliance Checklist, Enterprise Acceptable Use Policy (AUP)
Phase 5: People & Process (Weeks 9-10)
Manage change and create implementation roadmaps.
- Week 9: Change Management - Overcoming employee resistance, Human-in-the-loop workflows
- Week 10: Implementation Roadmap - PoC → MVP → Pilot → Production
- Deliverables: Internal Communications Plan, Gantt Chart / Timeline
Phase 6: Capstone (Weeks 11-12)
Complete strategy engagement simulation.
- Task: Full simulation - Receive a "Client Brief" (e.g., Insurance company automating claims)
- Deliverable: Comprehensive Strategy Deck covering Solution Design, Financials, Risk, and Roadmap
- Review: Peer review focused on "Defensibility" of the business case
The Consultant's Toolkit
Practical tools and frameworks you'll master.
Financial Modeling
- • Advanced Excel/Google Sheets
- • Token estimation models
- • TCO calculators
- • 3-year ROI projections
Diagramming & Architecture
- • Mermaid.js / LucidChart
- • High-level system architecture
- • Process mapping
- • Implementation roadmaps
Strategic Frameworks
- • Gartner Hype Cycle
- • RICE Score (Prioritization)
- • NIST AI Risk Management
- • Impact/Effort Matrix
Choose Your Learning Path
Flexible options to fit your schedule and learning style.
Self-Paced
- Lifetime access to all materials
- Pre-recorded video lectures
- Community forum access
- Certificate upon completion
Cohort-Based
- Everything in Self-Paced
- Live weekly sessions
- Direct instructor access
- Peer collaboration
- Real-time Q&A
Premium
- Everything in Cohort-Based
- 1-on-1 mentorship sessions
- Career coaching
- Guaranteed portfolio review
- Job placement assistance
AI consultant training FAQ
What is AI consultant training?
AI consultant training is a structured path for people who advise on AI rather than only write model code. This 12-week course covers opportunity diagnosis, business cases, ROI, governance, compliance, and implementation roadmaps so you can run an engagement from discovery to handover.
Is this an AI consultant masterclass or a roadmap?
Both. The curriculum is a consultant roadmap (strategy → cost → compliance → delivery) taught as a 12-week masterclass with optional cohort and 1-on-1 options. It is not a Python bootcamp — that is the AI Engineer course.
Who is this AI consultant course for?
Management consultants, technical product managers, and solution architects who need to brief executives, size AI work, and keep implementations inside cost and legal limits.
Ready to Become an AI Consultant?
Join the next cohort and master AI strategy, ROI, and implementation in 12 weeks.