AI Lead Consultant, AI Practice

Greenlight Consulting•Toronto, ON

7d•CA$90,000 - CA$130,000•Hybrid

About The Position

Greenlight helps organizations solve complex business challenges through intelligent automation, agentic AI, and custom technology solutions. Our teams work directly with clients to understand their operations, identify opportunities, and rapidly build solutions that create measurable business value. We combine deep consulting expertise with hands-on engineering to bridge the gap between strategy and execution. We are building a future where consultants and engineers work alongside AI to deliver faster outcomes, stronger businesses, and transformative customer experiences. We use Anthropic's Claude as a core delivery tool - and this role sits at the center of how we define what gets built and why it matters. The AI Lead Consultant is the analytical and commercial intelligence layer of every Greenlight AI engagement. You are not an engineer - you are the person who ensures the engineer builds the right thing. Working in a two-in-a-box model alongside a Forward Deployed Engineer, you own the what and the why of every automation initiative: why this process, what it should do, how success is measured, and what the business must look like after the AI agent is deployed. The most common failure mode in enterprise AI is not a technology failure - it is a requirements failure. Engineers build exactly what they are asked to build, and what they are asked to build is often a precise replication of a broken process, wrapped in AI. The result: faster execution of the wrong workflow. This role is the structural safeguard against that outcome. You will travel to client sites, run discovery workshops, challenge process assumptions, design the business logic that AI agents will execute, build the business case that justifies investment, and produce the requirements that the FDE builds against without ambiguity. You are client-facing, delivery-accountable, and commercially aware. This is not a back-office BA role.

Requirements

3-5 years in business analysis, management consulting, process improvement, or operational transformation - with demonstrated experience leading reengineering initiatives, not just documenting current-state workflows
Ability to facilitate structured discovery workshops with cross-functional and executive-level stakeholders
Hands-on use of Claude, ChatGPT, Copilot, Gemini, or comparable LLM platforms in a professional context - as a power user, not just a casual user
Strong written communication - polished, structured deliverables produced under time pressure without extensive editorial support
Structured thinker - organizes ambiguous situations into clear frameworks, prioritized issues, and decision-ready recommendations
Intellectually courageous - willing to challenge a client's stated requirements and push back on automate-as-is when the process design is flawed
AI-fluent - understands how agents reason, what they handle well, and where human judgment is still required; does not need to code but cannot be a black box about how the technology works
Executive presence - presents findings and recommendations to C-suite audiences with confidence and appropriate brevity
Claude Cowork and Skills framework - working knowledge as a power user; ability to test agent outputs and give feedback on logic design
LLM platforms - Anthropic Claude (primary), Microsoft Copilot, Google Gemini, OpenAI; understands conceptual differences between platforms and where each fits
Process mapping tools - Visio, Lucidchart, Miro, or equivalent; professional-grade swimlane and workflow diagrams
ROI and financial modelling - Excel; able to build and defend a credible business case without a finance team doing the heavy lifting
Agile delivery fundamentals - sprint cycles, user stories, acceptance criteria, backlog participation

Nice To Haves

Lean Six Sigma Green Belt or higher - applied project experience, not certification only
CBAP, PMI-PBA, or equivalent BA certification

Responsibilities

Lead structured current-state process discovery sessions with client stakeholders - walkthroughs, observation, value stream mapping - at a fidelity that captures decision logic, exception handling, system touchpoints, and handoff points
Challenge the client's stated requirements with disciplined questioning: if a process step exists because 'we've always done it this way,' surface and resolve that before it becomes an automation constraint
Apply Lean thinking to identify waste, bottlenecks, and manual interventions that should be eliminated - not automated - before an agent is built
Produce redesigned future-state process maps that represent the optimized workflow the AI agent should execute, not the legacy process with AI layered on top
Facilitate executive-level process reviews to validate redesigned workflows and secure stakeholder alignment before build begins
Author clear, complete, and structured requirements for AI agent automations - written precisely enough for engineering and accessibly enough for business sign-off
Define agent decision logic in structured formats: decision trees, if-then-else flows, escalation triggers, exception routing rules, and confidence threshold criteria - without writing code
Document scope boundaries explicitly: what the agent handles autonomously, what requires human-in-the-loop review, and what must always be escalated
Write user stories and acceptance criteria that specify the actor, the action, the business outcome, and the measurable success condition
Identify and document edge cases, exceptions, and failure modes the agent must handle gracefully - derived from discovery, not assumption
Validate requirements completeness with the FDE before build begins - no ambiguity that would require the engineer to make business decisions mid-build
Develop and maintain working fluency in Claude Cowork and the Skills framework as a power user - understanding how Skills are structured, how agents reason through tasks, and how human-in-the-loop checkpoints are configured
Use Claude directly during discovery and validation - testing sample inputs against prototype agents to assess output quality before requirements are finalized
Distinguish between process steps best suited to AI agent automation, workflow tools, or continued human execution - and make that recommendation as part of the automation blueprint
Understand prompt design principles conceptually - sufficient to review and give feedback on agent logic without authoring the technical implementation
Recognize when a requirement is technically unfeasible for the current platform and escalate early rather than allowing the FDE to discover it mid-build
Stay current on Claude and broader AI platform updates - translating new capabilities into automation opportunities for clients
Build automation business cases that quantify: current-state cost, projected AI-enabled capacity, error reduction value, compliance risk mitigation, and net investment payback period
Develop ROI models at conservative, base, and optimistic confidence levels - tied to measurable process assumptions the client validates
Prioritize automation opportunities by value-to-complexity ratio, enabling clients to sequence their AI roadmap by impact and feasibility
Produce executive-ready business case presentations that translate quantitative ROI into strategic narrative - what this automation enables the business to do that it could not before
Track realized value post-deployment against projected figures - closing the loop on delivery credibility
Design and facilitate discovery workshops, process reviews, requirements walkthroughs, and alignment sessions throughout the engagement lifecycle
Translate AI concepts - agents, Skills, prompt chains, human-in-the-loop design - into language that resonates with operations managers, compliance officers, and C-suite audiences
Manage competing stakeholder priorities and process ownership disputes that arise when automation redesigns cross departmental boundaries
Serve as the day-to-day business-side point of contact for client stakeholders, protecting the FDE's focus time for engineering delivery
Produce high-quality written deliverables: process maps, requirements documents, business cases, workshop readouts, and steering committee packages - written to consulting-grade standards
Surface expansion opportunities during active engagements - adjacent automation candidates, new use cases, follow-on scope - and route them to the Pre-Sales SE or Engagement Manager
Participate in pre-sales discovery calls and scoping sessions - bringing analytical and process credibility that validates Greenlight's approach before a deal is signed
Contribute to proposals by authoring the process design and business case sections that ground the technical solution in measurable client value
Build reusable practice assets: process assessment frameworks, requirements templates, BRD structures, ROI models, and industry-specific workflow patterns
Contribute to thought leadership - insights on process reengineering, AI requirements design, and automation ROI that build Greenlight's market profile