AI Account Director in Toronto

A technology innovation firm that has launched 750+ applications over the past 18 years for global brands including Suncor, Loblaw, Petro Canada, RBC, Porter Airlines, Chatters, David Yurman, Meridian, Empire Life, First Canadian Title, Gateway Casinos, and the Canadian Standards Association. We bridge the gap between AI's promise and business reality, delivering pragmatic AI solutions that create measurable business outcomes rather than theoretical possibilities. Our approach is business-first: we start with client objectives and work backward to ensure every AI initiative delivers measurable ROI. We are looking for builders who share this philosophy.

Our work increasingly takes us beyond individual AI use cases into designing enterprise agentic frameworks — the orchestration, observation, and decision-modelling layers that unify our clients' existing AI platforms into coherent, governable, brand-safe systems. We are connected into the Canada AI Alliance and Global AI Leaders Alliance networks, giving us cross-industry visibility into how the most demanding AI organizations are building.

Role Summary

• AI consulting sales to enterprise accounts: find the wedge, run the pursuit, close the work, and grow the account after delivery starts.
• They sell AI advisory and implementation into retail and grocery, financial services, manufacturing, automotive, and healthcare sectors. Buyers are executives who are past the pilot stage and want business outcomes, not model demos.

What you own

• Run client Discovery calls & Discovery wedges - AI use case demos, AI-DLC framework workshop, agentic framework workshop, AI ROI calculator wedge, AI Talent playbook wedge, AI adoption playbooks wedge etc.
• Pursuit management: Run the deal: qualification, stakeholder mapping, pursuit plan, timelines, internal resourcing.
• Solution shaping: Frame the client problem and the commercial case. Our AI and consulting leads design the solution; you own the narrative, the value case, and the fit.
• Proposals and RFPs: Own the proposal and its commercial structure, including pricing and scope trade-offs.
• Negotiation and close: Carry the number. Own MSAs, SOWs, and renewal negotiations through to signature.
• Client partnership: Be the client's senior point of contact after signature. Protect delivery quality, surface issues early, and stay in the account.
• Account growth: Mine and farm the accounts you close. Expand from the first engagement into adjacent business units and use cases.

What you need

• 8+ years selling enterprise technology services, with a track record of closing multi-million-dollar engagements into executive buyers.
• A consulting or professional-services background. You can hold a business conversation about operations, margin, and risk before anyone opens a slide.
• Fluency in AI: what agentic systems actually do, where they fail, and how enterprises govern them. You do not need to build them.
• AI-native working habits: You use these tools daily in your own work — research, account planning, proposal drafting, follow-up.
• Comfort as the senior person in the room with a CIO, COO, or divisional GM.

Strong Assets

• Existing relationships in one or more of our target sectors.
• Demo delivery: you can run a working demo yourself rather than scheduling one.
• RFP and proposal writing strength.
• Partner-led selling through hyperscalers or systems integrators.

How Performance is measured

• Qualified pipeline generated, self-sourced share.
• Win rate and average engagement size.
• Account expansion: revenue growth in year two of an account versus year one.
• Client retention and reference quality.

What You Will Work On

They deliver solutions across several domains. Here are examples of the types of projects you would contribute to:

• Designing the cross-platform agentic framework — orchestration, observation, and decision modelling — for an enterprise client unifying Salesforce Agentforce, Snowflake Cortex, Sigma, and Enterprise ChatGPT into a single governable system.
• Building multi-agent systems that reduce manual decision-making by 30% and improve response times by 40%.
• Implementing AI-driven quality monitoring for manufacturing clients that reduces batch rejections by 25%.
• Developing hyper-personalization engines for retail and luxury clients that increase digital conversion rates by 20–27%.
• Creating data pipelines and AI infrastructure that enable new AI initiatives while reducing data preparation time by 60%.
• Architecting observability, evaluation, and governance frameworks that pass enterprise brand, legal, and privacy review.
• Building reference architectures and reusable IP that scale across multiple engagements.