COO – Regional Operations — Applied-AI Portfolio

The mandate

An applied-AI portfolio has strong research teams and a growing customer pipeline, but too many prototypes remain between demonstration and supported deployment. Data access, evaluation, integration, controls and service ownership are resolved afresh for each project. The board has planned a new regional COO appointment to create repeatable research-to-product operations before growth multiplies unfinished obligations.

Approximately 950 employees and material partners span research, product, engineering, customer delivery, data operations, security, commercial and support from Toronto. The onsite COO owns regional operations, delivery systems, workforce deployment, facilities, service readiness and operating transformation, reporting to the Group Chief Executive or designated executive committee sponsor.

The operating model will define stages from customer problem to live service. Discovery, feasibility, data readiness, evaluation, pilot, production acceptance and support each need evidence, owner and exit criteria. Teams may iterate within a stage, but they cannot label a demonstration as production progress when control and operating work remains unowned.

Portfolio intake must test the decision being improved. A use case needs sponsor, users, baseline, data rights, consequence and adoption route. The COO will stop work commissioned as broad experimentation when no executive can state what successful deployment changes. Research curiosity can continue through its own governance, not disguised as customer delivery.

Data readiness is an operating dependency. Access, quality, representative coverage, retention and change need acceptance before schedules become commitments. Customer data may remain in controlled environments, requiring realistic integration and support. Synthetic or sample data cannot prove production readiness when rare cases drive consequence.

Evaluation will align to intended use. Accuracy alone may be insufficient; robustness, drift, human override, latency, cost and subgroup outcomes can matter. Independent risk and product authorities retain approval. Operations will ensure their requirements are scheduled and evidenced rather than compressed at the end.

Handover from research to engineering needs artefact discipline. Code, model, environment, dependencies, assumptions and known limitations must be reproducible by the receiving team. Researchers should remain available for defined transition, but an individual's notebook or tacit explanation is not a supported product asset.

Customer implementation will use a common control spine. Identity, data flows, monitoring, incident routes, release, rollback and user training should have standard patterns. Exceptions may be necessary for regulated or sovereign environments; each requires owner, cost and an agreed route out of bespoke support.

Service ownership begins before launch. Availability, response, model change, data drift and customer communication need named leaders and capacity. The COO will ensure the organisation can sustain weekends, holidays and regional coverage where contracts require it. A launch celebration is not proof of an operable service.

Workforce deployment should match bottlenecks. Scarce evaluators, platform engineers, domain specialists and implementation leads will be planned across the portfolio. Priority changes must show displaced work. Constant executive escalation will be treated as evidence that intake and capacity governance are failing.

Delivery economics will include compute, data preparation, specialist review, integration, support and change. Teams will forecast cost-to-complete and lifetime service burden. Customer-specific revenue cannot be considered attractive if it creates an unpriced permanent engineering squad.

Regional consistency matters, but Toronto cannot impose routines that ignore customer or regulatory context elsewhere. The COO will establish common evidence and decision rights while allowing approved local execution. Cross-region lessons will be transferred through observed practice and artefacts, not slide repositories.

Operating reviews will focus on readiness, blocked decisions, cost-to-complete, adoption and live-service health. Research output and deployment output will remain distinct. Leaders must surface weak evidence early; incentives that reward milestone declaration without sustained use will be changed.

What you will own

• Research-to-product operating stages and evidence.
• Use-case intake and portfolio readiness.
• Data, evaluation and handover operations.
• Customer implementation and service ownership.
• Scarce-capability and workforce deployment.
• Delivery economics and bespoke-support control.
• Regional consistency and operational learning.
• Operations leadership and succession.

The first 12 months

Within 45 days, classify active work by true readiness, identify unsupported live services and stop new commitments lacking sponsor or data evidence. Name owners for critical transition gaps.

By month six, implement intake and production-acceptance gates, common deployment controls and capacity planning. Move priority products through reproducible research handover and staffed service launch.

At twelve months, increase prototype-to-supported-deployment conversion by 30 percentage points, reduce average production-transition time by 35% and bring cost-to-complete variance within 10%. Ninety-five per cent of live services must have tested incident and rollback ownership, with no independent evaluation or security authority overridden.

What the sponsor will examine

• Demonstrations separated honestly from deployment readiness.
• Customer decisions and users defined at intake.
• Research artefacts reproducible beyond their authors.
• Support capacity established before launch.
• Portfolio priorities constrained by scarce capability.
• Economics including the lifetime cost of bespoke work.

The person

You bring 22–28 years in AI, software, technology services or product operations, including COO or regional operating authority. Your record includes research commercialisation, multi-customer deployment, service operations, portfolio economics and leadership across a perimeter approaching 1,000 people.

Candidates must show a promising prototype they stopped or rescoped because production evidence was absent, and a research handover that became independently supportable. This permanent role is onsite in Toronto with extensive customer and regional presence.

Compensation and terms

Base compensation is C$460,000–620,000 plus annual incentive and LTI linked to supported deployment, transition speed, service reliability, economic control and leadership capability. The permanent onsite Toronto COO reports to the Group Chief Executive or designated executive committee sponsor. This is a planned new appointment.

Confidentiality

The portfolio, research, customers, datasets, deployment gaps, service economics and operating design remain confidential. Further information follows conflicts, credentials and signed confidentiality. Applicants must not contact AI companies, research institutions, customers or delivery partners to infer the client.