This person owns the Requirements Matrix, sets the direction of the discovery effort, and is accountable for the decision-grade output the client will use to determine whether Genesys Cloud can carry their knowledge management requirements or whether a gap justifies a broader tool evaluation.

Responsibilities

Own the Requirements Matrix.

Drive the matrix to completion through structured discovery with business lines, operations, and the contact centre. Establish a clear, traceable, prioritised set of knowledge management requirements that can survive scrutiny from both the client and the vendor.

Set and guide the overall discovery strategy.

Define the discovery approach, sequence the workshops and interviews, identify the right stakeholders across each business line, and keep the workstream aligned to the client's three target outcomes — operational improvement, expanded self-service, and agent enablement.

Map requirements line‑by‑line against Genesys Cloud AI capability.

Take the Phase 1 Requirements Matrix and assess each requirement against the native Genesys Cloud knowledge management and AI feature set — knowledge surfacing, semantic search, agent assistance, summarisation and virtual agents. Document what the platform supports natively, what it supports with configuration or workaround, and what it cannot support. Identify which gaps are acceptable trade‑offs and which are material enough to justify looking beyond the approved‑vendor scope.

Gather and model total cost of ownership, including AI token consumption.

Collect the licensing, token allocation, configuration, integration, content migration, and ongoing administration cost inputs.



Model what each AI capability costs to run at full agent population scale against the client's allocated‑token fair‑usage terms, and flag the usage threshold at which a licence change becomes financially necessary. This is one of the most commercially consequential pieces of analysis in the engagement.

Compile the evaluation criteria and author the Tool Evaluation Report.

Define weighted, defensible evaluation criteria with the client. Produce the Tool Evaluation Report as the primary decision artefact — findings, capability assessment, TCO, risks, and a clear recommendation.

Write the Governance Playbook.

Document how knowledge will be created, reviewed, approved, published, versioned, retired, and audited. Define roles, review cycles, quality standards, escalation paths, and the metrics by which knowledge health will be measured on an ongoing basis.

Design templates and best practices.

Produce the article templates, writing standards, style guidance, and authoring best practices that content owners will use day to day — written for a government setting with plain‑language, accessibility, and official‑languages obligations in mind.

Observe and assess proofs of concept.

Participate in PoC sessions, test requirements against what is demonstrated rather than what is claimed, and feed findings back into the evaluation. Apply the client's established pilot pattern — demonstrate the capability,



assess the process and operating changes it requires, then execute those changes — rather than treating feature enablement as the end point.

Act as the client‑facing lead for the workstream.

Provide regular status, risk, and decision reporting to the client's transformation lead and to Miratech and Bell programme leadership.

Qualifications

10+ years in management consulting, business consulting, or enterprise transformation, with a substantial portion of that time focused on knowledge management, content operations, or contact centre operations.

Demonstrated experience leading a requirements gathering and vendor or tool evaluation effort end to end, through to a written recommendation that an executive used to make a decision.

Hands‑on working knowledge of enterprise knowledge management platforms and KM administration — configuration, content architecture, permissions, publishing workflow, and reporting. Experience with more than one platform is strongly preferred.

Working understanding of contact centre AI capability and its dependency on knowledge quality — knowledge surfacing, semantic search, interaction summarisation, agent assistance and virtual agents. The successful candidate must be able to explain to a business audience why content structure determines AI accuracy.

Familiarity with consumption‑based AI licensing and the ability to model cost at scale.

Proven ability to build a TCO model from incomplete inputs and defend the assumptions behind it.

Exceptional written output. This role produces the Tool Evaluation Report and the Governance Playbook; writing quality is a primary selection criterion, and candidates should expect to

📌 Business Consultant (Winnipeg)
🏢 Mercor
📍 Winnipeg