Licensing & capacity
Prerequisites, license position, deployment state, and viable pilot population.
Temen combines a read-only Microsoft 365 and Copilot evidence review with business discovery to turn AI ambition into a sequenced, owned strategy.
The assessment does not infer strategy from configuration alone. Temen verifies the tenant foundation, then works with leaders and process owners to define outcomes, risk boundaries, ownership, and an investment sequence.
Prerequisites, license position, deployment state, and viable pilot population.
MFA, Conditional Access, privileged roles, guests, OAuth consent, and access ownership.
SharePoint and OneDrive sprawl, sharing, stale content, labels, ownership, and authoritative sources.
Administrative policies, web and content settings, agent publishing, and lifecycle boundaries.
Role-level work scenarios scored for value, feasibility, risk, repetition, and measurability.
Owners, intake, review, acceptable use, human approval, monitoring, and escalation.
A useful strategy makes the intervention decision explicit. Temen compares the work, information, controls, integration, ownership, measurement, and operating burden before recommending what to enable, automate, build, or defer.
The work already happens in Microsoft 365 and the primary gaps are information hygiene, policy, licensing, training, or adoption.
The process is recurring, rules carry most decisions, and AI is useful only for bounded extraction, classification, or drafting.
The experience needs proprietary knowledge, tools, structured outputs, integrations, evaluation, and a purpose-built user journey.
The outcome is unclear, data is not authoritative, risk cannot be bounded, ownership is missing, or a simpler change solves the problem.
Every phase ends in a customer decision and inspectable artifacts - not a generic maturity score.
Review the tenant, workflows, information, users, friction, current controls, and strategic constraints.
Advance, combine, defer, or reject ideas using value, feasibility, data, integration, oversight, and measurement criteria.
Assign owners, dependencies, controls, investment gates, adoption work, and measures to a prioritized roadmap.
Change the five planning signals. The sampler demonstrates Temen’s decision logic; a real engagement uses verified evidence, interviews, and customer-approved decisions.
These anonymized patterns are grounded in Temen’s assessment tooling, automation statements of work, and custom-agent delivery documentation. They show engagement logic and designed outcomes - not invented customer metrics.
Leaders wanted Copilot progress while licensing, identity, sharing, policy, usage visibility, and pilot ownership were understood differently across teams.
Temen’s read-only assessment pattern reviews prerequisites, identity, SharePoint and OneDrive controls, Copilot policy, aggregate adoption signals, agent governance, and viable pilot capacity.
Prioritize foundation remediation, select role-level scenarios and owners, launch a controlled cohort, then use a 30/60/90-day value review before expansion.
Useful support history lived across tickets, documents, email, and archived attachments. Similar language did not always mean the same technical context.
Strategy work bounded the engineer’s job, approved sources, sanitization options, retrieval design, citations, human validation, evaluation, Teams experience, and production ownership.
Prove one grounded investigation workflow first; treat diagnosis and system change as human decisions; expand only after retrieval, security, and acceptance evidence pass.
Teams repeatedly entered the same opportunity data into proposals, SOWs, approvals, storage, and project handoff while commercial exceptions still required judgment.
Temen separated deterministic fields and business rules from bounded AI narrative, then mapped templates, approval gates, exception ownership, records, and recovery.
Use Power Automate for orchestration and system writes, approved templates for structure, AI only for grounded drafting, and people for pricing, scope, and final release.
The goal is not to produce the longest list of use cases. It is to identify a small first wave whose value, information, control, adoption, and ownership can be demonstrated.
Prepare an operating review from meetings, files, metrics, and open decisions.
Are the sources authoritative and are decisions clearly owned?
Copilot or Cowork pilotHelp engineers retrieve related incidents and prepare an evidence-backed response.
Can access-aware retrieval distinguish similar cases and show its sources?
Grounded custom agentCreate proposals and SOWs from approved opportunity data.
Which fields are deterministic, and who approves scope, pricing, and terms?
Workflow automationCoordinate remote-hire access, equipment, learning, and communications.
Are employee data boundaries, approvals, exceptions, and completion evidence defined?
Workflow + agent assistanceReview invoice exceptions and prepare the resolution package.
Can rules identify the exception before AI explains it, and who authorizes the change?
Automation with human reviewMonitor a service and begin troubleshooting when a device or workflow fails.
What may the system observe, recommend, attempt, escalate, and never do autonomously?
Bounded agentic workflowThe final package separates verified evidence, customer decisions, recommendations, dependencies, and assumptions so the next phase can be scoped without repeating discovery.
Business priorities, candidate scenarios, decisions, and the recommended engagement path.
Evidence by domain, findings, limitations, risks, and prioritized recommendations.
Advance, combine, defer, or reject decisions with value, feasibility, and control rationale.
Owners, remediation, cohort, success measures, dependencies, and 30/60/90-day gates.