Ohio University Copilot Pilot: Modest ROI, Role-Based Gains, Governance & Training

Ohio University’s yearlong pilot of Microsoft 365 Copilot delivered a pragmatic, measured verdict: the tool can help with routine drafting and information retrieval, especially for communication-heavy administrative work, but the headline productivity payoff is modest and highly role-dependent — and realizing meaningful ROI will require focused training, governance, and careful measurement. (ohio.edu)

Overview​

Ohio University (OHIO) ran a structured pilot of Microsoft 365 Copilot from May 2024 through May 30, 2025, enrolling roughly 130 faculty and staff across planning units to evaluate real-world value, security posture, and return on investment for the add-on Copilot license. Participants agreed to active involvement in a Microsoft Teams AI channel, periodic feedback, documentation of use cases, and adherence to institutional data-security standards. Monthly meetings and structured feedback mechanisms were used to collect qualitative and quantitative data. (ohio.edu) (ohio.edu)
The pilot’s summary findings are concise and actionable:

• Moderate productivity gains for specific drafting tasks (Outlook, Word, PowerPoint), with the greatest benefits reported by users in communication-heavy administrative and research-support roles. (ohio.edu)
• Measured time savings were real but limited: many users reported typical time savings of 1–3 hours per week, while others experienced less consistent impact.
• Ease of trial vs depth of use: Copilot is easy to try but harder to integrate into consistent workflows that deliver sustained ROI without training and tailored prompts. (ohio.edu)
• Training and trust gaps: participants wanted clearer guidance on prompt design and transparency about what data Copilot uses and how outputs are generated. (ohio.edu)

These findings align with larger, independent evaluations of Copilot in enterprise and public-sector pilots, which show measurable time savings in focused tasks but a wide variance by role and use case. For example, a large public‑sector study found average daily savings in the tens of minutes, while early randomized experiments across firms reported ~30 minutes per week saved on email reading and faster document completion. (barrons.com, arxiv.org)

---

Background: What is Microsoft 365 Copilot and why institutions pilot it​

Microsoft 365 Copilot in brief​

Microsoft 365 Copilot integrates generative AI into Office apps and Microsoft Graph to produce context-aware drafts, summaries, and analyses inside Word, Excel, PowerPoint, Outlook, and Teams. The service leverages tenant data surfaced through Microsoft Graph (subject to permission trimming) and coordinates large language models to generate outputs that are grounded in organizational data. Microsoft positions Copilot as a productivity layer that augments familiar workflows rather than replacing them. (learn.microsoft.com)
Pricing and licensing for Copilot are non-trivial for institutional buyers. Copilot has typically been offered as an add-on to Microsoft 365 at roughly $30 per user per month (or $360 per year), though Microsoft’s commercial packaging and discounts at scale make effective pricing variable. Institutions must weigh license cost against measured and projected time savings per customer segment. (barrons.com, microsoft.com)

Why universities pilot Copilot​

Higher-education institutions pilot Copilot for several interlocking reasons:

• To quantify the time-savings in day-to-day administrative and academic workflows;
• To validate data protection and governance claims, including whether the enterprise-protected Copilot avoids using tenant data to train public models;
• To create role-based adoption guidance (for faculty, staff, and supporting units) and to assess if Copilot supports teaching, research, or back‑office tasks;
• To develop prompt libraries, training materials, and governance policies before wider deployment. (ohio.edu)

OHIO’s pilot exemplifies this cautious, evidence-driven approach: a set cohort, explicit governance rules, repeated feedback cycles, and a formal evaluation window.

---

Pilot design and participation: what OHIO did, step by step​

• Selection and composition: Participants (~130) were nominated by planning-unit leadership to create a diverse, cross-functional sample including administrative staff, research support, instructional faculty, and technical support roles. (ohio.edu)
• Engagement rules: Participants agreed to active participation in a Microsoft Teams AI channel, monthly meetings, and documentation of use cases and experiences to inform IT policy and ROI assessment. (ohio.edu)
• Security posture: All users were required to follow OHIO’s Secure Use of Artificial Intelligence standard; institutional guidance emphasized using the protected/enterprise version of Copilot for sensitive data to ensure that tenant content is not used to train external models. (ohio.edu)
• Feedback loop: Regular qualitative and quantitative surveys, monthly touchpoints, and usage telemetry were collected to estimate time saved, identify friction points, and capture role-specific impact.

This mix of controls — governance, training, telemetry, and community engagement — is widely recommended for pilots because it balances exploratory experimentation with risk mitigation. Industry playbooks emphasize the same elements (KPIs, governance, limited-scope pilots, and explicit success criteria) when evaluating Copilot deployments.

---

What OHIO found: deep dive into the insights​

1) Task types that delivered value​

• Drafting and communication: Copilot produced the clearest, repeatable wins for drafting and rephrasing email replies, generating meeting summaries, and creating first drafts for Word documents and slide decks. These are high-frequency, standardized tasks where an AI draft materially reduces keystrokes and cognitive overhead. (ohio.edu)
• Meeting notes and thread summarization: Teams and Outlook integrations helped condense long threads and extract action items — useful for users juggling many concurrent projects.

2) Measured savings — modest but visible​

• The pilot reported typical time savings for some users in the 1–3 hours per week range. That’s meaningful at the individual level but modest relative to the per-seat license cost for broad deployment, particularly if only a subset of staff realize that benefit. OHIO’s conclusion explicitly frames these gains as role-specific rather than universal.

Context from independent studies: large-scale government pilots and academic experiments report similar ranges—average daily savings in the tens of minutes (converging to a few hours weekly), with entry-level and communication-focused roles often seeing larger relative gains. (barrons.com, arxiv.org)

3) Role and use-case sensitivity​

• Administrative and research support roles saw the most consistent value because their workflows include high volumes of template-based communication, report drafting, and document curation.
• Instructional faculty and technical support roles reported less consistent benefit: tasks are often more context-sensitive or require deep subject-matter judgment that current Copilot models struggle to deliver reliably. (ohio.edu)

4) Adoption obstacles: training, trust, transparency​

• Users wanted better training on prompt engineering and clearer documentation on what Copilot reads from their tenant, how it uses Microsoft Graph, and how to verify outputs.
• Institutional trust hinges on transparent assurances about data usage: OHIO’s security pages and instructions emphasize using the enterprise-protected Copilot so that tenant content is not used to train external models — a key governance distinction for universities. (ohio.edu)

5) Sentiment and future outlook​

• Despite tempered immediate gains, many participants were cautiously optimistic: they expect Copilot or similar assistants to increase utility over time as models and integrations improve and as institution-specific prompt libraries and templates are developed. (ohio.edu)

---

Critical analysis: strengths, limits, and hidden costs​

Strengths​

• Tight app integration: Copilot’s major advantage is that it sits inside the apps users already use — Word, Outlook, Teams — reducing friction and enabling immediate trialability. This lowers adoption barriers compared with standalone AI tools. (learn.microsoft.com)
• Quick wins on repetitive drafting: Where tasks are templated or formulaic, Copilot can markedly reduce drafting time and mental context switches.
• Enterprise governance features exist: Microsoft provides tenant-level protections, admin controls, and Microsoft Graph permission trimming that — if correctly configured — address many compliance concerns. OHIO’s pilot verified these controls as usable in an academic context. (ohio.edu)

Limits and risks​

• ROI depends on concentrated benefits: A license cost (roughly $30/month) is easy to justify when time savings aggregate across many users or a smaller group obtains large per-user savings. OHIO’s mixed results show that broad licensing without targeted selection risks poor ROI. (microsoft.com)
• Hallucinations and factual errors: LLM-driven outputs can be confidently incorrect. For tasks where accuracy is critical (legal, medical, grading rubrics), human verification remains essential.
• Training and cultural change costs: Realizing consistent time savings requires investment in prompt training, templates, and embedment into workflows — costs that are often overlooked in vendor ROI claims. Independent pilot guidance emphasizes these non‑licensing investments as decisive for success.
• Operational overhead: Governance, DLP rules, connector hygiene, license management, and ongoing monitoring create recurring IT and compliance workload. These must be budgeted alongside license costs.
• Potential for skill erosion and over-reliance: Repeated delegation of drafting tasks can reduce staff exposure to certain skills (e.g., writing precision). Organizations need guardrails and periodic audits.

Unverifiable or conditional claims​

• Any single metric about exact ROI must be treated with caution without full lifecycle financial modeling: vendor claims about aggregate productivity gains often assume optimistic adoption and task distributions. OHIO’s pilot provides institution-specific evidence that counters a one-size-fits-all ROI narrative. Readers should treat broad vendor ROI claims as directional and insist on local pilots and KPIs.

---

Practical guidance: how to run a high‑value Copilot pilot (recommended playbook)​

• Define success before licensing
• Select 2–4 high-frequency, low-risk use cases (email triage, meeting summaries, slide-first drafts).
• Establish measurable KPIs (minutes saved per user per week, reduction in review cycles, adoption rates).
• Start small and measurable
• 50–300 users, role‑based cohorts (admin, research support, instruction), 8–16 week timeboxed pilots.
• Pair with governance
• Enforce the Secure Use of AI standard and use enterprise-protected Copilot for any sensitive data. Configure DLP and conditional access. (ohio.edu)
• Invest in prompt libraries and training
• Create and distribute validated prompt templates and run short clinics on prompt design. Collect exemplar prompts and share in a central repository.
• Instrument and iterate
• Collect telemetry and qualitative feedback monthly; triangulate minutes saved with user satisfaction and quality metrics (error rates, rework).
• Cost modeling
• Calculate cost per minute saved = (license + enablement + monitoring costs) / (hours saved × users). Use conservative adoption rates for budgeting.

Short checklist for IT teams:

• Inventory candidate workflows and classify data sensitivity.
• Pilot with teams where benefits concentrate (communications, reporting).
• Assign an AI governance owner and a change sponsor.
• Publish acceptable-use rules and retention/escrow policies.

---

Implications for teaching, research, and campus operations​

• Teaching: Copilot can accelerate creation of syllabi, rubrics, and feedback templates, but faculty should be trained to verify pedagogical accuracy and to adapt prompts to academic integrity considerations.
• Research: Research administrators and grant offices may benefit from summarization and document drafting, but research data and unpublished manuscripts should be routed only through protected, compliant channels.
• Campus operations: Finance, HR, and student services may achieve measurable process acceleration where template-based correspondence is common — making them prime candidates for targeted licensing. (ohio.edu)

---

Conclusion: a balanced path forward​

Ohio University’s Copilot pilot is a useful case study for any organization evaluating Microsoft’s AI assistant: Copilot delivers real but measured value for specific tasks and roles, and the payoff is far from automatic. The key lessons are pragmatic:

• Deploy Copilot where structured, high-volume drafting and summarization occur.
• Budget for training, governance, and change management in addition to license fees.
• Use short, instrumented pilots to test assumptions and quantify ROI before wide rollout.
• Treat vendor productivity claims as useful hypotheses, not guarantees — verify them with local data. (learn.microsoft.com, barrons.com)

OHIO’s approach — transparent pilot design, security-first controls, community engagement, and cautious optimism — provides a replicable model for universities and enterprises alike that want to explore productivity AI without surrendering governance or fiscal discipline. (ohio.edu)

---

For WindowsForum readers and IT leaders, the most actionable takeaway is straightforward: pilot deliberately, measure conservatively, and scale only when data shows sustained, role-specific gains that exceed the full costs of license, enablement, and governance.

---

Source: Ohio University OHIO conducts pilot program assessing artificial intelligence tool - Microsoft 365 Copilot