What Peer Institutions Learned

Evidence on IT Consolidation Risks and Models in Research-Intensive Universities

Author

UCLA Library Data Science Center

Published

April 1, 2026

IT consolidation is not new in higher education. Over the past fifteen years, universities including UC Berkeley, UT Austin, University of Michigan, Yale, Cornell, and the University of Washington have undertaken large-scale initiatives similar to One IT. Their experiences — documented in academic senate reports, independent audits, and peer-reviewed research — reveal consistent patterns: both the conditions under which consolidation succeeds and the specific failure modes that recur across institutions.

This page draws on a systematic review of those initiatives to support the One IT Research Working Group’s risk identification work. It names and defines the documented failure modes, identifies the architectural models involved, and summarizes what the evidence says about reducing risk.

Architectural Models

Before examining risk, it helps to name the three structural options that universities have chosen — because the risks are not evenly distributed across them.

Model Structure Research IT Impact Efficiency Profile
Full Centralization All IT staff and resources absorbed into a single central organization reporting to a CIO High risk: “one-size-fits-all” service levels fail specialized laboratory and data needs Maximum potential for licensing and hardware savings; rarely fully realized
Federated / Layered Commodity services centralized; specialized staff remain embedded in academic units, often with dual reporting Better retention of discipline-specific expertise; researchers retain local relationships Moderate savings through common back-ends; not predicated on staff reductions
Ad-Hoc / Decentralized Department-level autonomy; no coordination layer High responsiveness; significant fragmentation, redundancy, and security gaps Low; the starting condition most consolidations are trying to move away from

The federated/layered model — sometimes called the Academic Divisional Computing (ADC) model in the UC system — is the approach most consistently associated with successful outcomes in peer institution cases.1 The RDWG’s layered model recommendation aligns with this approach.

The following risk patterns appear repeatedly across peer institution case studies. They are named here to give the Working Group shared vocabulary for identifying conditions that predict poor outcomes.

1. The Dual-Change Trap

What it is: Attempting to reorganize the workforce and implement major new technology or processes simultaneously. The cognitive and operational load of two concurrent large-scale changes consistently produces institutional turmoil, erodes trust, and degrades both the people-side and the process-side outcomes.

Peer evidence: Yale and Cornell both explicitly named this pattern after their own difficult experiences, warning against “too much change” for a single institution to absorb.2 Cornell’s post-implementation review concluded that even in a carefully sequenced transition, “some institutional knowledge and customization were still lost” — and that was with sequential change.3 Berkeley attempted restructuring and process automation simultaneously; the resulting institutional disruption was characterized in the Stony Brook Senate’s cross-institutional review as a “mood of total chaos.”4

The signal:

  • Organizational rationalization (role reclassification, unit restructuring, reporting-line changes) proceeding concurrently with or ahead of service design, governance design, or working group recommendations
  • Structural decisions closing before the design questions are answered

2. The Governance Bypass

What it is: Launching or advancing consolidation without meaningful faculty and staff involvement in governance. “Meaningful” is the operative word: advisory councils with no real authority do not satisfy the governance requirement. When affected constituencies perceive that decisions are being made without them, resistance becomes durable and often escalates.

Peer evidence: The Stony Brook Senate’s cross-institutional review of Berkeley’s Operational Excellence found “a significant lack of faculty involvement,” with governance limited to advisory councils that had “little real authority.”5 Faculty surveys documented changes as “time wasters,” with the loss of local staff directly hindering core departmental functions.6 Yale administrators later identified “doing a better job to engage faculty” as their most important lesson learned — in retrospect.7

At UCLA, the Academic Senate’s formal consultation request on OneIT documented a “perceived deliberate circumvention of the structures and mechanisms of shared governance,” noting that IT staff are “valued, known members of departmental communities” whose removal would replace established relationships with a “frustrating and opaque customer-service experience.”8

The signal:

  • Structural or staffing decisions that precede or bypass formal shared governance processes
  • Advisory bodies without authority over outcomes
  • Timelines that compress the deliberation window

3. The Commodity Fallacy

What it is: Treating research IT as functionally equivalent to enterprise IT — email, networking, desktop support — and applying the same standardization logic to specialized research computing, data management, and laboratory support. The fallacy is that because all of it is “IT,” it can be governed and supported the same way.

Peer evidence: Berkeley’s clustering of administrative and IT services failed most visibly in research contexts, where faculty found that the loss of local staff made it impossible to support specialized activities.9 The Stony Brook Senate review of seven peer institutions found that “knowledge loss was consistent and underaddressed” — not as a one-off, but as a pattern across all cases studied.10

The HPC dimension provides a measurable instance of this problem: university-operated research clusters are growing at a CAGR of 17.8% while industrial AI infrastructure grows at approximately 78%.11 Fragmented governance — not investment alone — is identified as a primary driver of this gap.

The signal:

  • Service design that uses the same frameworks for research computing, library data infrastructure, and enterprise functions like payroll or email
  • Ticket-based support replacing relationship-based support for research workflows
  • Service level agreements that do not account for the time-sensitivity and domain-specificity of research

4. The Brain Drain

What it is: The loss of tacit institutional knowledge that occurs when consolidation disrupts the people who carry it. This is distinct from simple attrition — it is accelerated and concentrated by consolidation pressure. It occurs through three documented mechanisms: retirement of senior staff who leave with experiential knowledge; mid-career exits when staff feel their expertise is devalued or their autonomy removed; and “process unlearning,” where localized workflows and domain-specific knowledge are replaced by standardized enterprise processes.12

Peer evidence: The University of Georgia system consolidated ten institutions between 2013 and 2023. Empirical findings from participant interviews consistently identified “a lack of transfer of knowledge and experience” — not just within institutions, but between mergers.13 Even when mergers succeeded on paper, lessons were not codified for later efforts, meaning the same knowledge was lost repeatedly. Five years post-consolidation, recurring themes included “uncertainty and unexpected workload,” “communication gaps,” and “managing culture gaps.”14 Cornell acknowledged that even with a careful model, “some institutional knowledge and customization were still lost.”15

The signal:

  • Role reclassification that changes title without considering domain identity
  • Career ladders that do not recognize discipline-embedded expertise
  • Attrition among long-tenured staff during the transition period
  • Standardization that removes the informal workflows through which research knowledge is actually shared

5. The Savings Mirage

What it is: Projected savings that fail to materialize at the scale promised, often because the cost of change management, service degradation, and staff attrition is not included in the original projection — and because technology costs and complexity evolve over a multi-year implementation in ways that erode efficiency gains.

Peer evidence: Berkeley’s Operational Excellence initiative projected $75 million in annual savings. A 2013 assessment characterized realized savings as “difficult to quantify” and “still a work in progress.”16 UT Austin projected $30–40 million in annual savings with 500 positions eliminated; after intense faculty and staff resistance, the university severed ties with Accenture and scaled scope to a small pilot in two departments, with setup costs dropping from $54 million to under $7 million.17

University of Michigan took a more measured approach, explicitly acknowledging that “cost is a hard goal to meet” because technology needs change over the course of a multi-year project — and reoriented around value gained rather than dollars saved.18 The Stony Brook Senate’s review of seven institutions found that savings were “generally assumed but rarely documented” — and identified this as the most reliable predictor of failure across all cases studied.19

The signal:

  • Projected savings presented without a forensic assessment of internal cost structures
  • Savings estimates that do not account for transition costs, change management, and service quality degradation
  • No mechanism for reporting realized versus projected outcomes after the fact

6. The Top-Down Mandate

What it is: Implementing consolidation as a directive rather than a co-designed transition, with decisions made at the executive level and communicated downward. This pattern does not just produce resistance — it eliminates the feedback loops that would allow the institution to course-correct before significant harm is done.

Peer evidence: Berkeley’s Operational Excellence was characterized by top-down implementation that “initially bypassed traditional shared governance structures.”20 Because faculty and staff were excluded from the design process, problems that could have been anticipated were not — and resistance outlasted the initiative itself.21 UT Austin’s faculty-staff coalition successfully targeted the legitimacy of the top-down process — not just the substance of the proposal — forcing a near-complete reversal.22

The signal:

  • Consolidation design that proceeds without co-design with affected units
  • Communication that presents decisions as finalized before consultation
  • Advisory processes initiated after key structural commitments are already made

7. The Consultant Playbook

What it is: The application of corporate restructuring frameworks to university contexts by external consultants who have limited familiarity with the academic mission. The pattern is consistent: recommendations emphasize increasing “span of control,” reducing management layers, and identifying “redundancy” using corporate metrics that do not capture the value of specialized academic units.

Peer evidence: The AAUP’s analysis of Bain & Company’s university engagements (Berkeley, Cornell, North Carolina) found that recommendations were frequently “boilerplate,” failing to account for mission and culture, and that projected savings “frequently fail to materialize in full” — with promised reinvestments in the academic mission often not occurring.23

UT Austin is the clearest reversal case: the consolidation committee was chaired by an Accenture executive, and critics successfully surfaced Accenture’s history of cost overruns in Texas state contracts — including a $64 million overrun on a state child support system — as evidence that the analytical framework could not be trusted.24 The university subsequently severed the relationship and scaled back to a minimal pilot.

The signal:

  • Savings projections that cannot be traced to institution-specific data
  • Recommendations that treat different academic units as equivalent based on headcount or job title rather than function
  • External frameworks applied without a baseline assessment of local culture and cost structure

Across the same institutions, a set of practices is consistently associated with better outcomes. These are not aspirational — they are documented from cases where consolidation proceeded without the failure modes above.

Inclusive governance from the start

University of Michigan’s Administrative Services Transformation began with a three-year assessment period, during which every process brought into the shared services center was reengineered with direct feedback from subject matter experts in colleges and schools.25

The layered support model

The ADC model — technically centralized, operationally embedded — is the approach most consistently cited in successful cases. Stanford’s “IT relationship managers” and NYU’s locally embedded teams are the institutional forms this takes: centralization of commodity services, preservation of domain expertise at the edge where research happens.26

Value-over-cost framing

Michigan explicitly argued that “cost is a hard goal to meet” and instead focused on capabilities gained — improved security posture, elevated desktop services, better data access.27 Institutions that led with cost savings (Maine, Michigan’s earlier attempts) consistently generated resistance and morale decline.

Incremental and opt-out structures

Yale introduced consolidation through “nudge” strategies — automated tools that led departments toward standardization over time rather than mandated transitions.28 Michigan allowed departments to opt out of specific shared service functions if they could demonstrate local necessity, while still paying a baseline contribution to central infrastructure.29

Sequencing: governance before rationalization

Yale, Cornell, and Michigan each concluded that service design and governance design must precede organizational restructuring — not run concurrently with it.30 Institutions that inverted this sequence encountered turmoil they could not recover from within the initiative’s timeline.

The RDWG’s four recommendations — ORDR, Bruin Research Data Hub, Expanded Research Data Services, and Expanded Research Infrastructure — map directly onto the success patterns the peer evidence identifies.

RDWG Proposal Peer Evidence Alignment
ORDR (coordination unit, service catalog, compliance lead) Addresses the “ad hoc and siloed” RDS governance problem documented across R1s;31 provides the coordination layer comparable to Michigan’s shared services design and NC State’s Research Facilitation Service
Bruin Research Data Hub (baseline storage, central + cloud) Addresses the commodity infrastructure gap — the layer appropriate for centralization — without touching embedded expertise
Expanded Research Data Services (built on Library/DSC) Aligns with the layered model: specialized, researcher-facing services remain embedded where domain expertise lives
Expanded Research Infrastructure (Hoffman2, P3/P4 enclave, Cloud for Research) Addresses the HPC governance fragmentation identified as a driver of the gap between university and peer research computing capacity

The RDWG did not argue for preserving fragmentation. It argued for replacing fragmentation with coordinated, research-led governance and shared infrastructure — while preserving the embedded expertise that research work depends on. That is the pattern the peer evidence supports.

Key terms used in IT consolidation discourse. Shared vocabulary helps a working group with diverse backgrounds converge on common analysis.

Organizational models

Academic Divisional Computing (ADC) A model originating in the UC system in which IT staff are centrally managed but physically and operationally embedded in academic divisions.32 Staff maintain deep knowledge of their unit’s research and teaching mission while providing central IT with security compliance and procurement leverage.

Administrative Services Transformation (AST) A common branding for large-scale shared services initiatives at research universities. Used at University of Michigan and elsewhere. Often scoped to include both IT and non-IT administrative functions (HR, finance).

Federated model An IT governance structure in which some functions are centralized (infrastructure, security, commodity services) while others remain distributed in academic units. The spectrum runs from loosely federated (high unit autonomy) to tightly federated (ADC-style, with central management and local embedding).

Shared Services Center (SSC) A centralized unit that provides administrative or IT services to multiple departments on a standardized basis. Success depends on whether the services provided are genuinely commodity in nature or require local customization.

Governance and process

Shared governance The formal structures through which faculty and academic units participate in institutional decision-making. In the university context, shared governance is not a courtesy — it is the mechanism by which research mission alignment is maintained in administrative decisions. Bypassing it is consistently associated with resistance and reversal.

IT Rationalization The process of systematically reducing duplication, standardizing platforms, and consolidating vendor contracts. Can be implemented narrowly (technology and licensing only) or broadly (including staffing). Michigan used the term to describe a technology-focused process that did not predicate savings on headcount reduction.33

Operational Excellence (OE) UC Berkeley’s 2009–2014 consolidation initiative, heavily influenced by a Bain & Company report.34 The term has become shorthand in academic senate discourse for consolidation initiatives that prioritize administrative efficiency over faculty and research mission alignment.

Rationalization In the One IT context, the process of assessing which IT roles and functions across UCLA units should be consolidated into DTS. Distinct from “shared services” (new organizational structures) and “ADC” (embedded models).

Span of control A corporate management metric measuring the number of direct reports per supervisor. Used by management consultants as a proxy for organizational efficiency. Peer institution reviews document that applying span-of-control targets to university units fails to account for the value of small specialized teams serving high-complexity functions.35

Financial mechanisms

Baseline tax A model used at University of Michigan in which departments that opt out of specific shared services functions still pay a baseline contribution to support the common infrastructure. Preserves local agency while ensuring the central layer is funded.36

Recharge model A funding arrangement in which a central unit provides services to academic units on a cost-recovery basis. In a consolidation context, a recharge model converts an embedded service relationship into a vendor relationship — removing staff from the unit while making the unit pay per service. Does not necessarily preserve professional development pathways or community membership.

Burning platform A framing strategy in which financial crisis is used to justify rapid, large-scale change. Common in consolidation initiatives; often used to accelerate timelines and suppress deliberation.

Failure modes and key concepts

Brain drain In the consolidation context, the concentrated loss of institutional knowledge that occurs when senior or specialized staff leave during a transition — through retirement, voluntary attrition, or reclassification into roles that do not match their expertise. Distinct from normal turnover because it is accelerated and concentrated.37

Dual-Change Trap Named by Yale and Cornell administrators: the failure mode that occurs when institutions attempt to reorganize the workforce and implement major new technology or processes simultaneously.38 Produces institutional turmoil because neither change can stabilize while the other is in flux.

Commodity services IT functions that are broadly standardized across institutions and users: email, networking, identity management, basic desktop support. Appropriate candidates for centralization. Contrasted with specialized research and domain-embedded services, where the commodity model consistently fails.

Shadow IT Technology and systems managed by individual departments or researchers outside central IT oversight. A symptom of the commodity fallacy — when central IT cannot serve specialized research needs, units build their own capacity.

Institutional knowledge The accumulated expertise, relationships, and informal know-how that staff develop over time — including knowledge of how to navigate unit-specific workflows, researcher preferences, and compliance requirements. Not captured in documentation systems. Primary casualty of rapid or top-down consolidation.

Research Data Services (RDS) Researcher-facing services that support the full data lifecycle: data management planning, curation, archiving, format conversion, compliance support, and discovery. Distinguished from IT infrastructure (storage, compute) by their domain-embedded, consultative character. Ithaka S+R characterizes RDS governance at most R1s as “decentralized and ad hoc.”39

Footnotes

  1. Susan Grajek, “The Many Faces of Shared Services at the University of California,” EDUCAUSE Review, 2014. https://er.educause.edu/articles/2014/8/the-many-faces-of-shared-services-at-the-university-of-california↩︎

  2. Stony Brook University Senate, Report on Shared Services (2014), pp. 12–14. https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  3. Ibid.↩︎

  4. Ibid.↩︎

  5. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  6. Berkeley Faculty Association, “Operational Excellence,” accessed 2026. https://ucbfa.org/reforming-the-university/operational-excellence/↩︎

  7. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  8. UCLA Academic Senate, “OneIT Request for Senate Consultation,” DMS Issue 10123, accessed 2026. https://dms.senate.ucla.edu/issues/issue/?10123.OneIT.Request.for.Senate.Consultation↩︎

  9. Berkeley Faculty Association, “Operational Excellence,” accessed 2026. https://ucbfa.org/reforming-the-university/operational-excellence/↩︎

  10. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  11. Peng Shu et al., “Survey of HPC in US Research Institutions,” arXiv:2506.19019v1 (University of Georgia, 2025). University clusters CAGR = 17.8%; industrial hyperscale CAGR ≈ 78%; DOE national labs CAGR ≈ 43%. https://arxiv.org/html/2506.19019v1↩︎

  12. David W. DeLong, Lost Knowledge: Confronting the Threat of an Aging Workforce (Oxford University Press, 2004). See also: https://www.researchgate.net/publication/281663514_Lost_Knowledge_Confronting_the_Threat_of_an_Aging_Workforce↩︎

  13. Chandra Torrence and Cassandra Caldwell, “Consolidations in Higher Education: A Business Program Case Study,” Journal of Higher Education Theory and Practice, 2018. https://www.researchgate.net/publication/328543715_Consolidations_in_higher_education_a_business_program_case_study↩︎

  14. Strategic Restructuring in Higher Education: A Case Study of a Consolidation of Two Institutions, Columbus State University ePress, 2019. https://csuepress.columbusstate.edu/cgi/viewcontent.cgi?article=1410&context=theses_dissertations↩︎

  15. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  16. Andrew Szeri et al., “Doing Much More with Less: Implementing Operational Excellence at UC Berkeley,” CSHE Research and Occasional Papers 10.13 (2013). https://cshe.berkeley.edu/sites/default/files/publications/rops.cshe_.10.13.szeri_el_al.operationalexcellence.6.24.2013_0.pdf↩︎

  17. “In a reversal, UT to cut ties with Accenture (for now), modifies job-cutting plan,” CWA-TSEU (Communications Workers of America — Texas State Employees Union), 2014. https://cwa-tseu.org/in-a-reversal-ut-to-cut-ties-with-accenture-for-now-modifies-job-cutting-plan/ (Primary source; union-affiliated news site.)↩︎

  18. “Enhancing IT’s Institutional Value through IT Rationalization,” EDUCAUSE Review, 2018. https://er.educause.edu/blogs/2018/4/enhancing-its-institutional-value-through-it-rationalization↩︎

  19. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  20. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  21. Berkeley Faculty Association, “Operational Excellence,” accessed 2026. https://ucbfa.org/reforming-the-university/operational-excellence/↩︎

  22. “In a reversal, UT to cut ties with Accenture (for now), modifies job-cutting plan,” CWA-TSEU, 2014. https://cwa-tseu.org/in-a-reversal-ut-to-cut-ties-with-accenture-for-now-modifies-job-cutting-plan/↩︎

  23. Andrew Feffer, “Bain and the University,” AAUP Union College Chapter, Winter 2021. https://minnow-hexaflexagon-rghl.squarespace.com/s/Andrew-Feffer-Bain-Report-2021-plus-exec-summ-1.pdf (Critical analysis; AAUP local chapter publication.)↩︎

  24. “In a reversal, UT to cut ties with Accenture (for now), modifies job-cutting plan,” CWA-TSEU, 2014. https://cwa-tseu.org/in-a-reversal-ut-to-cut-ties-with-accenture-for-now-modifies-job-cutting-plan/↩︎

  25. “Shared Services Center to be part of Administrative Services Transformation,” Michigan Record, University of Michigan, 2013. https://record.umich.edu/articles/a4210-shared-services-center/↩︎

  26. Susan Grajek, “The Many Faces of Shared Services at the University of California,” EDUCAUSE Review, 2014. https://er.educause.edu/articles/2014/8/the-many-faces-of-shared-services-at-the-university-of-california↩︎

  27. “Enhancing IT’s Institutional Value through IT Rationalization,” EDUCAUSE Review, 2018. https://er.educause.edu/blogs/2018/4/enhancing-its-institutional-value-through-it-rationalization↩︎

  28. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  29. Ibid.↩︎

  30. Ibid.↩︎

  31. Rebecca D. Frank and Judith C. Dorabiala, “The Research Data Services Landscape at US and Canadian Higher Education Institutions,” Ithaka S+R, 2021. https://sr.ithaka.org/publications/the-research-data-services-landscape-at-us-and-canadian-higher-education-institutions/↩︎

  32. Susan Grajek, “The Many Faces of Shared Services at the University of California,” EDUCAUSE Review, 2014. https://er.educause.edu/articles/2014/8/the-many-faces-of-shared-services-at-the-university-of-california↩︎

  33. “Enhancing IT’s Institutional Value through IT Rationalization,” EDUCAUSE Review, 2018. https://er.educause.edu/blogs/2018/4/enhancing-its-institutional-value-through-it-rationalization↩︎

  34. Andrew Szeri et al., “Doing Much More with Less: Implementing Operational Excellence at UC Berkeley,” CSHE Research and Occasional Papers 10.13 (2013). https://cshe.berkeley.edu/sites/default/files/publications/rops.cshe_.10.13.szeri_el_al.operationalexcellence.6.24.2013_0.pdf↩︎

  35. Andrew Feffer, “Bain and the University,” AAUP Union College Chapter, Winter 2021. https://minnow-hexaflexagon-rghl.squarespace.com/s/Andrew-Feffer-Bain-Report-2021-plus-exec-summ-1.pdf↩︎

  36. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  37. David W. DeLong, Lost Knowledge: Confronting the Threat of an Aging Workforce (Oxford University Press, 2004). https://www.researchgate.net/publication/281663514_Lost_Knowledge_Confronting_the_Threat_of_an_Aging_Workforce↩︎

  38. Stony Brook University Senate, Report on Shared Services (2014). https://www.stonybrook.edu/commcms/senatecas/_pdf/SharedServices-Report-Final.pdf↩︎

  39. Rebecca D. Frank and Judith C. Dorabiala, “The Research Data Services Landscape at US and Canadian Higher Education Institutions,” Ithaka S+R, 2021. https://sr.ithaka.org/publications/the-research-data-services-landscape-at-us-and-canadian-higher-education-institutions/↩︎