Overview
Endpoint sprawl is not, by itself, a reason to buy unified endpoint management. The executive issue is whether fragmented controls are creating measurable gaps in inventory, policy, patching, access, or support. Consolidate only where those gaps are material, then introduce Artificial Intelligence (AI) as bounded assistance before allowing it to make changes at scale.
What Is Happening
Hybrid work, Bring Your Own Device (BYOD), multiple operating systems, contractor access, and decentralized IT have pushed more control toward endpoints. A USENIX Security study based on more than 1,000 hours of Security Operations Center (SOC) field observation, plus interviews across seven additional SOCs, documented a shift toward endpoint-centric management in the SOC environments studied and also identified privacy and analyst-burnout costs.1
The old progression of Mobile Device Management (MDM) to Mobile Application Management (MAM) to Unified Endpoint Management (UEM) is too simplistic. Current UK National Cyber Security Centre (NCSC) guidance treats MDM, MAM, and hybrid approaches as different control boundaries with different assurance, privacy, and usability tradeoffs.2 The Center for Internet Security (CIS) similarly provides different control considerations for BYOD and fully managed deployment models.3
The management boundary should reflect device ownership, data sensitivity, required assurance, and the access granted. Regulatory and contractual requirements are seen as a floor, not a ceiling. Where managed-device posture is required, application-level controls alone may not suffice; where privacy or employment constraints limit device-level control, a narrower boundary or separate access path may be needed. .
The Implications
For CIOs, the problem is control fragmentation not tool count. Three interoperable tools that produce a reconciled asset view, consistent policy, reliable patch evidence, and clear ownership may be preferable to one “unified” platform with unmanaged exceptions.
Consolidation also changes risk rather than eliminating it. In 2025, threat actors exploited vulnerabilities in Ivanti Endpoint Manager Mobile (EPMM), and CISA subsequently analyzed malicious components recovered from a compromised EPMM environment.4 NCSC guidance separately notes that MDM platforms can carry considerable privilege because they can alter device configuration, install applications, and execute scripts.5 Research on Endpoint Detection and Response (EDR) (adjacent to, but not the same as, UEM) has also demonstrated that trusted endpoint-control capabilities can be repurposed for command execution and data exfiltration.6
Consolidation can reduce fragmented control while concentrating administrative authority, creating a different control-plane risk that must be governed explicitly. The evidence demonstrates that concentration risk exists; it does not establish that centralized management is inherently riskier than fragmented management.
Tactive View
The market framing is backwards when “AI-powered UEM” becomes the buying category. AI should be an authority layer on top of an endpoint-control system. What matters first is a trustworthy inventory, consistent policy, defined ownership, supportable workflows, and a management plane secure enough to deserve broad authority. Do not overreact to AI feature releases or assume fewer consoles automatically mean better control. Weak endpoint standards simply automate inconsistency faster.
Board-ready language. We are not consolidating to reduce tool count. We are consolidating where the current operating model cannot provide consistent inventory, policy, patch, and posture evidence. AI will begin as decision support, not autonomous change authority.
What Changes Operationally
Endpoint rationalization crosses digital workplace, security operations, identity, service desk, architecture, privacy, procurement, and asset management. It should therefore be treated as a managed endpoint service, not only as a tooling project.
That service needs a named owner, defined corporate/BYOD/contractor/exception classes, an approved management boundary for each class, documented change authority, exception handling, operational measures, and clear handoffs between endpoint operations, security, identity, and the service desk.
Without those elements, replacing several tools with one can centralize the same operating-model ambiguity. Ownership is also not merely an implementation detail. Consolidation can redistribute administrative authority, budget, policy control, support accountability, and local autonomy across workplace, security, identity, and decentralized IT teams. The CIO should treat unresolved decision rights and funding ownership as governance risks rather than assume that appointing a service owner resolves the underlying conflict.
AI introduces a second operating question: how much authority should the system receive? Table 1 separates AI use into three increasingly consequential modes. The distinction matters because the risk changes significantly when AI moves from explaining a problem to changing the environment.
Table 1. AI Authority and Operating Prerequisites
| AI mode | Appropriate use | Operating prerequisite |
|---|---|---|
| Assist | Search, summarize, explain, troubleshoot, surface documentation | Controlled data access and human review |
| Recommend | Draft policies/scripts, suggest remediation, identify likely misconfiguration | Standardized policies, named owners, human approval, auditable changes |
| Act | Deploy policy, isolate devices, revoke access, perform remediation | Reliable telemetry, measured change performance, tested rollback, bounded authority, strong governance |
Table 1 is deliberately asymmetric: moving from Assist to Recommend should be easier than moving from Recommend to Act.
A 2025 SOUPS study examined Large Language Model (LLM) use in security incident response rather than endpoint administration, so its results should not be treated as UEM performance data. In that specific experiment, involving 18 analysts and 50 real incidents, autonomous summaries omitted critical details in 35% of cases and introduced factual inaccuracies in 42%.7 Collaborative use reduced analyst effort and improved consistency. The relevant lesson for endpoint operations is narrower: increasing AI authority requires stronger validation and rollback than using AI as an assistant. CIS guidance published in 2026 likewise highlights additional security and operational risks when probabilistic AI systems connect to external tools capable of taking real actions.8
Decision rule: AI authority should rise with operational maturity—not with the sophistication of the product licence.
Decision Aid: Endpoint Rationalization Gate
Before initiating consolidation, the CIO should be able to distinguish a genuine control problem from dissatisfaction with the current toolset.
Table 2 is a Tactive decision heuristic synthesized from the cited control, operational, and incident evidence. It is not an empirically validated scoring model. Its purpose is to force explicit decisions and local evidence requirements before procurement or scale.
A “Pass” means the foundation is sufficiently understood to proceed; “Investigate” means uncertainty should be reduced through a bounded experiment; a “Fail” identifies a condition that should stop expansion. The final column identifies measures whose thresholds should be set locally during the baseline phase rather than imposed generically.
Table 2. Endpoint Rationalization Gate
| Gate | Pass | Investigate | Fail / stop | Local measure to define |
|---|---|---|---|---|
| Coverage | Material endpoint populations and exceptions are inventoried with an assigned control model | Some populations or ownership classes remain uncertain | IT cannot state what it manages, who owns it, or which devices are exceptions | % inventoried; % with named owner; % with assigned control model |
| Consistency | Inventory, patch, compliance, and policy evidence reconcile across material populations | Gaps exist but are bounded and manually reconcilable | Control evidence conflicts materially or remediation ownership is unclear | Policy-conflict rate; reconciliation time; unresolved compliance exceptions |
| Integration | Endpoint posture reaches security, identity, and service workflows that depend on it | Integration exists only for selected populations or depends on manual transfer | Posture cannot reliably influence access, response, or compliance decisions | % of material access/response decisions receiving trusted posture |
| Control-plane security | Privileged access, MFA, role separation, logging, platform patching, testing, and rollback are defined | Several controls exist but administrative exposure or recovery is unclear | Broad endpoint authority exists without proportionate privileged-access and recovery controls | MFA coverage; privileged-account count; recovery-test status; unresolved control exceptions |
| AI utility | Defined task, baseline, approval boundary, audit trail, and rollback mechanism | Use case appears useful but value or failure handling is unproven | AI is being purchased because it is available rather than because a measurable problem exists | Time saved; rework/error rate; override rate; rollback frequency |
Table 2 also makes sequencing explicit. A failure in Coverage or Control-plane Security should stop expansion. AI cannot compensate for weak foundations in the first four gates.
The measures are intentionally local. A regulated banking workflow, a teaching environment, and a hospital endpoint population may have materially different tolerances. The important discipline is to establish the threshold before the experiment begins so that success is not defined retrospectively.
Do Not Consolidate When
Retain multiple management tools where control evidence already reconciles reliably, ownership and escalation are clear, security and identity systems receive trustworthy posture information, operational handoffs meet service requirements, specialist endpoints have legitimate control needs, and migration cost exceeds the likely control improvement.
Tool rationalization is valuable only when it improves the service or its risk posture. A smaller tool count is not an outcome.
Where to Focus First
Start where fragmentation is visible and the blast radius is manageable: unreconciled inventories, conflicting policies, inconsistent patch evidence, slow remediation handoffs, overlapping tools with unclear ownership, or inconsistent treatment of personal devices.
Do not force specialized clinical, operational-technology, or research endpoints into a common model merely to improve a consolidation metric. Exceptions should be intentional and governed.
Illustrative Scenario: Higher Education
The following is a composite hypothetical designed to illustrate the decision framework, not a reported incident.
A university has central IT managing staff laptops, faculty IT groups managing laboratory Macs, and BYOD access for academics and students. A critical browser vulnerability emerges shortly before semester starts. Central IT can patch its own fleet within 24 hours but cannot reconcile faculty-managed patch status. Centrally enrolling every personal or research device before teaching resumes is operationally unrealistic and would create unnecessary privacy and governance friction.
The degraded operating mode is selective access: sensitive administrative applications require trusted endpoint posture, while lower-risk teaching services remain available through application-level controls. Faculty-managed laboratories receive temporary exception handling with named owners and accelerated patch evidence.
The CIO's decision is not to force universal UEM enrollment. It is to standardize inventory and compliance evidence for institution-owned endpoints, retain app/data controls for appropriate BYOD populations, and put explicit ownership and expiry conditions around exceptions.
The tradeoff is deliberate architectural diversity in exchange for continued teaching and research operations—while eliminating accidental control gaps.
Recommended Actions
The recommended sequence in Table 3 is designed to prevent a rationalization exercise from becoming a procurement exercise too early. Each phase produces evidence for the next decision rather than assuming enterprise consolidation is the final destination.
Table 3. 30/60/90-Day Decision Sequence
| Horizon | Action | Decision output |
|---|---|---|
| 0–30 days — Frame | Map endpoint populations, ownership, controls, exceptions, policy owners, duplicated tooling, patch evidence, and handoff delays. Establish local thresholds for the Table 2 measures before testing changes. | Control-gap record, success criteria, and priority problem; no procurement decision yet |
| 31–60 days — Experiment | Select one or two high-friction populations. Test existing capability before acquiring new tooling. Run one bounded consolidation experiment and, where justified, one low-risk AI-assist experiment. | Evidence against predefined success and rollback criteria |
| 61–90 days — Govern and decide | Review security, privacy, architecture, support, identity, cost, decision rights, funding ownership, and recovery implications. Decide adopt locally / route / stop / scale. | Approved operating model, decision rights, funding ownership, and named service owner |
| Next two quarters — Adopt and scale | Scale controls that demonstrated repeatable value. Retire duplicate tools only after service measures stabilize. Move AI from Assist to Recommend only where auditability and human approval are adequate. | Controlled scale plan, support model, and standardized service measures |
The important point in Table 3 is the decision at 61–90 days. A pilot is successful when it reduces uncertainty, not simply when the technology works. “Stop” and “adopt locally” are legitimate outcomes. Do not turn the pilot into a stealth rollout. Scale evidence, not enthusiasm.
Tradeoffs and Execution Burden
Consolidation costs rarely sit in the UEM licence alone. Table 4 highlights where organizational effort and hidden expenses usually accumulate. The effort bands are directional because estate diversity, contract structure, privacy requirements, and existing integration maturity materially change the burden.
Table 4. Execution Burden and Likely Friction
| Work | Burden | Likely owners | Main friction and cost sources |
|---|---|---|---|
| Estate/control-gap mapping | Light–Moderate: several weeks | Digital workplace, asset management, SecOps | Poor inventories, unclear ownership, exception discovery |
| Consolidation pilot | Moderate: cross-functional, 1–2 quarters | Endpoint owner, security architecture, identity, service desk | Parallel licensing, policy retesting, device re-enrolment, coexistence, integration engineering |
| Enterprise consolidation | Heavy where estates are diverse | CIO sponsor, platform owner, procurement, architecture, security | Contract exit costs, retraining, support surge, application dependencies, privacy constraints |
| Control-plane hardening | Moderate–Heavy | CISO, privileged access team, platform owner, architecture | Administrative identities, APIs, service accounts, recovery design, legacy integration |
| Assistive AI pilot | Light–Moderate | Endpoint operations, SecOps, AI governance | Data access, auditability, baseline measurement |
| Autonomous remediation | Heavy; not default | Platform, security, risk, service owners | Telemetry integrity, change risk, rollback, blast radius, governance approval |
As Table 4 shows, the most underestimated burden is often coexistence. Old and new management models may need to operate in parallel until inventory, policy, support, and compliance evidence show that migration is complete. CIOs should therefore budget for the transition state, not only the target platform.
AI features should also be tested for exitability, not only usefulness. Where policy generation, agent workflows, remediation logic, or audit history become dependent on proprietary AI services, include exportability, disablement, and fallback-to-manual operation in the exit criteria. AI should not quietly create a second layer of platform dependency after the organization has just spent effort rationalizing the first.
Bottom Line
Act on endpoint-control fragmentation. Pilot consolidation where measurable gaps exist. Keep multiple tools where controls already reconcile effectively. Pilot AI as assistance. Pause autonomous endpoint changes unless operational maturity, telemetry, governance, and rollback controls justify the authority.
The defensible decision is to standardize the management boundary, prove that consolidation improves the endpoint service, secure the control plane, and grant AI authority only as confidence in the operating model grows.
Evidence and Sources
- Jones, Kailani R., Dalton A. Brucker-Hahn, Bradley Fidler, and Alexandru G. Bardas. 2023. “Work-From-Home and COVID-19: Trajectories of Endpoint Security Management in a Security Operations Center”. 32nd USENIX Security Symposium.
- National Cyber Security Centre. 2025 review. “Device Security Guidance: Bring Your Own Device (BYOD), Action 4—Deployment Approaches”. Version 2.1.
- Center for Internet Security. 2026. “CIS Controls v8.1 Mobile Companion Guide”. March 2.
- Cybersecurity and Infrastructure Security Agency. 2025. “Malicious Listener for Ivanti Endpoint Mobile Management Systems”. AR25-261A.
- National Cyber Security Centre. 2025. “Principles for Secure Privileged Access Workstations: Principle 4—Scale the Solution”. March 25.
- Alachkar, Kotaiba, Dirk Gaastra, Eduardo Barbaro, Michel van Eeten, and Yury Zhauniarovich. 2025. “EvilEDR: Repurposing EDR as an Offensive Tool”. 34th USENIX Security Symposium.
- Kramer, Diana, Lambert Rosique, Ajay Narotam, Elie Bursztein, Patrick Gage Kelley, Kurt Thomas, and Allison Woodruff. 2025. “Integrating Large Language Models into Security Incident Response”. Twenty-First Symposium on Usable Privacy and Security.
- Center for Internet Security. 2026. “Artificial Intelligence and Large Language Models Companion Guide”. April 20.