Reducing Legacy Operations and Maintenance (O&M)

The agencies that will lead the next generation of government technology are already making a strategic choice: stop letting legacy operations and maintenance consume the budget that should fund AI, automation, and modern service delivery.

For U.S. State, Local, and Education (SLED) agencies, this is no longer an abstract IT conversation. Legacy O&M is a service delivery constraint, a cybersecurity liability, a workforce succession risk, and a growing barrier to the public trust that government depends on.

Most SLED agencies still depend on mission-critical applications built decades ago, systems supporting benefits administration, tax processing, licensing, permitting, transportation, public safety, higher education finance, and citizen services. These systems continue to work, which is exactly why they are so difficult to displace. They carry years of accumulated business rules, data dependencies, and institutional knowledge that no one has fully documented.

That is precisely the trap. The fact that a legacy system still functions is not evidence that it is cost-effective, secure, or capable of supporting what comes next.

NASCIO’s 2026 State CIO priorities place artificial intelligence first, application modernization second, and cloud solutions third. That ranking sends a clear signal: the path to AI-enabled government runs directly through legacy modernization. Agencies that cannot free up capacity, data, and talent from O&M obligations will find themselves unable to compete for the outcomes citizens and legislatures now expect.

Legacy O&M costs appear predictable because they are embedded in recurring budgets. In practice, they create a compounding drag that most agencies undercount.

The visible line items, aging infrastructure, specialized support contracts, software licenses, data center operations, disaster recovery complexity, manual testing, and custom interfaces, are only part of the picture. The hidden costs are where the real damage accumulates: every new policy requirement, reporting mandate, security control, or constituent-facing enhancement becomes harder and more expensive to implement on aging platforms.

The federal government offers a stark benchmark. GAO reported in 2025 that federal agencies spend approximately 80 percent of over $100 billion in annual IT and cyber-related investment on operations and maintenance of existing systems, including legacy infrastructure. While SLED appropriations are structured differently, the directional pattern is consistent across government: maintenance crowds out innovation before it ever gets a budget line.

The ROI conversation has to change. Modernization should not be justified only as a future-state technology investment. It must be measured against the compounding cost of doing nothing, including the escalating cost of the next emergency patch, the next compliance gap, and the next workforce retirement.

SLED agencies modernize under constraints that federal agencies and commercial enterprises do not face in combination: public budget scrutiny, extended procurement cycles, constrained staffing models, and constituents who now expect digital experiences equivalent to the best consumer applications they use every day.

A citizen who renews a subscription in two clicks does not understand why renewing a professional license still requires paper forms, batch delays, or manual reconciliation. That expectation gap is a political and reputational risk, not just a service quality issue.

EY research found that 71 percent of government IT decision-makers cite the cost of transitioning from legacy systems as prohibitively high. That statistic reflects a real constraint, but also a flawed frame. Most large-scale modernization programs that fail do so because they were designed as single, monolithic replacements rather than phased, value-first transformations. The answer to “we cannot afford to modernize” is not to delay further. It is to sequence modernization so that early phases fund later ones.

There is also a workforce dimension that is often underweighted in the business case. COBOL, PL/I, Assembler, JCL, and legacy database expertise remain operationally critical, but the engineers who carry that knowledge are retiring. This is not a theoretical risk for most SLED agencies. It is already visible in the difficulty of filling vacancies, in the cost of specialized support contracts, and in the institutional brittleness that results when a single individual understands how a critical system actually works.

Modernization is not a risk to be avoided. Continued dependency on aging systems and a shrinking talent pool is the actual risk.

• Infrastructure and Platform Cost Reduction. Migrating workloads away from aging on-premises environments, through data center exit, cloud migration, database modernization, or replatforming, typically eliminates significant fixed cost. The savings are immediate once the legacy environment is decommissioned, which is why sequencing decommission alongside migration is essential
• Legacy Skills Dependency. Modernized applications can be supported by a broader engineering talent pool. Reducing dependency on skills that are increasingly scarce and expensive is both a cost savings and a continuity risk reduction. For SLED agencies facing retirement waves, this is often the most urgent driver.
• Change Cost and Cycle Time. Modern architectures, automated testing, DevOps pipelines, and improved observability allow agencies to implement policy, compliance, and service changes faster and at lower per-change cost. The compounding savings from faster policy delivery are routinely overlooked in static ROI models.
• Security and Compliance Gap Remediation. Aging systems frequently lack modern identity controls, encryption, monitoring, vulnerability management, and audit automation. Reducing those gaps lowers both the direct cost of remediation and the exposure cost of the incidents that legacy vulnerabilities enable.
• Data Liberation and Analytics Enablement. Once legacy data is freed from brittle extraction processes and governed appropriately, agencies can support analytics, fraud detection, AI initiatives, and cross-agency insight without point-to-point integration complexity. This is the enabling layer for AI; modernization is how agencies make their data usable.

For many SLED agencies, modernization is first justified as an operating expense strategy: reduce recurring maintenance costs, lower support contracts, consolidate infrastructure, automate manual operations, and reduce dependency on aging technologies. Those savings matter. But they are only part of the financial case.

The larger impact is that OPEX reduction can also protect future capital budgets.

Legacy environments often force agencies into repeated capital reinvestment cycles. Hardware refreshes, storage expansions, disaster recovery upgrades, proprietary platform renewals, specialized networking investments, and end-of-life replacement projects all require CAPEX simply to keep existing systems running. In effect, agencies end up using capital dollars to preserve technical debt rather than improve public service delivery.

By reducing the operational burden of legacy systems through application modernization, cloud adoption, platform consolidation, automated testing, improved observability, and AI-enabled operations, agencies also reduce the need for future capital investments tied to outdated infrastructure. Instead of funding periodic emergency replacements, data center expansions, or proprietary platform upgrades, agencies can redirect capital toward higher-value priorities: cybersecurity modernization, constituent experience, analytics, fraud detection, AI readiness, and cross-agency data intelligence.

This creates a compounding financial effect:

• Reduced OPEX lowers annual budget pressure.
• Lower budget pressure reduces the need for reactive CAPEX refresh cycles.
• Avoided infrastructure replacement preserves capital for strategic initiatives.
• Modern architectures scale more efficiently without proportional capital growth.

For SLED agencies operating under multi-year budget constraints, that distinction is critical. Modernization allows agencies to move from reactive capital spending, where funding is consumed sustaining aging systems, to proactive investment models that support digital government and long-term mission outcomes.

In practical terms, agencies can stop buying expensive infrastructure to sustain yesterday’s technology and start investing in platforms designed for tomorrow’s public-sector services.

The most common barrier to modernization in public-sector environments is not budget. It is a false binary: either preserve the legacy estate indefinitely or undertake a massive rip-and-replace program that carries unacceptable operational and political risk.

This framing persists for understandable reasons. Large, failed modernization programs, benefit systems that went dark, tax systems that missed processing windows, licensing platforms that created backlogs, leave institutional memory that shapes procurement conservatism for years. The lesson learned is almost always the wrong one. The failure was not modernization. It was a poorly scoped, poorly phased, poorly governed program that tried to replace too much at once.

For most SLED agencies, the lower-risk path is application-by-application, workload-by-workload transformation. This begins with discovery: identifying which systems carry the highest O&M cost, the highest operational risk, the greatest security exposure, and the most significant service delivery impact. Sequencing modernization around measurable value, rather than technical elegance or vendor convenience, is what separates programs that succeed from programs that stall.

This is the context in which mLogica’s LIBER*M AI-Native + Deterministic Modernization approach is differentiated. The methodology combines AI acceleration, for discovery, documentation, code analysis, and pattern recognition, with deterministic automation that provides the repeatability, traceability, and auditability that mission-critical public-sector transformation requires. In SLED environments, “trust the black box” is not a viable operating model. Every modernization output must be explainable, verifiable, and defensible to auditors, oversight bodies, and legislative stakeholders. That is precisely what the deterministic layer delivers.

The most credible O&M reduction business case is not built on industry benchmarks or vendor projections. It is built from an agency’s actual application portfolio, validated through a scoped proof-of-value before full program commitment is made.

A rigorous business case addresses six components:

• Current annual run cost, infrastructure, licensing, labor, support, testing, incidents, and compliance burden, fully loaded.
• Modernization investment by phase, not as a single program total that triggers procurement conservatism.
• Expected savings by category, platform reduction, automation, staff productivity, license retirement, and data center optimization, tied to specific applications, not portfolio averages.
• Avoided costs, end-of-life risk, emergency support exposure, security remediation, and vendor lock-in premium.
• Operational benefits, faster service delivery, improved resilience, better reporting, reduced audit burden.
• Workforce transition plan, how institutional knowledge is captured, documented, and transferred before it retires with the people who carry it.

The agencies that build these cases most effectively do so through an independent portfolio assessment before selecting a modernization approach. Assessment findings, not vendor proposals, should drive the sequencing and scope decisions.

SLED agencies do not need to modernize everything at once. They need to stop allowing legacy O&M to define the ceiling of what is possible.

• Establish the baseline. Understand what the agency spends on legacy O&M today, fully loaded. Most agencies significantly underestimate this number because hidden costs are distributed across multiple budget categories. A structured portfolio assessment surfaces the real figure and identifies where the concentration of risk and cost is highest.
• Sequence for value, not for technical convenience. Identify the two or three applications where modernization would release the most O&M cost or reduce the most operational risk. Use those as the first phase. Early wins that demonstrate measurable savings create the political and financial runway for subsequent phases
• Design for decommission from day one. Modernization programs that fail to decommission legacy environments promptly lose most of their anticipated savings. The business case must include a clear decommission plan and timeline. Running parallel environments indefinitely is not a transition strategy, it is a cost doubling.

mLogica offers a structured Legacy Portfolio Assessment for SLED agencies, a time-bounded engagement that delivers a current-state O&M cost baseline, risk concentration map, and prioritized modernization sequencing recommendation. Contact us to learn how agencies comparable to yours have moved from legacy dependency to modernization momentum.

• NASCIO 2026 State CIO Top Ten Priorities, nascio.org
• GAO-25-107795: Federal IT Modernization and Legacy Systems, gao.gov (2025)
• EY Government State and Local 2025 Survey Findings, ey.com