Gartner: How Compounded Technical Debt is Stalling AI Progress in Manufacturing

Manufacturing CIOs are pouring money into AI. But most of them are running into a wall they built themselves, years of accumulated technical debt across IT, operational technology (OT), and engineering technology (ET) systems that were never designed to support machine learning, predictive analytics, or automated decision-making. The ambition is there. The infrastructure is not.

A February 2026 Gartner® report, 2026 Top Trends for Manufacturing CIOs: Challenges, identifies compounded technical debt as one of three headwinds threatening to stall manufacturing progress over the next three years. According to the report,  Aging IT/OT/ET systems and complex integrations are stalling AI scalability and increasing cost, risk and rigidity.

This is not a theoretical problem. Forty-eight percent of manufacturers with defined modernization plans are currently modernizing their core systems. Still, they are doing so to cut costs (39%), adopt new technology (37%), and improve security (29%), not to reduce technical debt itself. Yet when compared against other objectives or short-term needs, technical debt is often seen as being a lower priority to the point that 7% of organizations treat technical debt reduction as a top-three driver of modernization. That gap between AI ambition and infrastructure readiness is where projects stall, budgets overrun, and competitive advantages evaporate.

Key takeaways for manufacturing leaders

1. Technical debt is no longer an IT problem. It has become decentralized across business units, OT systems, and engineering platforms. PMO leaders and operations heads own parts of this debt, whether they realize it or not.

2.  AI adoption depends on data quality, and data quality depends on systems. Manufacturing execution systems and enterprise asset management platforms over a decade old cannot produce the clean, integrated data AI requires. Without modernization, AI projects will continue to underdeliver.

3. The cost of deferring modernization exceeds the cost of change. Gartner® repeatedly observes that organizations defer system upgrades, citing the "cost of change," but this deferral compounds the problem and drives higher downstream costs.

4. Fifty-four percent of manufacturing I&O leaders say rapid technical debt accumulation from digital transformation is their top challenge. Another 48% face high costs and risks from critical technology dependencies. These numbers confirm the scale of the problem.

5. Categorizing technical debt is the starting point. Not all technical debt requires immediate remediation. Breaking it into categories of maintainability, compatibility, portability, security, and performance efficiency lets organizations prioritize what matters most.

What technical debt looks like on the factory floor

Technical debt in manufacturing is not the same as it is in software development. In a software company, technical debt usually means shortcuts in code. In manufacturing, it means a production line running on a manufacturing execution system (MES) from 2011 that cannot communicate with the ERP system installed in 2018, which, in turn, does not integrate with the data lake the analytics team set up last year.

The Gartner report describes how growth through mergers and acquisitions, disconnected IT projects, and patchwork integrations have created manufacturing system architectures riddled with inconsistently deployed applications, heavily customized commercial off-the-shelf software, and siloed homegrown systems. These systems are not legacy in the sense that they don't work. They work well enough to keep operations running. But they were never designed to share data across functions, feed AI models, or support real-time decision-making at portfolio scale.

For PMO leaders managing complex manufacturing projects, this fragmentation creates a specific pain point. Project data lives in one system, financial data in another, resource allocation in a spreadsheet, and risk registers in email threads. When leadership asks for a portfolio-level view of all active projects, the PMO team spends days assembling it manually. That is technical debt manifesting as lost time and reduced visibility.

Why is this problem accelerating now?

Manufacturing organizations have been living with technical debt for years. What has changed is the arrival of AI, which demands exactly the infrastructure these organizations lack.

AI needs clean, connected, real-time data.

AI models are only as good as the data they consume. When a manufacturer's project data is scattered across disconnected systems, with different formats, update frequencies, and access controls, no amount of AI investment will produce useful results. The bottleneck is not the algorithm. It is the data pipeline.

 The Gartner® report makes this point directly: a mature technology ecosystem built on rich, high-quality data has become essential for harnessing the transformative potential of AI and related technologies. Organizations that skipped the unglamorous work of system integration, data standardization, and platform modernization are now discovering that they cannot simply bolt AI onto a broken foundation.

Competitive pressure is forcing premature AI adoption.

The pressure to adopt AI before addressing the underlying infrastructure creates a vicious cycle. Manufacturing operations often play catch-up to other business functions in technology modernization, according to Gartner®. When the board asks why the company hasn't deployed AI-driven demand forecasting yet, the CIO faces a difficult choice: admit that the core systems can't support it, or attempt an AI deployment on unstable ground.

 Many choose the latter. The result is AI projects that consume budgets, deliver disappointing results, and compound the technical debt problem by adding another layer of poorly integrated technology to an already tangled architecture.

Management systems are expanding to measure the value of digital investments.

A subtler shift is happening in how manufacturing organizations measure operational excellence. The definition of site-level operational excellence now includes measuring the value of targeted digital investments, such as establishing resilient platforms and hybrid infrastructures that address technical debt and minimize future risk. When executive teams start asking about the ROI of their digital portfolio, the fragility of the underlying systems becomes impossible to ignore.

The real cost of ignoring technical debt

The financial impact of technical debt in manufacturing is substantial, though it rarely appears as a single line item in the budget. It shows up in several ways.

Project overruns and delayed timelines

Integration complexity is consistently underestimated in manufacturing technology projects. When a new product introduction (NPI) project requires data from four different systems that don't communicate with each other, the project team spends weeks on manual data reconciliation. Those weeks translate to delayed launches, missed market windows, and lost revenue. Organizations that implement structured project management practices can mitigate some of this, but the root cause remains: the systems themselves are the constraint.

Inability to scale smart manufacturing initiatives

Smart manufacturing, the combination of IoT sensors, predictive maintenance, digital twins, and AI-driven production optimization, requires an integrated technology stack. A digital thread that connects design, manufacturing, and service data cannot function when the underlying systems are siloed. Technical debt does not just slow down individual projects; it blocks entire categories of capability improvement.

Rising security risk

Outdated systems are harder to patch, harder to monitor, and more vulnerable to attack. The Gartner report ‘2026 Top Trends for Manufacturing CIOs: Challenges’ specifically calls out the security risks of technical debt, particularly when critical infrastructure, product, or equipment recipes are involved. For manufacturers already contending with rising cybersecurity threats across their portfolios, unpatched legacy systems represent an expanding attack surface.

Talent challenges

Younger engineers and project managers do not want to work with 15-year-old systems. They expect modern interfaces, integrated data, and AI-assisted workflows. When a manufacturer's technology stack feels outdated, it becomes harder to attract and retain the skilled workers the industry already struggles to find. This compounds the workforce shortage challenges that aerospace and defense manufacturers are already facing.

How manufacturing leaders should approach technical debt reduction

The Gartner® report: 2026 Top Trends for Manufacturing CIOs: Challenges recommends several concrete actions that manufacturing CIOs and PMO leaders should prioritize.

Categorize debt before attempting to fix it.

Not all technical debt is equal. Gartner® recommends categorizing it along five dimensions:

• Maintainability: How difficult and expensive is it to maintain current systems?

• Compatibility: Can systems exchange data and work together?

• Portability: Can workloads be moved to modern platforms?

• Security: Are systems exposing the organization to preventable risk?

• Performance efficiency: Are systems bottlenecking operations?

 This categorization prevents the common mistake of treating all technical debt as equally urgent. Some debt is cosmetic. Some is dangerous. Knowing the difference is the first step toward a realistic remediation plan.

Assess debt with four fitness factors.

Gartner recommends evaluating each category of debt against business value, financial resources, and direct and indirect risks. This framework helps leaders answer the question: "Given our constraints, where should we invest first?" For a manufacturer running multiple concurrent capital projects, this prioritization is the difference between spreading resources too thin and making targeted, high-impact improvements.

Embed debt reduction into annual OKRs

One-time remediation projects fail because they compete with short-term priorities and lose. Gartner® recommends embedding technical debt reduction into annual site objectives and key results (OKRs) by mapping each debt category to clear objectives and key results. This makes debt management a continuous process rather than a project that gets funded in good years and cut in bad ones.

Thirty-seven percent of manufacturing I&O leaders cite difficulty demonstrating business value as a barrier to debt reduction. Tying debt reduction to OKRs aligned with revenue, throughput, or risk mitigation gives leaders the language they need to defend the investment.

Reframe the conversation

Technical debt reduction will never win a budget fight if it is positioned as an IT cost. The Gartner® report recommends reframing technical debt as an impediment to AI and to realizing its business value. When a CIO can show the board that a $2 million debt reduction initiative will save $8 million in failed AI projects over the next three years, the math speaks for itself.

Take the first step

The 2026 Gartner report on manufacturing CIO challenges provides the research and framework manufacturing leaders need to assess their technical debt exposure and build a practical remediation plan. Download the full report to understand how technical debt, alongside ransomware threats and geopolitical disruption, is shaping the manufacturing sector's technology agenda for the next three years.

Disclaimer

Gartner, 2026 Top Trends for Manufacturing CIOs: Challenges, By Brady Barnes, Simon Jacobson, Katell Thielemann, Chris Campbell, Bettina Tratz-Ryan, Sudip Pattanayak, Scot Kim, Jonathan Davenport, Kentaro Shikanai, Alexander Hoeppe, 18 february 2026

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