Across the global industrial landscape, nondestructive testing (NDT) faces a pivotal moment. The sector is grappling with two simultaneous forces: a shrinking and aging workforce on one hand and accelerating technological change on the other. These converging trends are not unique to NDT; they mirror global demographic and digital transformations that are reshaping manufacturing, energy, transportation, and infrastructure inspection. Understanding and adapting to these shifts will define the industry’s resilience and its relevance in the decades to come.

The Demographic Crossroads

A 2025 McKinsey report on global population decline warns that two-thirds of the world’s population now live in countries where fertility rates are below replacement levels. The effects of this demographic contraction are expressed with youth scarcity, aging populations, and shrinking labor pools, which are already rippling through specialized technical fields.

In NDT, these realities translate into one of the most pressing issues faced by training centers, inspection service providers, and overall employers: a workforce pipeline crisis. Not enough skilled people to fill the rising demand for inspection.

Stakeholders consistently point to an insufficient supply of qualified inspectors, especially in advanced modalities such as phased-array ultrasonic testing (PAUT), full matrix capture (FMC), eddy current array (ECA) and computed tomography (CT). The problem extends beyond technical competence; it also highlights the total lack of awareness of the diverse existing NDT careers path, the inconsistent training standards in our industry, and a shortage of not only inspectors, but also of experienced instructors capable of bridging theory and field application.

Efforts to attract new entrants to NDT face several barriers. Public understanding of the profession remains low compared with better-known disciplines such as welding, robotics, or aerospace engineering. Students and parents often learn of NDT only by chance or word of mouth. Training institutions, meanwhile, report difficulties recruiting instructors with both pedagogical skill and real-world experience, while inspection companies note that many entry-level technicians require extensive retraining after certification.

For many years, NDT was considered a hidden gem. A career path largely unknown to the broader public. While limited awareness remains a challenge, it is no longer the sole bottleneck. Today, the issue is compounded by a broader demographic shift: a shrinking labor pool. As global depopulation accelerates, the shortage of individuals available to enter and remain in the profession is becoming a critical constraint, even if every single candidate was aware of NDT’s existence.

Rethinking Workforce Sustainability

With this in mind, expanding the talent pipeline remains essential, but demographics suggest that NDT must also learn to do more with less. One strategy to rethink sustainability and workforce coverage is by retaining and retraining senior professionals,  while enabling Inspection Service Providers (ISPs) to access additional and on-demand expertise.

Flexible, remote, or consultancy-based models allow experienced Level III inspectors to remain active while mentoring the next generation. One way of achieving this could be with technology.

Digital platforms for remote data review and collaborative analysis are becoming more and more accessible and could support this shift, allowing experts to contribute from any location, even after “soft” retirement.

This approach not only preserves institutional knowledge but also redefines how expertise is distributed. Instead of concentrating specialists within a handful of large organizations, digital connectivity enables knowledge to circulate globally. This strengthens smaller service providers, universities, and regional industries that might otherwise struggle to access high-level guidance.

This also introduces a new rationale for integrating inspection data into digital twins: it enables more effective remote collaboration and evaluation by allowing senior inspectors to access not only inspection data, but also relevant environmental conditions to support their analysis and oversight.

From Training to Simulation: The Digital Classroom

The next frontier in workforce development also lies at the intersection of pedagogy and technology. Traditional NDT education has long prioritized hands-on training with physical specimens, emphasizing tactile understanding with manual scanning, material response, and environmental settings. Yet the availability of diverse, realistic flawed specimens is still limited, which constrains the depth of practice students can achieve.

Emerging training modalities, including augmented reality (AR), virtual reality (VR), and interactive simulation, now offer scalable alternatives. When properly implemented, these tools can expose trainees to hundreds of virtual or pre-recorded flaws, geometric variations, and scanning challenges before they ever handle a real component. Much like aviation embraced flight simulators decades ago to standardize pilot readiness, NDT can adopt virtual environments to expand access to high-quality practice and improve consistency across programs.

Skeptics rightly note that simulation cannot (yet) fully replace physical inspection experience. However, as a supplement (especially when guided by qualified instructors), it can equalize opportunity and expand training capacity without compromising quality. Combined with remote mentoring and asynchronous learning platforms, simulation technology may help close the gap between global demand and local instructional resources.

The Ultimate Technological Shift: From Proprietary to Open

Beyond workforce development, a deeper transformation is underway in the tools and data systems used to conduct inspections. For decades, NDT instrumentation and software have evolved within closed, proprietary ecosystems. Each manufacturer typically maintained its own data format, interface, and analysis environment, which limited interoperability and constrained innovation. This fragmentation not only complicates workflow integration but also restricts the ability to apply advanced analytics or artificial intelligence (AI) across datasets from multiple sources.

A growing movement toward open ecosystems and standardized data formats aims to change that. The principle is straightforward: inspection data should be accessible without dependence on a specific vendor’s proprietary technology or software development kit (SDK) and the user should be free to use their data however they see fit. Data structures and contextual information should also be expressed in an unambiguous manner, using standardized ontologies that support integration with external systems. But data shouldn’t be the only aspect of an open ecosystem. Accessible and documented application programming interfaces (APIs) can also enable researchers, software developers, and end users to build custom solutions tailored to their own operational needs.

Such openness can accelerate innovation in the field of NDT. The same dataset can be reused for different analyses, such as AI-assisted defect recognition, automated reporting, or integration with digital-twin systems. More importantly, it democratizes participation, allowing specialized SMBs, small laboratories, or institutions in emerging markets to contribute meaningfully to global progress.

Regulatory Momentum Toward Interoperability

The trend toward openness is not unique to NDT. The medical imaging sector, for example, has long relied on standardized formats such as DICOM to enable cross-platform data exchange. Now, broader regulatory frameworks are reinforcing this evolution. The European Union Data Act, taking full effect in September 2025, will require manufacturers of connected devices to provide users with access to the data generated by their products. The goal is to promote competition, consumer rights, and data-driven innovation by preventing vendor lock-in.

For the NDT community, this policy signals a shift toward legally mandated interoperability. Vendors selling equipment in Europe will need to comply with these requirements, effectively aligning industrial inspection data with the same accessibility standards already emerging in healthcare, energy, telecommunications and other industries.

Settling on the Right Approach

To succeed in the move toward openness, especially regarding data access, the NDT industry must align on a common approach. Without consensus, the proliferation of incompatible file formats, ontologies, and data definitions would undermine the very goals of accessibility and shareability. So far, the main players have seemed to align on a singular, well-known toolkit: Hierarchical Data Format 5 (HDF5). HDF5 is an open-source framework originally developed for large-scale scientific computing by agencies such as NASA. Its ability to handle complex, multi-dimensional datasets while embedding metadata (information about the data itself) within the same file structure has seemed to make it the logical choice for many. This capability is especially suitable for NDT, where inspection data is not only complex but also comes in various forms (e.g., A-Scans, strip charts, images) and often accompanied by rich contextual details such as probe geometry, test parameters, material type, and component identifiers.

However, HDF5 alone would still put NDT at risk of not aligning ontology, structure, or definitions. This is why the .nde file format (originally developed by Evident Industrial) adopts a dual approach, combining HDF5 with JavaScript Object Nation (JSON): a lightweight, human-readable way to describe metadata. By combining both, JSON Schemas, another proven, open-source technology, provides a way to validate metadata structure while HDF5 provides high-performance capabilities for reading and writing raw data. An NDT-specific JSON schema can define how this information is organized and used when saving data for structure verification. Descriptions like weld geometry, specimen material properties, transducer type, sensors position on the part, and so on, are key information in the inspection world. This structured approach relying on well adopted tools lays out the groundwork for a standardized .nde file format. It’s a foundation for open specifications designed to represent ultrasonic, radiographic, and other inspection data of any complexity and consistently across vendors.

Toward a Culture of Collaboration

Open data standards alone are not sufficient. They must be paired with open collaboration. Online repositories such as GitHub now host public projects detailing proposed HDF5 structures, metadata definitions, and sample code for reading and visualizing NDT data. By engaging with these resources, practitioners, academics, and developers can contribute to refining the common framework. This shared infrastructure fosters transparency, reproducibility, and trust. Such qualities are becoming increasingly essential as AI and machine-learning models begin to play a larger role in defect evaluation and decision support.

In parallel, the rise of inspection-as-a-service and regional digital hubs suggests a future in which expertise and computing power can be distributed dynamically. Technicians in the field may stream live scan data to remote specialists, who in turn leverage automated algorithms to pre-analyze results before final human review and decision-making. These hybrid models aim not to replace inspectors but to amplify their capabilities, reducing fatigue, minimizing subjectivity, and enabling faster, more consistent assessments, as the demand on them becomes unavoidably more stringent.

AI-Augmented Decision Making

Artificial intelligence has already demonstrated value in recognizing flaw patterns, segmenting imagery, and assisting with code-compliance checks. In the current phase of adoption, however, AI is best viewed as an augmentation tool rather than a replacement for qualified personnel. Standards bodies are responding accordingly. ASNT has released guidance documents on AI and machine-learning integration, while ASME is developing code provisions for validating automated analyses.

When paired with open data, AI systems can be trained on diverse and historical datasets from multiple sources, improving their robustness and generalizability. Over time, these systems could act as a second set of eyes, performing pre-screening, quality audits, or long-term asset health tracking across fleets of components. Such capability will become indispensable as the availability of skilled inspectors continues to decline, driving a technological shift across the industry.

Case Studies in Open-Format Adoption

Early adopters of open-data principles illustrate what this future might look like. Small engineering firms have already leveraged publicly available code libraries to build their own visualization tools for composite inspections, while service providers in the power-generation sector have begun integrating ultrasonic and visual datasets into unified digital twins for plant maintenance. Others have developed vendor-agnostic corrosion-mapping applications that use AI to refine defect characterization over time, learning from every inspection performed. These examples demonstrate that accessibility, rather than exclusivity, drives innovation.

The Path Forward

The convergence of demographic and technological pressures leaves the NDT industry with few alternatives but to evolve. Addressing the workforce shortage requires cultural as well as technical adaptation. Valuing mentorship, adopting flexible work models, and expanding access through simulation and remote collaboration may offer the strategic foundation the NDT industry needs to address its current challenges. At the same time, digital transformation demands a commitment to interoperability, open standards, and shared governance of data.

Ultimately, the shift toward open ecosystems is not a threat to competitiveness but a catalyst for collective progress. By reducing barriers between systems and stakeholders, the industry can unlock efficiencies, accelerate research, and enhance safety outcomes. As connected devices proliferate and data volumes grow, the ability to manage, interpret, and share information seamlessly will define the next generation of NDT.

The challenge is not merely to adopt new tools. It is to align them with human purpose and ensure that every technological advancement strengthens the people, processes, and principles that underpin quality assurance. If successful, the result will be a more transparent, collaborative, and sustainable inspection ecosystem, one that can meet the needs of an increasingly complex and resource-constrained world.