Key Takeaways  

• Scan-based progress monitoring provides objective, field-verified visibility into construction progress by comparing recurring reality capture data against project models, schedules, and milestones. 

• Semiconductor construction teams use scan-based progress monitoring to validate installation readiness, monitor planned-versus-actual performance, and identify issues before they disrupt tool installation or commissioning. 

• Unlike visual reporting alone, scan-based progress monitoring verifies whether installed work is spatially accurate, coordinated, and completed to the standard required for the next phase of construction. 

The Visibility Gap in Semiconductor Project Delivery 

Semiconductor fab construction leaves little room for uncertainty. Programs are delivered against compressed schedules, dense utility coordination, strict tolerances, and milestones tied to cleanroom readiness, tool installation, commissioning, and the path to production. A delay in one area quickly cascades across trades, installation sequences, and downstream activities. 

Despite this complexity, many project teams still rely on manual updates, visual walkthroughs, and subjective percent-complete reporting to understand whether work is advancing as planned. These methods are useful, but they provide an incomplete picture. Progress can appear satisfactory at a high level while hidden installation issues, incomplete interfaces, or unverified field conditions create risk beneath the surface. 

Scan-based progress monitoring connects reported progress to what has actually been installed. By capturing recurring point cloud data and comparing current conditions against project models, schedule activities, and defined milestones, owners and delivery teams can see where progress is aligned, where it is falling behind, and where emerging issues could affect the broader program.  

The value of scan-based progress is not a better visualization of the site. It is greater confidence in decisions about schedule, coordination, earned value, and installation readiness. 

The Limitations of Traditional Progress Reporting 

Construction progress is typically communicated through contractor updates, photographs, site observations, and estimated completion percentages. Each depends on interpretation.  

Consider how a walkthrough may confirm that equipment is present or utility systems are in place. However, it will not always verify whether installed work is complete, coordinated, or positioned correctly. Likewise, a visual snapshot documents activity but offers limited spatial context. And a reported percentage often reflects effort expended, rather than measurable installed scope. These limitations matter more in semiconductor environments, where progress is not defined only by whether something has been installed. 

Teams need to know whether it is in the correct position, aligned with the coordinated model, ready at its interfaces and tie-ins, within tolerance, and able to support the next scheduled milestone. A utility rack may appear substantially complete but contain an unresolved conflict, such as a routing clash with adjacent systems or an unfinished tie-in, that blocks follow-on work. Similarly, a tool area may look ready but have datum, clearance, or hook-up issues.  

Without field-verified information, these problems often stay hidden until they begin affecting downstream activities. 

Turning Point Clouds Into Project Intelligence 

A point cloud alone does not provide schedule confidence. The value comes from connecting captured reality to the project controls that guide delivery. A scan-based progress monitoring workflow typically involves: 

1. Establishing a reliable survey control framework 

1. Capturing site conditions at an agreed cadence or around critical milestones 

1. Registering each capture against the project coordinate system 

1. Comparing installed conditions with building information modeling, design models, or verified baselines 

1. Linking measurable scope to schedule activities, zones, and work packages 

1. Identifying planned-versus-actual schedule and cost variance and reporting it in a format that supports timely action 

This workflow turns reality capture from a periodic documentation exercise into a repeatable project management process. Instead of reviewing isolated images or generalized progress statements, teams examine field evidence tied to the same systems, areas, schedule activities, and milestones used to manage the program. 

Why Fabs Require a Different Level of Visibility 

Semiconductor construction concentrates highly coordinated systems in tightly controlled spaces. Cleanrooms, subfabs, utility corridors, process systems, electrical infrastructure, and automated material handling systems (AMHS) must come together within defined tolerances and sequences, so progress in one area is rarely isolated.  

Delayed overhead utilities affect cleanroom completion and tool hook-up. Incomplete subfab systems prevent equipment installation. Supports installed out of position can force downstream trades to adjust, reroute, or rework connected systems. Frequent design changes and undocumented field changes can further complicate sequencing, model alignment, tool moves, and utility reroutes. 

Fab owners, construction managers, and delivery teams therefore need more than a broad sense that construction is moving forward. They need to know that the right work is complete, in the right place, at the right time, and to the standard required for the next activity.  

Recurring scanning helps surface these issues earlier, including installed work that does not match the coordinated model, zones progressing more slowly than their surroundings, systems reported as complete but not ready for follow-on trades, and work installed outside tolerance. A delay discovered in a coordination meeting is usually recoverable. The same delay discovered during tool move-in or installation has far greater consequences for labor, sequencing, and the path to production. 

Note that scan-based project monitoring does not replace the judgment of project managers or field teams. Instead, it gives them better evidence, so progress discussions move quickly from debating the status of the work to deciding what happens next, helping protect critical schedule milestones. 

Supporting Tool Move-In and Installation Readiness 

Tool installation is one of the most schedule-sensitive stages of fab delivery. By the time equipment is ready to move into position, the surrounding environment must support move-in, installation, connection, qualification, and commissioning. Small discrepancies in location, elevation, or interfaces can cause significant disruption.  

Scan-based workflows validate equipment and support locations, tool datums, hook-up interfaces, AMHS alignment and clearances, floor conditions, and completion of prerequisite work. Rather than relying on visual confirmation or general completion statements, teams can verify that physical conditions match the requirements of the installation plan. 

The Importance of Survey Control and Geospatial Governance 

Not every reality capture workflow provides the same confidence. For progress information to support high-stakes decisions, the data must be accurate, consistently registered, and managed within an agreed framework covering survey control, measurement tolerances, capture standards, version control, quality assurance requirements, data ownership, and reporting responsibilities. Without this foundation, teams risk comparing inconsistent captures against outdated models or making schedule decisions from data that was never intended to support that level of precision. With it, each capture contributes to a trusted digital record of the program rather than a standalone dataset. 

A trusted, survey-controlled foundation is what separates verification from visualization. While a camera-based or AI-supported platform can show that activity has occurred, a survey-controlled, scan-based workflow can determine whether installed work is spatially correct and aligned with the project baseline. For semiconductor programs, that distinction is the difference between knowing something was installed and knowing it was installed correctly, within tolerance, and in time for the next scheduled activity. 

From Reporting Progress to Schedule Confidence 

Recurring captures create value beyond the active construction phase. They contribute to a field-verified digital thread supporting as-built models, digital twins, future tool moves, utility reroutes, retrofits, and expansions around live production. The result is a digital baseline that continues to support decisions after construction is complete. 

The goal of scan-based progress monitoring is to strengthen established project controls or field expertise with objective, measurable evidence. For semiconductor owners and delivery teams, that means earlier identification of schedule risk, less ambiguity in reporting, reduced exposure to avoidable rework, and greater confidence in installation readiness.  

Fab program managers already know that work is happening. True schedule confidence comes from knowing the right work has been completed correctly, in the right location, and in time for what comes next. 

Digital twins are rapidly moving from buzzword to boardroom priority across the aviation industry. For airports, the appeal is clear: a dynamic, 3D representation of infrastructure and operations that can improve decision-making, from managing passenger flow to preparing for advanced air mobility. Yet for many, digital twins still feel complex and undefined. The challenge is no longer recognizing their potential — it’s identifying and delivering tangible value.

Start With Outcomes, Not Data

A common misstep is collecting vast amounts of data without a clear purpose. While the urge to “capture everything” is understandable, managing data across thousands of assets, from light switches to structural systems, can quickly become costly and unmanageable.

Instead, digital twin strategies should be anchored in outcomes and return on investment. Ask questions such as: What problems are we solving? What decisions will this data improve? Focusing on high-value use cases positions digital twins as a tool for action, not just a repository.

In the aviation digital twin ecosystem, priority should be given to critical assets such as runways, taxiways, airfield lighting, navigational aids, HVAC and MEP systems, and baggage handling. Failures in these areas can halt operations entirely. By targeting assets that impact safety, throughput, and compliance, airports can enhance security, reduce operational risk, improve reliability, and maximize performance and ROI.

Governance Enables Long-Term Value

Building a digital twin is not a one-time effort; it’s an ongoing investment that enables smarter, more informed operations over time. As airports move from 2D environments to integrated digital ecosystems, they gain new opportunities to connect data, processes, and teams.

To unlock this value, digital twins must stay aligned with the real world. This requires continuously updating data to reflect how assets evolve and capturing accurate as-built information. When done well, this creates a strong foundation for moving from reactive to proactive operations.

Without this discipline, even advanced digital twins lose relevance. When models diverge from reality, confidence erodes and value declines. Establishing clear roles, responsibilities, and data standards is essential to sustaining long-term success.

Think Long Term, but Start Small

A long-term roadmap, often spanning five to 10 years, helps align stakeholders, standardize data practices, and scale capabilities. However, success doesn’t require a full-scale rollout from day one.

Focusing on a single high-impact use case is often the most effective starting point. Whether optimizing energy usage, improving passenger flow, or enabling predictive maintenance, targeted pilots can quickly demonstrate value and build momentum.

These early “quick wins” are critical, not only for proving the technology, but for securing buy-in across the organization.

AI: The Force Multiplier

Artificial intelligence is amplifying the value of digital twins. Combined with sensor data, AI enables airports to shift from reactive operations to predictive, and even prescriptive, decision-making.

Leading airports are already demonstrating this:

• Vancouver International Airport uses AI-driven simulations to anticipate congestion and adjust staffing.
• San Francisco International Airport integrates live data into its digital twin to enhance operational visibility and forecasting.
• Dallas/Fort Worth International Airport applies machine learning to monitor infrastructure health and reduce unplanned downtime.

These examples highlight an important shift — digital twins are evolving from static representations into intelligent systems that help airports anticipate and act before issues occur.

Preparing for Advanced Air Mobility

Digital twins will play a critical role in preparing for advanced air mobility. The rise of electric vertical takeoff and landing aircraft introduces new infrastructure requirements, particularly around electrification and charging.

With digital twins, airports can simulate scenarios, evaluate trade-offs, and plan effectively long before making physical investments

Beyond Technology: People and Integration

Technology alone is not enough. Success also depends on people, processes, and alignment across operations, IT, planning, and maintenance teams. Change management is often the biggest hurdle.

Integration is key to unlocking the full value of digital twins. By connecting systems such as CMMS, GIS, BIM, and IoT platforms, airports can unify data and insights — creating a whole that delivers far greater value than its individual parts. Without this interoperability, digital twins risk becoming isolated systems.

Turning Potential into Performance

Digital twins offer enormous promise, but realizing that promise requires focus and discipline. By prioritizing outcomes, enabling strong governance, leveraging AI, and starting with targeted use cases, airports can move beyond experimentation and achieve real operational impact.

In doing so, they won’t just build smarter models — they’ll build smarter, more resilient airports.

Data centers have become one of the most debated forms of infrastructure development in North America. To some communities, they represent unchecked growth: massive buildings, heavy power demand, water concerns, environmental impact, and relatively few permanent jobs. To others, they are the essential engines of the modern economy, quietly powering everything from health care and emergency services to manufacturing, education, cloud computing, and artificial intelligence. 

There is some truth in both perspectives. But the debate is too often framed as a binary choice: Should we approve the building of data centers in our communities or should we reject them outright? But that is the wrong question. The real issue is not whether data centers should be built, but how they can be built so they have a positive impact on the community. 

When planned and governed thoughtfully, data centers can function much like modern utility infrastructure: capital-intensive, low impact on daily life, fiscally transformative, and compatible with local priorities. When approvals are rushed, poorly conditioned, or disconnected from community expectations, they can just as easily generate frustration and backlash. The difference lies in policy and design. 

Data Centers as Critical Infrastructure 

The rapid expansion of cloud computing, digital services, and AI has elevated data centers from a niche real estate class to critical infrastructure. Their growth is not speculative. It is structural. These facilities now underpin health records, financial systems, logistics networks, industrial automation, education platforms, public safety systems, and more. 

Seen through this lens, data centers resemble other forms of utility infrastructure. They are designed for reliability and scale, not visibility or foot traffic. Their value lies less in daily employment counts and more in systemic support for the modern economy. 

That framing does not excuse poor siting or weak oversight. It does, however, suggest that communities should evaluate data centers using infrastructure criteria, including long-term fiscal impact, resource stewardship, and compatibility with local planning goals, rather than comparing them to uses such as retail or manufacturing. 

The Economic Reality: High Value, Low Daily Burden 

One of the most common criticisms of data centers is that they do not create enough permanent jobs once construction is complete. That observation is fair, but incomplete. 

Data center projects often involve hundreds of millions, or even billions, of dollars in upfront investment. Construction generates substantial demand for site preparation, civil work, utility coordination, electrical systems, structural materials, concrete, steel, fiber, cooling equipment, and security infrastructure. For many rural and suburban communities, that phase alone can create a meaningful economic lift for regional contractors, suppliers, and skilled trades over multiple years. 

More importantly, once operational, data centers often represent exceptionally high assessed value relative to their land footprint. In many jurisdictions, that tax base can materially support schools, fire protection, libraries, roads, and other public services without creating sustained traffic, significant housing pressure, or major day-to-day service demand. 

Unlike stadiums or entertainment complexes, data centers deliver fiscal benefits quietly. Their daily operational footprint is small, but their contribution to local budgets can be transformational, provided tax incentives are structured carefully and do not erode long-term value. 

Jobs: Fewer, But Higher Skill and Negotiable 

It is true that data centers generally do not generate the same number of permanent jobs as factories or distribution hubs. But the jobs they do create are typically technically challenging, stable, and well-paid, particularly in nonurban markets. 

Facility operations, systems maintenance, controls, networking, electrical support, and security roles frequently exceed local median wages and do not always require a traditional four-year degree. With the right approach, data centers can become entry points into skilled technical careers. 

Crucially, communities are not passive participants in this outcome. Local governments can require local hiring goals, apprenticeship programs, and partnerships with community colleges or trade schools as conditions of approval. When workforce provisions are negotiated upfront, data centers can contribute to long-term skills development rather than remaining isolated facilities. 

Power Demand and the Grid: From Risk to Opportunity 

Energy use is the most visible and politically sensitive issue surrounding data center growth. Large facilities undeniably increase electricity demand, and unmanaged growth can strain local grids. But higher demand does not automatically translate into higher residential power bills. That outcome depends on policy and design choices. 

Large data center operators are often willing, and financially able, to fund substation upgrades, transmission improvements, and interconnection costs. Many also procure long-term clean energy from solar and wind sources through power purchase agreements, effectively underwriting new generation rather than drawing solely on existing supply. 

When communities and regulators require projects to pay their marginal infrastructure costs and commit to clean energy additionality, data centers can help accelerate grid modernization instead of burdening it. Energy impacts, in other words, are a design and governance issue, not an inevitability. 

Water Use: An Issue That Has Evolved 

Water consumption has become one of the most emotionally charged objections to data center development, particularly in agricultural or drought-prone regions. Some older cooling designs do use significant volumes of water, and communities are right to scrutinize this closely. 

What is often overlooked is how rapidly cooling technology is changing. Many modern data centers now rely on closed-loop or non-evaporative cooling systems that consume little to no potable water for cooling. In these designs, water use is largely limited to ordinary domestic needs. 

Local governments retain the authority to require these approaches. Permits can mandate no potable water for cooling, require reclaimed or nonpotable sources, impose water-use caps, and include drought-response triggers. When these conditions are written into approvals, water risk becomes a managed variable rather than an open-ended concern. 

Land Use, Traffic, and Community Character 

Compared with warehouses, distribution centers, and manufacturing plants, data centers provide relatively little disruption to neighbors once construction ends. They typically generate minimal daily traffic, limited deliveries, and no customer visitation. 

Concerns about appearance and noise are legitimate, but solvable. Architectural standards, setbacks, landscaping, lighting controls, and enforceable property-line noise limits are well-established planning tools. Noise mitigation specifically is a mature engineering discipline when addressed during design rather than after complaints arise. 

The lesson is straightforward: Communities that specify expectations upfront tend to avoid conflict later. 

The Case for Conditional Approval 

The strongest defense of data center development is not blind enthusiasm, but conditional negotiation grounded in governance. 

Communities that successfully host data centers typically insist on:

• Clear community benefits agreements
• Transparent reporting on energy, water, and emissions
• Project-funded grid and infrastructure upgrades
• Modern cooling and noise-control standards
• Local workforce and training commitments 

These conditions shift the conversation from “trust us” to “here are the enforceable terms.” That shift often determines whether a project faces opposition or earns acceptance.

Asking the Right Question 

Data centers do not belong everywhere, and they should not be approved automatically; but neither should they be dismissed as inherently negative or extractive. 

The better question for communities is this: Can this project strengthen our tax base, modernize our infrastructure, protect our resources, and create pathways for residents? 

In many cases, the answer is yes, if leaders use the tools already available in zoning, permitting, and development agreements. When they do, data centers can become not symbols of unwanted growth, but examples of how modern infrastructure and local stewardship can coexist. 

Critical minerals are central to the economic competitiveness and national security of the United States. From smart phones to high-speed guided missiles, critical minerals are essential components of defense systems, energy technologies, and the digital economy.

Of the 50 minerals identified as critical by the U.S. Geological Survey, 32 are found in substantial quantities across Africa. Moreover, Africa holds approximately 30% of the world’s known mineral reserves, including cobalt, manganese, lithium, bauxite, and copper. For the U.S. to capture greater value from African critical minerals, it should utilize the differentiators that set it apart, namely, its wealth of technology firms that specialize in data.

An Atlantic Council report, “Mining Corridors as Catalysts for US–African Partnerships: Building on the Lobito Model,” released last month by Aubrey Hruby, highlights these data-driven advantages, as well as the strength of models like the Lobito Corridor, which stand as the most significant U.S.-backed infrastructure investment in Africa in a generation.

What is the Lobito Corridor Model?

The report contends that logistic corridors and processing hubs offer the most promising pathway to unlock African critical mineral wealth at scale. These corridor projects integrate mining operations with transportation networks and energy systems, reducing logistics costs, deepening regional integration, creating employment opportunities, and advancing economic diversification.

The leading example of this approach is the Lobito Corridor, an 800-mile, multimodal transport network connecting Angola’s port to the mineral rich areas of the Democratic Republic of Congo and Zambia’s Copperbelt. For the Lobito Corridor model to be replicated across additional African mining corridor and hub projects, data sharing must be prioritized.

Map of the Lobito Corridor railway connecting Angola’s Lobito port to the DRC and Zambian copper mining regions. Source: The Lobito Corridor Investment Promotion Authority (LCIPA)

For the many African critical mineral projects U.S.-based organizations and entities have been involved in so far, data has existed in silos. The data was collected and organized disparately, adversely affecting efficiency and collaboration. The report points out that information gaps between stakeholders can also raise risk premiums, slow project development, and disadvantage both host governments and credible investors.

Supporting Critical Mineral Corridors with Interoperable Data

To address this challenge, the U.S. must expand its role in Africa’s critical minerals ecosystem, and effectively pursue the Lobito model. To be successful, the environmental, social, infrastructure, and other interconnected requirements must be supported and advanced in a coordinated, comprehensive, and data-driven way.

The Atlantic Council and others have advocated for a shift toward establishing interoperable baseline data for an entire region from the outset. The U.S. has a prime opportunity to leverage its world-leading ecosystem of technology firms that excel at data capture and sharing to create this comprehensive data foundation. Greater data interoperability would allow all stakeholders, including partner nations, commercial operators, security agencies, environmental groups, and others, to access the same information and work in parallel rather than sequentially.

Widening access to data and increasing transparency also enables more actors to participate simultaneously. As a result, this shared data model can significantly compress timelines, improve coordination, and ensure that each party contributes to broader corridor development using a common, authoritative dataset.

Pursuing Cooperation Through Data Transparency and Governance

Having a unified data baseline not only enhances the overall efficiency of African critical mineral efforts across an entire corridor, but also benefits multiple stakeholders, including African partners, by equipping them with accurate, actionable information.

In today’s digital world, data is as valuable a resource as the critical minerals themselves. Hoarding or selectively sharing it mirrors other exploitative practices. An equitable critical mineral partnership seeking mutual prosperity therefore begins with data transparency. Beyond being ethically responsible, making data openly available to all participants from the outset is a far better strategy for long‑term business sustainability.

The Lobito model already stands in stark contrast to initiatives and projects that have historically been exploitative rather than cooperative. This approach can be reinforced by engaging technology firms whose advanced capabilities in data collection, management, and governance are matched by their dedication to transparent, responsible practices.

Mitigating Vulnerabilities with African Critical Minerals

The report also notes that dependence on adversarial nations for African critical minerals creates strategic exposure, leaving the U.S. vulnerable to potential supply disruptions, pricing manipulation, and other economic pressures that could undermine long-term industry stability.

At the same time, the U.S. does not possess the critical minerals it requires to build the systems and technologies essential to its defense and economic competitiveness. Compounding this challenge, the country is depleting significant stockpiles of ammunition, including interceptor missiles that require critical minerals, due to ongoing conflicts around the world.

The U.S. can mitigate these vulnerabilities by using both the corridor model abroad and the innovative data capabilities of its technology leaders at home. Already, U.S.-based firms like Woolpert, which has a significant presence on the continent and multiple offices in South Africa, see clear opportunities to play a meaningful role in these emerging corridors through their geospatial data expertise.

Data Is Vital for Future Corridor-Based Partnerships

As recommended in the Atlantic Council report, the data capabilities of the U.S.’s top technology firms can support countless use cases, including environmental monitoring, infrastructure planning, permitting, mineral exploration, community engagement, and more. When paired with an approach like the Lobito model, the U.S. can foster genuine cooperation, build trust, and help create long-term, mutually beneficial African critical mineral partnerships.

Demand for advanced semiconductor fabrication facilities is higher than ever. Projections indicate that the semiconductor industry will reach $1-$1.1 trillion by 2030, largely fueled by the growth of artificial intelligence and data centers. To capture this growing market, it is paramount that legacy facilities modernize quickly.

Today, there are about 26 fab facilities in the U.S., 17 of which are legacy facilities, and only nine meet the requirements to be considered modern. A legacy semiconductor fab facility is one built before the widespread adoption of building information modeling. These pre‑BIM fabs often rely on outdated drawings and models that no longer reflect actual field conditions.

Over years of operation, pre-BIM fabs accumulate tool relocations, rerouted utilities, and undocumented modifications, resulting in an unreliable “source of truth” slowing modernization efforts. By creating a trusted as-built digital baseline that accurately reflects real-world conditions, legacy facilities can rectify decades of undocumented change.

The Challenges Pre-BIM Fabs Encounter When Attempting to Modernize

The facility documentation for many legacy semiconductor fabs rarely reflects real‑world conditions. Since the fab’s original construction, numerous upgrades, tool relocations, and utility reroutes have occurred without proper updates to the master models or drawings. As a result, the existing documentation is often incomplete, inconsistent, or entirely outdated.

When pre-BIM fabs, under increasing market pressure, begin planning their next wave of upgrades, they inevitably encounter critical information gaps. Without accurate, up-to-date data about what is physically installed, including updated tool locations, utility re-routes, and past modifications, teams struggle to plan schedules and design new layouts effectively. The lack of accurate as-built information also increases the likelihood of coordination friction and issues such as spatial clashes and misalignments, causing delays and costly installation rework.

Establishing a trusted, as-built baseline closes the gap between historical documentation and present-day conditions. Accurately capturing and updating the fab’s current state empowers teams to plan and execute future upgrades far more seamlessly.

What Does “Trusted” Mean in a Fab Context?

In a semiconductor fab context, the term “trusted” refers to the reliability, accuracy, and professional rigor behind the data used for planning, design, and construction. Another key component of trustworthy data is that it has been thoroughly validated by a geospatial solution expert. By contrast, “untrustworthy” data is incomplete, inconsistent, and does not reflect the current built environment, providing fabs with little, if any, value.

All fabs must work with third party vendors to scan their facilities and generate geospatial data. Unfortunately, vendors may fail to capture site information comprehensively, or they may not establish the governance needed to maintain an accurate, continuously updated baseline. When this occurs, facilities often revert to manual methods, such as tape measures and on-site guesswork, which cause erroneous measurements and error propagation. In many cases, pre-BIM fabs will accept the tolerance issues that arise from manual methods over the hassle of working with an inaccurate digital baseline.

Legacy facilities must apply careful scrutiny when selecting a partner to develop a digital baseline of their construction site. While many vendors can access the latest hardware, such as wearable cameras or off-the-shelf scanning tools, few possess the surveying discipline or fab-specific experience required to generate high-precision, actionable as-built data. A trusted provider will have deep surveying expertise and understanding of measurement accuracy, error propagation, and the downstream impact poor data has on modernization initiatives.

Establishing a Comprehensive Workflow

In addition to creating a trusted as-built baseline, pre-BIM facilities will need to have a workflow in place that covers everything from control and scanning through modeling and validation. The first step is developing a strong control network, which provides the precise reference framework for all subsequent measurements.

When the control network is robust, scan data is accurate, producing a trustworthy model, which is foundational for pre-BIM fabs to make future upgrades. A robust control network also requires the right control point density, scaled to the complexity of the environment. In practice, this means tighter spacing and a higher density of points in congested, high-precision areas where line-of-sight is limited and tolerances are tight, and wider spacing with fewer points in simpler, more open areas. Getting this density right is foundational to building a trustworthy control network, which in turn produces accurate scan data.

A strong control network, however, is just the starting point. The subsequent workflow is what makes the scanned data usable in an operational fab. Once control is established, scanning needs to be planned around real constraints such as line-of-sight, congested chases, overheard utilities, white steel, sub-fab spaces, and busy active work areas. These scans must be consistently registered back to the control network so captured point clouds from different levels (utility level, sub-fab, interstitial, fab, etc.) and time periods align without drift or errors.

From there, modeling and validation translate these scans into coordinated, decision-ready information such as utility routing, clearances, and install zones. Quality assurance and quality control then verify that the data matches field conditions and flag anything uncertain or out of tolerance. This practical, end-to-end chain is what turns scan data into a trusted baseline that teams can build and install against, supporting tool install readiness and reducing clashes before crews arrive on site.

The Importance of Governance: Prioritizing Scope When Fabs Can’t Model Everything

As legacy, pre-BIM fabs modernize, they will need to make sure their digital baselines stay current, which will require ongoing governance. Effective governance is not a one-time task but a continuous process. Even after a high-quality as-built model is established, minor changes can jeopardize its accuracy. Put simply: The model must evolve as the facility evolves.

Note that effective governance does not mean re-scanning the entire facility every time a small change occurs. For extremely large fab facilities, frequent rescanning is neither practical nor efficient. A bay containing equipment that has remained largely unchanged does not require the same level of scanning attention as an area that has undergone substantial modifications.

Fabs must identify and prioritize those areas that have experienced the most change. Then, processes can be established to regularly re-scan and update high-activity areas, especially high-density regions where tools are constantly getting installed, demoed, or relocated. This targeted form of governance will enable owners and project teams to make faster decisions, improve install readiness, and execute upgrades with greater schedule predictability, without wasting resources capturing stable areas that haven’t meaningfully changed.

Being Competitive Means Working with a Partner Who Goes the Extra Mile

The next decade of the semiconductor industry will be a critical time. Those pre-BIM fabs that modernize quickly and achieve meaningful efficiencies first will gain a strategic advantage over the competition. Conversely, those that continue to face clashes, misalignments, or unexpected field conditions during upgrades will inevitably fall behind.

To stay competitive, legacy facilities must work with a partner willing to go the extra mile; one that closes the gap between documentation and field reality, rather than simply pointing out problems. While many companies can identify where a model and reality differ, such as incorrectly sized or located openings, a truly best-in-class partner goes further.

By transforming a model so it accurately reflects real-world conditions, and by delivering a governed, decision-ready baseline that is accurate, validated, and maintained over time, a great partner enables a pre-BIM fab to pursue modernization with confidence. In the end, the goal isn’t just an updated model of the fab; it’s a direct path to faster install readiness and greater schedule certainty.

Mission critical construction environments, such as data centers, semiconductor fabrication facilities, and life sciences manufacturing plants, present unique challenges that demand robust geospatial management and specialized expertise. While each facility type has its own nuances, they all share a common challenge: incorrectly sized openings, including those for cables, pipes, HVAC duct transitions, and equipment access panel openings.

This universal and seemingly innocuous issue of openings that are too small or too large or in the wrong location can lead to serious consequences. Without the help of construction validation processes supported by digital intent models, mission critical facilities may experience significant construction delays, substantial cost overruns, and long-term operational failures, such as equipment damage caused by overheating or contamination.

What Happens When Openings Aren’t Correctly Sized?

The primary issue caused by incorrectly sized or improperly placed openings in walls and floors within mission critical facilities is noncompliance with fire-safety requirements. These facilities must adhere to strict building codes and regulations governing wall and floor penetrations, involving multiple stakeholders throughout design, construction, coordination, programming, and final sign-off. This includes wall installation, mechanical/electrical/plumbing installation, architect/engineer approval, and general contractor accountability.

Each stage requires coordinated input and can influence the work of subsequent trades. If an opening is incorrectly located, it can compromise structural integrity. If it is undersized, MEP systems may not fit properly, which can also hinder the use of modular racks. These issues ultimately introduce negative impacts across the project, affecting schedule, cost, change management, risk management, and stakeholder engagement.

It’s worth noting that for greenfield data center builds, the critical coordination of wall openings is rigidly built into the facility’s modular design, making mistakes of this nature less likely. Wall openings serve as pathways between two elements inside the facility — and if those elements aren’t positioned correctly, the accuracy of the opening’s location becomes irrelevant. In the context of new builds, therefore, the precise placement of prefabricated assemblies is more important than ensuring the opening itself is in the correct location.

The Advantage of Catching Issues Digitally Rather Than Physically

Project owners are acutely aware of the consequences that arise from incorrectly sized or improperly positioned openings. When they inevitably encounter these mistakes, they must undertake tedious and costly rework and modifications, eating up precious time. This challenge is especially pronounced with legacy, or pre-building information modeling, semiconductor fabs that often operate with drawings and models that no longer reflect field conditions.

Although project owners cannot ensure total perfection across all openings on a mission critical construction site, they can control how early incorrect openings are identified. With construction validation processes supported by digital models that accurately reflect what has been built, teams can resolve sizing problems digitally before they become costly issues in the field.

Diagnosing opening issues begins with design teams generating a digital intent model. A geospatial solution partner then translates that model onto the ground, verifies construction against it, and continuously tracks changes as they occur. Typically, geospatial teams use multiple tools — including laser scanning, control networks, building information modeling analytics, and Power BI dashboards — to support progress tracking, identify incorrect openings, and validate installations.

In the mission critical sector, digital and virtual services have become fully embedded into the construction process and are pivotal to improving schedule accuracy, cost certainty, change management, and risk mitigation. Nevertheless, these tools are the minimum entry point. In such highly complex construction environments, simply identifying issues isn’t enough; especially when the solution is not always obvious.

Geospatial Technology is Table Stakes, People-Centric Collaboration is the Difference Maker

Consider a math professor who marks only the incorrect answers on their students’ tests. These redlines tell the students which questions they got wrong but don’t provide the formulas to help them arrive at the correct answers. The same can be said about mission critical construction environments with incorrectly sized openings, in that a geospatial partner or construction validator should do more than just flag issues; they should propose genuine solutions.

A geospatial partner should support the entire resolution process, working directly with contractors to adjust designs, improve installation methods, and maintain accuracy across both on-site and off-site fabrication. This ongoing digital project collaboration delivers a notable return on investment by reducing rework, avoiding delays, supporting “build-right-first-time” outcomes, and reducing requests for information costs by resolving issues digitally rather than through on-site rework.

What makes digital project collaboration so effective is that it is a people-centric method. The geospatial partner seeks to elevate the work of all the entities and vendors involved. Best-in-class partners that utilize this approach often do not require construction companies to use a certain type of software or technology, nor do they demand that their client remove any part of

their supply chain or even the contractor that made the opening error in the first place. Rather, this people-centric construction process aims to establish digital oversight on complex projects for better program certainty.

Minimizing Physical Mistakes Amid Escalating Demand

The development of mission critical facilities shows no sign of slowing down. As organizations push for faster, more cost-efficient builds to meet escalating demand, the value of robust digital oversight will only continue to grow. Catching issues like incorrectly sized openings early, and then resolving those discrepancies digitally rather than physically, will empower teams to deliver higher-quality facilities at the speed today’s market requires.

To kick off 2026 right, every day this month our global leaders throughout Woolpert shared their perspectives on what to watch for across the architecture, engineering, and geospatial industry in the year ahead. The idea was to spotlight areas of opportunity and areas of concern, sparking conversation and enhancing our collective ability to build a better tomorrow.

“One of our pillars to success at Woolpert is industry leadership, and it’s the insights of our global team that drive it,” Woolpert President and CEO Neil Churman said. “We’re fortunate to have an amazing group of thought leaders who truly value education, collaboration, and innovation. Their insights keep our company at the forefront of industry and technology trends to help our clients see around corners, deliver faster and more efficiently, and support our ever-changing world.”

The report below provides a high-level view of their insights shared this month.

Healthcare design is integral to the success and effectiveness of any healthcare facility. Research shows that the design of healthcare environments can positively or negatively affect health outcomes and psychological well-being for patients and their loved ones. It also influences the efficiency, safety, and stress levels of clinical staff.

In addition to designing healing environments that incorporate natural lighting, positive distractions, noise reduction, and ease of navigation, architects must allocate space for current and future equipment needs. This balancing act will become increasingly challenging with the recent influx of artificial intelligence and robots into these environments.

As Woolpert’s global director of healthcare, I believe much has been said in the industry about the impact AI and robotics will have on healthcare. However, what is often overlooked is how these technologies will affect the built environments of hospitals, medical centers, and healthcare systems.

How New Technology Influences Healthcare Design

In October, I joined Woolpert to build on the exceptional work of Bermello Ajamil, a Woolpert Company, and to expand the firm’s healthcare design practice worldwide. Previously, I served as president and CEO of E4H Architecture, the nation’s largest architecture firm dedicated exclusively to healthcare. Throughout my career, I have witnessed periods of rapid technological advancement that transformed healthcare design, and I believe we are now on the cusp of a similar transformation.

I’ve seen electronic health records and cloud storage render filing cabinets and supply closets obsolete, and marveled as telehealth and virtual care bridged hundreds of miles to diagnose patients with a phone call. Momentous changes such as these have paved the way for what healthcare systems face today as AI and robotics permeate processes, workflows, and physical environments.

AI has evolved into real-life care delivery, supporting diagnostic imaging, clinical decision-making, and even drug research and development. AI-enabled robots are also rapidly gaining traction in the healthcare industry. As with past technological shifts, today’s healthcare designers must create spaces that seamlessly incorporate and accommodate these innovations while remaining flexible enough to handle the unpredictable future.

The Architectural Response to the Clinical AI Revolution

New healthcare facilities have the advantage of being designed from the outset with spaces dedicated to AI command centers, high-tech robots, and robust data infrastructure. Existing facilities, however, don’t have that luxury.

Healthcare designers must determine how much space robots will require: Where will they be stored? How and where will they charge? Where will maintenance occur? Will specific rooms be needed for these functions? In most cases, facilities will need to reallocate space — either by repurposing general-purpose areas or constructing entirely new rooms.

Next‑generation robots are intelligent, AI‑powered, and increasingly vital to health systems’ delivery of care. As they begin autonomously navigating hospital corridors, they will likely require dedicated pathways. Hallways may need delineated lanes and specialized public‑awareness signage to prevent congestion and accidents. Designers might even incorporate creative solutions such as interior drones and ceiling‑ or floor‑mounted navigation systems.

Operating rooms also need to accommodate advanced robotics and multimodal imaging technologies. Robotic surgical systems and other high-tech equipment are expected to make these specialty rooms even larger.

Building on the success of telehealth, one approach hospitals and medical facilities are using to increase accessibility is creating dedicated spaces for both healthcare providers and remote patients. These spaces are specifically designed with widescreen visibility, high‑definition cameras, and sound‑isolating features for privacy. This strategy benefits patients by allowing them to remain comfortably at home rather than making costly, time‑consuming trips to healthcare facilities.

Energy and Data Considerations: The Additional Demands of AI and Robots

Healthcare designers also must consider energy requirements when preparing medical facilities for the widespread adoption of AI and advanced robotics. These technologies require significant energy to function and generate massive amounts of data, which in turn demands immense computational power.

Additionally, the future of healthcare includes smart‑room technology, giving patients easy control over multiple aspects of their environment, including lighting, temperature, sound, and streaming services. While these amenities enhance comfort and improve the patient experience, they will similarly introduce additional power and data requirements.

Effective strategies healthcare designers can leverage to address these challenges include integrating renewable energy sources, implementing advanced cooling and heat recovery systems, and building robust data infrastructure. Of course, all of these solutions will necessitate sufficient space allocations.

Although AI is driving increased energy demands, it can also help optimize energy management. AI- and machine learning-driven building management systems, for instance, can reduce hospital energy consumption by optimizing HVAC systems, lighting, and other utilities based on real-time data, occupancy, and predictive analytics.

The Importance of Flexibility in Healthcare Design

As with most technological leaps forward, healthcare designers will face unknowns as they work to create optimal spaces to heal, work, and thrive. The spatial and energy demands of AI-enabled robots in healthcare are no exception. We can position current and future healthcare facilities for success by designing adaptive, scalable spaces. The more modular and flexible these designs are, the more seamlessly they can incorporate technological upgrades.

Construction projects for semiconductor fabrication facilities are highly complex and capital-intensive, with a typical fab now costing $10 billion and requiring 6,000 workers over three years to complete. Like other mission critical facilities — such as data centers and pharmaceutical manufacturing plants — semiconductor fabs are in extremely high demand worldwide due to the ever-growing need for the chips that power the modern technology people use every day.

Each site requires spatial precision in design and construction to ensure millimeter-level accuracy. If measurements are even a hair off, critical components — such as tools and automated material handling systems — may need to be repositioned, adversely affecting schedules, time-to-production, and already immense budgets. The pressure for “right first time” accuracy is further intensified by the shortage of skilled labor in key trades and the industry’s shift toward off-site fabrication.

As a result, fabs must now be delivered with fewer resources while maintaining, and ideally increasing, speed and quality of execution to meet aggressive timelines. Geospatial management provides the precision, automation, and digital continuity required to address these constraints.

The Primary Challenges the Semiconductor Industry Currently Faces

One of the semiconductor industry’s biggest challenges today is the need for precise on-site measurement. Compared to traditional construction projects, semiconductor fabrication sites are exceptionally dense and intricate, housing thousands of highly specialized tools and systems that must operate in perfect harmony. Every component — from piping, electrical conduits, and gas and chemical lines to cleanroom equipment — must be installed with millimeter tolerances to maintain precise process control.

This level of precision and complexity means that if components arrive on-site and fail to fit as intended, the consequences can be costly and disruptive. Imagine a scenario where one contractor installs structural steel and another fabricates equipment to fit between those steel elements. If the steel columns are even a few millimeters out of position, the prefabricated components may not fit. These seemingly innocuous deviations can cascade into major delays and months of rework, derailing schedules, and creating cost overruns.

Another challenge is the mounting emphasis on off-site fabrication, where components are built at a different location and then delivered to the construction site for installation. For these components to fit perfectly upon arrival, precise coordination and measurement are essential — a task complicated by the involvement of multiple contractors. From electricians and pipefitters to plumbers and wire installers, each relies on their own technical drawings. When these drawings differ, which is often the case, problems are inevitable.

Additionally, the semiconductor industry faces persistent workforce shortages. Large projects can require thousands of workers and dozens of contractors. Although the CHIPS Act injected significant funding into semiconductor companies, their design requirements are so specialized that only a finite number of professionals are available to support these projects. In short, demand for new semiconductor facilities far exceeds the supply of qualified workers.

At the same time, timelines continue to become more aggressive. Chipmakers need fabs online quickly to meet global demand, stay ahead of technology cycles, and maintain a competitive advantage. In the past, a six- or eight-month delay might have been tolerated. Today, companies expect projects to be completed as quickly as possible, a challenge that is exacerbated by the industry’s inability to find skilled labor. These demands are increasingly difficult to meet without new digital strategies and tighter geospatial governance.

The Benefits of Geospatial Management for Semiconductor Fab Construction

To address these challenges, it is paramount that companies work with a geospatial solutions expert throughout the construction and design process. An ideal partner will integrate high-accuracy surveying, building information modeling (BIM) validation, and continuous model-to-field alignment — from site preparation through tool installation — reducing rework and redesigns, minimizing wasted effort, and keeping projects on schedule. This will ultimately accelerate the construction of semiconductor fabs.

There is a critical need for governance around measurement and positioning on semiconductor projects. In these congested environments, all the different contractors installing multi-million-dollar tools and components need a common spatial framework to ensure millimeter-level accuracy. Geospatial management provides that framework by establishing a control network — a grid of physical reference points on the ground that allow engineers to accurately position themselves on-site. These reference points create a critical link between the digital design world (BIM models) and the physical construction environment.

By bridging the gap between digital and physical, this geospatial framework delivers a unified view of the site, where all spatial data originates from a single source of truth. Verified spatial data is shared across engineering, procurement, and construction partners, original equipment manufacturers, and owner stakeholders. With all stakeholders accessing the same accurate data, decisions are based on actual site conditions, significantly reducing manual verification, rework cycles, and field-based problem solving.

Geospatial management also plays a critical role in construction validation. A best-in-class geospatial solutions partner continuously checks installation accuracy as the project progresses and identifies any deviations before they spiral into larger downstream issues. If the geospatial partner detects something out of tolerance, it alerts the contractors and project managers early so corrective action can be taken before subsequent work begins. This proactive approach enables cleaner installations, fewer clashes, and measurable improvements in project timelines. It also reduces reliance on large on-site labor teams to resolve clashes and positioning errors.

The Role of Digital Twins in Semiconductor Fab Construction

The structured as-built data produced through geospatial management forms the foundation for high-fidelity digital twins — a fully digital representation of a semiconductor facility. While digital twins vary in complexity, at their most advanced level they enable powerful simulations of plant performance. These simulations help users identify inefficiencies and make data-driven adjustments before implementing changes in the physical environment.

For semiconductor fabrication facilities, digital twins support a variety of different applications, including energy optimization, predictive maintenance, and future retooling. The result: reduced labor demand, lower rework ratios, compressed installation timelines, improved ramp-to-yield performance, and accelerated time-to-value — critical advantages in an increasingly competitive semiconductor manufacturing landscape.

Of course, the effectiveness of a digital twin model depends entirely on the quality of the data used to build it. Like with a large language model, inaccurate input data produces unreliable output. The issue for the semiconductor industry is that the multiple contractors involved in these projects are each responsible for a specific scope, and so they create individual models, which are combined into a federated model representing the entire project. However, design changes and on-site adjustments often occur throughout the project, and updating these complex digital twin models is time-consuming and costly. As a result, many models end up being inaccurate.

Leading geospatial solution providers overcome these issues by delivering the building blocks for digital twins. Woolpert, for example, can digitize legacy sites that predate BIM and modern modeling standards to create accurate base models. The multidisciplinary firm can also compare contractor models against actual site conditions, identify discrepancies, and make necessary adjustments. This rigorous validation ensures that the final models truly reflect the as-built environment, enabling the most reliable simulations.

How to Choose the Right Geospatial Partner

Building a semiconductor fab facility is an extraordinarily involved undertaking with many different players and moving pieces. Experience is essential to navigate challenges effectively. Similarly, geospatial management for these types of critical facilities is incredibly complicated, requiring experience across disciplines — especially digital twin development.

For companies looking to accelerate and enhance fab construction and operational readiness, partnering with an experienced, multidisciplinary geospatial solutions provider is crucial. This partner must understand the processes and pain points unique to semiconductor fab construction and have a proven track record of success. With timelines being more aggressive than ever, working with the right partner can mean the difference between staying ahead of schedule or falling behind and incurring significant costs.