Data Engineering | Construction Metrics

Autodesk says Forma will replace Revit over time, without a forced switch

Sun, 27 Sep 2026 10:25:58 -0400

Autodesk’s FAQ for its Autodesk University 2026 announcements asks whether Forma, its cloud platform for architecture, engineering and construction, will replace Revit. The answer, in a September 15 article by Autodesk executive vice president Amy Bunszel, is “Over time, yes.” She writes that more of the design, analysis, coordination and documentation work done in Revit will move into Forma, that Autodesk will keep investing in Revit, and that it will not force a sudden transition. At a general contractor, the change reaches the VDC coordinator who pulls design models into clash detection and the document controller who manages the project’s file set.

The question has circulated since the conference. Hayley Swords, who hosts the Built For Tomorrow construction technology leadership podcast, asked it on LinkedIn. She wrote that Autodesk is not saying Revit is going away, and she described a gradual shift in which Revit becomes one part of a larger connected system of tools. Autodesk’s FAQ also describes a gradual change, and it answers the replacement question yes.

What changed

Autodesk has moved in steps over the past year.

On September 16, 2025, Autodesk announced that Revit would become the first Forma Connected Client, its label for a desktop product tied directly into Forma. That announcement said, “Forma is not replacing Revit, it’s extending it.”
On March 24, 2026, Autodesk Construction Cloud became part of Forma, and its products took Forma names. Autodesk said no data migration was required and that licensing, permissions, folder structures and integrations stayed as they were.
On August 20, 2026, an Autodesk News Q&A on the connected client said more work will happen directly in Forma over time.
On September 15, 2026, the AU 2026 FAQ answered the replacement question yes, and Autodesk said Civil 3D will become the next connected client.

Autodesk Construction Cloud product names since March 24, 2026
Old name	New name
Autodesk Construction Cloud	Autodesk Forma
Autodesk Docs	Forma Data Management
Autodesk BIM Collaborate Pro	Forma Design Collaboration
Autodesk Build	Forma Build
Autodesk Takeoff	Forma Takeoff
Autodesk Estimate	Forma Estimate
Preconstruction Bundle	Forma for Preconstruction
Model Management Bundle	Forma for Model Management
Construction Operations Bundle	Forma for Construction Operations
Autodesk Construction Cloud Connect (ACC Connect)	Forma Construction Connect

Source: autodesk.com

Forma Data Management, formerly Autodesk Docs, is now what Autodesk calls the common data environment for all of Forma. A common data environment is the shared repository where every company on a job finds the current drawings, models and approvals. The AI agents Autodesk announced for construction teams at the same event are covered in our September 25 post.

What it does

Today the connected client is a design-side feature. According to Autodesk’s Revit 2027 help pages and its August Q&A, an architect working in a Revit cloud model can reference other design models in a Forma scenario, add terrain, buildings and parcel data from the Forma Data Marketplace, and run Forma analyses such as wind without leaving Revit. Autodesk says this cuts the import and export steps between Forma and Revit.

For a contractor, the AU 2026 changes that touch coordination and document control sit in Forma Data Management and Model Coordination. Today a GC often holds a file until it clears review, then copies it to another folder with different permissions so subcontractors can see it. Autodesk says Smart Controls change who can see a file when its approval status changes, so the file stays in one place. In Model Coordination, Autodesk says teams can view point clouds with the federated model and run clash checks against a saved search set. It also says schedule activities can now be linked to model elements for a 4D simulation in Forma.

Civil 3D comes next. Autodesk says it will start as a tech preview that references Forma and Revit designs and shares one geolocation engine with them, which is meant to reduce the manual coordinate transforms that leave civil and building models misaligned.

Evidence so far

These claims come from Autodesk:

Connected workflows reduce the information teams rebuild as a project moves between phases.
Smart Controls remove the duplicate files created by copying between folders.
The shared geolocation engine reduces manual transforms and model misalignment.

Autodesk’s article describes the Kraft Group using Forma Data Management and Forma construction tools at Gillette Stadium. Measured results for these workflows, from Autodesk or from independent testers, are not yet public.

Autodesk’s own help pages set limits on the Revit tech preview. Autodesk recommends against using its features on production projects. Once a project administrator adds a project to the preview, the project cannot be removed from it. Processing the project’s Revit cloud models can take up to 30 minutes, and Autodesk lists slower Synchronize with Central on large, complex models as a known issue.

How to measure it on your projects

Track the handoff count per coordination cycle. That is the number of times someone exports, re-uploads or copies a model or sheet set between the design team’s issue and the coordination meeting, including folder copies made only to change who can see a file. This is the manual work Autodesk says Forma removes, and you can count it today on any platform.

Capture the baseline on one active job before changing anything:

For a month of coordination meetings, have the VDC coordinator log each export, upload and folder copy, with the file name and the minutes it took.
Mark the handoffs that exist only to change permissions. Smart Controls is aimed at those.
Count the clash runs that needed a model re-upload first.

Try the Revit tech preview on a copy of a project, because joining cannot be undone and Autodesk advises against production use. A first step this week is to start the handoff log at the next coordination meeting on one job.

Availability and cost

Revit as a Forma Connected Client is a tech preview for Revit 2027 and later. It works on Revit cloud models in projects stored in the United States, European Union or Australian data regions, and a project administrator turns it on one project at a time. The Autodesk pages cited here do not list a price for it.

Civil 3D will start as a tech preview, and Autodesk gave no date. Autodesk says Forma Data Management Essentials is included in standalone subscriptions to AutoCAD, Revit and Civil 3D. Several of the construction features announced at AU 2026 are in beta on separate timelines, and Autodesk points customers to its Forma product roadmap for dates.

The Impact of AI on Construction: Pros and Cons Revealed

Tue, 09 Jul 2024 17:32:01 +0000

As artificial intelligence continues to transform industries across the board, the construction sector is no exception. We’ve been exploring AI’s potential to revolutionize our workflows. But like any powerful tool, AI comes with both benefits and challenges. Let’s break down some key pros and cons we’ve encountered:

Pros

Document Intelligence

By leveraging AI with Retrieval-Augmented Generation (RAG) on our SharePoint sites, we’ve unlocked a treasure trove of institutional knowledge. AI can now quickly sift through years of project reports, safety guidelines, and best practices to provide relevant insights on demand.

Predictive Maintenance:

AI algorithms play a crucial role in predictive maintenance, enabling us to proactively identify potential issues with machinery and equipment. By analyzing historical data and real-time performance metrics, these advanced algorithms can predict equipment failures before they occur, thereby minimizing unplanned downtime and optimizing operational efficiency. This proactive approach not only saves time and resources but also enhances safety by preventing potential hazards on job sites. By leveraging the power of AI, we can ensure that our equipment remains reliable and safe, ultimately contributing to a more productive and secure working environment.

Design Optimization

Generative AI plays a pivotal role in assisting our architects and engineers by leveraging advanced algorithms and machine learning techniques to explore innovative design solutions. By harnessing the power of Generative AI, we are able to optimize designs not only for cost, sustainability, and structural integrity but also for energy efficiency, aesthetic appeal, and user experience. This cutting-edge technology empowers us to push the boundaries of creativity and efficiency, ultimately leading to the development of forward-thinking and sustainable architectural and engineering solutions.

Cons:

Data Hallucinations

We’ve encountered instances where AI confidently presents false information as fact. This “hallucination” problem requires constant human oversight to catch and correct. It’s crucial for AI developers to implement robust fact-checking mechanisms to ensure that the information generated is accurate and reliable. Additionally, ongoing research and development in the field of AI ethics and accountability are essential to address these challenges. As AI technology continues to advance, the need for human involvement in validating the output becomes increasingly evident. Finding the right balance between autonomous AI capabilities and human intervention is a key area of focus for ensuring the responsible and effective deployment of AI systems.

Inconsistent Calculations

While AI excels at processing vast amounts of data, we’ve found its mathematical accuracy can be surprisingly unreliable for critical structural calculations. This has prompted further research into enhancing the accuracy of AI algorithms for specialized tasks such as structural engineering. The complexity and nuances of structural calculations require a more robust and nuanced approach, considering not only the mathematical precision but also the real-world implications of the results. As AI continues to evolve, it is crucial to address these limitations and refine the algorithms to ensure their reliability in critical scenarios.

Over-reliance Risks

There’s a danger of team members becoming too dependent on AI, potentially eroding crucial problem-solving skills and domain expertise. As AI technology continues to advance and automate various tasks, there is a risk of employees relying excessively on AI for decision-making, analysis, and creative problem-solving. This over-reliance on AI may lead to a decline in critical thinking abilities, innovation, and hands-on experience within specific domains. It is essential for organizations to foster a balance between leveraging AI tools for efficiency and preserving the cognitive capabilities and specialized knowledge of their teams.

Implementation Challenges

Integrating AI systems with our existing software stack and training our workforce to use them effectively has been a significant undertaking. Not only have we had to ensure seamless compatibility between the new AI components and our current infrastructure, but we have also dedicated substantial resources to educate and empower our employees to leverage these advanced tools. This comprehensive effort has involved restructuring internal processes, providing specialized training programs, and fostering a culture of continuous learning and adaptation. As we continue on this journey, we are seeing the transformative impact of AI on our operations, decision-making processes, and overall business performance.

The Path Forward

Despite these challenges, we believe AI’s potential in construction is immense. The key lies in thoughtful implementation:

Establish clear processes for human verification of AI outputs
Invest in ongoing AI literacy training for all employees
Collaborate with AI providers to address industry-specific needs
Use AI as a complement to, not a replacement for, human expertise

By embracing AI’s strengths while mitigating its weaknesses, we can build safer, more efficient, and more innovative projects for our clients.

In an upcoming post, we’re going to go through the exact strategies we took to overcome the challenges presented here. Stay tuned.

What has your experience been with AI in construction? I’d love to hear your thoughts in the comments.

Why start a career in the construction industry as a data engineer?

Fri, 01 Oct 2021 14:06:44 +0000

This post originally appeared on the ConstructionDataJobs.com Blog.

I’m a data engineer myself, and many of my colleagues are surprised that I work almost exclusively in the construction industry. They think, “Isn’t that industry stuck in the past? I bet you can’t get much done.” And, while it is true that construction is almost the least digitized, that is changing, fast.

When I was first approached by my first customers in the industry to modernize their workflows, I became struck by a number of factors that made me decide to dedicate my time to helping construction companies exclusively.

1. There’s a lot of low-hanging fruit

The biggest draw is simply that since there has been such a delay in modernization, there’s a ton of opportunities that are straightforward, even easy, for someone with a data engineering skillset. I look for any process that still exists on paper, in spreadsheets, or even in dedicated software tools who’ve just seen better days. The terms “data pipeline” and “data warehouse” are not ubiquitous in this industry, but many of the customers I work with are excel masters, and jump at the opportunity to pull up-to-date data from project management and scheduling software into their models.

2. Despite the industry lagging in technology adoption, that is changing rapidly

Yes, there’s low hanging fruit for data engineers, and on top of that, there’s a young workforce that has much higher expectations when it comes to user experience and data capabilities. This is the generation that grew up with social media, now getting into management positions at major companies and thinking, “Why is our technology so terrible, or nonexistent?” Well, now that is about to change because a large group of motivated managers can now allocate a budget to move their company to the next level.

3. There is a big opportunity to design your work environment

So, there’s obviously a lot of pent-up demand, but at the same time there’s a shortage of qualified and motivated data engineers. This means that your skills are highly valued, and you have many options for employment: full-time, part-time, and consulting gigs all exist and you can choose those which fit your lifestyle goals and ambition. It’s never been a better time to jump into this industry, and it’s only going to get better.

4. The industry is going to space

I say this semi-seriously. It is true that space exploration has had a renaissance as of recently. It is also true that as more people go to space, more things will have to be built and sent there, constructed in a zero-G environment, and even built in space and sent home. And if that prediction comes true, there will be high demand for robust, scalable data pipelines that connect systems that are both terrestrial and beyond. I’m looking forward to it!

Get out there and help this industry build the future!

For a list of data engineering jobs in the construction industry, check out ConstructionDataJobs.com

3 Approaches to Data Engineering in the Construction Industry

Thu, 16 Sep 2021 19:36:08 +0000

Understanding where you’re at in capturing and using data is a good way to build a road map to the future.

Two years ago, the president of Primus Builders knew he had a data-analysis problem, but he had no idea how big that problem actually was. All he really knew was that none of his tech solutions were “talking” to one another, leaving him with islands of data and redundant workarounds. Then he called in an expert and realized his problem was much worse.

That’s because Primus Builders was where a lot of construction firms are on their data-analysis journey — siloed point solutions. In practice, that meant the company was wasting money on technology it no longer used — and hadn’t been for years — without anyone realizing it. It also meant workers were duplicating efforts to produce reports, but the information in those reports often conflicted because there was no single source of truth. Finally, it meant the company was at risk for hacking, ransomware attacks and other security breaches.

“The problem that a lot of people have is how do I get these applications to talk? How do I take my financial data and combine it with my schedule and my project management? There’s not really a clean way to do it,” said John Robba, chief technology officer for Primus Builders. “But collecting data is one thing. The real question is, what do you do with it? And that’s usually the hardest thing for companies to answer. So, the first thing is knowing what’s the goal you’re trying to accomplish? Then you can back into the details, and it will be very apparent if you selected the right partnerships and have the right internal resources to pull that off.”

Before any of that happens though, Robba and others say it’s important to have a clear understanding of where your company is in its data-analysis journey — and how to move forward.

But for commercial construction firms looking to start a data-analysis journey — and capitalize on the promise of data analytics—the path is anything but clear. FMI reports that 96% of data collected in the engineering and construction industry goes unused.

That’s a big mistake.

“The organizations that take the time to gather the data, analyze it and turn it into actionable insights will gain a competitive advantage. The ones that bury their heads in the sand and hope it goes away will be quickly left behind,” warn the FMI report experts.

For many firms, there are more questions than answers on how to begin analyzing and using data. A good place to start is understanding where you are in this process. Here’s a high-level look at three states of progression in the journey toward data analysis most firms go through that can serve as a road map to help you move forward.

Understanding Three Basic Approaches to Data Analysis — and Where You’re at on Your Journey

Step 1: Data Collected in siloed point solutions

What it looks like: Companies in this scenario typically use multiple apps and tech tools to gather data. Although processes are being digitized, the apps and tools don’t integrate with one another, and thus, data becomes siloed in the various solutions. That means that to get a report, someone has to manually go into the different tools or apps and export the data. In fact, 42% of companies use four to six apps on their construction jobs, and 27% report that none of those apps integrate with one another. As a result, data is transferred manually nearly 50% of the time, according to the 2020 JBKnowledge Construction Technology Report.

Why it creates problems: It’s not just the siloing and manual data transfer that are problems. Using multiple apps and tools also means workers have to sign in and navigate multiple passwords, interfaces and hardware. Because of that friction, workers sometimes simply don’t use the tools at all. Even if there is consistent use of the tech tools, someone ends up spending a lot of time trying to figure out if the data is consistent and updated. Answering the question, “What happened on the job site today?” becomes a data wrangling challenge, instead of a quick and easy answer.

How to move forward: Rather than manually pulling data from individual apps to run reports, companies can begin using a data warehouse or data lake to pull in data from the various apps and tools, which can then be used to run reports. This data integration can be almost instantaneous and automatic. But these products and services can also be pricey. Another option for firms with capable IT departments is to create an in-house data warehouse using services such as Amazon Web Services. Either way, companies can start small with incremental steps and grow from there.

Step 2: Arguments over conflicting data: “Who’s right?”

What it looks like: Just as it sounds, this scenario finds two or more workers arguing over whose data is correct. For example, the field might have a different data point for hours worked than the office. Companies in this stage often have started to integrate and automate data capture, but they haven’t yet developed systems and approaches to ensure a single source of truth.

Why it creates problems: Without that single source of truth, trust in the system is lost, and buy-in from executives in the C-suite, all the way down to the field workers, is lost with it. When that happens, even the best tech tools and integration won’t produce useful data because people aren’t taking the time to input it. That’s more than just academic. Fully 52% of rework is caused by poor project data and miscommunication, meaning $31.3 billion in rework was caused by poor project data and miscommunication in the U.S. alone in 2018, according to an FMI/Autodesk report.

How to move forward: The solution to this problem is something called metadata — a set of information that describes and gives information about other data. In practice, this means that definitions must be provided for data in the user interface or dashboard. For example, what is the specific definition for hours worked? This question may seem obvious, but without clear definitions, workers may make assumptions that corrupt the data. Once data is defined with metadata, companies must implement a data-documentation system that verifies where the data is coming from and how it gets input. This process is known as data lineage. Data lineage includes the data origin, what happens to it and where it moves over time. Data lineage provides visibility into data and simplifies the ability to trace errors back to the root cause.

Step 3: Enterprise-wide data accessibility

What it looks like: Companies in this phase of the data-analysis journey can run data reports and view data dashboards quickly and easily. The data is secure and reliable, and there is a trusted single source of truth. That in turn allows companies to use different tools that they wouldn’t otherwise because all the inputs are readily available. For example, with BIM, changes can be made in a 3D render using real-time job site data. That means if an architect or owner wants to make a change, a cost analysis can not only be done in real time, but also based on how your project’s going at that point in time. And most importantly, there is a clear process for resolving discrepancies. The best companies use a combination of documentation, monitoring tools, and a dedicated support team to maintain data accuracy, but also allow stakeholders to avoid making judgements on inaccurate data.

Why it works: Because these companies see real-time data, they can use that insight to make actionable decisions about day-to-day operations that lead to greater efficiency and profitability. Questions that used to take hours or days to get answers can now be cleared up in seconds. This ability allows companies to be more aggressive and creative with different building scenarios that lead to still greater efficiency and cost savings. More time gets spent on high-value efforts rather than trying to find and massage data. Everyone in the organization is able to confidently and accurately answer the question, “What happened on the job site today?” The benefits of being able to answer that question (and others like it) are obvious to most contractors. In fact, 70% of contractors believe that advanced technologies can increase productivity (78%), improve schedule (75%) and enhance safety (79%), according to the 2019 USG + U.S. Chamber of Commerce Commercial Construction Index.

How to fully capitalize on it: Companies that have moved into this realm haven’t had to reinvent the wheel. Most other industries have made headway on how to move to enterprise-wide data accessibility, and a number of tools, services and products exist to help. Like Primus Builders, these companies have also prioritized data analysis by hiring experts to help them untangle the different apps and tools they use and develop a cohesive data-analysis strategy. No one tool or hire will solve this problem; it has to be an organization-wide initiative that plays to everyone’s strengths.

Moving Forward on Your Journey

Clearly, enterprise-wide data accessibility could benefit all contractors. But getting there requires a new way of thinking about building — and knowing when and from whom to ask for help along the way. The good news is that contractors don’t have to figure it all out immediately. They can move in the direction of enterprise-wide data accessibility in incremental steps. They can also look to other industries and experts for support and guidance.

The most important thing, though, is to start moving in the direction of enterprise-wide data accessibility — before it’s too late. Some topics to research moving forward:

Hiring a data team.
Understanding where data came from with data lineage.
Understanding why data may appear inaccurate with data observability.
Understanding how data is defined with a data dictionary.

“We’ve seen many customers come in with pieces of the puzzle, but they need help with staffing and tooling to put the full picture together,” explained Matt Monihan, CEO of ResponseVault, a data engineering firm that specializes in the construction industry.

Ultimately, experts like Robba warned getting that full picture is becoming more and more vital to not only remaining competitive, but also surviving.

“Wake up, because you’re already living the nightmare. You’re already in the weeds, you just don’t know how tall the weeds are or even where the road is, or how far it is to the road to get out of the weeds,” Robba said. “Most people running organizations are going a million miles an hour. And data analytics is one of those things where you have to have the expertise. And then you actually have to take the time to sit down and go, ‘Yes, I’m going to focus on this and put an action plan together.’ But the bottom line is, you need to invest in your future now, because your competition definitely is.”