Construction Tech on the Move

 Startups are Starting to Come to Market

Over the last few months there has been a series of capital raises and an IPO for Australian construction technology companies. This is a highly competitive landscape that is developing quickly with a focus on large scale procurement platforms. The table does not include the many local startups with products for site inspections and defects, safety and compliance and so on.

In a nice example of spillover effects, after the sale of Aconex founders Rob Phillpot and Leigh Jasper set up Xenoca and Significant Capital Ventures as VC funds for joint investments, and have invested in one IPO and four startups that have disclosed capital raises. Leigh Jasper has also invested with Ian Beatty and Salta Capital’s Andrew Sypkes and David Tarascio in venture fund SecondQuarter, which has raised $50m+ and has invested in Propeller (3D mapping), and ActivePipe (real estate marketing).

Leigh Jasper is also on the board of Salta Properties. Salta and Smorgon backed the modular Tribe Hotel designed by Mark and Melissa Peters and built in Perth in 2018 by Probuild, with Mantra as operator. Mantra was acquired by Accor, who announced in March 2019 plans to develop more than 50 of the modular hotels.

Taronga Ventures

Taronga Ventures “invests in emerging innovation, technology and business models shaping the future of the built environment. We offer direct venture investment, as well as programs and advisory services to support the growth of our portfolio companies and their impact on the traditional real estate sector.” They also have a few construction tech investments. Their RealTech fund was launched last year, the program director is Julian Kezelman. Their startups are:

Procurement platforms are looking like the next big thing in construction tech. In the UK Pagabo launched a procurement platform in February, mainly for the public sector, using framework agreements. Here is their relevant page which says:“Benefit from streamlined procurement and best value on all your mid-sized construction projects through our National Framework for Medium Works.Running until December 2022, this fully OJEU compliant Framework can be used to commission a full range of building works, valued between £250k to £10m.Providing real value and competition, it includes 46 regional and national contractors, split across 3 project value bands. And there’s complete flexibility in how you award your contract - either Direct Award your preferred contractor or go through a Further Competition with a selected few.”

The digital construction production system

Where is the technological frontier in Construction?

 

 

The fourth industrial revolution has already affected the construction industry through demand for structures for renewable energy and buildings like data centres, warehouses, ‘dark’ kitchens and supermarkets for online delivery services. Some of these buildings and structures already use forms of applied AI in their management and operation.

 

The construction industry is wide and diverse, and the various parts of the digital construction production system are in various stages of development. Over time the development of AI and associated digital fabrication and production technologies will reshape the existing industry, led by fundamental changes in demand (the function, type and number of buildings), design (the opportunities new materials offer), and delivery (through project management). However, these developments are  

 

Automation technology is at the point where intelligent machines are moving from operating comfortably in controlled environments, in manufacturing or social media, to unpredictable environments, like driving a car or truck. In many cases, like remotely controlled and autonomous trucks and trains on mining sites, the operations are run as a partnership between humans and machines, or as Brynjolfsson and McAfee put it “running with the machines not against them”. These innovations might reasonably be expected to affect site processes and project organization, as concrete and steam power did in the past. Table 1 has examples of where the technological frontier was in 2020 for plant and equipment and construction materials, as an indication of the range and extent of this wave of innovations. Missing from these lists is smart contracts using blockchain. 

 

Invention and innovation based around BIM, digital twins, digital fabrication and advanced manufacturing technology is starting to fundamentally affect the construction production system through economies of scale. Over time this will alter the balance between on-site and off-site production of building modules and components, and how they are handled, assembled and integrated. Because there are many different types of building in many places, production methods vary widely across the industry, so the use of these new materials and technologies will be varied. 

 

Transport costs have always been important, but the option of site production has been limited due to standardization of mass produced components. The combination of BIM, online design databases and digital fabrication allows on-site production of some building components. Combining robotic and automated machinery with digital fabrication and standardized parts opens up many possibilities. 

 

Past technological changes in construction operated over the three dimensions of industrialization of production, mechanization of work, and organization of projects. Automation and AI can also be expected to work along these dimensions as the fourth industrial revolution reconfigures them by linking data through the life of a project. The role of AI enhanced cloud-based platforms that integrate design, production and delivery of components and materials with digital production technologies that allow mass customisation will be significant in the production of components and materials.

 

Table 1. Examples of the construction technological frontier in 2020

Plant and equipment

New materials

Autodesk BUILD Space – Boston UK construction manufacturing hub Exoskeletons – Esko, HULK Remote control equipment – CAT, Komatsu Drone monitoring – Skycatch, Icon, Vinci Smart helmets –  Trimble Hololens, Daqri Platforms – Katerra Apollo, Project Frog Build autonomous skidsteer FBR Robotics ‘Wall as a service’  Otis ‘Elevator as a service’ Sensor fitted cranes Automated engineered wood factories

3D concrete printing with boom system – ICON, Aris, 3D Constructor 3D concrete printing with gantry suspended nozzle – D-Shape, BIG, US Marines Onsite metal printing – GE, MX3D, Aurora  3D printing of combined steel and concrete Roller press printing of smart fabrics  4D printing of shape memory materials  Molecular engineering of materials   Improved concrete additives and sealants Components with cloud-linked sensors Cloud-based fixtures and fittings

Source: Company and industry reports.

 

For mechanization, the characteristic changeability of construction sites is challenging for automated and robotic systems, and it might take decades of investment for machines able to do site work or for humanoid robots to do human tasks. In some case a human supervisor operating a team of robots or several pieces of equipment, each with limited autonomy, might work better. A worker with a smart helmet could monitor these machines both on the project and in the site model. Beyond site preparation however, there may not be many tasks left if site processes are restructured around components and modules that are designed to be assembled in a particular way, and machines to assemble those components and modules can be fabricated for that purpose. For an industry with an aging workforce there is the potential of exoskeletons for site work, a form of human augmentation that combines human skill with machine strength.

 

For organization of projects digital platforms providing building design, component and module specification, fabrication, logistics and delivery can be expected to become widely used. Platforms provide outsourced business processes, usually cheaply because they are standardized, and are available to large and small firms. Also, platforms use forms of AI to monitor and manage the data they produce, the function of intelligent machines. Examples are Linkedin (matching jobs and people), Skype (simultaneous translation of video calls), AWS and other cloud-computing providers, and marketing, legal and accounting software systems. Cheap, outsourced, cloud-based business processes can lower fixed costs and thus firm size, because firms can focus on their core competency and purchases services as necessary as they scale, leading to more entry and more innovation. If these digitised business processes are cost-effective and become widely used, they can provide much of the data needed to train machines as project information managers.

 

The BIM model of the project links the design and fabrication stages to the site and the project[i]. Digital fabrication produces components and modules designed to be integrated with on-site preparatory work and assembled to meet strict tolerances. Project management would be more focused on information management, and the primary role of a construction contractor might evolve into managing a new combination of site preparation work and integration of the building or structure with components and modules, some of which may be produced on-site in a Fab if economies of scale permit. 

 

In this case, the industry would, perhaps slowly, reorganise around firms that best manage on-site and off-site integration of digitally fabricated parts. With outsourced business processes and standardized site and structural work, that would be a key competitive advantage of a construction firm. Firms would become more vertically integrated if they become fabricators as well, reinventing a business model from the past when large general contractors often had their own carpentry workshops, brick pits or glass works and so on.    

 

While firms involved in construction of the built environment are facing technological advances that will affect many aspects of the technological system, this is a process that happens over years and decades. It takes 30 to 50 years between invention of a major new technology like cars or computers and its use becoming widespread, examples are discovering the double-helix and biotechnology, the dynamo and electricity, and the first electronic computers in the 1940s. 

 

How long a transition to a new production system largely built on automation and digital fabrication coordinated by AI takes might take is unknown. While machines can replicate individual tasks, integrating different capabilities and getting everything to work together is another matter. Combining a range of technologies is needed for workplace automation, but solving problems involves specific technical and organizational challenges, and once the technical feasibility has been resolved and the technologies become commercially available it can take many years before they are adopted. 

 

This suggests there will be many new roles emerging in construction over coming years, for project information managers, BIM supervisors, integration specialists and other fourth industrial revolution workers. Because these jobs will be primarily on new projects, they will not quickly replace the many existing jobs in the industry that maintain the built environment. 

 

Nevertheless, the technological frontier is moving again, and new construction projects will generally utilise the most cost-effective technology. Current AI technology provides services such as GPS navigation and trip planning, spam filters, language recognition and translation, credit checks and fraud alerts, book and music recommendations, and energy management systems. It is being used in law, transport, education, healthcare and security, and for engineering, economic and scientific modelling. Advanced manufacturing is almost entirely automated. 

 

In the various forms that AI and digital fabrication takes on their way to the construction site, they will become central to many of the tasks and activities involved. In this, building and construction may no different from other industries and activities, however the path of AI in construction will be distinct and different from the path taken in other industries. This path dependence can vary not just from industry to industry, but from firm to firm as well.

 

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[i] In 2019 the International Standard 19650 was released, providing a framework for creating, managing and sharing digital data on built assets. https://www.iso.org/obp/ui/#iso:std:iso:19650:-1:ed-1:v1:en 

Construction and Advanced Technologies

US Survey Data and the Construction Industry 

 

The previous post was on the United States Census Bureau Annual Business Survey (ABS). In 2018 the ABS included a technology module with three questions about the extent of technology use between 2015 and 2017: the availability of information in digital format (digitization), expenditure on cloud computing services, and use of a range of advanced business technologies. The first results were released in a working paper from the National Bureau of Economic Research in January. There were 583.000 responses to the survey, and two thirds of the firms employed under 10 people and were less than 20 years old.

 

The survey links technologies across firm size and age categories, as well as the co-presence patterns for the technologies at the firm level. It also identifies which technologies are in the early stages of diffusion as indicated by the rates of testing versus the rates of actual use of technologies by firms. The survey shows construction is not significantly lagging other industries in the US in digitization and use of cloud services, however it is doing less testing and development of advanced business technologies.

 

The main finding of the survey was “Despite increasingly widespread discussion in the press of machine learning, robotics, automated vehicles, natural language processing, machine vision, voice recognition and other advanced technologies, we find that their adoption rates are relatively low. Furthermore, adoption is quite skewed, with heaviest concentration among a small subset of older and larger firms. We also find that technology adoption exhibits a hierarchical pattern, with the most sophisticated technologies being present most often only when more-basic applications are as well.” 

 

 

Size and number of firms in US construction

 

The structure of an industry is the number of firms categorized by size, typically the number of employees. Firms are classified as small, medium or large, with the numbers used varying by country and industry, as the tables below show. Data on firms (often called enterprises in the statistics) is presented using the International Standard Industrial Classification. Section F in ISIC includes the complete construction of buildings (division 41), the complete construction of civil engineering works (division 42), and specialized construction activities or special trades, if carried out only as a part of the construction process (division 43). Also included is repair of buildings and engineering works. Although there are national variants on the Standard Industrial Classification format SIC codes therefore represent industries, and firms are classified (or often self-classify) to industries on the basis of common characteristics in products, services, production processes and logistics.

 

In the US the Census Bureau collects data on industries and enterprises, the latest data for 2012. The website has this notice: “Due to limited resources and competing priorities of critical programs within the Census Bureau, the Enterprise Statistics Program has been suspended.” Reflecting the scale of the American economy, the size range of firms is much greater than the EU and the largest firms much larger. Over 95 percent of US firms are small, in this case with less than 100 employees, and have on average five or six employees. However, there were 212 firms with 1,000 or more employees that had a total 630,000 employees, of which nearly 160,000 were employed by the nine largest firms. 

 

Table 1. US Construction 2012

Enterprise employment size	Number of enterprises	Sales or revenue $1,000,000	Annual payroll $1,000,000	Number of paid employees
All enterprises	581,601	1,349,346	260,606	5,006,131
Less than 100 employees	576,272	812,924	154,461	3,336,286
100 - 499 employees	4,788	226,818	46,899	817,823
500 - 999 employees	na	82,320	14,787	222,481
1,000 - 2,499 employees	141	79,475	14,968	211,141
2,500 - 4,999 employees	45	62,749	10,516	145,875
5,000 - 9,999 employees	17	38,072	7,497	113,133
10,000 employees or more	9	46,988	11,476	159,392

Source: US Census Bureau 2012, table 2; na is not available due to sampling issues. 

 

The data, which emphasises the number of firms, is deceptive because of the very large number of small firms the entire industry is often characterized as unconcentrated. Viewing the construction industry as predominantly made up of small firms supports the view of the industry as fragmented with the characteristics of perfect competition. That description is too broad, some segments are much less fragmented than others. Competition among large contractors and among specialty supplier firms is oligopolistic, while small contractors are closer to perfect competition. There are few significant barriers to entry to the construction industry for small firms, so labour-intensive subcontractors and small contractors can be assumed to operate under perfect competition. There are relatively few contractors capable of managing large projects, and the barriers to entry at this level in the form of prequalification are significant, based on track record, financial capacity and technical capability.

 

 

Technology testing and diffusion

 

The testing-versus-use rates across different technologies are used to assess which technologies are in earlier phase of diffusion, that is, where testing is high relative to use. From the survey data the Construction industry is neither a leader nor a laggard in the availability of information in digital format. Manufacturing, Information and Professional Services are the industries with the highest rate of adoption of digitization, with firm size the primary correlate of adoption. For expenditure on cloud computing services Construction is lagging, with use rates below the average and well behind Professional Services. Overall, cloud services purchases have much lower diffusion rates compared to those for digital information. On these two questions of digitization and cloud usage Construction is comparable to the Agriculture, Retail and Transport industries on the extent of adoption, which is significantly lower than the rate in Information, Professional Services and Health Care industries.

 

Where Construction is well behind is in the testing and use of a range of advanced business technologies. The butterfly chart below shows sectoral diffusion rates for all business technologies considered together. Manufacturing leads with about 15% of firms indicating use of at least one business technology, followed by Health Care (14%), Information (12%), Education (11%) and Professional Services (10%). The lowest diffusion rates for the technologies are in Construction, Agriculture, Mining and Utilities, Management and Administrative, and Finance, Insurance and Real Estate sectors. 

 

 

Figure 1: Extensive and Intensive Margin Measures of Use and Testing Rates for Business Technologies by Sector 

 

Across all AI-related technologies, the aggregate adoption rate for all firms in the economy was 6.6% meaning that approximately 1 in 16 firms in the US were utilizing some form of AI in the workplace. The AI adoption rate varies greatly by firm size. Adoption rates (defined as usage or testing) increase from 5.3% for the group of firms with the smallest number of employees to 62.5% for firms with 10,000+ employees. Scale appears to be a primary correlate of AI usage, and its use by large firms means the employment-weighted adoption rates (estimates of the fraction of workers employed by firms using the technologies for advanced business technologies) are five times higher than the firm rates (i.e. because large firms are using AI the number of employees working with AI is five time greater than the number of firms using AI). There is increasing concentration of both employment and advanced technology adoption in fewer, larger firms.

 

The analysis finds “In general, the business technologies explored in the module’s third question are more prevalent in larger and older firms. This skewness in technology prevalence suggests that these technologies may have a disproportionate economic impact despite their generally low adoption rates’ and “This may potentially have far-reaching implications on topics such as inequality, competition and the rise of “superstar” firms, especially if AI is shown to have widespread productivity benefits. If only a select group of firms are able to fully realize the benefits of AI, we can expect further divergence for the “frontier” and most productive set of firms.”

 

From table 1, in the US in 2012 there were 9 construction firms with 10,000 employees and 17 with 5-10,000 employees, employing nearly 280,000 people between them (out of 580,000 firms and 5 million employees). Although there will be small, young firms experimenting with AI and other technologies, the data suggests some of these large firms will be investing in advanced technologies like AI, robotics and augmented reality at a scale the rest of the industry cannot. This has already been seen with the use of BIM, which is spreading to smaller firms in the industry a decade after many larger firms began the process of implementation. Another example is the way some large contractors are already running their own platforms for procurement and project management, which their suppliers and subcontractors have to use. These are closed, internal platforms. However, there are also open platforms developed by digital systems integrators such as Project Frog. 

 

It seems clear that digital platforms providing building design, component and module specification, fabrication, logistics and delivery will become widely used. Platforms provide outsourced business processes, usually cheaply because they are standardized, and are available to large and small firms. Also, platforms use forms of AI to monitor and manage the data they produce, the function of intelligent machines. Examples are Linkedin (matching jobs and people), Skype (simultaneous translation of video calls), AWS and other cloud-computing providers, and marketing, legal and accounting software systems. Such cheap, outsourced, cloud-based business processes can lower fixed costs and thus firm size, because firms can focus on their core competency and purchases services as necessary as they scale, leading to more entry and more innovation. 

 

Table 2. Dimensions of Development

Dimensions	Construction and the fourth industrial revolution: Possible developments
Production of components and materials	Platforms integrate design and production with full visualisation of voice-controlled 3D models of buildings, components and location. Selection of components and modules from online design libraries, both open-source and private.  Developments in digital fabrication, design software and molecular engineering allow a range of new production technologies to spread through the industry. Economies of scale for on-site versus off-site production will determine where and what components are produced and how.
Mechanization and automation of tasks	Site workers have exoskeletons and smart helmets available.  Many on-site tasks can done by teams of robots and/or machinery and equipment, operated remotely with some autonomy. Assemblers can be designed and fabricated to install components and modules, which can be designed to be handled by assemblers.
Organization of projects	Cloud based platforms integrate delivery of the physical project with its digital model, with real-time data and monitoring of activities and tasks.  Standardized, outsourced cloud-based business processes are used, so contractors focus on integration of site work, site production and component assembly.

 

 

In the various forms that advanced technologies take on their way to the construction site, they will become central to many of the tasks and activities involved. In this, building and construction may no different from other industries and activities, however the development path in construction will be distinct and different from the path taken in other industries. This path dependence can vary not just from industry to industry, but from firm to firm as well. Because the construction industry’s technological system of production is so wide and deep this will affect a large number of firms and people, and through them the wider economy and society. Invention and innovation based around BIM, digital twins, cloud computing, digital fabrication and advanced manufacturing technology, will fundamentally affect the production system through economies of scale and scope. 

 

 

Advanced Technologies Adoption And Use By U.S. Firms: Evidence From The Annual Business Survey, by  Nikolas Zolas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Cathy Buffington, Nathan Goldschlag, Lucia Foster and Emin Dinlersoz. 2020. National Bureau of Economic Research, Cambridge, MA Working Paper 28290 http://www.nber.org/papers/w28290

Digitization and advanced business technologies in industry

Data on the technology frontier at the industry level

 

 

There is a commonly held view that construction is a digital laggard.  A widely cited McKinsey report in 2016 argued: “the construction sector has been slow to adopt process and technology innovations …. The industry has not yet embraced new digital technologies that need up-front investment, even if the long-term benefits are significant. R&D spending in construction runs well behind that of other industries: less than 1 percent of revenues, versus 3.5 to 4.5 percent for the auto and aerospace sectors. This is also true for spending on information technology, which accounts for less than 1 percent of revenues for construction, even though a number of new software solutions have been developed for the industry.”

 

Technology use and diffusion is an important dynamic in industry development, but good data is rare. Most surveys are of specific industries or selected firms (typically large ones), and surveys with large samples that would be more representative of firms across the economy by including small and medium size firms are scarce. This particularly affects built environment industries like construction and professional services because of the large number of small firms in those industries. 

 

The United States Census Bureau conducts an Annual Business Survey (ABS), and in 2018 the ABS included a technology module with three questions about the extent of technology use between 2015 and 2017: the availability of information in digital format (digitization), expenditure on cloud computing services, and use of a range of advanced business technologies. The survey was a partnership between the Census Bureau and the National Center for Science and Engineering Statistics, and the first results have been released in a working paper from the National Bureau of Economic Research. The results are summarized below.

 

The survey data is at a high level of generality, to make the questions relevant across the variety of firms and industries included. There are also issues around how the data has been modelled and analysed, and the adjustments for high counts for firms that minimally use or are not using advanced business technologies at all. Importantly, the survey shows construction is not significantly lagging other industries in the US in digitization and use of cloud services, however it is doing less testing and development of advanced business technologies. 

 

The main finding of the survey was “Despite increasingly widespread discussion in the press of machine learning, robotics, automated vehicles, natural language processing, machine vision, voice recognition and other advanced technologies, we find that their adoption rates are relatively low. Furthermore, adoption is quite skewed, with heaviest concentration among a small subset of older and larger firms. We also find that technology adoption exhibits a hierarchical pattern, with the most sophisticated technologies being present most often only when more-basic applications are as well.” 

 

There were 583.000 responses to the survey. Two thirds of the firms employed under 10 people and were less than 20 years old, making this the most comprehensive survey of diffusion of advanced business technologies done so far. The industry breakdown of firms is in Table 1, with the built environment industries of construction, real estate and professional services well represented. This makes the data particularly interesting. 

 

Table 1. Industry breakdown of firms

Sector	Distribution %
Agriculture, Mining, Utilities	2
Construction	10
Education	1
Finance, Insurance, Real Estate	10
Health Care	9
Information	2
Management & Administrative	5
Manufacturing	8
Other (Arts, Food, Other Services)	14
Professional Services	17
Retail Trade	13
Transportation & Warehousing	4
Wholesale Trade	5

 

Following are extracts from the NBER paper on the results of the survey on the three technology questions: the availability of information in digital format, expenditure on cloud computing services, and use of advanced business technologies with a focus on the use of AI. The next post will discuss the survey and its implications for construction. 

 

 

Digital Share of Information by Business Activity 

 

The first question in the 2018 ABS technology module queried firms on the type of information stored digitally. In all sectors, financial and personnel information are the most likely to be digitized, followed by customer feedback and marketing. This is the case for Construction, with financial, personnel and marketing digitized. The lowest rates of adoption are in production and supply chain activities.

 

Figure 2 is a butterfly chart of adoption and use rates for digital information by sector, where the ranking of sectors by adoption and intensity of use rates parallel each other. The right panel of the chart represents, by sector, the adoption rates of digital information across all surveyed information types. The segments within each bar in the chart capture adoption rates by the number of information types in digital format. In all sectors, a large share of adopters report having three or more types of information digitized. 

 

The left panel of Figure 2 represents intense use of digitization. Most firms report digitizing at least two types of information, regardless of sector, the fraction of firms digitizing only one type of information intensively is relatively small in each sector. Overall, digitization appears to be highly prevalent across sectors. Manufacturing, Information and Professional Services are among the highest adopters of digitization, with size being a primary correlate of adoption. 

 

Figure 2: Extensive and Intensive Margin Measures of Digitized Information by Sector

 

Cloud Service Purchases by IT Function 

 

This section describes the adoption patterns for cloud service purchases across size, age and sector. Like digitization, the highest adoption and intensive-use rates are in Information, followed closely by Professional Services and Education. The lowest rates are in Agriculture, Mining, Utilities, Retail Trade, and Transportation and Warehousing, and the Other category. Figure 4 reveals that cloud services purchases have much lower diffusion rates compared to those for digital information in any given sector. 

 

Billing and Security are the most common IT functions for most sectors, with certain sectors, including Construction, predominantly relying on the cloud to perform collaborative or synchronized tasks. The Data Analysis function has the lowest number of firms reporting some cloud purchase, Billing and Account Management has the highest number of firms, closely followed by Security or Firewall and Collaboration and Synchronization functions. 

 

Although the adoption rates for business IT functions in the cloud is significantly lower than the adoption rates of storing information digitally, this technology is widespread across various applications, with nearly a third of each different type of IT function being performed in the cloud and being used intensively. 

 

Figure 4: Extensive and Intensive Margin Measures of Use Rates for Cloud Service Purchases by Sector - Conditional 

Advanced Business Technologies 

 

In this section we analyze firm responses to the business technologies question. Due to their wide technological scope, we link the responses here with the previous technology adoption questions and perform a deeper set of analyses assessing the range of response categories. Very few firms use the business technologies included in the module, and many answered, “Don’t know”. Based on our tabulation weights, only 10.3% (8.5% non-imputed) of firms adopt at least one of the listed advanced business technologies. 

 

The highest use frequencies are in touchscreens and machine learning. For touchscreens the adoption rate is 6.1% of firms. Machine learning comes second but the rate is low at 2.9%. Voice Recognition and Machine Vision, which are can be considered examples of Machine Learning applications, have the next two highest use rates. 

 

The overall diffusion of robotics is very low across firms in the U.S. The use rate is only 1.3%, concentrated in large, manufacturing firms. The distribution of robots among firms is highly skewed toward larger firms. The least-used technologies are RFID (1.1%), Augmented Reality (0.8%), and Automated Vehicles (0.8%). 

 

Looking at the most common types of business technologies adopted by sector in Table 2 there is substantial variation. All sectors (except Manufacturing adopt Touchscreens followed by Machine Learning or Voice Recognition. Manufacturing is most likely to adopt Machine Learning followed by Touchscreens and Robotics. RFID technology is most commonly used in the Retail, Wholesale, and Transportation and Warehousing sectors, consistent with these industries tracking physical goods through supply chains. 

 

 Table 2. Top Use Sub-Categories for Business Technologies by Sector (p. 60).                                     

Sector	1st	2nd	3rd
Agriculture, Mining, Utilities	Touchscreens	Machine Learning	Automated vehicles
Construction	Touchscreens	Machine Learning	Voice Recognition
Education	Touchscreens	Machine Learning	Voice Recognition
Finance, Insurance, Real Estate	Touchscreens	Voice Recognition	Machine Learning
Health Care	Touchscreens	Voice Recognition	Machine Learning
Information	Touchscreens	Machine Learning	Voice Recognition
Management & Administrative	Touchscreens	Machine Learning	Voice Recognition
Manufacturing	Machine Learning	Robotics	Touchscreens
Other Arts, Food, Other Services	Touchscreens	Machine Learning	Machine Vision
Professional Services	Touchscreens	Voice Recognition	Machine Learning
Retail Trade	Touchscreens	Machine Learning	RFID
Transportation & Warehousing	Touchscreens	Machine Learning	RFID
Wholesale Trade	Touchscreens	Machine Learning	RFID

 

The testing-versus-use rates across different technologies are used to assess which technologies are in earlier phase of diffusion, that is, where testing is high relative to use. In Figure 6, the vertical axis represents the ratio of the fraction of firms testing to the fraction of firms using. The technologies are represented by the circles. The size of each circle corresponds to the use rate for that technology with larger circles representing higher rates of use. Technologies are ordered in the figure by usage rate, low to high. 

As shown in panel a, the technology with the highest testing-to-use ratio is Augmented Reality, where nearly half as many firms as those using the technology report testing it. The next highest ratios are observed in RFID and Natural Language Processing and the lowest ratios are in technologies that are relatively more diffused (and hence, used), such as Touchscreens, Machine Learning and Machine Vision. For Touchscreens, for instance, only about 15 firms report testing the technology for every 100 that use it. It is notable that most testing-to-use ratios are below 0.3, indicating that there are fewer than 30 firms testing the technology for every 100 using it. 

The remaining panels of Figure 6 plot the testing-to-use ratio for technologies by firm size, age, and manufacturing status. Panel b displays ratios by firm size, where small firms are defined as those with 1-9 employees and large firms are those with at least 250 employees. The blue circles capture usage among large firms and the orange circles represent usage among small firms. The sizes of the circles are smaller for small firms for each technology, consistent with the earlier finding that larger firms tend to use the business technologies at a higher rate, in general. 

 

Figure 6: Testing-to-Use Ratios 

a. Testing-to-Use Ratios for all Business Technologies (All Firms) 

 

b. Testing-to-Use Ratios for all Business Technologies (By Size) 

 

c. Testing-to-Use Ratios for all Business Technologies (By Age) 

 

d. Testing-to-Use Ratios for all Business Technologies (By Manufacturing Status)

 

The butterfly chart in Figure 7 provides sectoral diffusion rates for all business technologies considered together. Manufacturing leads with about 15% of firms indicating use of at least one business technology, followed by Health Care (14%), Information (12%), Education (11%) and Professional Services (10%). The lowest diffusion rates for the technologies are in Construction, Agriculture, Mining and Utilities, Management and Administrative, and Finance, Insurance and Real Estate sectors. 

 

Figure 7: Extensive and Intensive Margin Measures of Use and Testing Rates for Business Technologies by Sector 

 

The third question asked directly about the use of advanced “business technologies,” including those typically categorized as “AI.” These technologies include automated guided vehicles, machine learning, machine vision, natural language processing, and voice recognition software. Respondents are presented with a list that covers robotics (i.e., “automatically controlled, reprogrammable, and multipurpose machines”), various cognitive technologies (i.e., applications that help machines to “perceive, analyze, determine response and act appropriately in [their] environment”, a standard definition of AI), radio frequency identification, touchscreens/kiosks for customer interface, automated storage and retrieval systems, and automated guided vehicles. 

 

Across all AI-related technologies, the aggregate adoption rate for all firms in the economy is 6.6% meaning that approximately 1 in 16 firms in the US are utilizing some form of AI in the workplace. This adoption rate is significantly lower than the adoption rate highlighted in the AI survey by the European Commission and other private surveys by McKinsey, Deloitte, and PwC. However, it is important to consider the sampling methods of those surveys. None of the other surveys claim to be nationally representative and tend to focus on larger, publicly traded companies. In contrast, the ABS sample includes many small firms where AI adoption is very low. This is important because AI adoption rate varies greatly by firm size. Adoption rates (defined as usage or testing) increase from 5.3% for the group of firms with the smallest number of employees to 62.5% for firms with 10,000+ employees. 

 

In other words, scale appears to be a primary correlate of AI usage, likely due to both the large quantities of data and computing power required to fully realize the most popular types of AI currently available. This may potentially have far-reaching implications on topics such as inequality, competition and the rise of “superstar” firms, especially if AI is shown to have widespread productivity benefits. If only a select group of firms are able to fully realize the benefits of AI, we can expect further divergence for the “frontier” and most productive set of firms. 

 

Our data and explanatory variables are simply too crude to provide a reliable predictor for the precise types of firms that adopt certain technologies and those that do not. While we can claim that size is a reliable predictor of adoption, even amongst large firms, we see heterogeneous patterns of adoption depending on the technology type. In other words, there are simply too many unknown factors that cannot be measured by traditional metrics (such as firm size, age and industry) that appear to drive technology adoption. 

 

 

Conclusion 

 

We have provided an introduction to the technology module in the 2018 ABS and placed it in the larger context of related work at the Census Bureau to collect comprehensive data on technology adoption and use by U.S. firms in order to provide a more accurate picture of the state of advanced technology use in the U.S. economy. Because of the large pool of respondents (about 850,000 firms) in the 2018 ABS, the module represents a unique opportunity to offer insights on technology adoption and use across all sectors of the economy and across a variety of key firm characteristics. The same technology module is expected to be a part of the 2021 Annual Business Survey.

 

A primary contribution for the paper is to develop a nationally representative set of technology adoption and use measures based on the survey results, which in public use tabulations report aggregate response counts for each technology question (see public use tabulations at: https://www.census.gov/data/tables/2018/econ/abs/2018-abs-digital-technology-module.html ). 

 

Advanced Technologies Adoption And Use By U.S. Firms: Evidence From The Annual Business Survey, by  Nikolas Zolas, Zachary Kroff, Erik Brynjolfsson, Kristina McElheran, David N. Beede, Cathy Buffington, Nathan Goldschlag, Lucia Foster and Emin Dinlersoz. 2020. National Bureau of Economic Research, Cambridge, MA Working Paper 28290 http://www.nber.org/papers/w28290