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Artificial Intelligence (AI) in Construction - Thematic Intelligence

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    Report

  • 70 Pages
  • June 2023
  • Region: Global
  • GlobalData
  • ID: 5851870
The construction industry has been slower than other sectors in implementing AI as it is one of the least digitized industries. However, AI adoption in the construction industry is gaining traction and will continue to grow over the coming years. AI has the potential to transform processes across the project lifecycle. According to publisher forecasts, the total AI market will be worth $908.7 billion in 2030, having grown at a compound annual growth rate (CAGR) of 35.2% from $81.3 billion in 2022.

The industry is at a disadvantage when considering AI adoption as it is a difficult industry to digitalize. The vast and complex data sets needed for Building Information Management (BIM) have been historically difficult to process. However, due to developments in chips and cloud computing this is becoming possible. The construction industry's tight profit margins, critical deadlines, and health and safety issues, also mean the already under digitalized sector has been hesitant to invest in new technology. However, the construction sector could reap benefits from efficiencies gained using AI. AI applications in the construction industry can facilitate planning, design, modelling, safety, site monitoring and maintenance, and emission tracking.

The construction industry is widely considered one of the least digitized industries and has been slower to adopt AI than other sectors. This is due to a number of unique challenges the industry faces. Historically, computers did not have the processing power necessary to crunch the data needed for BIM, as it is often vast and unstructured. Access to this data was also notably inconvenient. However, in 2023, due to developments in computer power, semiconductors, and cloud computing, which facilitates data sharing, AI implementation has grown considerably.

Scope

  • This report provides an overview of the AI theme. The detailed value chain comprises of four segments: human AI interaction, decision making AI, motion and creation. Leading and challenging vendors are identified across both segments.
  • It identifies construction challenges, such as ESG, labor shortages, and safety, and an impact assessment of AI on the construction industry, addressing these challenges.
  • It includes three case studies, outlining market-leading use cases of AI in construction to solve specific challenges such as operational technology vulnerabilities and secure procurement processes.
  • It contains comprehensive industry analysis, including forecasts for AI revenues to 2030, and insight from the publisher's Job Analytics and Social Media Analytics databases. It contains details of M&A deals driven by the AI theme, and a timeline highlighting AI milestones and events in construction.
  • The report has extensive coverage and analysis of relevant companies' positions in the AI theme. This includes leading adopters, vendors, and specialist AI vendors in construction.
  • It includes the publisher's unique thematic scorecard that ranks construction companies according to their positioning in the ten themes most important to the industry, of which metaverse is one.

Reasons to Buy

  • This report will help you to understand AI and its potential impact on the construction sector.
  • Benchmark your company against your competitors, by comparing how prepared 46 companies in the construction sector are for AI disruption.
  • Identify and differentiate between the leading AI vendors and formulate an adoption plan for your company.
  • Position yourself for future success by investing in the right AI technologies. Cut through the noise with the publisher's priority ratings for each AI technology for each segment of the industry (conceptual design, feasibility studies, planning and permitting, financing, design and engineering, construction, and operations and maintenance).
  • Develop relevant and credible sales and marketing messages for construction companies by understanding key industry challenges and where AI use cases are most useful.
  • Identify attractive investment targets by understanding which companies are most advanced in the themes that will determine future success in the construction industry.

Table of Contents

  • Value chain
  • Construction challenges
  • The impact of the AI on construction
  • Case studies
  • Data analysis
  • Companies
  • Sector scorecard
  • Glossary
  • Further reading
  • Thematic methodology
List of Tables
  • Key players in the AI value chain
  • The AI value chain
  • Trend analysis of leading AI influencers in the construction industry over the last 12 months (May 2022- May 2023)
  • Challenges
  • At least one AI advanced capability will impact every stage of the construction value chain
  • The AI story
  • The AI industry will grow at a CAGR of 35.2% between 2022 and 2030
  • Mergers and Aquisitions
  • AI patent activity has grown steadily since 2013 in construction, peaking in 2021, at 1,014 patents
  • Between June 2021 - May 2023, over 260,000 AI-related construction patents were published in China
  • Mentions of AI by construction companies has been increasing, peaking in 2021 at 7,586
  • Mentions of AI appear insignificant when compared to themes like geopolitics
  • AI job postings increased by 53% between May 2021 and May 2023
  • Companies in the US and India are the major hirers for AI in construction jobs
  • The AI value chain
  • Adopters
  • Vendors
  • Scorecards
  • Glossary
  • Further reading
  • Glossary
List of Figures
3D models uploaded to ALICE are made into a 4D schedule
  • Oculo is attached via helmet to document the project
  • Brainpool.ai developed a platform for structural timber design
  • TogalGPT can be accessed on site using a mobile device
  • Carbon and Energy KPIs are shown on the dashboard
  • SAM, a semi-automatic mason, is a bricklaying co-bot

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • ABB
  • AECOM
  • Aifnet
  • Deep Dream
  • Aiva
  • Akzo-Nobel
  • Teradyne
  • Alibaba
  • ALICE
  • Alpha
  • Alphabet (DeepMind Google)
  • Alphabet (Google,Waymo)
  • Amadeus
  • Amazon
  • Apple
  • Aromyx
  • Art
  • Artbreeder
  • Aryballe
  • Augmenta
  • Aurora
  • Autodesk
  • Baidu
  • Balfour
  • Basler
  • Beatty
  • Bechtel
  • Bentley
  • Big Sleep
  • Boomy
  • Briq
  • Buildots
  • Built
  • Canvas
  • Code
  • Cognex
  • Collov
  • Construction
  • Converge.io
  • DeepAI
  • Dixon
  • Doxel
  • Ecrett
  • Eye
  • Face (Bloom)
  • Festo
  • Generic
  • Giatec
  • GM (Cruise)
  • GPT-4, MuseNet
  • Grumman
  • Honeywell
  • Hugging
  • Hyundai (Boston Dynamics)
  • IBM
  • iFlytek
  • iRobot
  • Jasper
  • Kernel
  • Keyence
  • Laing
  • Larsen & Toubro
  • Lockheed
  • Machine
  • Martin
  • Megvii (Face++)
  • Merative
  • Meta
  • Meta (ESMFold)
  • Microsoft
  • Midea
  • Midea (KUKA)
  • Mobileye
  • MOS
  • Motional
  • Music
  • Neuralink
  • NightCafe
  • Northop
  • nPlan
  • Nvidia
  • Oculo
  • Odometric
  • Omron
  • OpenAI
  • Opoplan
  • Oracle
  • O'Rourke
  • Output
  • Pangea
  • Paradromics
  • Plot-Z
  • Pony.ai
  • Procore
  • Research
  • Robotics
  • Robotiq
  • SAS
  • SenseTime
  • Shimizu
  • Shutterstock (Amper)
  • Siemens
  • Skanska
  • StarryAI
  • STRABAG
  • Synchron
  • Systems
  • Tangible
  • Tastewise
  • Tencent
  • Teradyne
  • Tesla
  • Tesmec
  • Thales Group
  • ThyssenKrupp
  • Togal.AI
  • Trimble
  • Versatile.ai
  • ViAct
  • Vinci
  • Willmott
  • WOMBO (Dream)