2026 Global: Artificial Intelligence (Ai) In Project Management Market-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) In Project Management Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in project management market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in project management market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Ten major companies leading the Artificial Intelligence in Project Management market combine enterprise software incumbents, specialized construction and engineering platforms, and AI-first vendors that embed predictive scheduling, risk forecasting, resource optimization, and natural-language automation into project workflows. Autodesk leverages generative design and construction-focused AI to connect BIM, scheduling, and cost models for design-to-construction coordination, driving fewer clashes and faster delivery times. Oracle and SAP integrate AI across their enterprise project portfolios—using machine learning for forecasting, portfolio prioritization, anomaly detection in costs and timelines, and intelligent resource allocation within ERP-embedded project modules. Microsoft brings Azure AI, Project for the web enhancements, and Copilot capabilities to enable conversational project queries, automated status summaries, and data-driven schedule adjustments tied to Microsoft 365 and Teams collaboration fabric. Procore, focused on construction project management, embeds machine learning for photo analytics, risk flags, and cost-schedule forecasting to reduce onsite rework and accelerate closeouts. PlanGrid/Autodesk Build (Autodesk family) and ALICE Technologies specifically target scheduling and sequencing: ALICE runs AI-driven simulations of millions of schedule alternatives from BIM inputs to find faster, lower-risk execution plans, particularly for complex builds. nPlan and Doxel apply predictive analytics and computer vision to construction progress data to forecast delays and productivity shortfalls weeks in advance, enabling proactive mitigation and improved cash-flow planning. Smartsheet and Asana extend AI features for cross-functional project portfolios—automating status extraction, prioritization suggestions, risk detection from unstructured updates, and intelligent task assignment that align teams to strategic milestones. IBM and Accenture (including specialist arms like QuantumBlack) serve large capital-program and industrial clients by combining advanced analytics, digital twins, and bespoke ML pipelines to predict schedule slippage, optimize resource mixes, and enforce governance across multi-project portfolios. Lastly, emerging AI-first vendors such as Mastt and specialized consultancies (examples: The Hackett Group, LeewayHertz, and boutique AI integrators) focus on owner-side portfolio visibility, automated reporting, and domain-tailored AI models that convert dispersed project data into unified risk and cost forecasts for portfolio executives.
These ten firms differ by target customer, data integration approach, and AI modality: enterprise ERPs (Oracle, SAP, Microsoft) favor broad portfolio controls and governance; construction specialists (Procore, ALICE, nPlan, Doxel, Mastt) emphasize on-site telemetry, BIM integration, and sequencing optimization; project collaboration platforms (Smartsheet, Asana) apply NLP and automation to knowledge work; and consulting/AI engineering leaders (IBM, Accenture/QuantumBlack, specialist consultancies) deliver bespoke models, digital twins, and change programs to scale AI across complex capital programs. Common use cases across the market include predictive schedule and cost forecasting, automated progress capture and variance detection, optimized resource leveling and procurement timing, smart change‑order impact analysis, and AI-assisted reporting and stakeholder communication—each reducing uncertainty, compressing decision cycles, and lowering contingency burn by converting historical project data, sensor feeds, and contract documents into actionable insights. Adoption barriers remaining across buyers are data fragmentation, integration complexity with legacy systems, model explainability for safety‑critical builds, and the need for domain-specific training data; vendors address these with connectors to ERP/BIM, transfer-learning techniques, human-in-the-loop workflows, and packaged industry models to accelerate value realization.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in project management market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in project management market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Ten major companies leading the Artificial Intelligence in Project Management market combine enterprise software incumbents, specialized construction and engineering platforms, and AI-first vendors that embed predictive scheduling, risk forecasting, resource optimization, and natural-language automation into project workflows. Autodesk leverages generative design and construction-focused AI to connect BIM, scheduling, and cost models for design-to-construction coordination, driving fewer clashes and faster delivery times. Oracle and SAP integrate AI across their enterprise project portfolios—using machine learning for forecasting, portfolio prioritization, anomaly detection in costs and timelines, and intelligent resource allocation within ERP-embedded project modules. Microsoft brings Azure AI, Project for the web enhancements, and Copilot capabilities to enable conversational project queries, automated status summaries, and data-driven schedule adjustments tied to Microsoft 365 and Teams collaboration fabric. Procore, focused on construction project management, embeds machine learning for photo analytics, risk flags, and cost-schedule forecasting to reduce onsite rework and accelerate closeouts. PlanGrid/Autodesk Build (Autodesk family) and ALICE Technologies specifically target scheduling and sequencing: ALICE runs AI-driven simulations of millions of schedule alternatives from BIM inputs to find faster, lower-risk execution plans, particularly for complex builds. nPlan and Doxel apply predictive analytics and computer vision to construction progress data to forecast delays and productivity shortfalls weeks in advance, enabling proactive mitigation and improved cash-flow planning. Smartsheet and Asana extend AI features for cross-functional project portfolios—automating status extraction, prioritization suggestions, risk detection from unstructured updates, and intelligent task assignment that align teams to strategic milestones. IBM and Accenture (including specialist arms like QuantumBlack) serve large capital-program and industrial clients by combining advanced analytics, digital twins, and bespoke ML pipelines to predict schedule slippage, optimize resource mixes, and enforce governance across multi-project portfolios. Lastly, emerging AI-first vendors such as Mastt and specialized consultancies (examples: The Hackett Group, LeewayHertz, and boutique AI integrators) focus on owner-side portfolio visibility, automated reporting, and domain-tailored AI models that convert dispersed project data into unified risk and cost forecasts for portfolio executives.
These ten firms differ by target customer, data integration approach, and AI modality: enterprise ERPs (Oracle, SAP, Microsoft) favor broad portfolio controls and governance; construction specialists (Procore, ALICE, nPlan, Doxel, Mastt) emphasize on-site telemetry, BIM integration, and sequencing optimization; project collaboration platforms (Smartsheet, Asana) apply NLP and automation to knowledge work; and consulting/AI engineering leaders (IBM, Accenture/QuantumBlack, specialist consultancies) deliver bespoke models, digital twins, and change programs to scale AI across complex capital programs. Common use cases across the market include predictive schedule and cost forecasting, automated progress capture and variance detection, optimized resource leveling and procurement timing, smart change‑order impact analysis, and AI-assisted reporting and stakeholder communication—each reducing uncertainty, compressing decision cycles, and lowering contingency burn by converting historical project data, sensor feeds, and contract documents into actionable insights. Adoption barriers remaining across buyers are data fragmentation, integration complexity with legacy systems, model explainability for safety‑critical builds, and the need for domain-specific training data; vendors address these with connectors to ERP/BIM, transfer-learning techniques, human-in-the-loop workflows, and packaged industry models to accelerate value realization.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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