AI in Data Integration Market - Global Industry Size, Share, Trends, Opportunity and Forecast, Segmented By Application, By Business Function, By Deployment Mode, By Organization Size, By End-Use, By Region & Competition, 2021-2031F

Description

The Global AI in Data Integration Market is projected to grow from USD 22.48 Billion in 2025 to USD 57.72 Billion by 2031, achieving a CAGR of 17.02%. This market consists of software solutions that utilize machine learning and natural language processing to automate the ingestion, mapping, quality enhancement, and unification of diverse data sources. The primary drivers of this growth are the exponential increase in enterprise data volumes and the urgent requirement for real-time business intelligence, which pushes organizations to replace manual, error-prone extract, transform, and load processes with automated workflows. This transition enables businesses to substantially lower data latency and operational costs while improving the accuracy of their analytical insights.

Market Overview
Forecast Period	2027-2031
Market Size 2025	USD 22.48 Billion
Market Size 2031	USD 57.72 Billion
CAGR 2026-2031	17.02%
Fastest Growing Segment	Cloud
Largest Market	North America

However, market expansion is significantly hindered by a critical shortage of skilled professionals qualified to manage and deploy these complex adaptive systems. The disparity between the demand for advanced technical skills and the available workforce compels many enterprises to postpone implementation. As noted in CompTIA's 'IT Industry Outlook 2025', 66% of organizations plan to train current employees to bridge essential skills gaps in data and technology, underscoring the severity of the talent shortage that currently limits the scalability of AI-driven data integration initiatives.

Market Driver

The rapid increase in big data volume and complexity serves as a primary catalyst for the Global AI in Data Integration Market. As enterprises amass vast quantities of structured and unstructured data across hybrid environments, the inability to unify these fragmented assets results in significant operational bottlenecks. AI-driven integration is increasingly utilized to automatically map and synchronize disparate sources, resolving interoperability issues that manual coding can no longer address. This fragmentation poses a critical barrier to progress; according to the '2025 Connectivity Benchmark Report' by Salesforce in January 2025, 90% of IT leaders reported that data silos were creating business challenges in their organization, establishing an urgent mandate for intelligent, automated unification tools.

Concurrently, the operational necessity for cost reduction and workflow efficiency accelerates the adoption of autonomous, agentic AI solutions. Organizations are moving away from labor-intensive data pipeline maintenance toward adaptive systems that self-heal and optimize performance, thereby reducing the overhead associated with data engineering. This efficiency drive is financially vital; as noted in the 'Ascendion Recognized as a Global Leader in the ISG Provider Lens for Generative AI Services 2025' press release from October 2025, their agentic AI platform delivered up to 60% effort savings in data analysis for large banking clients. Consequently, budgets are shifting to support these modern architectures, with Informatica's 'CDO Insights 2025' report from February 2025 indicating that 86% of data leaders planned to increase their data management investments in 2025 to address these complexities.

Market Challenge

The severe shortage of skilled professionals constitutes a formidable barrier to the growth of the Global AI in Data Integration Market. As these solutions become increasingly complex, relying on advanced machine learning algorithms and natural language processing, the need for specialized talent to configure, manage, and maintain them rises disproportionately. Organizations often struggle to identify and retain personnel who possess the necessary blend of data engineering expertise and AI literacy. This scarcity forces businesses to delay or abandon critical integration projects, as they lack the internal capability to oversee the transition from manual processes to automated workflows, leading to reduced adoption rates as potential buyers hesitate to invest in technologies they cannot effectively support.

The magnitude of this workforce gap is evident in recent industry findings which highlight the disparity between technology adoption and employee readiness. According to ISACA, in 2024, 40% of organizations provided no AI training, while 85% of professionals indicated a need to acquire additional AI skills to perform their roles effectively. This disconnect creates a substantial bottleneck for vendors. Without a sufficient pool of qualified operators, enterprises encounter operational risks and prolonged implementation timelines, directly dampening the revenue potential and scalability of the broader market.

Market Trends

The adoption of Generative AI for automated schema mapping and transformation logic is fundamentally reshaping the market by lowering technical barriers to data interoperability. Modern integration platforms are increasingly embedding Large Language Models (LLMs) to interpret complex data structures and automatically generate the necessary code for schema alignment, replacing labor-intensive manual ETL scripting. This innovation allows non-technical users to execute sophisticated data mappings with natural language prompts, accelerating project delivery times. The industry prioritization of this capability is evident in investment trends; according to Nexla's 'State of Data + AI Trends Report 2024-2025' from February 2025, 59% of data integration professionals identified Generative AI and machine learning-driven integration as a key area requiring attention and investment to enhance workflow efficiency.

Simultaneously, the integration of vector embedding capabilities for unstructured data processing is expanding the scope of data integration beyond traditional structured formats. As enterprises race to build retrieval-augmented generation (RAG) applications, integration tools are evolving to ingest, vectorize, and index unstructured assets like PDF documents and customer logs directly into vector databases. This capability is becoming a critical infrastructure requirement for organizations aiming to leverage their internal knowledge bases for AI development. The demand for such processing power is substantial; according to Fivetran's '2025 and Beyond' report from June 2025, 89% of technology leaders planned to use proprietary data to train large language models in 2025, creating an urgent mandate for pipelines capable of handling high-dimensional vector data.

Key Market Players

Informatica
Fivetran
Microsoft Azure Synapse Analytics
IBM DataStage
Oracle Data Integration Platform
AWS Glue
Google Cloud BigQuery
SCIKIQ
Airbyte
SnapLogic

Report Scope

In this report, the Global AI in Data Integration Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

AI in Data Integration Market, By Application

Data Mapping
Big Data Processing
ETL
Schema Alignment

AI in Data Integration Market, By Business Function

Marketing
Operations
Finance
Customer Relationship Management
Human Resource Management
Others

AI in Data Integration Market, By Deployment Mode

On-Premise
Cloud

AI in Data Integration Market, By Organization Size

Large Enterprise & Small & Medium Enterprises

AI in Data Integration Market, By End-Use

Healthcare
BFSI
Manufacturing
Retail
IT & Telecom
Government & Defense
Others

AI in Data Integration Market, By Region

North America
- United States
- Canada
- Mexico
Europe
- France
- United Kingdom
- Italy
- Germany
- Spain
Asia Pacific
- China
- India
- Japan
- Australia
- South Korea
South America
- Brazil
- Argentina
- Colombia
Middle East & Africa
- South Africa
- Saudi Arabia
- UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI in Data Integration Market.

Available Customizations:

Global AI in Data Integration Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

1. Product Overview

1.1. Market Definition
1.2. Scope of the Market
- 1.2.1. Markets Covered
- 1.2.2. Years Considered for Study
- 1.2.3. Key Market Segmentations

2. Research Methodology

2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Key Industry Partners
2.4. Major Association and Secondary Sources
2.5. Forecasting Methodology
2.6. Data Triangulation & Validation
2.7. Assumptions and Limitations

3. Executive Summary

3.1. Overview of the Market
3.2. Overview of Key Market Segmentations
3.3. Overview of Key Market Players
3.4. Overview of Key Regions/Countries
3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global AI in Data Integration Market Outlook

5.1. Market Size & Forecast
- 5.1.1. By Value
5.2. Market Share & Forecast
- 5.2.1. By Application (Data Mapping, Big Data Processing, ETL, Schema Alignment)
- 5.2.2. By Business Function (Marketing, Operations, Finance, Customer Relationship Management, Human Resource Management, Others)
- 5.2.3. By Deployment Mode (On-Premise, Cloud)
- 5.2.4. By Organization Size (Large Enterprise & Small & Medium Enterprises)
- 5.2.5. By End-Use (Healthcare, BFSI, Manufacturing, Retail, IT & Telecom, Government & Defense, Others)
- 5.2.6. By Region
- 5.2.7. By Company (2025)
5.3. Market Map

6. North America AI in Data Integration Market Outlook

6.1. Market Size & Forecast
- 6.1.1. By Value
6.2. Market Share & Forecast
- 6.2.1. By Application
- 6.2.2. By Business Function
- 6.2.3. By Deployment Mode
- 6.2.4. By Organization Size
- 6.2.5. By End-Use
- 6.2.6. By Country
6.3. North America: Country Analysis
- 6.3.1. United States AI in Data Integration Market Outlook
  - 6.3.1.1. Market Size & Forecast
    - 6.3.1.1.1. By Value
  - 6.3.1.2. Market Share & Forecast
    - 6.3.1.2.1. By Application
    - 6.3.1.2.2. By Business Function
    - 6.3.1.2.3. By Deployment Mode
    - 6.3.1.2.4. By Organization Size
    - 6.3.1.2.5. By End-Use
- 6.3.2. Canada AI in Data Integration Market Outlook
  - 6.3.2.1. Market Size & Forecast
    - 6.3.2.1.1. By Value
  - 6.3.2.2. Market Share & Forecast
    - 6.3.2.2.1. By Application
    - 6.3.2.2.2. By Business Function
    - 6.3.2.2.3. By Deployment Mode
    - 6.3.2.2.4. By Organization Size
    - 6.3.2.2.5. By End-Use
- 6.3.3. Mexico AI in Data Integration Market Outlook
  - 6.3.3.1. Market Size & Forecast
    - 6.3.3.1.1. By Value
  - 6.3.3.2. Market Share & Forecast
    - 6.3.3.2.1. By Application
    - 6.3.3.2.2. By Business Function
    - 6.3.3.2.3. By Deployment Mode
    - 6.3.3.2.4. By Organization Size
    - 6.3.3.2.5. By End-Use

7. Europe AI in Data Integration Market Outlook

7.1. Market Size & Forecast
- 7.1.1. By Value
7.2. Market Share & Forecast
- 7.2.1. By Application
- 7.2.2. By Business Function
- 7.2.3. By Deployment Mode
- 7.2.4. By Organization Size
- 7.2.5. By End-Use
- 7.2.6. By Country
7.3. Europe: Country Analysis
- 7.3.1. Germany AI in Data Integration Market Outlook
  - 7.3.1.1. Market Size & Forecast
    - 7.3.1.1.1. By Value
  - 7.3.1.2. Market Share & Forecast
    - 7.3.1.2.1. By Application
    - 7.3.1.2.2. By Business Function
    - 7.3.1.2.3. By Deployment Mode
    - 7.3.1.2.4. By Organization Size
    - 7.3.1.2.5. By End-Use
- 7.3.2. France AI in Data Integration Market Outlook
  - 7.3.2.1. Market Size & Forecast
    - 7.3.2.1.1. By Value
  - 7.3.2.2. Market Share & Forecast
    - 7.3.2.2.1. By Application
    - 7.3.2.2.2. By Business Function
    - 7.3.2.2.3. By Deployment Mode
    - 7.3.2.2.4. By Organization Size
    - 7.3.2.2.5. By End-Use
- 7.3.3. United Kingdom AI in Data Integration Market Outlook
  - 7.3.3.1. Market Size & Forecast
    - 7.3.3.1.1. By Value
  - 7.3.3.2. Market Share & Forecast
    - 7.3.3.2.1. By Application
    - 7.3.3.2.2. By Business Function
    - 7.3.3.2.3. By Deployment Mode
    - 7.3.3.2.4. By Organization Size
    - 7.3.3.2.5. By End-Use
- 7.3.4. Italy AI in Data Integration Market Outlook
  - 7.3.4.1. Market Size & Forecast
    - 7.3.4.1.1. By Value
  - 7.3.4.2. Market Share & Forecast
    - 7.3.4.2.1. By Application
    - 7.3.4.2.2. By Business Function
    - 7.3.4.2.3. By Deployment Mode
    - 7.3.4.2.4. By Organization Size
    - 7.3.4.2.5. By End-Use
- 7.3.5. Spain AI in Data Integration Market Outlook
  - 7.3.5.1. Market Size & Forecast
    - 7.3.5.1.1. By Value
  - 7.3.5.2. Market Share & Forecast
    - 7.3.5.2.1. By Application
    - 7.3.5.2.2. By Business Function
    - 7.3.5.2.3. By Deployment Mode
    - 7.3.5.2.4. By Organization Size
    - 7.3.5.2.5. By End-Use

8. Asia Pacific AI in Data Integration Market Outlook

8.1. Market Size & Forecast
- 8.1.1. By Value
8.2. Market Share & Forecast
- 8.2.1. By Application
- 8.2.2. By Business Function
- 8.2.3. By Deployment Mode
- 8.2.4. By Organization Size
- 8.2.5. By End-Use
- 8.2.6. By Country
8.3. Asia Pacific: Country Analysis
- 8.3.1. China AI in Data Integration Market Outlook
  - 8.3.1.1. Market Size & Forecast
    - 8.3.1.1.1. By Value
  - 8.3.1.2. Market Share & Forecast
    - 8.3.1.2.1. By Application
    - 8.3.1.2.2. By Business Function
    - 8.3.1.2.3. By Deployment Mode
    - 8.3.1.2.4. By Organization Size
    - 8.3.1.2.5. By End-Use
- 8.3.2. India AI in Data Integration Market Outlook
  - 8.3.2.1. Market Size & Forecast
    - 8.3.2.1.1. By Value
  - 8.3.2.2. Market Share & Forecast
    - 8.3.2.2.1. By Application
    - 8.3.2.2.2. By Business Function
    - 8.3.2.2.3. By Deployment Mode
    - 8.3.2.2.4. By Organization Size
    - 8.3.2.2.5. By End-Use
- 8.3.3. Japan AI in Data Integration Market Outlook
  - 8.3.3.1. Market Size & Forecast
    - 8.3.3.1.1. By Value
  - 8.3.3.2. Market Share & Forecast
    - 8.3.3.2.1. By Application
    - 8.3.3.2.2. By Business Function
    - 8.3.3.2.3. By Deployment Mode
    - 8.3.3.2.4. By Organization Size
    - 8.3.3.2.5. By End-Use
- 8.3.4. South Korea AI in Data Integration Market Outlook
  - 8.3.4.1. Market Size & Forecast
    - 8.3.4.1.1. By Value
  - 8.3.4.2. Market Share & Forecast
    - 8.3.4.2.1. By Application
    - 8.3.4.2.2. By Business Function
    - 8.3.4.2.3. By Deployment Mode
    - 8.3.4.2.4. By Organization Size
    - 8.3.4.2.5. By End-Use
- 8.3.5. Australia AI in Data Integration Market Outlook
  - 8.3.5.1. Market Size & Forecast
    - 8.3.5.1.1. By Value
  - 8.3.5.2. Market Share & Forecast
    - 8.3.5.2.1. By Application
    - 8.3.5.2.2. By Business Function
    - 8.3.5.2.3. By Deployment Mode
    - 8.3.5.2.4. By Organization Size
    - 8.3.5.2.5. By End-Use

9. Middle East & Africa AI in Data Integration Market Outlook

9.1. Market Size & Forecast
- 9.1.1. By Value
9.2. Market Share & Forecast
- 9.2.1. By Application
- 9.2.2. By Business Function
- 9.2.3. By Deployment Mode
- 9.2.4. By Organization Size
- 9.2.5. By End-Use
- 9.2.6. By Country
9.3. Middle East & Africa: Country Analysis
- 9.3.1. Saudi Arabia AI in Data Integration Market Outlook
  - 9.3.1.1. Market Size & Forecast
    - 9.3.1.1.1. By Value
  - 9.3.1.2. Market Share & Forecast
    - 9.3.1.2.1. By Application
    - 9.3.1.2.2. By Business Function
    - 9.3.1.2.3. By Deployment Mode
    - 9.3.1.2.4. By Organization Size
    - 9.3.1.2.5. By End-Use
- 9.3.2. UAE AI in Data Integration Market Outlook
  - 9.3.2.1. Market Size & Forecast
    - 9.3.2.1.1. By Value
  - 9.3.2.2. Market Share & Forecast
    - 9.3.2.2.1. By Application
    - 9.3.2.2.2. By Business Function
    - 9.3.2.2.3. By Deployment Mode
    - 9.3.2.2.4. By Organization Size
    - 9.3.2.2.5. By End-Use
- 9.3.3. South Africa AI in Data Integration Market Outlook
  - 9.3.3.1. Market Size & Forecast
    - 9.3.3.1.1. By Value
  - 9.3.3.2. Market Share & Forecast
    - 9.3.3.2.1. By Application
    - 9.3.3.2.2. By Business Function
    - 9.3.3.2.3. By Deployment Mode
    - 9.3.3.2.4. By Organization Size
    - 9.3.3.2.5. By End-Use

10. South America AI in Data Integration Market Outlook

10.1. Market Size & Forecast
- 10.1.1. By Value
10.2. Market Share & Forecast
- 10.2.1. By Application
- 10.2.2. By Business Function
- 10.2.3. By Deployment Mode
- 10.2.4. By Organization Size
- 10.2.5. By End-Use
- 10.2.6. By Country
10.3. South America: Country Analysis
- 10.3.1. Brazil AI in Data Integration Market Outlook
  - 10.3.1.1. Market Size & Forecast
    - 10.3.1.1.1. By Value
  - 10.3.1.2. Market Share & Forecast
    - 10.3.1.2.1. By Application
    - 10.3.1.2.2. By Business Function
    - 10.3.1.2.3. By Deployment Mode
    - 10.3.1.2.4. By Organization Size
    - 10.3.1.2.5. By End-Use
- 10.3.2. Colombia AI in Data Integration Market Outlook
  - 10.3.2.1. Market Size & Forecast
    - 10.3.2.1.1. By Value
  - 10.3.2.2. Market Share & Forecast
    - 10.3.2.2.1. By Application
    - 10.3.2.2.2. By Business Function
    - 10.3.2.2.3. By Deployment Mode
    - 10.3.2.2.4. By Organization Size
    - 10.3.2.2.5. By End-Use
- 10.3.3. Argentina AI in Data Integration Market Outlook
  - 10.3.3.1. Market Size & Forecast
    - 10.3.3.1.1. By Value
  - 10.3.3.2. Market Share & Forecast
    - 10.3.3.2.1. By Application
    - 10.3.3.2.2. By Business Function
    - 10.3.3.2.3. By Deployment Mode
    - 10.3.3.2.4. By Organization Size
    - 10.3.3.2.5. By End-Use

11. Market Dynamics

11.1. Drivers
11.2. Challenges

12. Market Trends & Developments

12.1. Merger & Acquisition (If Any)
12.2. Product Launches (If Any)
12.3. Recent Developments

13. Global AI in Data Integration Market: SWOT Analysis

14. Porter's Five Forces Analysis

14.1. Competition in the Industry
14.2. Potential of New Entrants
14.3. Power of Suppliers
14.4. Power of Customers
14.5. Threat of Substitute Products

15. Competitive Landscape

15.1. Informatica
- 15.1.1. Business Overview
- 15.1.2. Products & Services
- 15.1.3. Recent Developments
- 15.1.4. Key Personnel
- 15.1.5. SWOT Analysis
15.2. Fivetran
15.3. Microsoft Azure Synapse Analytics
15.4. IBM DataStage
15.5. Oracle Data Integration Platform
15.6. AWS Glue
15.7. Google Cloud BigQuery
15.8. SCIKIQ
15.9. Airbyte
15.10. SnapLogic