RPA & AI Technology Evaluation
Who Is This Report For?
NelsonHall’s “RPA & AI Technology Evaluation” report is a comprehensive market assessment report designed for:
- Sourcing managers investigating ssourcing developments withinn Robotic Process Automation amd Artificial Intelligence technology
- IT and business decision makers explloring the benefits and inhibitors of RPA & AI, as evidenced form the clients and vendor capability
- Vendor marketing, sales and business managers developing strategies to target opportunities within RPA & AI
- Financial analysts and investors specializing in the IT services and BPS sector
Scope of the Report
This report analyzes the global market for RPA & AI and its constituent services. The report addresses the following questions:
- What is the current and future market for RPA and AI technology and services?
- What are the customer requirements and how are they changing?
- What are the benefits/results that vendors have been able to achieve for their clients?
- What services are customers buying from RPA and AI vendors?
- What technologies and platforms are being utilized and what are the latest developments and trends emerging?
- What is the size and growth of the RPA and AI market by geography?
- What is the size of the RPA and AI market by industry and which is seeing increased demand?
- What are the vendor selection criteria, challenges and critical success factors for vendors targeting RPA and AI?
- Additional topics include: industry-specific developments, trend identification, pricing models, deployment (cloud versus on-premise), and best practices in vendor selection and implementation.
Key Findings & Highlights
NelsonHall's market analysis of the RPA and AI industry and trends consists of 85 pages.
The global RPA market is worth $373m (estimated in 2017), with average CAGR of 72% through 2021. The cognitive automation market is worth $317m, with 71% CAGR projected through 2021.
Both markets are quite young, with providers still seeking leadership positions. The boundaries between RPA and AI are blurring, with RPA vendors increasingly including machine learning and cognitive automation in their roadmaps, and cognitive technology vendors such as WorkFusion developing RPA capability.
The primary focus of RPA is on back-office tasks characterized by high volumes of work, low overall variability, and high value derived from automation. Primary drivers for RPA deployment are FTE reduction, process cycle time improvement, and expansion of work capacity in preparation for disruptive market action (in either direction).
Most deployments are still delivered directly, but an increasing proportion are managed via solution integrators, on a spectrum ranging from split direct/partnered deployment to entirely reliant on SIs to provision the solution.
Deployments are being led by organizations in the BFSI and healthcare verticals, where high volumes of repetitive tasks with critical quality and security issues are driving automation uptake. These same security issues are driving RPA deployments as on-premise technology in these industries