AI Emerges As The Foundation Of Enterprise Decision-making
Los Angeles, April, 2021 Over the past 12 months, the number of artificial intelligence (AI) and advanced analytics projects in production has increased by 54%. Much of this growth is due to AI-enabled digital workplace solutions and self-services capabilities, allowing for low-touch operations needed during the pandemic. New use cases have emerged in areas such as advertising, media, and marketing where consumers now expect personalized content and communication. AI is creating a shift in the way enterprises consume data and make decisions. These emerging trends are covered in Avasant’s new Applied AI and Advanced Analytics Services 2021 RadarView report. The report is a comprehensive study of applied AI and advanced analytics services, including top trends, recommendations, and a close look at the leaders, innovators, disruptors, and challengers in this market. Avasant evaluated 40 providers across three dimensions: practice maturity, partnership ecosystem, and investments and innovation. Of those 40 providers, we recognize 22 as having brought the most value to the market during the past 12 months. The report recognizes service providers in four categories:
- Leaders: Accenture, Capgemini, Cognizant, HCL, IBM, Infosys, TCS, Wipro
- Innovators: Atos, DXC, Genpact, LTI, NTT DATA, Tech Mahindra
- Disruptors: Coforge, Mindtree, Mphasis, Sutherland, Zensar
- Challengers: EXL, Persistent Systems, UST
- As cloud platforms such as AWS, Google, and Microsoft elevate their AI-driven offerings, a new class of use cases are unleashing improved business models across industries such as retail, investment banking, insurance, healthcare, and consumer tech.
- The emergence of enterprise-class platforms that embed AI within enterprise resource planning (ERP), supply chain, analytics, and customer interaction solutions is convincing traditional organizations to aggressively adopt AI.
- As organizations become data-driven, the demand for technologies such as AI and natural language processing (NLP) to decipher personalized and relevant insights from large volumes of data is increasing.
- This has disrupted the way data and reports are consumed, as enterprises are shifting toward generating suggestions and insights. Dashboards and reports are augmented by contextual insights that are personalized to the user and the role.
- Despite high interest in AI, three key challenges need to be addressed to scale AI implementations: staggering energy consumption, biases in models, and high cost and computing space.
- Advances in GPU-accelerated infrastructure; increased initiatives that promote the adoption of inclusive, transparent, and trusted AI; and the emergence of quantum computing in AI applications will help tackle energy and computing concerns to a large extent.