Riding the Wave of AI: An In-Depth Look at Platforms Transforming Businesses

This report, a refresh of our 2020 report titled “AI Platforms – Next Frontier for Indian IT Services”, explores the evolution of AI platforms, over the past 2-3 years, down to the more recent emergence of AI-as-a-Service (AIaaS) and ML engineering platforms. The emergence of deep learning frameworks such as TensorFlow and PyTorch in the early 2010s marked a turning point, enabling businesses to build more sophisticated and scalable AI models. The rise of cloud computing and big data also paved the way for new types of AI platforms, including AIaaS, which enables businesses to access AI capabilities on a pay-per-use basis.

As applications and use-cases mature and find usability across a diverse range of industries, particularly in the areas of Natural Language and Vision, the impetus is seen to be shifting from a tech-centric view to an outcome-centric view.

Platformizing AI solutions is a powerful new trend that can help customers glean insights from data, generate real-time intelligence from their operations and processes, automate mission-critical applications and systems, and address issues of privacy and security. AI platforms bundle together best-of-breed components, data, advanced analytics and automation to drive efficiency and competitive differentiation.

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Key Findings

Have emerged as the pillars to build a post-pandemic ‘Digital Core’. New data strategies encompassing Data Mesh, Data Fabric, increasing public cloud spend and improvements in Language, Vision and Decision systems fueling the next phase of growth.
Autonomous Analytics, Deep Learning, Computer Vision, Sensor Tech drawing investor money across regions. North America leads by volume followed by APAC. Investments in India growing at 31% CAGR. 7 Core AI and 4 Applied AI themes moving towards commercialization.
The Platformization of AI is resulting from increasing algorithmization of ML models. Generative AI powered by LLMs like GPT-4/ChatGPT and Bard will fundamentally change Digital Product development in the coming days.

The current AI Platforms landscape consists of MLOps and Conversational AI platforms, intelligent automation frameworks, managed services offerings from systems integrators, AIOps platforms/offerings, Intelligent BPM solutions and Low-code development platforms for Artificial Intelligence.
The managed services model with its broad constituents – AI software platforms, AI applications, automation modules and professional services promises production-grade solutions to ensure sustainable AI adoption at scale.
6 key business drivers identified for the increasing adoption of AI are: improving data quality and reliability, readily available data models, on-demand accessibility and scalability, data-driven intelligent decision-making and now large language models looking at the possibility of integrating with existing applications.

~$10bn funding in 2021 and 2022 across ~700 funding rounds. The top recipients are Uniphore, Fractal, Verse, RazorPay. Healthcare, Transportation, Tech leading the way, major thrust on Conversational AI, vernacular language platforms and decision intelligence systems.
Post the Pandemic year, funding in AI companies has increased by 2.8x, 3-year funding growth stands at 95%.
India’s DeepTech ecosystem has a substantial AI play with ~2,000 start-ups.
Providers are trying to couple together data and business models in which the emphasis is on the use of data, rather than the application where the data has been hosted. Becoming more IP-driven, shifting from being people-driven.

The adoption of AI platforms is expected to continue increasing across various industries, as businesses seek to integrate AI into their existing workflows and systems.
With advancements in AI technologies such as generative AI, federated learning, and edge computing, AI platforms will continue to drive innovation and create new opportunities in the marketplace.
AI platforms will play a critical role in helping to derive insights from that data, enabling businesses to make better-informed decisions and improve outcomes.

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