Market Growth Accelerating Across AI Segments

The global Machine Learning market is expected to reach $309.68 billion by 2032, growing at a 30.5% compound annual growth rate (CAGR). The market was valued at $55.80 billion in 2024 and is projected to reach $282.13 billion by 2030, signalling explosive growth across the AI landscape.

Within this broader expansion, autonomous AI agents represent one of the fastest-growing segments, with demand expected to hit $93.20 billion by 2032.

Agentic AI: The Next Frontier

Agentic AI relies on large language models and generative AI to understand context, plan, make decisions itself, and execute in real time. This capability shift is driving the rapid adoption curve for autonomous AI agents across enterprise environments.

Domain-Specific Models Taking Hold

The trend toward specialised AI is accelerating. More than 50% of GenAI models used by enterprises will be tailored to a particular industry or business task by 2027. One example already in use is PubMedGPT, a domain-specific language model focused on healthcare literature, trained on research articles and medical terminology.

For context on model complexity: large language models contain hundreds of trillions of parameters, while small language models typically have around 1 million or 10 billion parameters.

Multimodal Enterprise Software on the Rise

80% of enterprise software will be multimodal by 2030, up from less than 5% in 2024. This shift reflects growing expectations for AI systems to process and integrate multiple data types simultaneously.

MLOps Driving Operational Maturity

MLOps, inspired by the DevOps methodology, is a set of practices for transparent and seamless collaboration of data scientists and operational specialists to build, deploy and maintain ML models. MLOps brings more transparency, eliminates communication gaps, and allows better scaling due to business objective-first design.

Real-World AI Applications Delivering Results

In 2020, Meta introduced a Facebook chatbot named Blender that could communicate on various subjects after being trained on 1.5 billion publicly available Reddit conversations.

In healthcare, Wanda is an ML-powered remote patient monitoring platform that utilizes machine learning algorithms, prediction, and risk analytics to enhance care plans through timely alerts to doctors.

Supply chain optimisation is seeing measurable gains: a supply chain software and service provider achieved 80% forecasting precision by using ML-powered automation for exception prediction and data processing.

In retail, personalisation efforts are delivering significant uplift. A retail company using ML tools achieved 6X CTR, 3X CVR, +30% retention, and -20% churn through mobile hyper-personalisation on edge devices. The largest retailer in Central and Eastern Europe saved significant money using a retail category management tool after nine months, eliminating staff who manually performed category management.

Source: SoftTeco