Agentic AI Market to Surpass $5.98 Billion by 2025, with Strong Growth Expected Through 2035

The Agentic AI Market Report forecasts revenue to surpass $5.98 billion in 2025, driven by rising demand for autonomous decision-making across industries. However, the lack of explainability and trust in these highly autonomous systems remains a core challenge, limiting adoption in regulated industries. The report includes revenue forecasting to 2035, qualitative analyses, and commercial prospects.

The Agentic AI Market Report forecasts revenue to surpass $5.98 billion in 2025, driven by rising demand for autonomous decision-making across industries. Key drivers include the increasing adoption of AI models capable of independent decision-making in sectors such as finance, healthcare, and manufacturing. However, the lack of explainability and trust in these highly autonomous systems remains a core challenge, limiting adoption in regulated industries [2].

The report, published by ResearchAndMarkets.com, includes detailed revenue forecasts to 2035, qualitative analyses, and commercial prospects. It highlights the growing potential in diverse tech applications, providing insights into the most lucrative places for investments and revenues. The report also forecasts revenue for five regional and 25 leading national markets, including North America, Europe, Asia Pacific, Latin America, and MEA. Additionally, it profiles leading companies involved in the Agentic AI Market, such as Adept AI, Alphabet Inc., Amazon Web Services, Inc., Amelia Botica's Cognigy AI, CUJO AI, Infinitus AI, IBM, Leena AI, LivePerson, Microsoft, NVIDIA Corporation, OpenAI, Salesforce AI, and Toyota [3].

One of the prominent factors driving the Agentic AI market is the growing demand for systems capable of making independent decisions without continuous human oversight. Industries ranging from finance and healthcare to manufacturing and defense are increasingly relying on AI models that can interpret data, define objectives, and execute tasks with minimal intervention. For example, JPMorgan Chase has recently begun integrating Agentic AI models into its fraud detection and trading platforms to enable more responsive and autonomous financial decision-making. In manufacturing, companies like Siemens are deploying agentic systems to monitor machinery, predict failures, and initiate maintenance activities autonomously [2].

However, a core challenge limiting the wider adoption of Agentic AI is the lack of transparency and explainability in the decision-making processes of these highly autonomous systems. Unlike traditional AI models that follow predefined logic or produce traceable outputs, Agentic AI systems are designed to act with a degree of independence, setting sub-goals, adapting to feedback, and initiating actions without explicit instructions. This autonomy, while powerful, raises serious concerns about accountability, safety, and ethical compliance, especially in regulated industries such as healthcare, finance, and law. For instance, an Agentic AI model used in autonomous trading or medical diagnostics may take an action based on internal reasoning that even its developers cannot fully interpret. In 2024, a high-profile case involving an AI agent used in insurance claim assessment in the UK drew regulatory scrutiny after it denied claims based on opaque decision logic, triggering a call for greater explainability standards [2].

In response to these challenges, companies like OpenAI and Anthropic are investing in interpretability research to address the gap in explainability. However, the pace of progress remains slow relative to the urgency of deployment. Until Agentic systems can reliably explain their decisions in a human-understandable manner, trust and adoption will remain limited, especially in mission-critical applications [2].

The report also highlights the potential for blockchain technology to enhance the security and transparency of autonomous vehicle data. A recent collaboration between Avalanche and Toyota aims to build a blockchain-based infrastructure for autonomous robotaxis. This partnership focuses on secure data management, transparent transaction processing, and scalable network performance to support real-time fleet operations. Blockchain can create immutable logs of vehicle telemetry, sensor data hashes, and service transactions, improving trust between fleet operators, riders, and regulators while enabling controlled data sharing [3].

In conclusion, the Agentic AI market is poised for significant growth, driven by the increasing demand for autonomous decision-making across industries. However, the lack of explainability and trust in these systems remains a critical challenge that must be addressed to facilitate wider adoption. The report provides valuable insights into the market's prospects, helping investors and financial professionals make informed decisions.

References:
[1] https://timesofindia.indiatimes.com/technology/tech-news/elon-musks-xai-launches-agentic-coding-model-grok-code-fast-1-how-it-is-different-from-other-ai-agents/articleshow/123588868.cms
[2] https://finance.yahoo.com/news/demand-autonomous-decision-making-catalyst-153600560.html
[3] https://en.coinotag.com/avalanche-and-toyota-could-explore-blockchain-for-autonomous-robotaxi-infrastructure/