Agentic AI shifts from hype to scalable enterprise solution amid cautious industry progress

As agentic AI transitions from laboratory curiosity to boardroom priority, industry leaders explore its potential to transform workflows while grappling with challenges around governance, measurement, and real-world deployment, signalling a cautious yet optimistic outlook for its role in the future of enterprise intelligence.

Agentic AI is moving from laboratory curiosity to boardroom agenda, promising to shift enterprise automation from reactive systems to software tha...

t can reason, act and adapt across complex workflows. Proponents say these “intelligent agents” will co‑ordinate tasks, collaborate with humans and continuously learn to close the gap between insight and action , but the path to durable, measurable value remains contested.

Industry momentum is visible. According to Analytics India Magazine, enterprises are embedding agentic systems across customer support, supply chain, marketing and demand planning, while consultancies and vendors are launching frameworks to help scale multi‑agent deployments. The article cites 2025 as a turning point in ecosystem activity and notes partnerships such as Accenture and HCLTech working with Google Cloud to accelerate multi‑agent adoption. HCLTech has separately announced an agentic AI smart manufacturing solution built on Google Cloud’s Cortex Framework and Manufacturing Data Engine, positioning agentic approaches as tools to detect defects and optimise operations in real time.

Market sizing reflects both enthusiasm and uncertainty. Research releases paint a wide range of futures: one report projects the enterprise agentic AI market at USD 71.91 billion by 2033, another places the broader agentic AI market at USD 107.28 billion by 2032, a separate forecast expects USD 93 billion by 2032, and yet other studies estimate figures as high as USD 199.05 billion by 2034 or USD 227 billion for agentic applications in cybersecurity by 2032. These divergent estimates underscore the fast‑evolving nature of the field and the challenge of defining market boundaries , whether counting narrowly defined enterprise agents, cybersecurity use cases, or the wider class of autonomous AI systems.

Where agentic AI sits on the technology adoption curve is equally contested. Analytics India Magazine describes it at the “peak of interest” on the hype cycle: widespread experimentation and pilot programmes, but relatively few large‑scale, production‑grade deployments. That assessment aligns with corporate attitudes reported elsewhere , Fortune 500 companies are “curious but cautious”, attracted by the promise of automation that reduces decision‑cycle time and reclaims human hours, yet wary because governance, reliability and explainability remain unresolved.

Measuring value is a central practical hurdle. The article argues that conventional ROI frameworks are inadequate for systems that learn and reconfigure themselves; instead it proposes a Return on (Agentic) Intelligence, or RO(A)I, encompassing metrics such as proportion of tasks executed autonomously, human hours saved and redeployed, cycle‑time reduction, and continuous improvement in outcomes. Industry observers say disciplined implementation, clear key performance indicators and scalable architectures are prerequisites for translating pilot activity into sustained impact.

Vendors are responding with productised platforms and end‑to‑end approaches. Analytics India Magazine highlights Tredence’s MilkyWay, a multi‑agent workflow and decision intelligence system the company says will co‑ordinate agents across data, analytics and insights workflows, increase analyst productivity fivefold and deliver up to 50% cost savings. Those figures are presented as the company’s claims; independent verification of such multipliers is not cited. Tredence’s stated strategy , a pilot‑to‑production pipeline, upfront success metrics and domain contextualisation via an in‑house innovation arm , echoes a broader industry shift from proof‑of‑concepts to production engineering.

Risk and governance remain front of mind. Analysts caution that agentic systems amplify challenges around explainability, auditability and alignment with regulatory requirements. The cybersecurity sector illustrates both potential and peril: one market forecast values agentic AI in cybersecurity at USD 227 billion by 2032, driven largely by automated threat detection and response, signalling urgent demand for robust controls as autonomous agents take on security‑critical tasks.

For enterprise leaders the calculus is pragmatic. Agentic AI offers the prospect of turning insights into timely, automated decisions at scale , improving metrics from customer satisfaction to on‑shelf availability and return on ad spend , but realisation depends on engineering rigor, measurable KPIs and governance frameworks that constrain risk while allowing agents to learn. Industry data shows significant investment and vendor activity; equally, the wide variance in market projections and the current concentration of pilots indicate that the technology’s transition from hype to durable, last‑mile adoption is still in progress.

If agentic AI is to become an operating system for enterprise intelligence, firms must prioritise measurable outcomes and controls over novelty. According to Analytics India Magazine, that means moving from “can we build it?” to “can we scale it safely, measure it, and sustain value?” , a test now being applied by both technology providers and the enterprises that will ultimately determine which agentic systems survive the peak of interest and deliver lasting business impact.

Source: Noah Wire Services