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Agentic AI has moved decisively beyond experimentation in shared services and GBS. Organizations are no longer asking whether autonomous agents matter, they are grappling with how fast they must move, where control must sit, and what foundations are required to scale safely.

What the data now makes clear is this: value does not break down at the use-case level. It breaks down at the enterprise level, where strategy, metrics, governance, data, orchestration, and talent fail to move in sync.

This Visual Analytics Workbook surfaces the non-negotiables emerging from early adoption and highlights where leaders must intervene now to avoid stalled pilots, inflated expectations, and unrealized ROI with exclusive data from SSON Research & Analytics.

AI agents (agentic AI) are taking automation beyond rigid rules - toward systems that can learn, decide, and act.

View the article for clear answers to the questions leaders are asking:

Q: What is an AI agent (agentic AI)?

A: A system that can make decisions and take actions with minimal human intervention - more adaptive than traditional automation.

Q: How is this different from RPA?

A: RPA follows pre-defined rules; AI agents can handle unstructured inputs and adjust their approach based on context.

Q: Where are AI agents creating impact today?

A: Across functions like operations, finance/risk, and customer service - helping teams automate work at scale and improve outcomes.

Q: What should we watch out for?

A: Governance and risk - bias, regulation, security, and workforce adaptation need to be built into adoption plans.

It also features an interview with Kartick Kalaimani (Dentsu), sharing a practical view on how “digital workers” can take on high-volume tasks so teams can focus on higher-value work.

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