Articles

Scaling agentic AI depends on closing the data gaps that show up when agents move from demos to day-to-day operations.

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

Q: Why do agents work in pilots, then fail in production?

A: Production adds messy inputs, inconsistent context, and higher stakes. Missing signals and unclear decision rules lead to wrong actions.

Q: What does “agent-ready data” mean?

A: Data that includes business context, freshness requirements, ownership, and clear boundaries for what an agent can and cannot do.

Q: What’s the most common hidden gap?

A: Undocumented process logic. Escalations, exceptions, and “how we actually decide” often live in people, not systems.

Q: What needs to be set before agents can execute actions?

A: Scoped permissions, approval gates for high-risk steps, audit trails, and rollback paths.

It also features insights from Vipin Kataria, Senior Lead Architect (Data/ML) at Picarro (and Agentic & Applied AI for the Enterprise Speaker), on what it takes to make enterprise data usable for agents at scale.

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Scaling agentic AI depends on process discipline, clear ownership, and governance that is in place before automation expands.

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

Q: Why do agentic AI pilots succeed, but scaling slows down?

A: Pilots run in controlled settings. At scale, process variation, data gaps, unclear accountability, and control requirements start to limit results.

Q: What does “standardize first” mean in practice?

A: Align teams on the same inputs, steps, decision rules, and exception handling so automation runs consistently across locations and systems.

Q: What needs to be set before agents can take action across workflows?

A: Ownership, access boundaries, escalation paths, and auditability, plus controls that match the risk of the process.

Q: What should we measure beyond hours saved?

A: Exception rates, rework, handoffs, intervention frequency, end to end cycle time, and control performance.

It also features insights from Jesús Villalobos Arce, Process Enablement GBS Americas (SSON Speaker), on how standardization and governance create the conditions for agentic AI to scale.

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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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