Banking: Moving data without moving it, and settling transactions without paperwork

94% of core banking modernization projects exceed their timelines because legacy systems are deeply entangled. IBM's response to this is Zero Copy Integration: instead of extracting data from mainframes and cloud systems to process it elsewhere, AI agents operate directly on the data where it already lives. This eliminates a category of workflow complexity that has caused delays and errors for decades. And 57% of banking executives say tokenization will allow AI agents to settle transactions autonomously, removing the manual back-office steps that currently sit between a transaction and its completion. C-suite executives in financial services need to ask whether their current workflow architecture requires humans to move data between systems, and whether that step is actually necessary.

Energy and utilities: AI that does not just alert, it acts

IBM's Grid Edge automation work in energy illustrates something important about what makes agentic automation different from older automation. When an AI agent monitoring IoT sensors detects a potential thermal failure in grid infrastructure, it does not send an alert and wait. It autonomously triggers a work order, checks parts inventory, and suggests power rerouting to prevent a blackout. The human receives a summary of what was done and what was recommended, not a notification that something needs attention. C-suite executives in energy and utilities who are still running operations where an alert goes to a person who then manually initiates each of those steps are running a slower and more error-prone version of a workflow that can now be largely automated.

Automotive and manufacturing: Production scheduling and predictive maintenance without manual intervention

40% of manufacturers are now using AI to automate production scheduling, per IBM's 2026 research. And autonomous predictive maintenance has changed how factory floor operations are managed. Agents detect wear patterns in robotics, pause specific production lines that need attention, and reschedule other lines to maintain total output while maintenance happens. The manual version of this process requires a maintenance engineer to notice a problem, escalate it, coordinate with production planning, and manually reschedule. The automated version happens faster, with less disruption, and without requiring a person to be in the right place at the right time. C-suite executives in manufacturing should be asking which of their current manual coordination steps exist only because the systems involved do not talk to each other.

Telecoms and media: Intent-based network management and hyper-personalized content at scale

In telecommunications, IBM's automation work has moved toward intent-based network management. A human defines the goal, something like prioritize emergency services bandwidth during a major incident, and the AI orchestrates the network configuration in real time without requiring manual technical steps. In media, IBM pilots have shown a tenfold increase in marketer efficiency by using AI to manage thousands of micro-segments simultaneously. A marketing team that previously managed broad campaign segments manually can now run highly specific targeting at a scale that was not operationally feasible before. C-suite executives in media and telco need to distinguish between automation that makes existing workflows faster and automation that enables entirely new operational models.