Much has been made of agentic AI’s potential to augment and extend the human workforce \u2014 and it’s notable that “agents” can describe both humans doing specific jobs and the artificial intelligence program crafted to do specific tasks. As more enterprises incorporate agentic AI into collaborative task flows, a big question persists: Who is responsible for the decisions an AI agent makes \u2014 or the outcome of the output the AI agent produces?<\/p>\n
In the articles below, reporters Terri Coles and Nathan Eddy talked to technology decision-makers about<\/a> the best practices humans can implement around AI-augmented collaboration. The collection highlights how effective AI management begins with effective human management \u2014 and outlines how to set up both human workers and AI agents for success<\/p>\n Terri Coles | July 16, 2026<\/a><\/p>\n Many enterprises perceive that the risk of AI\u2019s integration into business environments comes from the technology making occasional mistakes. However, the long-term risk comes when those enterprises lose the ability to understand why AI-assisted decisions happened, whether or not those decisions were correct.Read the whole article here<\/p>\n Terri Coles | May 1, 2026<\/p>\n Traditional ownership models defined by author, manager, and system owner roles no longer map cleanly in many work environments. If this shift isn\u2019t addressed, the results can include diffuse authorship, orphaned outputs, and fragmented accountability.Read the whole article here<\/p>\n Nathan Eddy | July 20, 2026<\/p>\n The way enterprises divide responsibility for AI strategy, governance and execution will influence whether AI becomes a coordinated business capability or another layer of operational complexity.Read the whole article here<\/p>\n Nathan Eddy | June 29, 202<\/p>\n AI agents are beginning to execute work on their own, with the ability to approve refunds, grant policy exceptions, update customer records and complete transactions without human approval. That promises faster service and greater efficiency, but it also forces enterprises to answer this question: When an AI agent makes a business decision, who owns the outcome?Read the whole article here<\/p>\n Nathan Eddy | June 9, 2026<\/p>\n The rise of AI agents is creating new pressure on the traditional relationship between CRM systems and contact center platforms because both increasingly want to become the primary<\/p>\n Nathan Eddy | May 22 2026<\/p>\n The growing use of AI \u201cclones\u201d designed to mimic executive communication styles and decision patterns could reinforce inconsistent judgment, uneven access to information and gaps in organizational context while creating a misleading sense of alignment across teams.Read the whole article here<\/p>\n \n En rapport:<\/strong> \n Rejoindre Justfine Infotech<\/strong> et d\u00e9velopper de v\u00e9ritables comp\u00e9tences num\u00e9riques en IA, automatisation, d\u00e9veloppement web, marketing digital, bureautique, e-commerce, travail ind\u00e9pendant et cybers\u00e9curit\u00e9<\/a>.<\/p>\n Programmes disponibles :<\/strong> WhatsApp :<\/strong>AI at work is creating decision debt. Can your organization still explain its own decisions?<\/h2>\n
Gen AI is creating work. Who owns the results?<\/h2>\n
The orchestration question: Who actually owns AI in the enterprise?<\/h2>\n
Agentic AI’s next challenge: tackling accountability<\/h2>\n
Who owns the customer truth in an AI-driven contact center?<\/h2>\n
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