<\/p>\n
\u00a0As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That\u2019s because the value in AI agents isn\u2019t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create \u201ca true competitive differentiation for an enterprise\u201d by making decisions based on this capacity to contextualize, he says. \u201cThat is where the next battleground will be.\u201d<\/p>\n
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. \u201cOrganizations that make this architectural shift become genuinely more adaptive,\u201d says Chatterjee. \u201cWhen a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.\u201d<\/p>\n
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.<\/p>\n
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks\u2014often without managerial coordination\u2014the lines of that established hierarchy become blurred.<\/p>\n
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers \u201cwill need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics\u201d to navigate new tensions that could arise in a hybrid workforce, says Shah.<\/p>\n
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.\u00a0<\/p>\n
Success metrics are the third and final pillar of ABT.\u00a0<\/p>\n
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output\u2014such as calls handled or reports filed\u2014no longer make sense.\u00a0<\/p>\n<\/p><\/div>\n
#Rethinking #organizational #design #age #agentic<\/p>\n","protected":false},"excerpt":{"rendered":"
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