As AI agents are integrated into an organization, enterprises will need to move from a set of linear processes and steps to reimagining work in a very different way, Shah explains. That’s because the value in AI agents is not as another layer in the existing technology stack, but as a connective tissue, he explains, moving between or across layers to coordinate higher-level work or retrieve and interpret data from many different applications. He says AI agents can create “a real competitive differentiator for an enterprise” by making decisions based on this ability to contextualize. “This is where the next battlefield will be.”
To build this connective tissue, leaders need to optimize their technology stack to drive high-quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that undertake this architectural shift actually become more adaptive,” says Chatterjee. “When a new business need arises, you don’t have to wait six months for a software vendor to build a feature. You configure an AI worker using natural language and connect it to the systems it needs. The time from business to production workflow is reduced from months to days.”
Workforce Redesigned
As AI agents are deployed for more use cases, enterprise leaders should consider what this means for mobility in their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical models 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 move through an organization based on their ability to optimize output from the teams below them. But with AI agents that can execute, coordinate, and optimize tasks – often without managerial coordination – the lines of that established hierarchy blur.
In a workforce that blends AI agents and human employees, managers will be freed from many performance-based tasks but will take on new responsibilities associated with managing hybrid teams. Shah says managers will need to be able to manage “issues related to trust, explainability, psychological safety, and even situation dynamics” to deal with the new stresses arising in a hybrid workforce.
The impact of agentic AI on existing workforce structures extends far beyond the management level. McKinsey estimates that by 2030, Three-quarters of current jobs This will require redesign, upskilling or redeployment and organizations will need to act quickly to modify recruitment, retention and remuneration.
From output to result
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, playing collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output – such as calls handled or reports filed – no longer make sense.
