Fig. 1 Distributed orchestration across systems: Autonomous agents coordinate signals and actions across enterprise systems, while human stewards such as Riya step in to shape judgment, communication, and customer experience at critical moments.
The disruption is not simply about running old workflows faster. It is rooted in the ability to coordinate decisions and orchestrate actions at a scale and speed that humans alone could never sustain.
- Unified data platforms give agents a single, coherent view of business and production (spanning everything from supply chain operations and manufacturing to digital service delivery), letting them read, reason, and act across what used to be rigid boundaries.
- People and agents share the same conversational layer (messaging apps, email, and internal social platforms) to surface work-in-progress and hidden dependencies that once stayed trapped in silos. Events in the conversations can trigger agents into action.
- Work assembles into fluid squads or swarms instead of fixed departments, with teams, resources, and priorities constantly reconfigured around live business environment signals rather than static org charts.
- AI agents roam across functions—for example, linking Procurement’s supplier signals to Operations’ capacity decisions and tying Design’s prototypes to Quality’s live defect data—so the enterprise behaves like one coordinated organism instead of a collection of fiefdoms.
The fundamental shift involves moving beyond rigid silos toward coordinated execution across the enterprise.
Customers never cared how a company’s structure was drawn. They experienced the result as marketing that did not match service, sales oblivious to past friction, loyalty programs misaligned with customer lifecycle signals, and billing systems blind to failures, which are symptoms of an architecture built for internal order rather than coherent relationships.
What is changing is the operating model: from siloed, hierarchical departments to dynamic, AI-augmented networks of squads (swarms) that self-adjust to demand and continuously learn from unified data and messaging platforms. This shift is likely to persist because it compounds advantages in speed, adaptability, and customer alignment—traits that replace static structures inadequate for today’s requirements.
An early hint of this “smarter orchestration” is visible in pioneering models. When a driver signs up for Uber, no single department “owns” onboarding. Algorithms orchestrate the capabilities Uber needs to operationalize the new hire. License checks, payout setup, app provisioning, and other related tasks are managed by the equivalent of “agents.” Humans step in for exceptions.
This model of agent-led execution and human governance is scaling across industries, from supply chain (leading to reductions of 32% in stockouts, reduction of excess inventory by 20% and improved seasonal stock alignment) to finance (in the UK, between UK banks, international banks, non-bank lending, etc., 2% of use cases have fully autonomous decision-making) [1,2].
Rather than running end-to-end processes, domain squads become stewards of values, risk, and guardrails.
The data shows that the case for Agentic AI is becoming stronger. This will lead to the dissolution of departments (the once universal containers for people, workflows/processes, and systems) into sparse squads.
Rather than executing end-to-end workflows, domain-specific squads spread across people operations, enterprise finance systems, strategic sourcing, and adjacent domains will become authorities in interpreting and managing enterprise values and goals. They will become the stewards of governance, fairness standards, risk appetites, regulations, and strategic guardrails within their domains. Agents will draw on the squad’s expertise to set boundaries but execute work across a capability mesh regardless of traditional lines.
For tomorrow’s enterprises, the essence is not just “who does the work,” but “who defines the work.”
Departments, in their traditional form, will fade and reemerge at a more fundamental level. Their role will shift from executing workflows to designing the technical, relational, and cognitive conditions for self-organizing work; they will evolve into guardians of expertise and institutional wisdom. Organizational architects will shape fairness, culture, and governance; enterprise custodians will balance risk and integrity; and digital trust engineers will craft secure, interoperable systems that ensure reliability by design. Governance anchors will help enterprise performance scale with wisdom, not mere task efficiency.
This shift dismantles the enterprise’s oldest bottleneck: silos. Once execution decouples from departments, companies can reconfigure around priorities in real time, becoming programmable systems that adapt as quickly as markets change. A “mesh of capabilities” replaces rigid structures, enabling enterprises to orchestrate modular functions such as payroll or compliance with Agentic AI. Freed from hierarchy, these capabilities dynamically combine and evolve within a responsive network where agents fluidly execute and recombine services under human oversight, to create a living and adaptive enterprise.
Consider the example of a hypothetical brand named Sassy Styles , a direct-to-consumer apparel brand known for its limited-run drops. An Agentic agent monitoring real-time inventory detects that a popular jacket in Maya’s size has gone out of stock minutes after she added it to her cart. It instantly secures a comparable piece from an upcoming micro-collection, price-matching it and upgrading shipping to express.
The system then routes the situation to Riya, a customer experience lead, along with context: Maya’s purchase history, style preferences, and recent support interactions. Riya sends a personalized note explaining the substitution, why the new item was selected, and invites Maya to share photos and feedback after it arrives. Maya not only loves the fit but also posts a mini review and styling tips, which Riya (after asking permission) turns into a featured “drop diary” on the Sassy Styles social handle.
The agent orchestrated inventory, data, and fulfillment in real time; Riya turned a potential disappointment into a moment of recognition, advocacy, and story for the brand.
In this emerging operating model, execution is increasingly delegated to autonomous agents, while human expertise migrates upstream to the design of norms, guardrails, and exception regimes.
In Part 4, we examine how this execution shift triggers a deeper change: processes themselves are reimagined, and autonomy evolves through distinct stages.
References
1This example is hypothetical and is included solely to clarify the concepts discussed.
[1] SupplyChains Magazine. The Impact of Agentic AI on Supply Chain Management. SupplyChains Magazine, March 27, 2026. Accessed March 31, 2026. https://supplychains.com/the-impact-of-agentic-ai-on-supply-chain-management/
[2] Bank of England and Financial Conduct Authority (FCA). Artificial intelligence in UK financial services – 2024. Report. Published November 21, 2024. Accessed March 31, 2026. https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
