For decades, business processes have been designed around one core assumption: scarcity. Scarcity of expert time, attention, and energy.

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As Vinod Khosla once explained, “we rationed the time of our most valuable talent, and built workflows, software, and approvals around that constraint.” Project plans, escalation paths, and even user interfaces were optimized to stretch limited human capacity as far as possible.

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AI agents are rewriting this story. Expertise is no longer scarce — it is available 24/7, on-demand, and in parallel at scale. Adding “another unit” of execution carries near-zero incremental cost.

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This shift has profound implications. If we simply layer AI onto legacy workflows, we’ll only get faster versions of what we already know. But if we redesign from scratch, removing the assumption of scarcity, our processes will transform completely.

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• Finance closes that run continuously, not once a quarter.
• Customer support that never queues.
• Compliance reviews that scale instantly without bottlenecks.

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Scarcity was the constraint that shaped decades of organizational design. With AI, that constraint dissolves — and so must the workflows it produced.

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The future of business isn’t about speeding up old processes; it’s about rearchitecting them for an era of abundance.

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What are your thoughts on how business processes should be rewired for an AI-first world?

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Frequently Asked Questions (FAQs)

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‍Q1. How does AI change the concept of scarcity in business processes?
Traditionally, workflows were built around bottlenecks of expert availability, approvals, or human energy. AI agents shift this by:

• Providing always-on availability (24/7 execution).
• Scaling expertise in parallel without proportional cost increases.
• Automating repetitive decision-making and task execution.
  This eliminates the assumption that talent or time is the limiting factor.

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Q2. What are examples of processes that could be redesigned without scarcity constraints?

• Finance: Continuous real-time closing instead of monthly/quarterly cycles.
• Customer Service: Zero queue wait times with AI agents resolving 90% of inquiries.
• Compliance & Risk: Ongoing audits and monitoring, instead of periodic reviews.
• Supply Chain: AI-driven demand forecasting and procurement, updated dynamically every few minutes.

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Q3. What risks or challenges come with removing scarcity assumptions?

• Over-automation: If controls are removed entirely, AI decisions may bypass essential oversight.
• Data fragmentation: AI agents require consistent, high-quality data across systems.
• Human role shifts: Without bottlenecks, employees must adapt to supervising, governing, and innovating rather than executing.
• Regulatory concerns: Continuous processes may need new standards for compliance and auditing.

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Q4. What industries stand to benefit the most from this shift?

• Finance & Banking: Automated reconciliation, fraud detection, continuous reporting.
• Healthcare: Scalable patient triage, monitoring, diagnostics.
• Retail & E-commerce: Real-time personalization and dynamic pricing.
• Manufacturing & Logistics: Predictive maintenance and AI-driven supply chains.

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Q5. How should organizations redesign processes for a “post-scarcity” AI environment?

• Start by mapping current bottlenecks (e.g., approvals, availability of SMEs).
• Redesign processes assuming those bottlenecks disappear.
• Introduce human-in-the-loop for high-stakes interventions, not routine tasks.
• Treat AI as a core team member in the workflow, not an add-on.
• Build governance frameworks to maintain transparency, accountability, and compliance.

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