Human Like Intelligent Systems in Procure to Pay

A panel of finance leaders and AI experts, including Mike Vaishnav, John Silverstein, and Niyati Chhaya, came together to explore a fundamental shift happening in finance: the rise of human-like intelligent systems in procure-to-pay.

Mike Vaishnav brings extensive experience across finance and operations in technology, manufacturing, and services, having led multi-billion dollar enterprises and served as CFO for PE and VC-backed companies. John Silverstein, with over two decades in Fortune 500 firms and startups, is known for his data-driven approach and ability to transform financial processes. Niyati Chhaya, Co-Founder and VP of AI at Hyperbots, combines deep industry experience from Adobe with advanced research in machine learning, holding a PhD in NLP and Computer Vision and contributing to over 40 patents and 50 publications.

The discussion did not center on whether automation is valuable. That debate is over. Instead, it focused on a more important question: why traditional automation is no longer enough and what replaces it.

Across procurement and accounts payable functions, finance teams have spent decades implementing systems designed to streamline workflows. Yet, despite these investments, much of the actual work like interpreting invoices, resolving exceptions, chasing suppliers, and validating data, still relies heavily on human effort.

Human-like intelligent systems aim to change that.

The Limits of Traditional Procure-to-Pay Automation

The panel began with a clear assessment of the current state of procure-to-pay (P2P).

Mike noted that most enterprises today operate with a layered automation stack, ERPs, procurement platforms, OCR tools, and workflow engines. These systems are effective at structuring processes, but they rarely eliminate them.

“They organize work very well,” he explained, “but they don’t actually do the work.”

This distinction is critical. Traditional systems depend on structured inputs and predefined rules. When invoices are formatted correctly, when purchase orders match perfectly, and when data is clean, workflows move smoothly. But real-world finance rarely operates under such ideal conditions.

Exceptions are the norm, not the exception.

John added that this is where most automation strategies break down. “The last mile of finance operations, the messy, unstructured, unpredictable part, is still entirely human-driven.”

This includes:

• Resolving invoice mismatches

• Following up with vendors

• Clarifying missing or inconsistent data

• Managing accruals and edge cases

• Handling non-standard procurement scenarios

These are precisely the areas where cost, delays, and inefficiencies accumulate.

What Makes a System “Human-Like”?

The conversation then shifted to what differentiates human-like intelligent systems from traditional automation.

Niyati defined it succinctly: “A human-like system doesn’t just follow instructions. It understands context, makes decisions, and takes action.”

This shift introduces several critical capabilities:

1. Contextual Understanding

Unlike rules-based systems, human-like AI can interpret unstructured data like emails, invoices, contracts without relying on rigid templates.

2. Reasoning and Decision-Making

Instead of escalating every exception, these systems evaluate scenarios and determine the best course of action.

3. Autonomous Execution

They don’t wait for human triggers. They initiate tasks, follow up, and complete workflows independently.

4. Continuous Learning

They improve over time by learning from past interactions, decisions, and outcomes.

5. Multi-Agent Coordination

Rather than a single system performing isolated tasks, multiple specialized agents collaborate to execute end-to-end processes.

This combination allows intelligent systems to behave less like tools and more like teammates.

From Workflow Automation to Work Elimination

One of the most striking themes of the panel was the distinction between automation and autonomy.

John framed it as a shift from “workflow optimization” to “workflow elimination.”

Traditional systems improve efficiency within existing processes. Human-like systems fundamentally redesign those processes by removing the need for manual intervention altogether.

For example:

• Instead of routing an invoice exception → the system resolves it

• Instead of notifying a supplier → the system communicates directly

• Instead of flagging missing data → the system retrieves or requests it

Mike emphasized the financial impact of this shift. “When you eliminate steps rather than optimize them, the cost structure changes entirely.”

This is where Hyperbots positions its AI Copilots, not as workflow tools, but as execution engines embedded within finance operations.

How Human-Like Systems Transform Procure-to-Pay

The panel explored how this model applies across the core components of P2P.

Supplier Onboarding

Traditional onboarding requires suppliers to input data manually, followed by validation and back-and-forth communication.

Human-like systems:

• Extract and validate data from documents automatically

• Identify inconsistencies

• Communicate directly with suppliers to resolve gaps

• Update systems without human involvement

The result is faster onboarding with significantly less friction.

Purchase Requisitions and Orders

In conventional systems, employees create purchase requests manually, often leading to incomplete or incorrect entries.

With intelligent systems:

• Requests can be initiated through natural language

• The system identifies suppliers, pricing, and categories

• Missing information is automatically clarified

• Purchase orders are created and routed autonomously

Niyati noted that this reduces both cycle time and error rates, while improving user experience.

Invoice Processing

Invoice processing is one of the most resource-intensive areas in P2P.

Traditional approach:

• OCR extracts data

• Rules match invoices to POs

• Exceptions are routed to humans

Human-like systems:

• Read invoices in any format

• Understand context beyond extracted fields

• Resolve mismatches autonomously

• Communicate with vendors for clarification

• Update ERP systems and close transactions

Mike pointed out that this is where the most immediate ROI is realized. “You’re not just speeding up processing. You’re removing the need for manual intervention in most cases.”

Exception Handling

Exception handling is traditionally the biggest bottleneck in finance workflows.

John emphasized that intelligent systems fundamentally change this dynamic.

“Instead of exceptions being routed, they’re resolved.”

This includes:

• Identifying root causes

• Cross-referencing data across systems

• Making decisions based on historical patterns

• Escalating only truly novel cases

The result is a dramatic reduction in manual workload.

Accruals and Month-End Close

Accruals are often managed through manual estimation and reconciliation.

Human-like systems:

• Identify incurred expenses in real time

• Track commitments and usage patterns

• Generate accurate accruals automatically

• Reduce dependency on manual adjustments

This leads to faster and more accurate financial closes.

The Role of Data: Foundation for Intelligence

Despite the advanced capabilities of these systems, the panel emphasized that data quality remains foundational.

John noted that even the most intelligent systems require consistent and reliable data to operate effectively.

However, human-like systems have an advantage: they can actively improve data quality.

Niyati explained that these systems don’t just consume data, they clean, enrich, and standardize it continuously.

This creates a feedback loop:

Better data → better decisions → better outcomes → even better data

Organizations that invest in this foundation see compounding benefits over time.

Building Trust in Autonomous Systems

One of the most critical challenges discussed was trust.

Finance teams are inherently cautious, and for good reason. Errors can have significant financial and compliance implications.

Mike highlighted that trust must be earned through performance, not promises.

The panel outlined several strategies:

Parallel Runs

Deploy AI systems alongside existing processes to compare outcomes.

Transparency

Ensure every action and decision is traceable and explainable.

Controlled Rollouts

Start with low-risk, high-volume processes before scaling.

Continuous Monitoring

Track performance metrics and refine systems over time.

Niyati emphasized that explainability is key. “If users can’t understand what the system is doing, they won’t trust it.”

ROI: Beyond Cost Savings

The financial case for human-like systems extends beyond simple cost reduction.

John outlined three dimensions of ROI:

1. Cost Efficiency

Reduction in manual effort, headcount dependency, and processing costs.

2. Cycle Time Compression

Faster procurement cycles, invoice processing, and financial closes.

3. Strategic Value

Improved decision-making through better data and insights.

Mike added that the most transformative impact comes from the third category.

“When finance teams spend less time on transactions, they can focus on strategy. That’s where real value is created.”

The Organizational Shift

Adopting human-like intelligent systems is not just a technology change. It is an organizational transformation.

Niyati emphasized that success depends on how these systems are positioned internally.

Teams must see them as enablers, not replacements.

John Silverstein noted that roles within finance are evolving:

• From data processors to decision-makers

• From task executors to system supervisors

• From reactive operators to proactive strategists

Organizations that embrace this shift will be better positioned for the future.

Why Hyperbots Is Leading This Shift

The discussion naturally converged on how these concepts translate into real-world systems.

Hyperbots’ approach to AI Copilots reflects the principles discussed throughout the panel:

• Autonomous execution across workflows

• Deep integration across systems

• Continuous learning and adaptation

• Built-in transparency and auditability

Rather than adding another layer of automation, Hyperbots aims to redefine how work gets done in finance.

The Road Ahead

The panel concluded with a clear message: the transition to human-like intelligent systems is already underway.

Organizations that adopt early are gaining measurable advantages in efficiency, accuracy, and agility. Those that delay risk falling behind in ways that compound over time.

Mike summarized it best:

“This isn’t about whether AI will change procure-to-pay. It already is. The question is whether you’re leading that change or reacting to it.”

John added that the competitive gap will not remain static. “The longer you wait, the harder it becomes to catch up.”

Niyati closed on an optimistic note:

“The technology is ready. The use cases are proven. What’s left is execution.”

Final Thoughts

Human-like intelligent systems represent a fundamental shift in how procure-to-pay operates.

They move beyond automation to autonomy.
Beyond workflows to execution.
Beyond efficiency to transformation.

For finance leaders, the opportunity is clear:
not just to improve existing processes, but to redefine them entirely.

And for organizations willing to embrace this shift, the rewards are not incremental, they are exponential.