Introduction

The rise of generative AI has changed how enterprises think about software development. With tools such as ChatGPT and AI coding assistants, development teams can generate APIs, database models, workflows, automation scripts, and even sophisticated application components faster than ever.

That naturally raises an important question for procurement, IT, and workforce leaders:

Should an organization build its own Vendor Management System (VMS) with AI, or buy an AI-powered enterprise VMS?

At first glance, building can look attractive. An organization can define its requirements, use AI to accelerate development, deploy the application in its preferred cloud environment, and maintain complete control over the technology stack.

However, building a few VMS functions is very different from building an enterprise VMS capable of managing contingent labor, suppliers, compliance, approvals, rates, timesheets, invoicing, services procurement, integrations, reporting, security, and AI-driven workflows.

The difference becomes clearer when we examine what actually goes into an enterprise-grade VMS.

What Can ChatGPT Actually Build for a VMS?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Requisition(BaseModel):
    title: str
    location: str
    skills: list[str]
    bill_rate: float
    currency: str

@app.post("/requisitions")
def create_requisition(req: Requisition):
    return {
        "status": "created",
        "requisition": req.model_dump()
    }

This can create a functional endpoint for submitting a contingent workforce requisition.

AI can also help generate:

For a proof of concept, this is extremely valuable.

The challenge begins when the proof of concept becomes an enterprise application.

A production VMS must answer questions such as:

The code may be easy to generate.

The business logic surrounding the code is the real complexity.

Building a VMS Is More Than Building Features

A VMS is not simply a collection of screens and APIs.

It is a system that connects multiple business processes across the contingent workforce lifecycle.

A typical enterprise VMS may need to support:

  1. Workforce requisitions

  2. Supplier management

  3. Candidate submissions

  4. Candidate evaluation

  5. Worker onboarding

  6. Compliance management

  7. Rate management

  8. Time and attendance

  9. SOW management

  10. Services procurement

  11. Invoice processing

  12. Supplier performance

  13. Workforce analytics

  14. Approvals

  15. Notifications

  16. Enterprise integrations

  17. Security and access controls

  18. Audit trails

Each capability also interacts with the others.

For example, changing a worker's assignment rate may affect approvals, supplier billing, purchase orders, invoices, reporting, and budget calculations.

That interconnected architecture is what makes enterprise VMS development substantially more complicated than creating individual features with AI.

AI Can Generate Approval Logic, But Businesses Need Configurable Governance

Consider a simple rule:

Requisitions above $100 per hour require Procurement approval.

An AI coding assistant could implement that requirement in seconds:

def determine_approval(bill_rate):
    if bill_rate > 100:
        return "PROCUREMENT"
    return "HIRING_MANAGER"

The function works.

But enterprise approval structures rarely remain this simple.

A global organization may have different rules based on:

The organization may also require delegation when an approver is unavailable, escalation when an approval is delayed, and complete audit history for every decision.

That means the real requirement is not merely an approval function.

It is a configurable approval framework.

It needs rule versioning, effective dates, delegation, escalation, exception handling, notifications, testing, permissions, and auditability.

AI can accelerate development of those components.

But the organization still has to design, govern, test, secure, operate, and maintain the system.

Building AI Candidate Matching Into a VMS

Candidate matching is another area where AI can provide significant value.

Suppose a requisition requires:

A candidate may have Python, AWS, Docker, Kubernetes, and nine years of experience.

An AI model can compare the candidate profile with the requisition and produce a structured assessment.

For example:

Match Score: 94

Matching Skills:
Python
AWS
Kubernetes

Relevant Experience:
9 years

Recommendation:
Strong match for the role.

This is considerably more flexible than basic keyword matching.

However, AI candidate matching inside an enterprise VMS requires more than an AI model.

Before candidate information reaches a model, the platform must determine whether the user is authorized to access that information.

The workflow may therefore look like:

Candidate Data
      ↓
Permission Check
      ↓
Data Filtering
      ↓
AI Model
      ↓
Validation
      ↓
Audit Log
      ↓
Recruiter

The AI model is only one component.

The surrounding controls determine how safely and reliably the model operates.

AI Governance Becomes Critical When Workforce Data Is Involved

A VMS can process commercially and operationally important information.

Depending on the implementation, this can include candidate information, worker details, supplier information, rates, contracts, assignments, invoices, compliance records, and workforce analytics.

An AI-enabled VMS therefore needs appropriate controls around:

For example, an AI candidate-matching workflow could enforce authorization before performing an evaluation:

def evaluate_candidate(job, candidate, user):

    if not user.has_permission("VIEW_CANDIDATE_DATA"):
        raise PermissionError("User is not authorized")

    safe_candidate = {
        "skills": candidate["skills"],
        "experience": candidate["experience"]
    }

    result = match_candidate(job, safe_candidate)

    audit_log(
        user=user.id,
        action="AI_CANDIDATE_MATCH",
        candidate_id=candidate["id"]
    )

    return result

This illustrates an important principle:

Enterprise AI is not simply about connecting an application to a language model. It is about controlling what the model can access, what it can recommend, and what the system can do with its output.

AI Agents Increase the Complexity Further

The next stage of AI development is moving beyond recommendations toward AI agents that can execute tasks.

Imagine an AI agent receiving the instruction:

Identify suitable suppliers for this requisition, evaluate their historical performance, prepare a supplier request, and wait for Procurement approval before sending it.

The agent could potentially access tools such as:

def find_suppliers(skill, location):
    return supplier_database.search(
        skill=skill,
        location=location
    )

def check_supplier_performance(supplier_id):
    return supplier_database.performance(
        supplier_id
    )

def create_supplier_request(supplier_id, requisition_id):
    return supplier_portal.send_request(
        supplier_id=supplier_id,
        requisition_id=requisition_id
    )

This creates exciting possibilities for AI-powered workforce management.

But it also introduces a different risk profile.

An AI system that only provides information is different from an AI agent that can call enterprise APIs and perform actions.

Organizations need to establish:

The more autonomy an AI system receives, the more important the underlying governance becomes.

The Integration Challenge in an Enterprise VMS

A VMS rarely operates as an isolated application.

It typically needs to exchange information with systems such as:

A simple integration may appear straightforward.

But enterprise integrations need to account for authentication, authorization, data mapping, transformation, validation, retries, error handling, monitoring, logging, versioning, and testing.

Consider an HRIS sending an organizational update to the VMS.

What happens if:

Writing an API connection may take hours.

Keeping that integration reliable as enterprise systems change is the larger challenge.

Compliance Is Another Major Layer

A VMS may need to ensure that a worker cannot begin an assignment until required checks and documentation are complete.

A simple implementation could check whether required documents exist:

def check_compliance(worker):

    required_documents = [
        "ID",
        "Work Authorization"
    ]

    return all(
        document in worker.documents
        for document in required_documents
    )

But real-world workforce compliance can vary by:

Rules can also change.

Organizations need ways to configure those requirements, monitor expiration dates, manage exceptions, record verification activity, and produce an audit history.

AI can help engineers build these capabilities faster.

It does not remove the organization's responsibility for operating them correctly.

The True Cost of Building a VMS With AI

The initial build calculation can look attractive:

AI + Developers + Cloud Infrastructure = VMS

The enterprise calculation is considerably broader:

AI-assisted development + Architecture + Engineering + Infrastructure + Security + Data + Integrations + Testing + Compliance + Monitoring + Support + Maintenance + AI Governance = Enterprise VMS

There is also a less visible cost: engineering opportunity cost.

Engineering teams have finite capacity.

If developers spend the next 12 to 24 months building requisition workflows, supplier portals, worker onboarding, compliance engines, timesheets, invoicing, reporting, integrations, and AI functionality, those resources cannot simultaneously focus on the company's core differentiating capabilities.

The question therefore becomes:

Where should the organization use its engineering capacity?

Build vs. Buy: When Does Building Make Sense?

Building a VMS can make sense in specific circumstances.

For example, an organization may consider building if:

For most organizations whose primary business is not VMS technology, however, the economics can look very different.

A financial institution, healthcare organization, manufacturer, retailer, technology company, or logistics business may generate its competitive advantage from its products, customers, operations, intellectual property, data, or workforce strategy.

Its competitive advantage is unlikely to come from operating an internally developed supplier management portal.

Why an AI-First VMS Changes the Build vs. Buy Decision

The alternative is to purchase an enterprise VMS that already provides the underlying workforce management infrastructure while incorporating AI into the platform.

This is fundamentally different from buying traditional software and attaching a chatbot to it.

An AI-first VMS can integrate AI into workforce workflows such as:

The important distinction is that AI operates on top of an established enterprise foundation.

Instead of spending engineering resources building every basic workforce workflow, organizations can focus on using AI to improve the decisions and processes that matter most.

AI-First Does Not Mean AI Without Human Oversight

An effective enterprise AI strategy should not be about handing every decision to an autonomous system.

Some workforce decisions require human judgment.

A better model is progressive automation.

For example:

  1. AI identifies relevant candidates.

  2. AI explains why they match.

  3. Recruiters review recommendations.

  4. AI identifies potential compliance issues.

  5. Procurement reviews commercial decisions.

  6. AI automates repetitive administrative work.

  7. Humans retain control over high-impact approvals.

This creates a balance between automation and human oversight.

The goal is not simply to make a VMS more intelligent.

It is to make the entire workforce management process more efficient while maintaining appropriate controls.

The Value of Continuous Platform Innovation

Building a VMS also creates a long-term maintenance commitment.

Technology changes continuously.

AI models evolve. Enterprise security expectations change. Integration standards change. Workforce requirements change. Regulations change. Users expect new experiences.

An internally developed VMS must evolve with all of these changes.

An enterprise VMS provider, on the other hand, can spread the investment in platform development, integrations, security, AI capabilities, and product innovation across its customer base.

This can allow organizations to benefit from ongoing innovation without maintaining the entire technology foundation themselves.

The decision is therefore not simply about today's development cost. It is about the cost and capacity required to keep the platform competitive over time.

The Better Question for CIOs, CTOs and Procurement Leaders

The build-versus-buy conversation often begins with:

“Can our developers build a VMS using AI?”

Today, the answer is increasingly yes.

But that is not necessarily the question that matters most.

A more strategic question is:

“What could our organization build if our engineering team did not have to build and maintain the VMS foundation?”

That question shifts the discussion from software development to business value.

Instead of building another approval engine, teams could develop predictive workforce planning.

Instead of maintaining another timesheet workflow, they could develop intelligent workforce analytics.

Instead of spending months maintaining integrations, they could focus on proprietary automation and customer-facing innovation.

Instead of recreating enterprise workforce infrastructure, they could use an AI-first VMS as the foundation for a broader workforce strategy.

Building With AI vs. Buying an AI-Powered VMS

The distinction can be summarized simply.

The right decision ultimately depends on the organization's strategy, resources, requirements, and appetite for long-term platform ownership.

But AI changes the equation in an interesting way.

AI makes building software easier. It does not necessarily make owning enterprise software easier.

What the VMS Experiment Really Reveals

Starting with a few lines of AI-generated code, it is possible to create a requisition API.

Then approval rules can be added.

Then candidate matching.

Then permissions.

Then audit logs.

Then AI agents.

Then integrations.

Then compliance.

And eventually the project becomes something much larger.

The challenge is no longer proving that AI can generate code.

The challenge is building a platform that an enterprise can trust with its workforce, suppliers, spend, compliance, data, and business processes.

That is the fundamental difference between creating VMS functionality and creating an enterprise VMS.

Conclusion: Build the Differentiator, Buy the Foundation

The rise of AI coding tools is changing software development. Organizations can build prototypes faster, automate engineering tasks, and create sophisticated application components with far less effort than before.

But an enterprise VMS is more than code.

It is an interconnected business platform requiring governance, integrations, security, compliance, reliability, configuration, analytics, support, and continuous innovation.

For organizations whose core business is not developing VMS technology, buying an AI-powered enterprise VMS can allow engineering teams to focus their capacity on areas where they can create genuine competitive differentiation.

The strategic question is therefore not:

“Can we build a VMS with ChatGPT?”

It is:

“Should we spend our engineering capacity building a VMS when an enterprise AI-first platform can provide the foundation?”

AI has made software development faster.

The next opportunity is deciding where that newly available development capacity can create the greatest business value.

For many enterprises, the answer may be to buy the workforce management foundation and use AI to innovate above it.

Summary

Building a VMS with AI can significantly reduce the effort required to create APIs, workflows, integrations, and other software components. However, an enterprise VMS involves much more than generated code. It requires security, compliance, governance, integrations, auditability, reliability, maintenance, and continuous platform development. AI can make development faster, but it does not eliminate the responsibility of designing and operating an enterprise-grade system. Organizations should therefore evaluate build versus buy based not only on development cost, but also on long-term ownership, engineering capacity, business requirements, and where their teams can create the most value.