Golden Pear Funding

Michelle Bonat

AI Platform Modernizer

Michelle Bonat is a technology, product and AI leader focused on turning enterprise AI investments into repeatable business capabilities. Her approach emphasizes reusable technology foundations, responsible deployment and measurable outcomes that make each AI implementation easier to scale.

Moving Beyond AI Projects

Enterprise AI does not have a pilot problem. Most organizations have no shortage of experiments, proofs of concept, copilots and, increasingly, AI agents. Many demonstrate real potential. Some produce measurable business value.

The harder question is whether organizations can repeat those results more efficiently and at greater scale.

When the second, fifth or twentieth AI use case enters production, are teams starting from scratch? Does each team have to determine how AI will securely access enterprise data, connect to existing systems, manage identity and permissions, comply with governance policies, monitor performance and escalate decisions to humans?

If every new AI use case starts from scratch, organizations are not scaling AI. They are scaling projects. The next stage of enterprise AI requires a shift in mindset from individual projects toward a reusable platform that can repeatedly turn AI into measurable business outcomes.

Establishing the Enterprise AI Foundation

Enterprises are complex, with different data, systems, workflows, security requirements and governance needs. A model may perform impressively in isolation, but putting AI into production means connecting it to the reality of the enterprise.

This becomes even more apparent with agentic AI. An agent may need to interact with multiple models, applications, APIs and data sources, maintain context across a workflow, operate within identity and access controls, apply business rules and know when to escalate to a human.

The common capabilities required to put AI into production, including secure integrations, orchestration, identity and access management, governance, observability, evaluation, monitoring and reusable workflow components, should increasingly become standardized and reusable. Standardize what every AI capability needs. Differentiate where the business creates value.

  Every AI deployment should do two things: deliver business value now and make the next deployment easier.  

A modular foundation also provides an alternative to assembling an expanding collection of AI components or relying heavily on a single vendor ecosystem. It allows organizations to create reusable capabilities without sacrificing flexibility.

Turning Business Outcomes into Reusable Capabilities

Platform thinking does not mean creating the platform first. Start with a business problem worth solving and develop the solution in a way that creates reusable capabilities.

At Golden Pear Funding, we have deployed AI in financial operations to address duplicate billing. Bills can arrive through different streams, with invoice, claim and payment information residing across multiple systems. Historically, identifying a potential duplicate was highly manual, with a single review taking approximately 30 minutes.

We developed an agent that performs the analysis before payment, comparing information across systems and identifying potential duplicates. It does not autonomously stop a payment. Potential duplicates are surfaced for human review, keeping responsibility for the consequential decision with a person.

The immediate value is reducing manual effort and allowing people to focus on exceptions requiring judgment. Putting the agent into production also required secure data access, system integrations, permissions, business rules, evaluation, exception handling, monitoring and governance.

Those capabilities should not belong exclusively to one project. They should become reusable components for the next AI capability. That is how an AI platform should emerge: one valuable business outcome at a time.

Establishing Trust in AI Systems

Platform thinking also changes how enterprises establish trust. With traditional machine learning, validation is often centered on model performance. Agentic AI changes the unit of evaluation.

An enterprise AI capability may include multiple models, agents, data sources, APIs, business rules, guardrails and human escalation paths. The question shifts from whether the model gave the right answer to whether the agent took the right actions, followed the right controls and achieved the right business outcome.

That requires evaluation across scenarios and failure modes, observability, appropriate human intervention and continuous monitoring. The appropriate level of autonomy should depend on the business outcome and the consequence of being wrong.

In our duplicate billing example, AI performs the analysis while a person makes the final determination. Higher-risk activities may require additional levels of review. Trust does not require AI to be infallible. It requires systems where unexpected outcomes are visible, contained and recoverable.

AI investments also need portfolio discipline. At Golden Pear Funding, potential use cases are evaluated across impact, feasibility and risk. The objective is not to deploy as much AI as possible, but to apply it where it can create measurable value at an acceptable level of risk.

The real measure of AI maturity is how much easier it becomes to deploy the next use case. If the tenth AI use case is as hard to put into production as the first, ten projects have been created, not a platform.

Deliver the business outcome in front of the organization, but make the next outcome easier to achieve.