How To Evaluate AI Vendors And AI Capabilities

Key Takeaways

AI vendor selection criteria provide a framework for comparing AI platforms, such as large language model providers, generative AI systems, and machine learning frameworks.

AI vendor selection criteria emphasize transparency of training data, governance controls, and integration ecosystems.

Evaluating AI in ERP involves examining configurability and compliance to ensure AI features align with operational complexity and regulatory requirements.

The distinction between AI-native vs AI-enabled ERP systems is whether artificial intelligence is built into the architecture or layered onto existing software.

Executives evaluating AI as part of digital transformation efforts face a difficult question: Which AI vendors and capabilities will translate into real business value?

While executives do not need to become AI experts to make informed decisions, they do need to refine how they evaluate AI vendor and ERP vendor claims.

Our AI assessment consultants always tell clients that the goal is not to chase innovation for its own sake. The goal is to select vendors whose AI capabilities align with your specific business objectives.

This post outlines practical AI vendor selection criteria that help leadership teams distinguish between true enterprise enablement and marketing-driven noise. It also introduces key concepts like AI-native vs. AI-enabled solutions and explains how to evaluate AI in ERP ecosystems.

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What We Mean by “AI Vendor”

“AI vendor” refers to a company that develops core AI technologies or platforms, like OpenAI and Anthropic. These are providers of large language models, machine learning frameworks, and foundation models. Their tools may be embedded into enterprise systems, licensed as APIs, or delivered as stand-alone platforms.

In contrast, when discussing AI in ERP, we are talking about how enterprise software vendors integrate or embed those AI capabilities into their platforms. In other words, SAP, Oracle, and other Tier I vendors are not AI vendors in the pure sense. Instead, they are enterprise software providers incorporating AI features developed in-house or sourced from broader AI ecosystems.

This distinction matters. Evaluating an AI vendor is different from evaluating how an ERP provider deploys AI. One focuses on foundational AI technology, while the other focuses on applied use cases in enterprise workflows.

AI Vendor Selection Criteria

When evaluating AI vendors—those that develop foundation models, generative AI platforms, and machine learning systems—executives should take a structured, risk-aware approach. These vendors provide the engines that power downstream applications, so the stakes are high.

Based on Panorama’s independent advisory experience, here are five criteria that leadership teams should apply:

1. Clarity of Model Use Cases

A credible AI vendor does more than promote cutting-edge research. They connect their technology to enterprise-relevant use cases with measurable outcomes. Executives should expect vendors to explain:

Which business functions their models are designed to support (e.g., customer service automation, content generation, anomaly detection).

How performance is benchmarked against industry-standard datasets.

Where their models are already deployed successfully in enterprise settings.

The strongest vendors go beyond “AI can do anything” claims. They articulate specific scenarios where their models deliver a competitive edge, supported by case studies or pilots.

2. Transparency of Training Data and Dependencies

Foundation models are only as trustworthy as the data that shaped them. Executives should scrutinize:

The vendor’s disclosure about training data sources.

The level of curation and filtering applied to minimize harmful content.

The ability to use proprietary enterprise data securely without commingling it with public datasets.

Vendors should provide clear guardrails around privacy, compliance, and intellectual property. If transparency is absent, the vendor relationship carries long-term legal and reputational risk.

3. Governance and Model Control

For enterprise adoption, strong governance is non-negotiable. AI vendors should provide:

Tools to monitor accuracy, detect bias, and assess explainability.

Options for fine-tuning and version control so models evolve responsibly.

Audit trails that demonstrate compliance with GDPR, CCPA, and industry-specific regulations.

Executives should also ask how the vendor addresses model drift—the decline in accuracy as real-world data changes—and whether retraining happens in controlled, auditable environments. Real-time learning may work in low-stakes applications, but high-stakes domains require periodic retraining with oversight.

4. Security, Reliability, and Service Model

AI vendors are not just research labs; they are technology partners. Executives should evaluate:

Uptime commitments and service-level agreements (SLAs).

Data security measures, including encryption, isolation, and breach response protocols.

The vendor’s roadmap for scaling capacity, latency management, and regional compliance.

In effect, AI vendors must be held to the same reliability standards as ERP or cloud infrastructure providers. Without this discipline, AI initiatives risk stalling in pilot mode.

5. Alignment with Enterprise Strategy

The best AI vendors do not just deliver models—they provide ecosystems. Executives should assess:

The vendor’s integration capabilities with ERP, CRM, and productivity platforms.

The availability of APIs, SDKs, and developer communities to support adoption.

Whether the vendor’s roadmap aligns with the organization’s priorities for efficiency, customer experience, and innovation.

Selecting the wrong AI vendor may lock the enterprise into a platform with limited adaptability. Selecting the right vendor creates a foundation for scaling AI responsibly across multiple business functions.

How to Evaluate AI in ERP Systems

The ERP category deserves specific scrutiny when evaluating AI claims. Many ERP vendors now include AI features in their cloud-based platforms—but those features are sometimes immature, isolated, or poorly aligned with business priorities.

Executives evaluating AI in ERP systems should consider:

1. Configurability

Can your internal team or system integrator tailor the AI to reflect unique workflows, exception handling, and business rules? Or are outputs limited to predefined use cases?

For example, a distribution company might need AI-driven order allocation during seasonal demand spikes, where exceptions and real-time decisions are common. If the system isn’t configurable, the AI may fail to adapt to operational complexity and become a surface-level feature rather than a strategic capability.

2. Compliance

Does the vendor provide embedded governance tools that ensure regulatory alignment and auditability as processes scale?

A clear illustration comes from a public sector client managing more than $110 billion in assets. The client trusted our enterprise software consultants to evaluate solutions while prioritizing compliance with GASB and state requirements. By selecting a platform that provided standard functionality and governance controls, the client enabled workflow automation without compromising compliance.

3. AI-native vs. AI-enabled

While AI-native enterprise systems are built with AI woven into the architecture, AI-enabled enterprise systems bolt on AI features to extend existing capabilities.

In most cases, the right answer is not either-or. Executives should focus on where AI capabilities drive differentiation in their industry and whether those capabilities are proven, scalable, and supported by strong governance tools.

Example of the AI-Native vs. AI-Enabled Distinction

Oracle today sits firmly in the AI-enabled category, but it is moving steadily toward AI-native characteristics in certain areas. Here’s a breakdown:

AI-enabled today: Oracle ERP Cloud and Fusion Applications embed AI in targeted functions—like predictive cash forecasting, intelligent account reconciliation, supplier risk detection, and expense anomaly detection. These features are layered into existing modules rather than representing a ground-up AI-first architecture.
Evolving toward AI-native: Oracle is investing heavily in what it calls “embedded AI” and “autonomous” capabilities. For example, its Autonomous Database uses machine learning at the core to optimize performance, security, and patching. This points to a more AI-native design philosophy in the infrastructure layer, even if the application suite is still largely AI-enabled.

A Final Word on Independence

Software vendors excel at selling a vision. However, most organizations need help filtering that vision through operational reality.

This is why Panorama Consulting emphasizes independent ERP consulting. We do not accept vendor referral fees, so our only interest is ensuring the selected platform fits your people, processes, and data.

When it comes to AI, this independence matters even more. Vendors are under pressure to claim AI capabilities, whether or not they are production-ready. Without a neutral third party to guide the evaluation, organizations risk mistaking hype for strategy.