A guide for legal, procurement, and compliance teams navigating AI vendor selection
As more organizations adopt AI to drive productivity and improve decision-making, the challenge becomes how to responsibly procure and evaluate AI tools. Selecting the right AI solution requires a strong understanding of AI capabilities and knowing the right questions to ask: questions about data processing, privacy, risk, transparency, and how AI should fit within your existing workflows. This article offers five key questions to ask before signing up for a new AI solution.
AI Procurement Requires a Different Approach
AI tools interpret, generate, and even act on information in ways similar to humans, but based on “thinking” that is sometimes difficult to trace. While AI can work faster than a person, its use introduces new risks, particularly with respect to data handling and the delivery of consistently accurate, explainable results.
Yet most organizations evaluate AI solutions through the same lens as traditional software, relying on standard security reviews and compliance checks. That’s not enough. Procurement practices must evolve to address the new capabilities and challenges AI introduces.
Know What You’re Buying: Not All AI is the Same
Before diving into these top five questions, it’s essential to recognize that not all AI is created equal. There are a few different ways AI capabilities are typically offered in the market. Knowing the “flavor” of the AI solution you’re considering will help you tailor your selection criteria and assess potential risks more effectively.
Generally, AI offerings for legal and business functions fall into three categories:
1. AI-Native
These vendors build solutions around AI and have end-to-end control over the AI. True AI-native vendors develop and own proprietary AI models, training them specifically for tasks within a specific domain. For example, a legal AI solution may be designed from the ground up to analyze contracts for specific clauses, provide billing intelligence, or automate legal workflows.
When evaluating AI-native vendors, you should expect deep domain expertise and a high level of confidence in their approach to data privacy, security, and ethical deployment. Their answers to questions about training data, explainability, and risk should reflect not only technical capability but also a robust policy framework.
2. AI-Enhanced
These solutions embed or integrate third-party AI models and services. Often referred to as “wrapper” solutions, they wrap their brand, workflows, and user interface around other providers’ capabilities. For example, a legal tech solution may integrate a large language model (LLM) from providers like OpenAI, Google, or Anthropic to power features such as document summarization or first-draft generation. Vendors may fine-tune these models or orchestrate multiple AI services to deliver specific functionality.
During due diligence, it’s critical to understand the underlying AI system being used. Questions around data handling, security, and subprocessors become more complex. You’ll need visibility not only into the vendor’s practices, but also into how data is governed and secured when interacting with third-party AI components. For instance, the issue of AI explainability would involve understanding how the vendor uses external AI models to produce reliable outputs.
3. AI-Enabled
In this model, you’re not buying an AI solution, but rather a service where AI is most likely being used to enable delivery. This includes offerings from Legal Process Outsourcing (LPO) providers, Legal Managed Services (LMS) firms, or even specialized AI consulting firms. These service providers leverage AI internally to perform tasks more efficiently, for example, performing document reviews, contract abstraction, e-discovery, or compliance monitoring. While you benefit from the AI’s capabilities, your primary contractual relationship is with the service provider and their operational teams.
Here, due diligence shifts from evaluating technology to assessing the service delivery model. Ask how data is handled and processed by both AI and humans. Look for evidence of strong AI governance, with clear policies and visible practices, including where you might expect to see humans-in-the-loop. Be sure to address contractual liability for AI-driven errors, overall data security, and expectations for ethical use of AI.
In the next section, we explore the top five questions you should ask when procuring AI. Knowing what AI category you are buying—from AI-Native to AI-Enabled—will help you refine these questions, assess the level of AI control and responsibility, and clarify where your organization remains accountable for security and ethics.
Top 5 Questions You Should Ask When Procuring AI
1. What Happens to Our Data—During and After Processing?
Understanding how a vendor handles your data is foundational. AI solutions often process highly sensitive information, including legal, financial, or contractual data. It’s essential to establish clarity around data handling, storage, access, and deletion.
Ask:
- Where is the data processed and stored?
- Is the data used to train models (and if so, how to opt out)?
- What are the retention and deletion protocols after processing?
- What rights do you have to export or audit your data?
Even if AI use appears limited, your information matters. It may include commercial or personal data. How long it persists, or how it may be used, could overstep boundaries and conflict with your business needs. You will likely also have your own data processing obligations to clients, partners, and regulators. Without asking, you might risk compromising these obligations or, worse, lose control over core business data.
2. How Transparent and Explainable Are AI Decisions?
If AI arrives at a conclusion, flags a clause, scores a vendor’s compliance, or summarizes a document, users should be able to understand why. Ultimately, people are accountable for these results, so teams need defensibility.
Ask:
- Can you trace the reasoning behind AI output?
- Are there processes that place humans-in-the-loop, and can you intervene to supersede or test AI results?
- Are all AI actions and decisions logged for auditing purposes?
Explainability builds trust and enables informed decision-making. Acting on AI outputs without understanding them introduces risk and can undermine consistency and defensibility. Clear explanations and defined pathways for human review help maintain continuity and reduce errors.
3. How Can We Be Confident in AI’s Ongoing Performance & Integrity?
AI models evolve, and their effectiveness can degrade over time due to factors like data drift or misuse. Once you rely on AI, you need confidence that it will remain accurate, secure, and reliable over the long term. This requires asking the vendor how they plan to maintain the AI’s integrity against both internal decay and external interference.
Ask:
- What processes prevent performance degradation or “model drift”?
- How do you ensure the integrity and quality of AI training and updates?
- What safeguards exist against attacks like prompt injections? (“Prompt injections” are when malicious users craft inputs that trick an AI into overriding its safety guidelines to perform some unintended action that could impose indirect harm on your organization.)
- Are independent audits of AI security and performance conducted regularly?
- What is your incident response plan for AI-specific security breaches or failures?
Your vendor should provide confidence that they can sustain an AI solution that remains dependable long-term. You need assurance the underlying models and systems remain accurate, secure, and resilient. Without robust measures against degradation, drift, or malicious interference, the utility of the AI you procure today may diminish in the future, exposing your organization to less reliable insights, potential security breaches, and liabilities.
4. How Does AI Fit into Our Existing Workflows?
Powerful AI only delivers value if it’s adopted. Solutions that require users to leave systems of record often struggle to gain traction. The best AI solutions will integrate with your current workflows or appear where your team already works.
Ask:
- Can AI be triggered within our current workflows?
- Does it integrate with core systems, such as ELM platforms?
- Can we upload our own playbooks, data, or criteria to contextualize the AI results?
If an AI solution disrupts your established processes or lives in isolation, it will fail to deliver on its promised value. Look for solutions that extend and strengthen how your teams already operate.
5. How Can This AI Empower Our Organization to Enforce Standards and Scale Best Practices?
A key attribute of AI, especially within the legal domain, is its ability to translate institutional knowledge into consistent, scalable action. This evolution happens gradually by:
- Replacing manual and repeatable workflows and checklists
- Embedding specific policies into processes
- Adapting to preferred positions and risk tolerances
The more an AI solution can bring these considerations directly into the AI framework, the more it can transform tacit expertise into an always-on, intelligent asset for your organization.
Ask:
- Can AI automatically apply our rules and tolerances?
- Can it proactively flag deviations from standards?
- Does it help capture and retain institutional knowledge?
- Can it identify best practices or surface prior “good” decisions?
AI that enforces standards and captures knowledge becomes a long-term strategic asset for governance, risk management, and organizational intelligence.
Bonus Considerations: Three More Questions Worth Asking
While the five core questions should anchor your AI procurement strategy, here are three additional considerations to help round out due diligence:
1. Do you disclose and document your third-party subprocessors?
AI solutions often rely on external infrastructure, such as cloud providers or foundation model APIs. It’s crucial to know who these third parties are and what access they have to your data to ensure your compliance chain is unbroken.
2. What certifications or attestations does your platform hold?
While not exclusively specific to AI, certifications like SOC 2 Type II, ISO 27001, or GDPR/CCPA compliance documentation provide a strong baseline signal of a vendor’s maturity, security commitment, and operational rigor.
3. What happens if the AI tool gets it wrong?
Ask about error handling, escalation processes, and explicit contract clauses related to liability or indemnity. No AI tool is infallible; what truly matters is how potential failures are managed, disclosed, and remediated.
These questions help you build a robust picture of risk posture, especially for solutions operating in regulated or mission-critical environments.
From Checklists to Confidence: Procuring AI, With AI
Many organizations still rely on static PDFs, unwieldy spreadsheets, or ad hoc reviews when assessing new AI tools. This traditional approach leaves room for inconsistency and a lack of defensibility. Leading legal and procurement teams are shifting toward systems that codify requirements and automate key parts of vendor due diligence.
Imagine being able to upload a vendor’s privacy policy, data processing agreement, or terms and conditions, and have them reviewed instantly against your internal, AI-powered checklist.
Without the aid of AI in this process, the sheer volume and complexity of legal and compliance documentation for AI vendors would overwhelm even the most diligent teams, leading to missed risks, slower procurement cycles, and less confident decision-making.
As organizations deepen their use of AI across contracts, spend management, compliance, and beyond, they also need smarter ways to evaluate and onboard AI vendors themselves.
By embedding the five questions introduced in this article, you can dramatically reduce procurement friction, mitigate risks proactively, and accelerate your organization’s ability to adopt AI with confidence. Leverage AI in your procurement process to transform institutional knowledge into an automated, always-on guardrail, ensuring AI procurement aligns with your standards, protecting your organization in an evolving digital landscape.
tl;dr
When evaluating AI vendors, understand what type you’re buying (AI-Native, AI-Enhanced, or AI-Enabled services) and ask these critical questions:
- Data Handling: Where is data processed/stored? Is it used for training? What are retention/deletion policies?
- Explainability: Can you trace decisions? Are there humans-in-the-loop? Are actions logged for auditing?
- Performance & Security: How is model drift prevented? What defenses exist against prompt injections? Are there regular audits?
- Integration: Does it work within existing workflows? Does it integrate with current systems? Can you upload your own playbooks?
- Standards Enforcement: Can it apply your specific rules automatically? Does it capture institutional knowledge? Can it identify best practices?
Bonus considerations: Third-party subprocessor documentation, security certifications (SOC 2, ISO 27001), and clear liability/error handling processes.
Bottom line: Traditional software procurement isn’t enough for AI. You need AI-specific due diligence that addresses data privacy, explainability, ongoing performance, workflow integration, and your ability to enforce organizational standards at scale.
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