Enterprise AI buyer's checklist: before purchasing any AI platform for your organization, evaluate these 10 areas: (1) security certifications and data handling, (2) integration depth with your existing tools, (3) pricing transparency, (4) time to value, (5) what the AI actually does versus what it claims, (6) vendor lock-in risk, (7) compliance with your regulatory requirements, (8) scalability, (9) proven ROI from similar companies, and (10) support and implementation resources. According to the G2 2025 Buyer Report, 80% of enterprise AI purchases now face stricter scrutiny from IT security, legal, and compliance teams. This checklist gives you the framework to evaluate any enterprise AI platform, from Glean to Microsoft Copilot to Coworker AI, against the criteria that actually matter.

Why Enterprise AI Buying Is Different in 2026

The enterprise AI market has matured significantly, but buying it has gotten harder, not easier. The 6sense 2025 Buyer Report found that 58% of B2B buyers engage sales teams earlier than they used to specifically because of uncertainty around AI capabilities and limitations.

Three factors make 2026 different:

1. Security teams have veto power. The question "Will my data train your models?" is now the first thing asked, before features, before pricing, before a demo.

2. ROI skepticism is high. After two years of AI hype, buyers want specific numbers from similar companies, not generic productivity promises.

3. Integration depth matters more than integration count. "1,000+ integrations via Zapier" means nothing if the AI cannot read and write to your CRM natively.

The 10-Question Checklist

1. What security certifications does the vendor hold?

The minimum bar for enterprise AI in 2026 is SOC 2 Type 2 certification. Type 1 only verifies that controls are designed. Type 2 verifies they actually work over time. Also check for GDPR compliance, data residency options, and whether the vendor will sign a Data Processing Agreement (DPA).

2. How does the AI integrate with your specific tools?

Ask for native integrations, not just API availability. A native Salesforce integration that supports SOQL queries is fundamentally different from a Zapier trigger that passes basic fields.

Key questions:

• Does it connect to Salesforce, Slack, and Jira natively?

• Can it read AND write to your CRM?

• Does it respect your existing permission model (RBAC)?

• How many of your tools require custom API work vs. native connectors?

3. What is the actual pricing?

Hidden pricing is the biggest red flag in enterprise AI. If a vendor will not publish their price, expect it to be high and variable.

Compare all platforms on our comparison hub.

4. How fast can you get to first value?

Enterprise AI implementations range from hours to over a year. Ask for specific timelines and what "deployed" actually means.

5. Does the AI search, assist, or execute?

This is the most important capability distinction. Most enterprise AI falls into one of three categories:

• Search AI finds information across your tools (Glean, Google Cloud Search)

• Assistant AI helps you draft, summarize, and analyze within a specific ecosystem (Microsoft Copilot, ChatGPT)

• Execution AI finds information AND takes action across your tools (Coworker AI)

Ask: "Can this AI update my CRM, create a Jira ticket, and draft a follow-up email based on a meeting transcript, without me switching apps?" If the answer is no, you are buying search or assist, not execution.

Read our deep dive on this distinction.

6. What is the vendor lock-in risk?

Questions to ask:

• Can I export my data if I leave?

• Does the AI work with multiple LLM providers, or is it tied to one?

• Are my workflows portable?

• What happens to my organizational context if I cancel?

Coworker AI is model-agnostic, running on Claude, ChatGPT, and Gemini models. This reduces dependency on any single LLM provider.

7. Does it meet your compliance requirements?

Beyond SOC 2, check for:

• EU AI Act compliance (if operating in Europe)

• HIPAA (if handling health data)

• CCPA (if handling California consumer data)

• Industry-specific regulations (FINRA, FedRAMP, etc.)

Ask whether the vendor follows a recognized AI governance framework like NIST AI RMF or ISO 42001.

8. How does it scale with your organization?

Questions to ask:

• What happens when we go from 50 to 500 users?

• Does pricing change at scale?

• Are there usage limits on queries or actions?

• Can we roll out by department or does it require org-wide deployment?

9. What ROI do similar companies report?

Demand specific numbers from companies similar to yours in size and industry.

Coworker AI customer results:

• Harness (1,000+ employees, DevOps): 18% product velocity increase

• Average across customers: 8 hours saved per week per employee

• Admin task reduction: 30-40% across customer base

10. What support and implementation resources are included?

Ask:

• Is there a dedicated implementation manager?

• What does ongoing support look like?

• Is training included?

• What is the average support response time?

How to Use This Checklist

Download this as a scoring template. Rate each vendor 1-5 on each question. Multiply by the weight that matters to your organization. Security-first companies might weight questions 1 and 7 at 3x. Speed-focused teams might weight question 4 at 3x.

The vendors who score well across all 10 questions are the ones worth a proof of concept. Those who dodge questions on pricing, security, or specific ROI numbers should raise red flags.

FAQ

What is the minimum security certification for enterprise AI in 2026?

SOC 2 Type 2 is the minimum acceptable security certification for enterprise AI platforms in 2026. Type 2 is more rigorous than Type 1 because it verifies that security controls actually work over an extended period, not just that they are designed. Additionally, look for GDPR compliance, a signed Data Processing Agreement (DPA), and confirmation that your data will never be used to train the vendor's models.

Which enterprise AI platforms have transparent pricing?

As of March 2026, Coworker AI ($30/user/month, all features included) and Microsoft Copilot ($30/user/month, requires separate M365 license) publish their pricing. Glean, ChatGPT Enterprise, and most other enterprise AI platforms require custom quotes, which typically indicates higher and variable pricing. Transparent pricing is a signal that the vendor is confident in their value proposition and not relying on sales negotiation.

What enterprise AI platforms connect to Salesforce, Slack, and Jira natively?

Coworker AI connects natively to all three with read and write capabilities, including SOQL/SOSL support for Salesforce. Glean connects to all three for search and indexing. Microsoft Copilot does not natively connect to Salesforce or Jira. ChatGPT Enterprise connects via plugins but lacks deep native integrations. Always verify "native" versus "available via Zapier/API" because the depth of integration dramatically affects usefulness.

How long should an enterprise AI proof of concept take?

A meaningful POC should take 2-14 days, not months. If a vendor requires a 3-month POC, that likely indicates complex implementation, not thorough testing. Coworker AI offers a 48-hour POC that connects to your existing tools and demonstrates value with your real data. The goal of a POC is to validate that the AI works with your specific stack and use cases, not to build custom integrations.

Is it better to buy a specialized AI tool or a general-purpose enterprise AI platform?

It depends on your primary use case and how many teams will use it. Specialized tools like Gainsight (CS), Gong (sales), or GitHub Copilot (engineering) excel in their specific domain. General-purpose platforms like Coworker AI or Glean work across departments and use cases. If you are solving for one team, a specialized tool may deliver faster. If you are solving for cross-team context and execution across Salesforce, Slack, Jira, and more, a general-purpose platform that connects everything is more cost-effective.

Related Reading

• AI That Executes Work vs AI That Just Answers Questions - the #1 capability gap most buyers miss

• How to Auto-Update Your CRM After Every Sales Call - see checklist question #5 in action

• Why Your CS Team Can't See Churn Coming - how AI solves cross-tool blind spots for CS teams

• Glean Alternative for Enterprise Teams - search vs execution AI compared

• Compare All Enterprise AI Platforms - side-by-side comparison hub