Which everyday work questions should you use to shortlist enterprise AI search platforms?

Use these eight everyday work questions — covering leave policies, onboarding, project decisions, tool access, customer accounts, experts, filters, and incident response — to shortlist enterprise AI search platforms. Run the same questions on every platform with identical source systems and test users; weak answers surface fast because each question mirrors real work conditions, not vendor-scripted demos.

Search problems cost real time. A Gartner survey published in 2023 found 47% of digital workers struggle to find the information or data they need to do their jobs effectively.

AI is now everywhere: 88% of organizations use it in at least one business function. Yet vendor demos rarely show the cost of poor search because they run on clean, scripted data. Your own questions, asked by your own people, give you a fair AI search platform comparison before contract talks begin.

How to shortlist enterprise AI search platforms with everyday work questions

Build your questions for AI platform assessment from real requests in HR, IT, sales, engineering, support, and finance. Run the same set on every platform, with identical source systems and test users. Score every response against the same five criteria for AI search tools:

• Does the answer address the question directly, in plain language?
• Does it cite sources a reviewer can open and check?
• Does it show only what that test user is allowed to see?
• Does it point to the current version of each source?
• Would the evaluator act on it without opening five more tabs?

Real questions beat vendor demo scripts because they carry your company's mess: old file versions, team jargon, and uneven access rules. Okta's Businesses at Work 2025 report found the average number of apps each company uses reached 101. A good test set forces each tool to reach across that sprawl, which is the core job of AI enterprise search.

We recommend a light first pass: a few high-frequency questions reviewed side by side. Save deep security, admin, and architecture reviews for the vendors that pass, so enterprise AI decision-making stays grounded in daily work instead of slideware.

1. Ask: "What is our current parental leave policy for employees in my region?"

The parental leave question shows whether a platform gives a direct answer or a pile of links. A good result states the policy in plain language, cites the current approved version, and reflects the asker's region. Platforms that need exact document titles or folder paths to find the policy should score lower.

A keyword search for "parental leave policy" often returns dozens of hits: the current policy, older versions, Slack threads, and town hall decks. Policy lookups make a fair test because every employee needs them, and HR can confirm which version is correct.

Run the leave policy test in three steps:

1. Ask the question as an employee in one region, then as a colleague in another.
2. Confirm each answer cites the policy version that applies to that person.
3. Rephrase it casually, such as "how much time off do new parents get," and compare results.

One prompt checks several enterprise search best practices at once: natural-language understanding, region-aware relevance, and citation quality. An employee in Germany shouldn't see the U.S. policy as the top answer.

2. Ask: "Where is the latest onboarding checklist for my role?"

The onboarding checklist question tests cross-system retrieval and version handling. The right checklist might sit in a wiki, a team drive, a help center, or a pinned Slack message. A strong platform ranks the current version first, explains why it's current, and keeps stale copies from appearing as equal options.

Onboarding content also exposes vocabulary gaps. HR might title the doc "new hire readiness plan" while a new engineer types "onboarding checklist." Semantic search, which matches meaning instead of exact words, should close that gap without anyone tagging documents by hand.

During enterprise AI search evaluation, ask how the platform decides which version is current. Useful signals include last-edited date, document owner, and how often colleagues on the same team open it. The test passes when a new hire finds the right starting point on day one without guessing which app holds the truth.

3. Ask: "What did we decide about this project, and who approved it?"

The project decision question separates document search from real company context. The answer usually spans a proposal, meeting notes, a ticket, a chat thread, and the people involved. A strong platform summarizes the decision, cites each source, and names the approver without claiming more certainty than the evidence supports.

Test ambiguity on purpose. Pick two projects with similar names, or a decision that changed twice over a quarter. Watch whether the platform asks a clarifying question or confidently blends the two. Getting this right matters: in a 2025 survey, 61% of respondents named erroneous or inconsistent answers as the top concern for broad AI adoption.

Ask each vendor to walk through how its system links people, content, and activity across your tools. The decision prompt is one of the sharpest ways to shortlist enterprise AI search platforms, since ranking files alone can't answer it.

4. Ask: "How do I request access to a tool, and can you start the request for me?"

The tool access question checks whether a platform stops at answers or helps finish the work. First, the response should cite the current request process, the relevant policy, and the right form. Then the platform should help the user take action, like drafting or opening the request, inside clear guardrails.

Access requests make a good AI search use case because they're frequent, repetitive, and easy to verify. A platform that only summarizes the IT wiki still saves a few minutes. One that also starts the request spares the employee a second trip to the ticketing system.

Before giving credit for automation, ask the vendor four questions:

• Who approves an action before it runs?
• Which permissions get checked first?
• Where does the audit trail live?
• Does automation follow the same trust model as search?

5. Ask: "What happened with this customer last quarter, and what should I know before the next meeting?"

The customer history question tests whether a platform connects account context across CRM records, support tickets, call notes, docs, and internal chats. A useful answer summarizes the quarter, flags open issues, lists recent commitments, and cites where each point came from. Missing recent activity is a reason to move a vendor down your list.

The same account looks different depending on who asks. A seller, a support lead, and a finance partner each see different slices, and the platform should respect those lines without leaking restricted details. Run the prompt as all three roles and compare the answers.

Then push with follow-ups such as "What changed since the renewal call?" or "Which product issue came up most?" Strong systems keep the thread going and refine the answer instead of starting over. Blending structured CRM fields with unstructured call notes is among the key features of AI search solutions, and weaker tools tend to show gaps here.

6. Ask: "Who is the best person to help with this issue, and what have they worked on?"

The expert question shows whether a platform can find people as well as files. Strong systems infer expertise from projects, documents, code, tickets, and collaboration patterns, then explain why each person is relevant. The right answer is usually someone with recent, hands-on work on the topic, who may be less visible than a senior leader.

Say a payments outage hits at 2 a.m. The engineer who rewrote the retry logic last month is a better contact than the director who presented the payments roadmap. A platform that weighs recent commits and tickets will surface that engineer first.

Run the expert prompt as an engineer and as a support manager, since each may need a different contact on the same topic. Expertise discovery matters most in distributed companies, where acquisitions and time zones hide who knows what, and where nearly half of employees say their work feels chaotic and fragmented. Good results also spare IT from maintaining yet another skills directory by hand.

7. Ask: "Can I narrow this down to only the documents that matter for this task?"

The narrowing question tests what happens after the first answer. People rarely stop at one search, so the platform should let users limit results by source, person, date range, content type, or team. Those filters should feel like everyday controls that don't require learning special syntax.

Ask the vendor to start from a broad prompt, like "Q3 pricing changes," and reach a precise result set in a few clicks. For a sense of the depth power users expect, review this advanced search filter documentation and ask each vendor to match it.

On your AI search platform features scorecard, count refinement as part of answer quality. Even excellent ranking needs a fast way to slice results once a task gets specific. Without filters, every broad answer turns into manual sorting.

8. Ask: "What is the approved process for handling a security incident like this one?"

The security incident question tests trust under pressure. The platform should surface the approved runbook or policy fast, with clear citations and timestamps, and show only what the responder is cleared to see. When you shortlist enterprise AI search platforms, weak results here rule a vendor out, even after strong lower-stakes answers.

Ask vendors how fast a permission change in the source system reaches search results, and expect minutes rather than days. Ask vendors for separate sync timelines for edits, new content, and access changes. During an incident, access shifts quickly, and restricted content shouldn't linger in anyone's results.

Use the incident prompt to open the governance review:

• Compliance controls that match your industry's requirements
• Admin settings that limit which teams can use which capabilities
• Model routing that sends sensitive queries through approved paths
• Data handling rules for both answers and automated actions

How to shortlist enterprise AI search platforms: frequently asked questions

1. What specific features should I look for in an enterprise AI search platform?

Look for direct answers with citations, deep connectors, permission-aware retrieval, fresh content, practical filters, workflow actions, and admin controls for governance and spend. The features that matter most let employees ask natural questions and trust the answer without leaving their workflow.

2. How do I assess the integration capabilities of different AI search tools?

Test with questions that need three or more systems to answer, then ask which sources contributed and how permissions sync from each one. Judge connectors by depth across content, metadata, actions, and permissions rather than by how many logos appear on a slide.

3. What questions can help evaluate the user experience of an AI search platform?

Ask whether people can search in plain language, refine results quickly, and act on an answer without copying and pasting between apps. Watch evaluators during the test, because a polished demo means little if they still hunt, stitch, and verify by hand.

4. How quickly can the platform adapt to new data sources?

Ask for separate timelines covering connecting a new source and indexing its existing content. Fast setup matters less than ongoing freshness, so new documents and updates should appear when employees need them.

5. What are the security and compliance considerations for enterprise AI search?

Confirm that permissions are enforced before content reaches the language model, and ask whether model providers retain or train on your data. Then review audit logs, usage controls, and the limits on which data feeds answers and actions.

Run these eight questions with your own people, sources, and permissions, and the platforms worth a deeper review will separate quickly from the rest. We'd be glad to run the same test with you on your company's real knowledge. Request a demo to explore how Glean and AI can transform your workplace.