Enterprise AI Platform vs. Glean
Glean helps employees find and use company knowledge. EnterpriseAI turns knowledge, rules, and data into a governed workflow that moves work forward.
A simple test: if the problem is "people cannot find the answer", Glean is strong. If the problem is "the work still does not move after the answer is found", EnterpriseAI is the workflow answer.
Plain-English buying comparison
Each row explains where EnterpriseAI should win, where Glean may still fit, and a concrete example of the difference.
| Criterion | Enterprise AI Platform | Glean |
|---|---|---|
| Best fit | Use EnterpriseAI when an enterprise needs AI to improve a real workflow, not just give people another tool. Example: a customer service, claims, compliance, approvals, or operations process with many people, rules, and systems involved. | Glean fits enterprises with knowledge scattered across many tools. Example: staff search across docs, chat, tickets, and intranet content from one place. |
| What changes in the business | The process changes: intake, triage, approvals, handoffs, evidence, and next actions become visible and governed. Example: fewer cases wait in email because the workflow shows who owns the next step. | The business changes because people find answers and context faster. Example: a support manager gets the right policy answer without asking three colleagues. |
| Data and context | EnterpriseAI connects the work to the data, policies, documents, and system context needed to make decisions. Example: a team member sees the policy extract, evidence, and case history in the same flow. | Glean focuses on enterprise search, permissions, connectors, and grounded assistants. Example: answers respect the documents a user is allowed to see. |
| Controls and approvals | Controls sit inside the work: human approval, audit trail, exception handling, and escalation. Example: AI can recommend an action, but the accountable person still approves it. | Controls focus on source permissions, knowledge governance, and enterprise admin settings. Example: search results inherit access from connected systems. |
| First useful project | Start with a high-value, repeatable workflow where speed, quality, and governance all matter. Example: an enterprise service journey with measurable cycle time, risk, and customer impact. | A useful first project is knowledge discovery for a large workforce. Example: replacing scattered internal search with a trusted work AI layer. |
| What to check before buying | Check whether the platform can own the operating workflow end to end, not just automate one step. Example: ask who sees the queue, who approves, and how exceptions are recorded. | Check whether better knowledge access is enough to change the workflow. Example: ask what happens after the answer is found and who owns the next action. |
How to make the buying decision
Use these notes to test whether the decision is really about changing a workflow, buying a broader platform, or improving individual productivity.
Choose based on the work that must change
EnterpriseAI should win when the buyer needs a governed workflow to move better, not just another tool around the edge of the work.
Make the first project measurable
The strongest business case starts with one high-value workflow, a clear owner, and a before-and-after measure such as cycle time, rework, quality, or service experience.
Keep human accountability visible
Enterprise buyers need to know where AI recommends, where people decide, and how exceptions are recorded before they can trust the workflow at scale.
Buyer questions
Questions executives and delivery teams should ask before choosing a direction.
When should a buyer choose EnterpriseAI?
Choose EnterpriseAI when the problem is a real workflow that needs clearer ownership, better evidence, human approval points, and measurable operating improvement.
When could Glean still be the right choice?
Glean can be the right choice when the buyer's main need matches its core category, such as broad platform standardisation, individual productivity, developer productivity, app automation, or agent building.
What should the buying team ask in the demo?
Ask for the same real example on both sides: where the work starts, who owns the next action, what data the AI can use, where a human approves, how exceptions are handled, and what metric improves first.
Compare EnterpriseAI against your real workflow
Bring one process, one bottleneck, and one success metric. We will show where EnterpriseAI fits, where another platform may be better, and what the first project should prove.