Enterprise AI Platform vs. Make
Make helps teams connect apps and automate scenarios visually. EnterpriseAI helps enterprises change governed workflows where people, policies, data, and decisions all matter.
A simple test: if success is "connect these apps and remove manual steps", Make is useful. If success is "change how this business process is owned, governed, and measured", EnterpriseAI is stronger.
Plain-English buying comparison
Each row explains where EnterpriseAI should win, where Make may still fit, and a concrete example of the difference.
| Criterion | Enterprise AI Platform | Make |
|---|---|---|
| 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. | Make fits cross-app automation and operations scenarios. Example: when a form is submitted, Make updates a CRM, sends a message, and creates a task. |
| 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 by removing manual handoffs between apps. Example: an operations coordinator stops copying data between tools. |
| 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. | Make uses app connectors, triggers, actions, and scenario data. Example: a workflow passes customer data from a form to email and CRM tools. |
| 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 are scenario-level and depend on builder discipline and workspace governance. Example: teams manage who can edit automations and what app credentials are used. |
| 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 a stable, low-risk app automation. Example: lead routing, notifications, report generation, or simple back-office handoffs. |
| 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 the workflow needs business governance beyond app automation. Example: ask how approvals, exceptions, and audit evidence are handled. |
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 Make still be the right choice?
Make 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.