Enterprise AI
"Enterprise AI is AI deployed inside a large organization with the controls scale requires: access rules, audit logs, data protection and change management."
What is an Enterprise AI?
Enterprise AI is AI deployed inside a large organization with the controls scale requires: access rules, audit logs, data protection and change management. Related product: Chat Agents.
Reviewed by Atul Diwedi, Co-Founder · Last reviewed
Deep Dive
The model that answers a chat for a 10-person company and for a 10,000-person one can be the same. What changes is everything around it: who is allowed to change the prompt, where the transcripts are stored and for how long, which of forty internal systems the agent may touch, how a change is tested before it reaches customers, and who is accountable when a wrong answer goes out. Enterprise AI is the term for AI agents deployed with those controls in place, usually across several departments, regions and languages at once.
How it works. The deployment is split into roles: an owner for knowledge, an owner for integrations, a security reviewer, and the business teams who use the agent. Access is controlled per role, with audit logs on prompt changes, knowledge updates and data exports. Data handling is written down: encryption at rest and in transit, retention periods, redaction of card numbers and health data from transcripts, and regional storage where law requires it. Integrations go through the IT team's standard review, with scoped credentials. Changes move through a staging environment and a test set of conversations before production. Reporting rolls up by department and region, and procurement adds a vendor assessment, a data processing agreement and an SLA.
Why it matters for procurement and IT. A pilot that works in one team often stalls at the enterprise gate, not because the agent is wrong but because nobody can answer the security questionnaire. Agreeing the controls early is what gets the second and third deployments approved. It also protects the business: an agent with write access to the CRM and no audit log is a risk, whatever the model quality.
Example. A bank's retail division wants a voice agent for card-block requests and branch hours in two languages. Before launch, security confirms transcripts are encrypted and card numbers are masked, legal signs the processing agreement, the call flow is tested on 300 recorded scenarios, and the agent gets read-only access to branch data plus a single write action: creating a card-block ticket that a person confirms. Rollout goes region by region, with the knowledge owner in each region reviewing a weekly sample. The second deployment, for the mortgage team, reuses every control and takes a fraction of the time.
Enterprise AI vs vertical AI. Vertical AI is specialization by industry: the agent knows dental or restoration vocabulary and workflows out of the box. Enterprise AI is specialization by organization size and governance. A large dental group needs both; a single clinic needs the vertical part and a simpler version of the controls.
What to measure. Time from request to approved deployment for each new team. Number of audit findings per quarter. Share of conversations with redaction applied where required. Change failure rate: production incidents per release. Adoption by department against the plan.
Botcadence offers an Enterprise plan with custom pricing alongside its self-serve tiers, encrypts data with AES-256 at rest and TLS 1.2+ in transit, and supports 50+ languages on chat agents; see data privacy in AI.
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Common Questions
Enterprise AI FAQs
Encryption standards at rest and in transit, data retention and deletion terms, where data is stored, who at the vendor can access transcripts, how model providers handle prompts, SSO and role-based access, and audit logs on configuration changes.
One department first with the full control set, then reuse the controls for each new team. Later rollouts should be measured in weeks rather than months because security and legal have already approved the pattern.
Yes, and it should start that way. Add write actions one at a time, each with a named approver, a staging test and an audit entry, so a wrong write can be traced and reversed.
Not always. What they need is separation of staging and production, scoped credentials per integration and logs the company can export. Ask the vendor how each is provided on the Enterprise plan.