While IT and security wait on each other, the AI rollout drags on

At the first meeting after your company decides to bring in business AI, IT asks which server to prepare, security asks what will leave the company, and the business teams ask when they can start trying it. If that meeting doesn't settle who handles what and when, the three groups wait for each other's answers, and the schedule stretches in the meantime.

An on-premises OwlAI rollout runs in five stages: preparation, installation, data connection, validation and expansion. Assign an owner to each stage in advance, and work doesn't stall at each handoff while someone is found to take it. SaaS skips stages 1 and 2 and starts at stage 3 after sign-up.

#Decide where the AI models run before you pick a server

The first decision in preparation is the deployment type and the AI model setup. What you need depends on whether you use external AI models, only self-hosted models on your own GPUs, or a mix of both. We recommend a Linux server with at least 16GB of memory and 80GB of disk for OwlAI itself, plus a separate GPU server if you run self-hosted models.

Most of the network and security checks also fall in this stage. Check the HTTPS certificate for the server address, whether you have an internal proxy or a TLS interception appliance, whether the firewall allows the external addresses the server must reach, and whether your server security policy blocks features needed to isolate AI code execution. This is also a good time for the security team to start its review.

#Check the server's readiness in advance with the pre-install check tool

Before installing, run the check tool that ships with OwlAI. It changes nothing on the server. It only reads the current state and reports on the operating system, Docker version, disk, network range conflicts, certificates and security module settings, marking each item pass, warning or fail. Every failed item comes with instructions for fixing it, so you can get the server ready before installation day.

Once the checks pass, unpack the install kit, add the configuration values and license file, load the images and start the services. Installation is complete when you sign in as the first administrator and confirm the AI model connection.

#The screen writes the SQL for the database account

Business databases are connected from the screen. Because OwlAI only reads databases, the screen shows the SQL that creates an account with query permission only, which you can hand straight to your DBA. After connecting, OwlAI analyzes table structures and column meanings in advance so the AI can find the right tables for a question. Upload internal documents into document collections, and attach the internal systems you need through connectors. Finally, create roles that decide who can use which databases and documents.

#Test with a pilot team's real questions before going company-wide

Pick one or two departments and have them use it for real work questions. Check whether common questions get accurate answers, whether data outside a user's permissions is blocked, and whether meeting minutes and documents come out in your company's templates. The answer ratings users leave and the activity logs show what needs refining. Questions that work well become playbooks (workflows that run a recurring task in a set order) and move into regular work.

#Company-wide expansion starts with sign-up policy and default roles

Before widening access to the whole company, set the sign-up policy and default roles. That way you won't need to adjust permissions by hand each time a new user joins. Set a usage limit for each person, and write down as operating procedures who approves updates and when, and how backups are done. For user training, use the per-feature user manuals.

#IT owns the servers, security owns egress, business teams verify the results

#GPU servers and security review set the timeline more than company size

Preparation is the stage most likely to run long, because the time it takes to secure GPU servers and complete the security review goes straight into the schedule. Installation depends on how quickly you fix the items the check tool fails; once the checks pass, the installation itself follows a set procedure.

Table analysis in the data connection stage takes longer the more tables you analyze, since analysis runs once per table. In an internal production environment, analysis of some 270 tables finished in just over 16 minutes (observed in October 2026). Tables that aren't analyzed aren't used in answers, so leaving out tables no one will ask about saves time and gives the AI fewer wrong tables to pick. Validation takes as long as the pilot departments need to run one full cycle of real work.

So the first meeting should settle when you'll secure GPU servers, when the security review starts, and which department runs the pilot. We go over the same factors with you during adoption consultation to set the schedule.