An OpenAI implementation should begin with a task whose quality, time and cost can be measured. Drafting a response, finding a procedure or extracting specific information from a document can provide a useful starting point. The project needs a workflow owner, representative data and clearly defined acceptance criteria.
This plan covers the decisions between an initial idea and a solution used by a team. Use it when discussing a project with a supplier, planning a pilot or assessing whether your organisation has the data and resources required for implementation.
1. Select a workflow and establish a baseline
Document who performs the task, what information they receive, which systems they use and where the result goes. Measure handling time and the number of errors or corrections. Include exceptions such as incomplete documents, ambiguous questions and cases that require a manager’s decision.
For the first implementation, choose a scope you can assess without reorganising the whole business. An assistant drafting responses for employees, for example, has simpler acceptance conditions than a system independently handling every customer conversation. This is a scope-planning example, not a description of a completed client project.
2. Choose between a team tool and an API integration
An established employee tool may be suitable when the task mainly involves text and documents and the user can supply the necessary context. A custom integration becomes more relevant when the feature must operate inside an existing application, retrieve system data or save a structured result.
Connecting OpenAI to a CRM requires decisions about which records can be read, which fields may be changed and who approves a write. The same applies to ERP, support and e-commerce systems: access should follow the specific task. The availability of an API and documentation affects the implementation effort.
3. Prepare knowledge sources and access rules
Organise documents, remove outdated versions and assign people to maintain them. If answers must reflect company procedures, define a source for each type of information. Retrieving relevant document passages and supplying them to a model as context, often called RAG, is one possible approach to this work.
Check permissions before making content available. Employees should receive answers based only on documents they are allowed to access. An instruction written for the model does not replace application-level access controls. The system also needs a defined response when the sources do not contain an answer.
4. Define data handling
List the data sent to the model, stored in the application and recorded in logs. Limit each set to what the task requires. Store API keys on the server; an application user should not receive a secret key in browser code.
Data not being used for training does not automatically mean it is not retained. API retention depends on the feature and configuration, and additional controls such as Zero Data Retention have eligibility conditions. Review OpenAI’s official data controls documentation for the intended features before choosing the architecture.
5. Evaluate quality using representative tasks
A test set should include common tasks, difficult cases, incomplete inputs and questions outside the intended scope. Record the expected result or assessment criteria for each example. For customer support, relevant measures include procedural accuracy, completeness and correct handover to a person.
OpenAI recommends task-specific assessment and continuous evaluation in its evaluation best practices guide. In practice, return to the same test cases after changing the model, instructions or knowledge sources.
Also test attempts to make the system disclose information or perform an unauthorised action. Customer messages and documents are inputs. They must not be allowed to expand an integration’s permissions or replace the application’s rules.
6. Calculate the cost of the complete workflow
The implementation budget includes assessment, data preparation, integrations, the interface, testing and launch. Running costs include API usage, infrastructure, knowledge maintenance, monitoring and exception handling. Comparing only the price of a single model call leaves out a substantial part of the expense.
To estimate operation, establish monthly task volume, average calls per task and the size of inputs and responses. Include retries and additional tools. Check model prices at the time of estimation on the OpenAI API pricing page.
For an illustrative time calculation, if 600 cases each require four fewer minutes of work per month, the difference is 40 hours. This is a scenario to test in a pilot. Subtract time spent reviewing and correcting results, then determine how the released capacity would change the team’s actual work.
7. Run a pilot and accept the solution
Before launch, define the user group, assessment period, spending limits and person responsible for reported issues. Users should understand when they are seeing an AI suggestion and how to correct it. Saving records, sending messages and other consequential actions need approval rules appropriate to the workflow.
Acceptance should cover test results, correct permissions, behaviour during external API failures and a way to return to manual handling. Documentation should identify account owners, configuration, knowledge updates and support arrangements. The pilot ends with a decision to expand, improve or discontinue the solution.
Start an OpenAI implementation with N0VA
N0VA is an OpenAI Select Partner. We combine application design with business process automation and integrations. To define a scope, we need a workflow description, sample tasks, a list of systems and a success criterion.
Send your workflow description to N0VA to discuss an OpenAI use case and define the first stage.
Frequently asked questions
How much does an OpenAI implementation cost?
Cost depends on integrations, data quality, the interface, permissions and testing. Separate implementation fees from API, infrastructure and maintenance costs. A credible budget requires a workflow description and expected usage volume.
How long does an OpenAI implementation take?
Timing depends on scope, data availability, system documentation and acceptance arrangements. Plan assessment, a pilot and team rollout separately. A concrete schedule requires these dependencies to be reviewed first.
Can OpenAI connect to an existing CRM?
Yes, if the CRM provides an appropriate API or another integration mechanism. Define read and write access, permissions, error handling and actions that require employee approval.
How do we assess the business case?
Compare task time, quality and cost before and during the pilot. Include corrections, human review and maintenance. Agree on success criteria before implementation starts.