You need to resolve a specific decision or a stalled AI initiative.
AI consulting →AI implementation.
Turn your AI plan into a solution employees can use every day.
Discuss your projectAdvice, strategy or implementation?
Several initiatives need priorities, ownership and a roadmap.
AI strategy →The process is selected; you want to test and introduce a solution.
AI implementation (this service)How do you move an AI pilot into everyday use?
AI implementation connects a chosen application to real data, users and working agreements. Beyond the model call, I implement access controls, error handling, checks and handover for daily operations. I start with a bounded test set and a fallback route. Expansion follows only when the agreed quality, cost limits and human checks work in practice.
When it is a good fit
A process has been selected, a process owner is involved and representative data is available. The goal is an application staff can actually use.
When to take a different approach
If the problem is still unclear, start with an assessment or strategy. Missing permission or source access must be resolved first.
What needs to be ready before AI goes live?
A successful demonstration does not establish that an application can be operated every day. For AI implementation, we document data origins, permitted actions and who can intervene. We also examine what happens when a provider is unavailable or an output is insufficiently clear. [1]
Before launch, we test the agreed task with ordinary and difficult examples. Model, prompt and source changes receive identifiable versions so differences can be investigated. Handover includes monitoring, incident handling and a fallback that the responsible operator can execute. [2][3]
Questions to settle first
- Who can stop or roll back the application?
- Which actions need review before reaching business systems?
Make acceptance measurable
- An assessed test set with results by failure category.
- A handover that includes an exercised fallback and simulated outage.
From pilot to daily use: who intervenes, and when?
This design example concerns a fictional application that prepares support replies and saves one ticket note after human approval. The table describes possible situations and responses to agree in advance. It contains no incidents or test results from OXIAE or a customer. Permissions, detection and conditions for resuming must be established separately for your application.
On a narrow screen, scroll the table horizontally to read every column.
| Situation to rehearse in advance | Immediate action | Responsible owner | Evidence required to release or resume |
|---|---|---|---|
| The pilot moves to a limited user group. | Record the source selection, model and prompt configuration, permissions and fallback. Admit only the agreed group; keep the existing manual process available. | The process owner decides whether to launch; the application administrator carries out the release. | A recorded question set, exception paths and disabling procedure have been reviewed. Administrators know which version was released and who handles open work. |
| A draft answer omits an important condition from the source. | Hold the draft and send the question to a staff member. Pause the affected answer path where necessary and investigate source selection, the retrieved passage and the wording separately. | The support owner assesses the content; the application administrator restricts the affected path. | The cause and affected scope have been investigated. The original case and similar exceptions are reassessed before the path is released. |
| Saving an approved ticket note times out; the outcome is unknown. | Mark the action for investigation. Look up the outcome using the same stable action reference before retrying. If that is not possible, ask a staff member to check rather than blindly writing again. | The integration administrator investigates the technical outcome; the support owner keeps the unresolved task visible. | There is one confirmed outcome or a visible manual task. The agreed duplicate-note check and unknown-outcome path have been exercised. |
| A staff member loses access to a knowledge source. | Block new access according to the permission model. Also check search results, existing session context and shared caches; blocking only the source link is insufficient. | The access administrator revokes permissions; the application administrator checks how the application applies the change. | A session with the changed permissions is not offered the restricted content again. Existing context and stored output have been handled under the agreed access and retention policies. |
| A model, prompt or source configuration changes. | Assess the change against recorded cases before release. Keep the previous configuration identifiable and determine how new actions will be stopped if a problem occurs. | The content owner reviews quality; the application administrator manages configuration; the process owner decides whether to release. | Relevant checks and differences have been reviewed. Restoring configuration and handling ticket notes already created have separate instructions and owners. |
What this helps you decide
Pausing a path prevents new actions. Restoring a previous configuration changes future behaviour. Neither automatically repairs notes already saved or messages already sent. A rollout plan therefore also explains how affected work is found, who assesses the consequences and which correction is permitted. This table is a design starting point, not evidence that these measures already work.
Apply this to your own project
- Can the designated administrator disable the affected function, and can staff then see where open work is handled?
- Does an uncertain write outcome remain visible until it is confirmed or taken over by a staff member?
- Do access checks cover revocation, an existing session and previously assembled context together?
- Does each change have an identifiable configuration, a content reviewer and a recorded release decision?
- Is there a separate recovery action for data already changed, with an owner authorised to make the correction?
Compare your options
Choose based on the task, the people involved and what you can maintain.
| Approach | Useful when | What to consider |
|---|---|---|
| Prototype | Test one technical assumption. | Not automatically suitable for production data or daily use. |
| User pilot | Test quality and working practices within a limited scope. | Needs representative examples and explicit boundaries. |
| Production rollout | Pilot criteria are met and maintenance has an owner. | Requires monitoring, support and a recovery procedure. |
What I need to start
- Approved data and access rights for the selected application.
- Examples with expected outputs and difficult exceptions.
- An owner for incidents, costs and quality checks.
How to assess the result
- The application passes a documented representative test set.
- Uncertain results go to a person or a safe fallback route.
- Access, logging, cost limits and rollback are tested.
What determines the cost?
A useful estimate starts with the scope. I distinguish development work from recurring usage and operating costs. These are the main drivers:
- Number of systems and data types.
- Consequences of errors and required review.
- Monitoring, usage volume and handover to maintainers.
Your challenge.
A practical application.
A successful AI demo needs to become a manageable business solution.
The AI strategy determines which problem to address first and what limits apply. If the selected application mainly concerns recurring processing, AI automation may fit; for other applications, I first define the software, sources and user steps that are needed.
What quality must the solution achieve before it can be used?
Agree the first step
Use the prerequisites, acceptance checks and cost factors above to define your assignment. Agree who can approve the outcome and who will maintain the solution. The project approach explains discovery, delivery and handover.
Read the project approachYour questions.
Clear answers.
Is a working demo enough to go live?
No. A demo only shows that a scenario can work. Rollout also requires checks for exceptions, access rights, outages, costs and maintenance ownership.
What is the difference between stopping and rolling back an AI function?
Stopping blocks new processing or actions. Restoring an earlier configuration changes behaviour after resuming. It does not correct data already saved. That requires a separate assessment of affected work and an authorised recovery action.
Can a failed write request be retried immediately?
A timeout does not always establish whether the change was made. First check the outcome using a stable reference or the integration's agreed mechanism. If the outcome cannot be established, create a visible task for manual review. A blind retry may cause a second change.
Who decides whether an AI application may resume after an incident?
Agree this before launch. The content owner assesses whether the work can be handled correctly again; the administrator assesses the technical correction and checks. The authorised process owner decides whether to resume based on those findings and any unresolved consequences.
Sources and technical basis
These primary sources explain the technical principles behind this guide. The project recommendations are OXIAE’s interpretation; they do not imply endorsement by the publishers. Product documentation applies to the named platform.
- AI Risk Management FrameworkNIST
Basis for managing AI throughout its lifecycle.
Link and relevance checked: - Evaluation best practicesOpenAI
A practical example of task-specific evaluation for changes.
Link and relevance checked: - LLM06:2025 Excessive AgencyOWASP GenAI Security Project
Risk guidance for limiting executable actions.
Link and relevance checked:
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