Q 821.

Before launching an AI solution into real operations, organizations must be confident that it performs reliably, ethically, and within acceptable risk limits. Unlike traditional systems, however, AI may evolve, adapt, or behave unpredictably in new contexts — making certification more complex.

 

Think of a process in your domain where an AI system is being prepared for deployment.

What checks, validations, or sign-offs should be part of the AI readiness or certification process?

Who should be responsible for approving it — and how often should re-certification occur?

 

⚠️ Note: Any answer that is generic or does not connect with a specific, relevant process will not be approved.

 

🏆 The best answer will be selected on the basis of:

 

• Relevance of the chosen deployment scenario

• Practicality of the proposed certification or validation process

• Clarity in defining ownership and governance

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Domain: Aerospace Heat Treatment (Solutionizing & T6 Hardening)

Framework: NADCAP (AC7102) | AS9100 Rev D

At our plant, the AI system we’re getting ready to use will monitor the complete heat-treat process for aluminum parts — from the solution cycle (T42) all the way through aging (T6).
In short, it keeps an eye on furnace temperature, soak time, quench delay and even freezer logs once the load is out.
The goal isn’t to replace operators; it’s to make sure every batch follows the qualified recipe without slips or missed alarms.

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Step 1 – Prove the AI actually works

Before going live, we run it side-by-side with our existing SCADA for about a month.
It has to track furnace temperature within about ±3 °C, match soak-time control within a minute and recognize every out-of-limit event.
We also test it by creating small faults for example, a 5 °C sensor bias or a short delay in the quench timer,  just to see if it catches them.
If it misses anything, it’s back to tuning.
The fail-safe is just as important: if a thermocouple drops out, the AI must freeze the recipe and alert the operator immediately.

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Step 2 – Keep everything traceable

Every batch record is digital.
The AI automatically links furnace data, part numbers, start–stop times, quench logs and operator IDs.
When it recommends a change — say, adding a minute of soak time or adjusting voltage slightly — the reason and timestamp are stored in the traveller.
If it wants to move outside the approved limits, a Level 3 heat-treat skilled engineer has to sign off first.
That way, we can show auditors or customers exactly what changed and why.

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Step 3 – Certification and periodic review

Before the system touches real production, four people sign off:
the Quality Manager, Process Engineer, Compliance/EHS officer and the Automation lead.
Once it’s certified, we still re-check it every six months or any time we change the furnace mapping, bath chemistry, or retrain the model.
During each review, we run at least three test loads and make sure our Cpk on temperature and soak time stays above 1.67.
If the AI’s accuracy drifts by more than about five percent, it automatically pauses itself and waits for inspection.

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A quick example

During one trial, the AI noticed the middle zone in Furnace #3 cooling a few degrees faster than normal halfway through the soak.
It raised a warning — “Zone 2 temp deviation – 4 °C” — and maintenance found a weak blower.
Fixing that early saved a whole batch from re-processing.
All of it was logged automatically with the operator’s name and the part numbers involved.

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In summary

AI can do a lot of heavy lifting in heat treatment — watching temperature curves, soak times, quench delays, and freezer conditions — but it still has to earn its trust.
We treat its certification just like we do our furnaces or gauges: prove it, record it and re-check it.
That keeps us compliant with NADCAP and AS9100, while giving the team a smarter set of eyes on every load we run.

 

Q 821: How Should Organizations Certify AI Before It Goes Live?

 

Before any organization starts working on a framework for AI preparedness, the organization needs to run an assessment of AI readiness. AI readiness means whether the organization has the mindset to adopt AI, do we have necessary data to work, do we have enough people in organisation who have capability and understanding of AI, present architecture scalable and secure to support AI loads, measurable outcomes and governance of data.dopting AI code assistant in a software development team. What is AI code assistance? It is a tool that uses AI to help developers to write code which are effective and efficient. The tool also helps in debugging, doing code review, manipulation of files, and command line execution.efore adopting AI code assistant there are couple of steps an organization needs to do to make sure this tool creates value for the investment made by the organization.

§  AI preparedness framework: It needs to work on a framework which will create a de-facto standard in the organization to refer before any team in the organization would like to adopt an AI tool in their process or automation.

§  Sandbox Testing: Choose a mix of team members from a project and ask them to test the AI code assistant in their day-to-day activity to test the accuracy, reliability and consistency. Ask them to track the experiences every day. Set a time limit as to till when they can evaluate the tool and submit their feedback.

§  Data compliance: In co-ordination with compliance team, the organization team needs to evaluate how the AI is handling our data especially if the data is labelled as confidential, internal. How the data is stored and where it is stored? if there is an exception on how data is being handle, the compliance team should be able to work with the AI service provider to make sure the data handling by service provider aligns with the organization's data compliance and also ensure all the regulatory compliances are adhered. Does the tool bring in any data from outside the organization?

§  Internal Controls/External certifications: Have internal controls on the AI tool as to what it has access to and how it can be used. The organisation can also work on securing certifications like ISO/IEC 42001. The certification will enable the organization to evaluate frameworks in place, data compliance, storage policies, data flow, security.

§  Security: Evaluate the tool on how much secure it is. Whether the learning models are accessing data outside the organization for training (any unreliable source or from competitor organization). How it will access organization data and train?

§  Efficiency: Steps should be taken on how to streamline the process which help in reducing the cost of managing the tools in the organization.

§  Cost: Evaluate the cost of implementing the AI tool in the organization including how many resources required, time to implement, etc.

§  Trainings: Schedule trainings from the service provider to the teams on how to use the tool effectively as part of the offerings.

 

Action post adoption:

§  AI policy for usage: A policy needs to be in place in the organization for users on how to use the AI tools in their day to day. Restrictions that have been applied including terms and conditions on usage. The trainings that a team member need to attend or complete before getting access to the tool which upskills the team member at the same time educate on features that are allowed to use with the tool.

§  Transparency: Keeping a tab whether transparency is maintained with tool and by service provider as to what has changed, how the data is being handle including storage policies with every new release or update of the AI tool. Also, if there is a change in user-agreement from the service provider.

§  Check on competition: Organization will need to keep a tab on similar AI tools in the market which might be making significant progress in terms feature and cost when compared to the tool that has been adopted by the organization.

 

 

In order to ensure that all critical checks, validations, and approvals are completed prior to the launch of an AI solution for a Customer Complaint Chatbot , There is a need to emphasis on governance, compliance, model integrity, transparency, operational security, ethical considerations, and ongoing oversight. These elements are important to certify that the system will meet the expectations.

1. Governance & Oversight

Effective governance is fundamental for an AI deployment. The AI solution has to  aligns with the organisation’s overarching AI policy, It should supports robust model lifecycle management which include and adheres to the relevant regulatory standards and guidelines.

When defining the scope of the Project ,clear accountability for the AI system must be defined such as who is the owner, validator, business sponsor, and compliance officer. The Business Sponsor provides Final sign-off for accountability and role allocation. Additionally, it is essential that the AI system is properly registered as a critical application under the Risk Framework.

2. Regulatory & Compliance Checks

The Compliance and Legal teams are responsible to ensure that the ChatBot demonstrate full adherence to financial regulations.Data privacy and protection checks are conducted to ensure use of data complies local regulatory requirement.

Since we will be interpretating  data to address customer grievances special attention need to be given on customer fairness and non-discrimination, The  outputs to customer queries must be audited for biases across sensitive attributes such as gender, race, and age. This needs to be part of the Compliance department checks post the implementation of the system.

3. Model Development Validation

When designing the Model it should undergoes rigorous validation to ensure data integrity and performance. The Data Governance Team will be responsible  to validate the data source  for accuracy, completeness, and traceability.

The R & D team need to ensure that the Algorithm selection is scrutinised and that the  chosen model is appropriate in the context of the Customer Complaints handling and aligns with risk appetite. Rigorous performance test is done against benchmark datasets to meet the expectation of customers in terms of output. Additionally, overfitting and stability are assessed through testing across various scenarios, and these checks require sign-off by an Independent Model Validator.

4. Explainability & Transparency

Transparency is crucial for building trust in the AI systems. The rationale behind model decisions should be meticulously documented by the business Owner. To ensure accountability, there must be a clear audit trail for every prediction, with Internal Audit overseeing this requirement.

Furthermore, the outcomes produced by AI systems must be explainable to customers, particularly in sensitive contexts. Both Compliance and Legal departments are responsible for ensuring customer-facing communications are clear and informative.

5. Operational Readiness & Security

The Integration team does, thorough operational readiness checks prior to deployment. Integration testing validates that the AI system performs as expected within existing IT environments, with IT and DevOps teams responsible for this validation. Information Security ensure Access controls are checked so that only authorised personnel have the appropriate levels of access and that data is properly segregated.

An incident management plan is defined to address potential issues, such as wrong information are being generated and given to customer thus creating more frustration and adds to reputational risk.

6. Ethical and Social Impact Assessment

It is the responsibility of the Business Sponsor to includes a thorough evaluation of the AI system’s ethical and social implications. The Committee has to assesses potential social, ethical, and reputational risks. It is essential to ensure meaningful human oversight for critical decisions since the Bot will interact with customers.

The Compliance unit has to build a Mechanisms for transparency, contestability, and recourse,inorder to ensure customers are treated fairly and can challenge or appeal decisions.

7. Deployment & Post-Deployment Controls

Post UAT is done and concluded, and before the system goes live, pre-production testing has to be conducted in a sandbox or shadow environment this will validate the model performance and provides an assurance that the system is ready for deployment

If all the key stakeholders mentioned above assume their roles and responsibilities in creating a robust system.

Here are the winners of Q821.

🏆 Winner – Adil Khan (Aerospace Heat Treatment😞 outstanding NADCAP-compliant AI certification plan with controlled trials, Cpk > 1.67, and 4-tier sign-off governance.
🥈 Runner-up – Sattar Mohammad Imran (Complaint Chatbot😞 comprehensive multi-departmental certification and oversight framework ensuring fairness and compliance.
🥉 Special Mention – Arul Palani (AI Code Assistant😞 strong readiness and policy-based certification approach using ISO/IEC 42001 standards.