Swapnil Madhav Chaukar

How transparent ? I think is a grey area question. Can AI be transparent on logic used behind providing solutions ? The answer is Yes and No.
The answer is as simple as the prompt provided and resources provided by user and as complex as are we providing a knowledge base and references to AI or sending AI on a goose chase on open internet. The transparency of AI agents depends upon what are we providing it as an input.
 At my place of work:  banking customer service domain where decisions can significantly impact a customer's financial life, AI transparency is not just a nice-to-have — it's crucial.
We can look at it from three different perspectives.
Depending on the level of complexity we have build an AI agent in a customer service environment. If it is low risk and low stakes or high risk and high stakes.
AI Transparency in Low Stake Transactions:
·         Short rationale: A brief explanation like “Based on your credit score and income, you're eligible for a lower interest rate.”
·         Confidence score: medium, but helpful to show how certain the AI is.
Why: Customers want quick answers but appreciate knowing why they got a certain suggestion, so when we provide a rationale that because of your low credit score this is the best interest rate you can get. It satisfies the customer’s query. Why is this low stake? Because it is just an information and customer might not be loosing anything monetarily.
AI Transparency in medium risk and Medium-Stakes Interactions (e.g., loan pre-approval, document verification)
·         Steps of rationale: what can be shared : Outline key factors considered (e.g., income, employment history, credit utilization).
·         Audit trail: Since this info is internally logged for compliance and review, not necessarily shown to the user.
Why: Customers may want to contest or understand decisions, and regulators may require traceability. For e.g. if a home loan application gets rejected or rate of interest changes upon careful review of applicants credit history, customers will definitely seek explanations. The AI agent build might not provide the rationale behind the decision taken since it is based on a lot of internal criteria and due diligence by specific branch managers.
Now if we consider a
AI agent transparency in High Risk and High-Stakes Transactions or Interactions (e.g., loan rejection, fraud detection, dispute resolution)
·         A more detailed explanation is necessary: A clear, legible reasoning with references to policy or thresholds is necessary so that the customers get a complete picture of why a certain decision was taken, what is the basis.
·         Audit trail: It should be available for internal review & regulatory compliance.
·         Confidence score: Important to show uncertainty or borderline cases.
Why: These decisions directly impact customer’s financial status, morale and can cause frustration or financial harm, so trust and fairness are critical. AI needs to be fair and transparent when the stakes are high.
 
How to Balance Explanations and Simplicity
Draw the line based on user intent and impact: If the customer is just browsing for options, keep it simple. If the customer is making a decision or facing a rejection, offer layered transparency — start simple, but allow deeper insights on request. We should lead with a progressive disclosure: Display short rationale first. Offer the customer, a “Why was this decision made?” button for more details. We can also give downloadable audit logs or summaries for compliance officers or advanced users. Golden Nugget Mining : Now what are some best Practices for AI Transparency in Banking
We should use simple language: Avoid technical jargon when explaining decisions. Be open and consistent: If the customers with similar queries fall under same criteria, ensure similar cases have similar explanations. Opportunity of a VOC : Let customers contest, provide feedback or ask for clarification. Comply with regulations: Align with GDPR, RBI, or other local financial regulations on automated decision-making.

Possible root causes for lapse in control phase
 
Employee or workers resistance to Change: Lack of buy in from production team, Staff, if they find the new update or process difficult or confusing may revert to old habits, Insufficient periodic dip checks or audits: Without regular evaluation of a improved step or process, it is difficult to catch non adherence and the processes start reverting to previous, less efficient states. Inadequate Training: employees may not have had adequate training to adapt fully to new systems, which in turn might lead to improper use that negates expected improvement in the output Shift of business focus: Over time, changes in company strategy, customer expectations, or market conditions can render initial improvements less effective. Lack of executive level Support: Lack of technical or managerial support and disinterest can cause the momentum brought by the change to reduce drastically.  
Tools and Techniques to Maintain Improvements
Smart Automated Tools: Implement tools that automate parts of the system to minimize human dependency and ensure consistency. For example, using AI-driven systems for intelligent call routing based on live data analytics.  
Recreate Framework and deploy updated SOPs: Documenting the new processes in detail, including any new workflows and best practices, helps ensure everyone is on the same page.  
Periodic Review and Audits: Regular management reviews like weekly business review and process audits can help maintain focus on the importance of the new process or method of working, ensuring it remains aligned with strategic objectives.  
Continuous Refresher Courses: Share the benefits and positive impact of the change on the business outcome Continuous training helps reinforce the importance of the new tools or processes and ensure staff remain proficient in its use. Motivates the staff to imbibe change  
VOB use to have a Balanced scorecard and revise Metrics and Dashboards: imbibe the efforts metrics of changed or updated process, Implement metrics to continuously measure performance against key performance indicators (KPIs) like average handle time and customer satisfaction rates. Dashboards can provide real-time insights to assist in making proactive adjustments.  
VOC and VOE: Once a change or improvement is implemented, we should regularly gather feedback from both customers and employees to identify areas for further improvement and address minor issues before they become major problems.  
 
Example
Suppose a contact centre introduced a new call distribution system that reduced average call wait times from 10 minutes to 2 minutes. A smart use of AI agent project is run and implemented for automated categorization of query type by a Bot or IVR before the call reaches a live agent, Initially, this was a significant improvement, but over time, wait times began creeping back up. 
Upon analysis, it was discovered that:
Impact from New Agents: Incoming staff were not being adequately trained on the new system due to high turnover rates and hurry in implementation. Lack of involvement of training department in live projects resulting in new agents being unaware of the changed or improved process System Updates: Regular updates that could enhance system efficiency were not being utilized due to a lack of technical support.  
To address these issues, the contact center:
Implemented a mentorship program, pairing new employees with seasoned agents to ensure smooth transitions. Scheduled monthly system reviews to incorporate and evaluate new updates. Developed a dashboard allowing real-time tracking of call metrics, providing instant feedback to operators. In a Nutshell
By implementing these strategies and tools, contact centres can better maintain improvements gained from new routing systems in Control Phase, adapt to changes proactively, and ensure customer satisfaction remains high. Keeping systems resilient against backsliding requires constant vigilance and adaptation to the changing environment.

When we talk about Continuous improvement, as a concept, we refer to a constant WIP mode of innovation, enhancement and incremental progress. Take all possible learnings and loop it back an input to further refine a product or process.
VOC, VOB, error types, new data or new pattern or behavior of a certain process or a machine that can be studied, performance monitoring. Getting RCAs. Feeding it back into the system and closing the loop of a continuous self-learning improvement process with the help of Artificial Intelligence.
 
Natural language programing Reinforcement Learning : AI agents can learn by interacting with end users and learning best practices from online forums and receiving feedback (rewards or penalties). Over a period of time, AI models or AI agents can improve their decision-making based on outcomes. Example: AI in customer service optimizing responses based on customer satisfaction scores. Also, in an AI agent environment if a customer rates a low score or not resolved on a survey. AI can ask customer if he or she wants to get redirected to additional support Utilize online libraries: System based or conventional training methods have a focused content and is periodically reviewed once or twice a year. Unlike traditional models trained once on a fixed dataset, online learning allows models to update continuously as new data arrives. Can prove to be extremely useful in dynamic environments like fraud detection system that can improve itself whenever a new fraud pattern emerges or email classification or query categorization. Optimized Human-in-the-Loop (HITL): AI backend can incorporate VOC of output or a human feedback to refine and improve performance. On a continuous basis which a key component of continuous improvement For example, customer service agents correcting AI-generated email drafts helps the model learn better phrasing, grammar, formatting, and tone. Use concepts like A/B Testing and Feedback Loops: A tried and tested AI system can test different strategies (e.g., email templates) and learn which ones perform better. Manual or online VOC and Feedback loops help the system adapt to changing customer behavior or business goals. e.g. In a Banking Email Customer Service Context:
AI can learn from: VOC (NPS scores, complaints and RCA) Agent corrections to AI-suggested replies, check of all queries are answered in a multi query email. Frequency and Escalation patterns (e.g., which types of queries lead to dissatisfaction) Compliance checks (to avoid regulatory violations)  
Though there will be challenges to guarantee a continuous improvement on AI based models, if we study it enough it can be overcome.
 
Challenges like
Propagation of systematic Bias. For e.g. an AI model might be more favorable to a certain type of machine or high-performance shift timing, or certain region in terms of Sales etc.
Distribution or pattern shift. Or drifting of parameters,
Real world the situation changes dynamically so AI will have to be trained to Adapt. Failing which it will follow a fixed pattern and might not necessarily be effective.
 
In manufacturing or Healthcare sector or if we speak from a Six sigma perspective AI can
 
Conduct SPC if we feed it in initial stage. Analyze process deviations If we find some points or processes out of control, we will implement solutions to get the process in control, AI agent can learn from such corrective actions It can also Suggest process changes to reduce defects depending upon previous corrective actions taken by us or information available online. Would be better poised to predict process output or future failures or improvement opportunities