Mongi Gazelle

Introduction

As an IT Program Manager working in cybersecurity, I interact daily with complex enterprise systems, overseeing the migration and onboarding of hundreds of applications in recent years. From this perspective, I can affirm that generative AI has emerged as a significant asset across multiple enterprise domains. Organizations including the Emirates Group, where I work currently, increasingly depend on large language models (LLMs) for functions such as customer support, marketing, fraud detection, and logistics optimization. At the same time, public debates—such as the 2024 Christie’s auction of AI-generated artwork—have amplified concerns about authorship, creativity, and the broader value of human contribution. These anxieties are beginning to shape business conversations as companies incorporate generative AI into customer-facing systems. Since the balance between Innovation, Responsibility, and Ethics is one my professional priorities, I tend to continuously analyze both the advantages and ethical challenges of using AI in customer engagement and outline a structured framework for its responsible deployment.

Customer Interaction takes center stage

Customer engagement is a prime-val area of potential automation. Unlike back-office operations, mistakes in customer support involve immediate consequences; this is especially true for organizations who deal with financial stability, personal health considerations, and legal obligations. In the past one year, banks, retailers, carriers and insurance companies all started to use LLMs at scale in their service workflows. These systems have been shown to improve reaction times, reduce operating costs and raise customer satisfaction (Zhao et al., 2023; Huang et al., 2024). But while great as they may be, these benefits do not come without ethical challenges.

Pros: Increase Service Value. The benefits of G.I.C. have numerous positive aspects for how they can significantly improve customer experience management:

• Immediate Assistance: Generative AI-driven systems provide around-the-clock, continuous help, which may be used from a distance or at any time, anywhere. This means that customers anywhere in the world can get their assistance promptly.
• Personalised Interactions: Individualized responses from models can be personalized based on previous conversations or user preferences for the individual person who had given a comment, letting personalization be carried out at large scale (Dwivedi et al., 2023).
• Operational Efficiency: Automation of repetitive tasks cuts labor costs and frees human personnel to address more complex or sensitive issues.
• Reduction of Routine Errors: The standardized information sourced from verified sources has been found to mitigate misinformation and enhance the reliability of support interactions (Colombo et al., 2023).

These advantages explain why organizations are starting to warm to generative AI, but they also highlight the absolute necessity for ethical filters.

Ethical Risks

Trust, Equity, and Human Agency. Generative AI remains powerful, although a number of significant risks have been found to arise from it:

Misinformation: LLMs can deliver confident but incorrect responses, presenting an issue of concern around the fields of healthcare and finance (Bender & Koller, 2021).

Privacy Concerns: Many users are quite unaware of how their data is collected, processed or recycled (Floridi & Chiriatti, 2020).

Bias and Discrimination: The systems that are trained on historical data run the risk of perpetuating social inequalities according to gender, race or socioeconomic status (Mehrabi et al., 2021).

Workforce Displacement: Customer-facing jobs — predominantly entry to mid-level — are particularly susceptible to automatise and the technology may perpetuate inequity (Brynjolfsson & McAfee, 2017).

Erosion of Authenticity: According to Mittelstadt (2023), simulated empathy calls into question whether AI-mediated relationships would be able to meaningfully replace caring for human beings.

These dilemmas echo those posed in debates over AI-generated art: Who has authorship over the work? Who benefits from automation? And which collective values are at stake?

A Framework for Ethical Integration

The question has changed from whether businesses should use generative AI to how much responsibility in how they use it. An informed intervention that works will see ethical principles translated into concrete decisions in both design and governance.

Core Ethical Principles: Transparency | Fairness | Safety | Accountability | Dignity.

Practical Guidelines

• Transparency: Provide clear information to customers about when AI is used and under what circumstances their data is handled.
• Human-in-the-loop: Implement AI to generate recommendations but allow human oversight to take place when making sensitive or high-impact choices – particularly in finance, healthcare, and legal environments.
• Data Governance: Implement restrictive data retention policies, encryption, and role-based access controls.
• Bias Tracking: Monitor outputs continuously against fairness metrics (Mehrabi et al., 2021).
• Workforce Strategy: Invest in upskilling and carve out new AI oversight and governance roles.
• Authentic design: Avoid deceptive interfaces which represent human-like environments – be guided by the principles of digital ethics (Floridi, 2019) and responsible innovation practices (Stilgoe et al., 2013).

This framework is centered around the idea that ethical AI relies on more than just technical solutions: it is about organizational values and governance structures.

From tools to agents: Evolving responsibilities.

Generative AI is rapidly moving from a support tool to something like an independent agent capable of performing things like password resets or invoice processing. As autonomy grows, fresh accountability issues arise:

Who has to make the decisions of an AI system? How can consumers challenge automated results? What are the mechanisms of auditability and accountability?

Though more and more regulations are established, the companies have to lead in order to become more proactive and not be dependent on an external regulation.

Conclusion

When done responsibly, generative AI can improve accessibility to customer service services, make it more efficient, and even out the distribution of jobs. It will, at least in theory, broaden the pool of knowledge available for service users and make service delivery smoother and more user-oriented. If organizations are only pursuing automation out of cost reduction, the risks of bias, misinformation, disrupted workforce and eroded trust remain high. This debate raises similar questions about AI-generated art: What happens to human creativity, empathy and agency that emerge even more fully when algorithms take on increasingly central positions? The future directions of generative AI ultimately come down to the values of the companies implementing it. If they are honest with each other in the information space, responsible and human-focused in the designs we send them, AIs can be very effective tools to aid humans and not replace them as it does now humans.

References

• Bender, E., & Koller, A. (2021). Climbing towards NLU: On meaning, form, and understanding. ACL.
• Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd. W.W. Norton.
• Colombo, C. et al. (2023). Generative AI in Customer-Service Workflows. Journal of Service Research.
• Dwivedi, Y. et al. (2023). AI for customer experience management. Journal of Business Research.
• Floridi, L. (2019). Ethics of Artificial Intelligence. Oxford University Press.
• Floridi, L., & Chiriatti, M. (2020). GPT-3: A New World? Minds and Machines.
• Huang, K. et al. (2024). AI-driven digital services. Information Systems Journal.
• Mehrabi, N. et al. (2021). A Survey on Bias and Fairness in Machine Learning.
• Mittelstadt, B. (2023). Principles Alone Cannot Guarantee Ethical AI. Nature.
• Stilgoe, J. et al. (2013). Responsible Innovation. Research Policy.
• Zhao, X. et al. (2023). Human-AI Collaboration in Customer Service. ISR.