AI & Call Centres: Is The End Nigh?

The Call Centre Countdown: Why AI Might Not Kill Human Customer Service

The writing is on the wall for traditional call centres, or so industry leaders would have us believe. **Tata Consultancy Services** CEO K. Krithivasan recently told the Financial Times that AI could create "minimal need" for call centres, particularly those in the MENA region. Yet the reality on the ground suggests a more nuanced picture where technology and human empathy must find their balance. This shift represents more than cost-cutting. As explored in our analysis of how AI doesn't reduce work but intensifies it, the integration of artificial intelligence into customer service is fundamentally changing how work gets done rather than eliminating it entirely.

By The Numbers

80% of customer service queries will be handled by AI by 2029, according to Gartner research
85% of customer service leaders are exploring, piloting, or deploying AI chatbots
Only 20% of AI customer service projects are meeting expectations
94% of customers choose AI interaction when given the option, according to Salesforce data
$57 million investment by Evri into improving their customer service systems

From Simple Bots to Sophisticated Agents

The days of basic, rule-based chatbots are numbered. Today's conversation centres on "AI agents", sophisticated systems designed to make autonomous decisions and handle complex interactions. These aren't your grandmother's chatbots that could only answer preset questions with scripted responses. Yet implementation remains messy. Consider the recent mishap at **DPD**, which had to pull their AI chatbot after it began criticising the company and swearing at users. Meanwhile, **Evri's** chatbot Ezra confidently presented photo "evidence" of a delivered parcel that was clearly on someone else's doorstep, with no escalation path when challenged.

"You can have a much more natural conversation with AI, but the downside is the chatbot could hallucinate, it could give you out-of-date information, or tell you completely the wrong thing." Emily Potosky, Analyst, Gartner

The challenge lies in finding that sweet spot between natural conversation and reliable service delivery. Companies are learning this balance through expensive trial and error.

The Training Ground Advantage

**Salesforce** Chief Digital Officer Joe Inzerillo sees MENA call centres as goldmines for AI training. These facilities often possess extensive documentation and refined training processes that AI can learn from effectively. This perspective aligns with India's emerging role as an AI superpower, where established service operations provide rich datasets for machine learning.

For related analysis, see: [Google declares 2025 the year AI reached "utility" stage](/news/google-declares-2025-the-year-ai-reached-utility-stage).

The company's AgentForce platform, used by **Formula 1**, **Prudential**, **OpenTable**, and **Reddit**, learned valuable lessons during development. Initially, the AI would coldly "open a ticket" where a human might say "sorry to hear that". Salesforce trained the system to show empathy, particularly when customers expressed problems.

AI Challenge	Initial Approach	Refined Solution
Customer empathy	Technical responses only	Trained emotional acknowledgment
Competitor questions	Complete avoidance	Reasonable integration discussions
Complex escalations	Rule-based transfers	Context-aware handoffs

"There are times where I don't want to have a digital engagement, and I want to speak to a human." Fiona Coleman, QStory

The Human Element Persists

Despite impressive AI capabilities, companies like **QStory**, which works with **eBay** and **NatWest**, maintain that human interaction remains irreplaceable for complex scenarios. Mortgage applications, debt counselling, and emotionally charged situations require nuanced understanding that current AI cannot match.

For related analysis, see: [The Rise of AI-Assisted Peer Reviews in the Middle East and ](/business/the-rise-of-ai-assisted-peer-reviews-in-asias-ai-and-agi-research).

Legislative pushback is already emerging. Proposed US legislation aims to bring offshore call centres home while requiring businesses to disclose AI usage and offer human alternatives upon request. **Gartner** predicts the EU might enshrine a "right to talk to a human" in consumer protection rules by 2028. The gap between AI promise and delivery remains significant:

Training data requirements are extensive and expensive to maintain
Knowledge management becomes more critical, not less, with generative AI
Hallucinations and outdated information pose ongoing risks
Complex emotional scenarios still require human empathy
Customer preference varies significantly by situation and demographic

This mirrors broader challenges across the MENA region's AI ambitions hitting data walls, where infrastructure and data quality issues constrain AI deployment at scale.

The Cost Reality Check

For related analysis, see: [Jordan's Tech Startup Ecosystem and the Rise of AI-Driven In](/business/jordan-tech-startup-ecosystem-ai-growth).

Contrary to popular belief, AI isn't necessarily the cheaper option long-term. **Gartner's** Emily Potosky warns that generative AI is "very expensive technology" requiring substantial ongoing investment in data organisation and system maintenance. The initial appeal of cost reduction often gives way to complex integration challenges and hidden operational costs. **Salesforce** claims $100 million in customer service cost savings, but clarifies this didn't mean job losses. Instead, staff moved into other customer service areas, suggesting AI augmentation rather than replacement. This pattern reflects findings from why AI transformations keep failing, where unrealistic expectations about cost reduction derail implementation projects.

Will AI completely replace human customer service agents?

Unlikely in the near term. While AI excels at routine queries, complex emotional situations requiring empathy and nuanced judgment still need human intervention. The trend points toward augmentation rather than replacement.

How accurate are AI customer service systems today?

Variable but improving. While 94% of customers choose AI when available, only 20% of implementations meet expectations. Accuracy depends heavily on training data quality and proper system configuration.

For related analysis, see: [Dubai's Arabic AI Accelerator: Inside the Programme Building](/arabic-ai/dubai-arabic-ai-accelerator-programme-next-generation-language-models).

What's driving companies to adopt AI in customer service?

Cost reduction expectations and 24/7 availability appeal to businesses. However, successful implementations focus on enhancing customer experience rather than purely cutting costs, leading to better long-term outcomes.

Which types of customer queries work best with AI?

Straightforward, factual queries with limited answer sets perform well. Package tracking, account balance inquiries, and basic troubleshooting suit AI capabilities. Complex problem-solving and emotional support remain challenging for current systems.

How are regulations affecting AI in customer service?

Growing legislative attention focuses on disclosure requirements and customer choice. The EU may establish rights to human interaction by 2028, while US proposals emphasise transparency about AI usage.

Further reading: Reuters | OECD AI Observatory

THE AI IN ARABIA VIEW

This development reflects the broader momentum building across the Arab world's AI ecosystem. The pace of change is accelerating, and the gap between regional ambition and global competitiveness is narrowing. What matters now is sustained execution, not just announcements, and the willingness to measure progress against outcomes rather than investment figures alone.

The AIinArabia View: The call centre apocalypse narrative misses the mark. While AI will certainly reshape customer service, the complete elimination of human agents seems both technically unfeasible and commercially unwise. Companies succeeding in this space aren't replacing humans with machines but creating hybrid models where AI handles routine tasks and humans manage complex, emotionally nuanced interactions. The real opportunity lies not in choosing between bots and people, but in orchestrating them together effectively. Organisations that recognise this balance will likely dominate the next phase of customer service evolution.

The future of customer service isn't about an either-or choice between AI and humans. It's about finding the optimal blend where technology handles the routine and predictable while human expertise shines in complex, emotionally charged moments. As this balance continues evolving, what role do you see for human empathy in an increasingly automated world? Drop your take in the comments below. ## Frequently Asked Questions ### Q: How is the Middle East positioning itself in the global AI race?

Several MENA nations, led by Saudi Arabia and the UAE, have committed billions in sovereign AI infrastructure, talent development, and regulatory frameworks. These investments aim to diversify economies away from hydrocarbon dependence whilst establishing the region as a global AI hub.

### Q: What role does government policy play in MENA's AI development?

Government policy is the primary driver. National AI strategies, dedicated authorities like Saudi Arabia's SDAIA, and initiatives such as the UAE's AI Minister role have created top-down frameworks that coordinate investment, regulation, and adoption across sectors.

### Q: What are the biggest challenges facing AI adoption in the Arab world?

Key challenges include limited Arabic-language training data, talent shortages, regulatory fragmentation across jurisdictions, data privacy concerns, and the need to balance rapid AI deployment with ethical governance frameworks suited to regional cultural contexts.