AI Integration in the Services Industry Insights

A Paradigm Shift in the Global Services Economy

As of 2025, AI in the services sector is crossing a critical threshold: it is no longer a “support tool” but the main engine at the centre of business processes. The GenAI excitement of previous years is giving way to more mature, outcome-driven, autonomous “agentic AI” systems.1 This shift is fundamentally changing how service providers engage with customers, run operations, and shape value propositions. Goldman Sachs’ 2026 global growth forecast of 2.8% is directly linked to AI integration across business and to investments exceeding $100B into these technologies.1

This new era is also defined by AI leaving screens and operating in the physical world (embodiment). Amazon coordinating over a million robots via DeepFleet AI or BMW using self-driving vehicles on production lines are signals that intelligence is becoming an actor that performs work—not just a decision-support layer.1 As boundaries blur, companies must shift from “How should we use AI?” to “How have we placed AI at the centre of our business?”1

Moving into 2026, AI’s economic impact extends beyond productivity gains. PwC estimates AI could contribute up to 26% to global GDP by 2030.3 Of this, $6.6T is expected from productivity gains and $9.1T from consumption-side effects.3 Within services, industries with high AI exposure are observed to grow revenue per employee ~3× faster than sectors that have not fully integrated AI.4

Technology Foundations and Economic Drivers

In 2025–2026, the core infrastructure dynamic shifts from model training to inference. Deloitte expects that by 2026, roughly two-thirds of AI workload will be inference—running trained models to execute real-world tasks.1 This initiates a new cost and strategy phase often described as the “inference economy.”

Edge computing and real-time processing

Success in services depends on processing data close to where it is produced (e.g., IoT devices or in-store sensors). Edge AI reduces dependency on central servers and minimises latency. Deloitte indicates AI-enabled edge computing will reduce latency in 80% of IoT applications by 2025.5 In services, this is critical for real-time customer recognition, autonomous payments, and live logistics tracking.

Technology area	Expected impact (2025–2026)
IT operations automation	75% full automation level
Data processing and analytics speed	50%+ increase
Cybersecurity cost savings	$3.2B annually
Corporate ethical guideline adoption	70% of companies

Source: 5

Multimodal AI and contextual understanding

By 2025, AI is no longer limited to text. Multimodal systems process images, video, audio, and text together, enabling deep contextual understanding.6 In services, this allows support teams to analyse screenshots, call recordings, and usage logs simultaneously and propose solutions in seconds.8 For example, a telecom provider can diagnose an issue by reading modem LED status from a photo and guide the user through a voice system in real time.8

Agentic AI: A New Workforce Model for Services

If 2024 was “the year of chatting with AI,” 2026 is described as “the year of hiring AI.”9 Agentic AI does more than answer questions: it breaks complex goals into sub-tasks, interacts with external tools to execute those tasks, and supervises outcomes autonomously.10

The challenge of moving from pilots to production

Deloitte’s November 2025 data shows only 11% of companies have moved AI projects into full production.1 The remaining 89% are still in strategy or pilot phases. 2026 is positioned as the year this “pilot gap” closes. The key is not adding technology to existing processes, but redesigning processes for AI. McKinsey highlights that high performers redesign workflows ~3× more than others.1

Operational mechanisms of agentic systems

Compared to traditional automation, agentic systems differ in three core areas: goal-oriented execution, memory/context retention, and tool integration.10 An agent can remember previous interactions, access databases via APIs, and—e.g., in financial reporting—pull data, analyse it, generate a report, and email stakeholders end-to-end.10 A Gravitee study reports that 72% of mid-to-large enterprises already use agentic AI and 21% plan to adopt within two years.11

Autonomous Transformation in Banking and Financial Services

Financial services is one of the most AI-intensive segments and is entering a “Banking 4.0” era.12 Banks are shifting from back-office efficiency gains to proactive, personalised advisory services.

Proactive banking and hyper-personalisation

By 2026, banks use autonomous agents that detect slowdowns in customer cash flow and propose suitable financing before customers experience hardship.13 70% of finance leaders plan to democratise personalised financial advice—previously reserved for high-income clients—via AI agents for all customers.13

Banking application	Efficiency improvement
Digital service resolution rate (Bradesco)	83%
Technology cost reduction	30%
Operational processing speed	20% faster
Operational cost savings	15% reduction

Source: 13, 14

Examples in production

Brazil-based Bradesco built an API layer (“Bridge”) to run autonomous workflows using Microsoft Azure AI. This achieved an 83% resolution rate while reducing technology costs by 30%.14 In Argentina, Banco Ciudad launched 10 different AI agents in only six months for customer service and workflow automation.14 Lloyds Banking Group trained 10,000+ employees in AI skills, reached 93% daily usage, and saved up to 200 hours per employee annually.14

Retail and E-commerce

In 2026, retail undergoes a paradigm shift where content and intelligence merge.15 AI becomes not just a recommendation engine but a force shaping the end-to-end retail journey.

Predictive discovery and visual search

Multimodal AI makes search more intuitive. Customers can find products not only via text queries but by describing a “mood” or “intent.”15 Retailers combining visual search with text queries report 30%–40% increases in conversion.16 Pinterest Lens-like systems can identify a furniture style from a photo and return results that match both visual and textual criteria (e.g., “blue mid-century modern sofa”).16

Operational excellence: inventory and packaging

Amazon saves millions in packaging costs using AI systems that analyse product dimensions, shipping requirements, and inventory data to select optimal box sizes.16 Walmart uses multimodal systems to monitor shelf stock via cameras and combine it with sales data to trigger automated restocking orders—preventing stockouts that human inspectors might miss.16

Travel and Hospitality

In one of the most “human” service domains, AI is used to reduce routine workload so staff can improve the quality of guest relationships.17

Smart hospitality and operational efficiency

Global chains such as Marriott and Hilton have integrated AI across booking systems, housekeeping schedules, and loyalty programs.17 Marriott’s “ChatBotlr” and Hilton’s IBM Watson-backed robot concierge “Connie” deliver personalised local recommendations 24/7.17 Hotels using AI for pricing and revenue management report 17% revenue increases and 10% occupancy uplift.17

Operational foresight and sustainability

AI is critical not only in customer service but also in back-office operations. Predictive maintenance detects failures weeks in advance (e.g., a cooling system trending toward outage), preventing disruptions that impact guest satisfaction.17 Groups such as Accor reduce food waste with AI; properties like Fairmont Jakarta reportedly cut waste by 39%.18

Logistics and Supply Chain

In 2025–2026, logistics enters a “predictive orchestration” phase.19 Instead of siloed data models, integrated layers spanning procurement, production, and logistics are becoming dominant.

Document intelligence and compliance speed

RAG (retrieval-augmented generation) has transformed document-heavy workflows such as customs form classification, invoice verification, and certificate-of-origin cross-checking.20 In cross-border trade, AI can reduce manual review time by up to 80% while improving compliance accuracy.20

Graph RAG and supply-chain resilience

“Graph RAG” adoption accelerates in 2026. It helps interpret complex relational data such as multi-tier supplier networks, inter-facility dependencies, and production constraints.20 During geopolitical shocks or disasters, AI can simulate how impacts cascade beyond primary suppliers down to sub-suppliers and ultimately to delivery outcomes—within seconds.

Logistics application area	Core gain
Demand forecasting	20% improvement using external signals (weather, social data)
Autonomous sourcing	RFQ creation + bid evaluation automation
Route optimisation	15% savings in fuel and time cost
Logistics AI market	$21B by 2029

Source: 11, 20, 21

Workforce Transformation and Economic Effects

AI’s impact on labour is not only job loss, but deep role transformation. PwC’s 2025 Global AI Jobs Barometer shows AI-skilled workers earn up to 56% higher wages.4

A “skills quake” and wage premiums

Skills in AI-exposed roles change 66% faster than in other jobs.4 This makes continuous learning mandatory. AI capability is no longer a purely technical advantage; it becomes a baseline requirement to access fast-growing labour markets.22 Strong growth is expected in roles collaborating with AI across IT, data analytics, health, and education.

Flattening organisational structures

AI takes over many tasks traditionally executed by middle managers—reporting, scheduling, and performance monitoring—flattening organisations. By 2026, 20% of companies are expected to use AI to eliminate more than half of middle-management positions.22 Remaining managers must focus on strategy and high-value work.

Risks and opportunities for early-career talent

Research indicates AI most strongly affects entry-level roles. Employment in “high AI exposure” positions—where 22–25 year-olds are overrepresented—has dropped 13% since 2022.23 This signals a need for graduates to prepare for roles such as “AI–human collaboration specialists” who operate and audit AI systems rather than focusing on manual data entry or basic analysis.22

Regulatory Framework: EU AI Act and Global Standards

AI use in services is now under legal scrutiny. The EU AI Act is becoming a “gold standard” that shapes global regulation.24

Implementation timeline and banned practices

February 2, 2025: “Unacceptable risk” practices are banned—including emotion recognition in workplaces and schools, social scoring, and manipulative techniques that can cause physical or psychological harm.25
August 2, 2025: Transparency and copyright obligations for general-purpose AI (GPAI) models take effect.26
August 2, 2026: Comprehensive compliance for high-risk AI systems (health, employment, and critical infrastructure) begins.25

Transparency and ethical governance

Customer trust in AI correlates directly with transparency. 75% of consumers want companies to adopt ethical AI governance programs.28 Informing users that they are interacting with a bot or labelling AI-generated content is no longer just “good practice” but a legal obligation.27

AI in Turkey: 2025 Vision and Strategic Positioning

Turkey views AI as a growth lever that can contribute roughly 1% annually to GDP.29

Savings and efficiency in public services

AI-enabled smart automation is estimated to unlock $4B–$5B in annual public-service savings.29 Smart urban mobility projects and AI-supported public transport are improving efficiency in major cities.

Turkey as a commerce hub and AI

Events such as WORLDEF in Istanbul (2025) highlight Turkey’s vision to strengthen its e-commerce advantage with AI.29 The Turkish services sector—especially logistics and retail—focuses on bringing autonomous agents into enterprise operations. The clarity of regulation and the management of workforce transformation will shape Turkey’s global competitiveness in this domain.

Strategic Conclusions

2025–2026 is the period when AI in services moves from the “invention” phase to the “execution” phase. Companies that keep AI at the edges of operations will not be able to compete with those that adopt AI as a core operating system.

Critical success factors

Redesign processes: Automating inefficient processes only makes mistakes happen faster. Processes must be rebuilt around agent capabilities.1
Invest in AI literacy: AI capability must become an organisation-wide culture, not a single department’s job. Skills such as prompt engineering and ethical oversight should be top priorities.14
Build on trust and governance: Regulations such as the EU AI Act should be treated as opportunities to build customer trust. Transparent, accountable systems are the key to long-term loyalty.9
Edge + inference focus: Move from central cloud-only approaches to hybrid models that prioritise real-time decisions at the point of data generation.2

The services sector is now dominated by autonomous agents, multimodal interfaces, and autonomous decision mechanisms. Organisations embracing this shift early are positioned to lead the global economy from 2026 onward.