The enterprise AI revolution isn’t unfolding in Silicon Valley boardrooms or academic research labs. Across industries, AI is reshaping customer engagement, product development, and decision-making; not to replace people, but to extend their capabilities and scale expertise.
From aviation to automotive, telecommunications to luxury retail, leading brands are integrating AI with robust data strategies to streamline operations, personalise services, and create new value streams.
The following case studies reveal how organisations across sectors are deploying AI as a strategic imperative. From chatbots that genuinely understand context to recommendation engines that predict customer needs, these implementations offer a glimpse into the future of business operations optimised with AI.
Each story demonstrates a different facet of AI adoption. However, these implementations share common threads that illuminate the path forward for enterprise AI adoption. Together, they paint a picture of an economy in transition, where competitive advantage increasingly flows to those who can best harness artificial intelligence.
Finnair Takes Off with AI: Transforming Aviation Customer Service
Finland’s flagship carrier, Finnair, achieved 80% query resolution and reduced agent training times by 30% after becoming one of the first airlines to deploy Salesforce’s Agentforce platform.
Finnair connects 12 million passengers annually to 1,000 destinations with a 90% on-time rate. Now the century-old airline is applying operational precision to customer service through AI. “Customer satisfaction is at the centre of everything we do,” says Tiina Vesterinen, VP of digital customer and revenue, “but in the aerospace sector, disruption is inevitable.”
Building on a 2018 Salesforce foundation with Service Cloud and Data Cloud for loyalty programmes, Agentforce became a “natural evolution.” The AI agents draw data from Finnair’s website, Service Cloud insights, loyalty information, and Amadeus reservation system bookings to handle customer queries.
Operating on Finland’s largest web shop, Agentforce manages chat functions for loyalty schemes, trip queries, and baggage allowances. Human agents now focus on complex issues while AI handles routine triage. “It makes their job easier and allows them to focus on those situations where they are needed the most,” Vesterinen explains.
Next comes integration with Amadeus ERP for flight inventory access, enabling proactive proactively answer travel-related questions like alternative routing during disruption. Data privacy remains central: “We should always be very careful about the use of the data and the consents that we have from the customer.”
Separately, Salesforce’s Dreamforce 2024 revealed broader adoption trends. Publisher Wiley saw 40% chatbot improvements, while retailer Saks deployed AI assistant “Sophie” within weeks. Marc Benioff’s vision targets one billion AI agent interactions by next year, positioning Agentforce against competitors who “force enterprises to DIY GenAI solutions.”
Euro Car Parts: WhatsApp Business Meets AI for Automotive Excellence
UK automotive giant LKQ Euro Car Parts replaced an end-of-life telephony system with Genesys cloud orchestration and WhatsApp integration, now handling over 500 new WhatsApp conversations daily while eliminating data blind spots across 330+ branches.
The spare parts retailer stocks 160,000 distinct automotive components across a network rivalling Amazon’s warehouse footprint. Yet despite this scale, head of sales excellence Chloe Thomson admits: “Unless you’ve ordered a spare part from us, you’ve probably never heard of us.” The company’s challenge wasn’t visibility; it was adapting to generational shifts in customer communication preferences.
Sales teams were increasingly using personal WhatsApp accounts to communicate with customers, creating data silos and compliance risks. The February deployment of Genesys’ cloud-based system brought these interactions in-house while providing unprecedented visibility into customer behaviour and preferences.
“We’ve never had as much data as we do now,” Thomson explains. “We don’t have any more blind spots. We can see everything. We can now analyse the peaks and troughs of the different queries we receive as well as the different sales that are coming in.”
The WhatsApp integration enables sophisticated interactions: customers photograph registration plates requesting brake pads or clutches, while sales advisors access customer preferences to suggest complementary products like brake fluid promotions. This contextual selling approach transforms routine transactions into revenue opportunities.
Beyond immediate sales benefits, the system provides strategic insights into customer journeys and sales team performance. Thomson notes the company is studying contact centre methodologies to enhance its sales office model.
Future plans include AI integration for data mining and sentiment analysis, plus gamification to celebrate top performers with transparent, data-driven metrics rather than subjective assessments.
Telecommunications Titans Pioneer Next-Generation Conversational AI
Two of Europe’s largest telecommunications companies have offered candid insights into the evolution of conversational AI. Their experiences provide a blueprint for organisations seeking to balance automation with authentic customer engagement.
BT and Deutsche Telekom revealed ambitious chatbot strategies at London’s Chatbot Summit, with DT’s decade-old Frag Magenta handling millions of daily queries and BT’s Aimee targeting 400 million customer conversations.
Deutsche Telekom’s Franz Weisenburger outlined their “decade-long journey” with Frag Magenta, the German telco’s AI-powered chat and voice bot managing everything from internet faults to contract extensions.
“As you can imagine, 10 years is a long time to learn about how to interact with customers, how to use technology in the right way,” Weisenburger explained. The company partnered with Rasa to scale massively across chat and voice channels, leveraging Level 3 conversational AI that understands context and handles unexpected queries.
DT’s future vision includes “digital twin” concepts where AI agents seamlessly replace human workers during absences. “We envision a digital twin concept — leveraging technologies like robots, avatars, and LLM technology — where we can seamlessly step in for that worker when they are away on vacation,” Weisenburger described.
BT’s Kevin Lee positioned their Aimee chatbot as evolving beyond customer service into business intelligence. “Because Aimee has been harvesting its large language model across millions of customers a day, she will start to know what features we actually need to build for that particular customer,” Lee noted.
BT envisions Aimee achieving a net promotion score (which benchmarks customer satisfaction) exceeding 80 (which is in the top percentile) by 2025, based on over 400 million customer conversations.
LVMH: The Art and Science of AI in Luxury Beauty
The French luxury conglomerate has deployed over 200 AI products across its 15 beauty brands, including Dior and Fenty Beauty, using “Quiet AI” to enhance human creativity rather than replace it while maintaining artisanal excellence across Create, Move, Show, Sell, and Service categories.
LVMH’s beauty division encompasses numerous prestigious names, each requiring distinct digital approaches based on heritage, market segments, and geographical reach. Chief data and AI officer Julie de Moyer explained their strategy: “We’re not just undergoing a single data transformation. We’re working on multiple transformations, each tailored to the needs of different brands and business domains.”
The company’s internal AI chatbot, MaIA, assists employees from content translation to mock-up generation. MaIA predicts that by 2027, 70% of consumer experiences in beauty will be influenced by data and AI; a forecast reflecting the industry’s rapid adoption pace compared to other sectors.
Central to LVMH’s approach is “Quiet AI” that subtly supports decision-making without overwhelming creativity. In Guerlain’s fragrance development, AI analyses thousands of ingredients to help perfumers identify promising combinations more efficiently.
However, de Moyer emphasises human primacy: “The ‘art’ is the craftsmanship—the designer, the perfumer, the creative mind behind the product. The ‘science’ is the data and technology that enhance the decision-making process.”
The five-category framework spans product innovation (Create), operational optimization (Move), targeted marketing (Show), personalised retail (Sell), and post-purchase relationship management (Service). Each category utilises AI to enhance specific business functions while maintaining luxury brand standards.
LVMH collaborates with Stanford’s AI Ethics Programme to ensure responsible deployment, building what de Moyer calls a “robust data platform” for swift processing and market responsiveness in the fast-paced beauty industry.
