This article was originally posted at: https://www.eglobalis.com/ai-and-customer-experience-the-smarter-faster-and-more-personal-duo-redefining-b2b-success/
In today’s digital age, the relationship between technology and customer experience (CX) has become almost inseparable. As artificial intelligence (AI) continues to evolve, it is fundamentally reshaping how businesses interact with their customers, offering personalized, efficient, and predictive solutions. For B2B enterprises, the integration of AI into customer experience strategies has become a cornerstone for staying competitive. Companies like Samsung, Oracle, SAP, and Salesforce, which have adopted AI early on, are reaping significant benefits, from enhanced customer satisfaction to operational efficiency. This article explores how technology and customer experience are becoming more interdependent, with a focus on AI’s role in B2B environments. We will delve into ten key areas where AI is transforming CX, provide examples of enterprise technology companies that have been using AI for over eight years, and offer practical insights on how your company can leverage these advancements. Additionally, we will examine real-world examples from B2B companies across Europe, China, Japan, the USA, Brazil, and South Korea to illustrate the global impact of AI on customer experience. Clearly, there are still cases of companies that did not even need technology to deliver an exceptional customer experience. A prime example is the medical device giant Medtronic, which has a very sharp and well-oiled culture, but it is another topic.
Personalization at Scale in B2B
AI enables B2B businesses to deliver hyper-personalized experiences by analyzing vast amounts of customer data. For example, Samsung has been leveraging its proprietary AI model, Samsung Gauss2, to provide tailored solutions for its B2B clients. This model processes multiple data types, including text, code, and images, to deliver customized services such as coding assistance for developers and document summarization for corporate users. Similarly, Salesforce has been using its Einstein AI platform since 2016 to offer personalized recommendations and predictive insights for its B2B clients. By adopting similar AI-powered customer data platforms (CDPs), your company can segment B2B audiences and deliver personalized marketing messages, enhancing customer retention and satisfaction. For instance, Oracle uses its Oracle CX Unity platform to unify customer data across touchpoints, enabling businesses to create personalized experiences at scale.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle with delivering relevant content to diverse customer segments.
- Solution: AI-powered CDPs like Oracle CX Unity and Salesforce Einstein unify customer data from multiple sources (e.g., CRM, ERP, and marketing platforms) to create a 360-degree view of the customer. This allows businesses to segment audiences based on behaviour, preferences, and purchase history, enabling hyper-personalized marketing campaigns.
- Example: A manufacturing company using Salesforce Einstein saw a 25% increase in customer engagement by delivering personalized product recommendations based on past purchases and browsing behaviour.
Predictive Analytics for Proactive Support
Predictive analytics powered by AI allows B2B businesses to anticipate customer needs and address issues before they arise. Samsung’s SmartThings Pro platform uses AI to optimize energy consumption in commercial spaces, predicting usage patterns and adjusting settings in real time to reduce costs by up to 30%. Similarly, SAP has been using its SAP Predictive Analytics tool since 2013 to help businesses forecast demand, optimize inventory, and improve service delivery. For example, a manufacturing client of SAP reduced downtime by 20% by leveraging predictive maintenance insights. Your company can implement predictive analytics tools to forecast customer behavior, optimize inventory, and improve service delivery, ensuring proactive support and operational efficiency.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often face inefficiencies due to reactive customer support and supply chain disruptions.
- Solution: AI-driven predictive analytics tools like SAP Predictive Analytics and Microsoft Azure AI enable businesses to forecast demand, predict equipment failures, and optimize inventory levels. This reduces downtime, improves service delivery, and enhances customer satisfaction.
- Example: A logistics company using SAP Predictive Analytics reduced delivery delays by 15% by predicting potential bottlenecks in its supply chain and rerouting shipments in real time.
AI-Driven Chatbots and Virtual Assistants
AI-driven chatbots and virtual assistants have revolutionized B2B customer service by providing instant, 24/7 support. Samsung’s Gauss2 model includes a conversational AI service that assists employees with tasks like document summarization and email drafting, significantly improving workplace productivity. Similarly, Salesforce’s Einstein Bots have been helping B2B clients automate routine inquiries since 2018, freeing up human agents for complex issues. For example, a global logistics company using Salesforce reduced its customer service response time by 40% through AI-powered chatbots. By integrating AI chatbots into your customer service strategy, your company can reduce response times, handle routine inquiries efficiently, and improve overall customer satisfaction.
How It Helps Companies Overcome Challenges:
- Challenge: B2B customer service teams are often overwhelmed by repetitive inquiries, leading to slow response times and frustrated customers.
- Solution: AI-powered chatbots like Salesforce Einstein Bots and Google Dialogflow automate routine inquiries (e.g., order status checks, FAQs) and escalate complex issues to human agents. This reduces response times and improves customer satisfaction.
- Example: A financial services company using Google Dialogflow reduced its average response time from 12 hours to 2 hours, resulting in a 20% increase in customer satisfaction scores.
Sentiment Analysis for Enhanced Engagement
AI-powered sentiment analysis tools help B2B businesses understand customer emotions and tailor their responses accordingly. Samsung uses AI to analyze customer feedback and improve its B2B solutions, ensuring that its products and services align with client needs. Similarly, Oracle has been using its Oracle Text Analytics tool since 2015 to analyze customer feedback from surveys, social media, and reviews. For instance, a retail client of Oracle improved its Net Promoter Score (NPS) by 15% by addressing negative sentiment identified through AI analysis. Your company can use sentiment analysis to monitor social media, reviews, and customer feedback, enabling you to address concerns and improve brand perception.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle to understand customer sentiment and address dissatisfaction in real time.
- Solution: Sentiment analysis tools like Oracle Text Analytics and IBM Watson Natural Language Understanding analyze customer feedback across multiple channels (e.g., surveys, social media, reviews) to identify trends and actionable insights. This enables businesses to address concerns proactively and improve customer satisfaction.
- Example: A healthcare provider using IBM Watson improved its NPS by 10% by identifying and addressing negative feedback related to appointment scheduling delays.
AI-Driven Customer Journey Mapping
AI helps B2B businesses map and optimize the customer journey by identifying pain points and opportunities for improvement. Samsung’s SmartThings Pro platform integrates AI to create seamless, omnichannel experiences for its B2B clients, from smart apartments to AI-powered offices. Similarly, Salesforce has been using its Journey Builder tool since 2014 to help businesses create personalized customer journeys. For example, a financial services client of Salesforce increased customer engagement by 25% by optimizing its journey maps using AI insights. Your company can use AI-driven journey mapping tools to identify bottlenecks and enhance the overall customer experience, ensuring a smooth and efficient journey.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle with fragmented customer journeys, leading to poor experiences and lost opportunities.
- Solution: AI-driven journey mapping tools like Salesforce Journey Builder and Adobe Journey Optimizer analyze customer interactions across touchpoints to identify pain points and optimize the journey. This ensures a seamless and personalized experience for customers.
- Example: A retail company using Adobe Journey Optimizer increased its conversion rate by 18% by streamlining the checkout process and reducing friction points.
Voice and Visual Search in B2B
AI has transformed how B2B customers search for products and services through voice and visual search technologies. Samsung’s AI-powered devices, such as its Neo QLED TVs and Family Hub refrigerators, use generative AI to create dynamic visual content, enhancing user engagement. Similarly, Oracle has been using its Oracle Digital Assistant since 2018 to enable voice-activated search and commands for its B2B clients. For example, a healthcare client of Oracle improved operational efficiency by 30% by enabling voice search for its inventory management system. Your company can adopt voice and visual search capabilities to make it easier for B2B clients to discover your offerings, improving accessibility and user experience.
How It Helps Companies Overcome Challenges:
- Challenge: B2B customers often struggle to find the right products or services quickly, leading to frustration and lost sales.
- Solution: Voice and visual search technologies like Oracle Digital Assistant and Amazon Lex enable customers to search for products using natural language or images, improving accessibility and user experience.
- Example: A manufacturing company using Amazon Lex reduced its average search time by 50%, resulting in a 15% increase in sales.
Dynamic Pricing and Revenue Optimization
AI enables B2B businesses to implement dynamic pricing strategies based on real-time market conditions and customer behaviour. Samsung’s AI solutions for retail stores analyse energy consumption patterns and adjust pricing strategies to optimize revenue. Similarly, SAP has been using its SAP Price and Margin Optimization tool since 2014 to help businesses set optimal prices based on market demand and competitor pricing. For example, a retail client of SAP increased its profit margins by 10% by leveraging AI-driven pricing insights. Your company can use AI-powered pricing tools to optimize pricing strategies and stay competitive in the B2B market.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle to set optimal prices due to fluctuating market conditions and competitor pricing.
- Solution: AI-powered pricing tools like SAP Price and Margin Optimization and Pros Pricing Software analyze market data, competitor pricing, and customer behavior to recommend optimal prices. This maximizes revenue and ensures competitiveness.
- Example: A logistics company using Pros Pricing Software increased its profit margins by 12% by adjusting prices dynamically based on demand and competitor actions.
Fraud Detection and Security
AI enhances customer trust by improving security and detecting fraudulent activities. Samsung’s Knox Matrix, a blockchain-based security solution, uses AI to create a secure environment for connected devices, ensuring data privacy and protection. Similarly, Oracle has been using its Oracle Fraud Detection tool since 2016 to help businesses identify and prevent fraudulent transactions. For example, a financial services client of Oracle reduced fraud losses by 50% by leveraging AI-driven fraud detection. Your company can implement AI-driven security solutions to protect customer data and build trust, ensuring a secure and reliable experience.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies face increasing threats from cyberattacks and fraudulent activities, which can damage customer trust.
- Solution: AI-powered fraud detection tools like Oracle Fraud Detection and SAS Fraud Management analyze transaction patterns and detect anomalies in real time, preventing fraud and enhancing security.
- Example: A financial services company using SAS Fraud Management reduced fraudulent transactions by 40%, saving millions of dollars annually.
AI in Product Development
AI helps B2B businesses design products that better meet customer needs by analyzing feedback and usage data. Samsung’s Gauss2 model supports product development by generating code and optimizing designs, ensuring that its B2B solutions align with market demands. Similarly, Salesforce has been using its Einstein AI platform to help businesses gather customer insights and develop products that meet specific needs. For example, a manufacturing client of Salesforce reduced its product development cycle by 20% by leveraging AI insights. Your company can use AI to gather customer insights and develop products that meet the specific needs of your B2B clients.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle to develop products that meet evolving customer needs due to limited insights and long development cycles.
- Solution: AI-powered product development tools like Salesforce Einstein and Autodesk Generative Design analyze customer feedback and usage data to identify trends and optimize designs. This reduces development time and ensures products meet customer needs.
- Example: A construction company using Autodesk Generative Design reduced its product development time by 25%, resulting in faster time-to-market and higher customer satisfaction.
Enhanced Customer Feedback Analysis
AI automates the analysis of customer feedback, enabling B2B businesses to identify trends and actionable insights. Samsung’s Gauss Portal uses AI to summarize and translate documents, providing valuable insights for corporate users. Similarly, SAP has been using its SAP Qualtrics platform since 2018 to help businesses analyze customer feedback and improve their offerings. For example, a retail client of SAP improved customer satisfaction by 15% by addressing feedback identified through AI analysis. Your company can use AI-powered feedback analysis tools to gain deeper insights into customer preferences and pain points, ensuring continuous improvement.
How It Helps Companies Overcome Challenges:
- Challenge: B2B companies often struggle to analyze large volumes of customer feedback manually, leading to missed insights and slow improvements.
- Solution: AI-powered feedback analysis tools like SAP Qualtrics and Medallia automate the analysis of customer feedback, identifying trends and actionable insights. This enables businesses to address issues quickly and improve customer satisfaction.
- Example: A telecommunications company using Medallia improved its customer retention rate by 10% by addressing feedback related to service quality and billing issues.
Real-World Examples from B2B Companies
Europe: Siemens
Siemens, a German multinational, has been using AI in its industrial automation and energy management solutions since 2015. By integrating AI into its MindSphere platform, Siemens enables predictive maintenance and energy optimization for its B2B clients, enhancing operational efficiency and customer satisfaction.
China: Huawei
Huawei, a leading Chinese technology company, has been using AI in its cloud and enterprise solutions since 2014. Huawei’s AI-powered cloud services help businesses optimize operations, improve customer support, and enhance data security, making it a trusted partner for B2B clients.
Japan: Hitachi
Hitachi has been leveraging AI in its social innovation business since 2013. By using AI to analyze data from infrastructure projects, Hitachi provides actionable insights to its B2B clients, improving decision-making and project outcomes.
USA: Google Cloud
Google Cloud has been using AI to enhance its enterprise solutions since 2014. With tools like AutoML and AI-powered analytics, Google Cloud helps businesses improve customer experiences through personalized recommendations and predictive insights.
Brazil: Totvs
Totvs, a Brazilian software company, has been using AI in its enterprise resource planning (ERP) solutions since 2015. By integrating AI into its platforms, Totvs helps businesses automate processes, analyze customer data, and improve service delivery.
South Korea: Samsung
Samsung has been a pioneer in using AI for B2B applications, particularly through its Gauss2 and SmartThings Pro platforms. These solutions enable personalized experiences, predictive analytics, and enhanced security for its B2B clients, showcasing the transformative power of AI in customer experience.
Conclusion
The interdependence of technology and customer experience is undeniable, and AI is at the heart of this transformation. From personalization and predictive analytics to chatbots and dynamic pricing, AI is revolutionizing how B2B businesses interact with their customers. Companies like Samsung, Oracle, SAP, and Salesforce have demonstrated the power of AI in enhancing customer experience over the past decade. By adopting AI-driven strategies, your company can improve customer satisfaction, optimize operations, and stay ahead of the competition. The future of customer experience lies in the seamless integration of AI, and businesses that embrace this trend will thrive in the digital era.
Sources:
- Samsung’s AI Strategy Centered on Customer Experiences
Samsung Newsroom
https://news.samsung.com/global/ai-leadership-%E2%91%A2-samsungs-ai-strategy-centered-on-customer-experiences - Samsung Eyes B2B Market with AI Solutions
The Korea Times
https://www.koreatimes.co.kr/www/tech/2024/09/133_382130.html - Samsung Unveils Bold AI Plans to Elevate Customer Experience
Rolling Out
https://rollingout.com/2024/12/10/samsung-ai-customer-experience/ - Generative AI for Customer Experience
Samsung SDS
https://www.samsungsds.com/kr/insights/generative-ai-for-customer-experience.html - Samsung to Bolster B2B AI Services
The Investor
https://www.theinvestor.co.kr/article/3470987 - Samsung, LG Ramp Up AI Development Race with Custom Language Models
Korea Bizwire
http://koreabizwire.com/samsung-lg-ramp-up-ai-development-race-with-custom-language-models/304195 - Siemens: AI in Industrial Automation and Energy Management
Siemens Press Release
https://press.siemens.com/global/en/pressrelease/siemens-expands-ai-capabilities-mindsphere - Huawei’s AI-Powered Cloud Solutions
Huawei Enterprise Blog
https://e.huawei.com/en/blog/ai-cloud-solutions - Hitachi’s AI-Driven Social Innovation Business
Hitachi Review
https://www.hitachi.com/rev/archive/2023/r2023_01/index.html - Google Cloud’s AI Tools for Enterprises
Google Cloud Blog
https://cloud.google.com/blog/products/ai-machine-learning - Totvs: AI in ERP Solutions
Totvs Corporate Website
https://www.totvs.com/en/solutions/ai-in-erp/ - Samsung Knox Matrix: AI and Blockchain for Security
Samsung Knox Blog
https://www.samsungknox.com/en/blog/knox-matrix-ai-blockchain-security - Samsung SmartThings Pro: AI for Energy Optimization
Samsung SmartThings Blog
https://www.smartthings.com/pro/ai-energy-optimization - Samsung Gauss2: AI for B2B Productivity
Samsung SDS Blog
https://www.samsungsds.com/kr/insights/gauss2-ai-productivity.html - Samsung Gauss Portal: AI for Document Summarization
Samsung Newsroom
https://news.samsung.com/global/samsung-gauss-portal-ai-document-summarization
That’s a long and comprehensive list Ricardo, and I think that the lenght of the list illustrates one of the big challenges: there are so many possibilities, perhaps too many! Somehow, and I don’t know exactly how, companies have to pick the one or two solutions or solutions categories that will make the greatest difference. A key to resolving this may for a leadership team to be crystal clear on the top two or three KPIs they want to improve over, for example, the next 18 months. If the top metric is Net Revenue Retention, that points to ML solutions that examine all of the operational, attitudinal and customer profile data that exists in all IT systems for all customers, and working out what has driven NRR in the past and can therefore have most impact in the future. Platforms like Qualtrics AI that only works on the tiny fraction of customers who respond to surveys are close to useless for this, in my opinion.
On the other hand, if the company has labor cost issues, perhaps the focus will need to be on that, and here is where Gen AI bots can help, especially in contact center work, for example.
Overall, I think B2B companies should therefore set out which are the top one to three KPIs they need to improve, and the selection process will be a lot easier.
Very interesting article, Ricardo. You shared a number of insightful case studies from companies that are deploying these technologies.
Business is moving toward a more AI process for managing customer service and CX. However, we’re not at a point where it can be 100% automated. There is still balance that must be achieved to get the customer to come back. This was a big article, but one of the ideas you covered was personalization at scale. It’s nice to be known, and the companies and brands that make the effort, even if it is AI-fueled, to make a customer feel like they are known, remembered, and appreciated will have a competitive advantage.
Very comprehensive analysis of the different ways AI can support the multiple challenges that B2B companies face. The key is to truly have a a 360-degree view of the customer. As Ricardo says, this allows businesses to segment audiences based on behavior, preferences, and purchase history, enabling hyper-personalization to better target and serve them.
Maurice, you’ve made an excellent point — and as always, your experience shines through in the depth of your analysis. You’ve highlighted one of the biggest strategic challenges companies face today: too many AI options and too much complexity in deciding which solutions will make the greatest impact.
-Your suggestion to focus on one to three core KPIs over the next 18 months is absolutely correct. Many companies make the mistake of chasing too many goals at once, which dilutes focus and reduces the likelihood of success. Defining clear, high-priority KPIs creates a strategic framework for AI adoption and ensures that resources are allocated effectively.
Your example of focusing on Net Revenue Retention (NRR) is particularly relevant. Machine learning (ML) models that analyze operational, behavioral, and customer profile data can uncover patterns and drivers of NRR — helping businesses make data-driven decisions to increase customer loyalty and retention.
I also agree with your point about labor cost issues. Generative AI bots can play a crucial role in reducing contact center workload and improving response times. Automating routine inquiries allows human agents to focus on complex, high-value interactions — driving both efficiency and customer satisfaction.
While setting clear KPIs is critical, the real challenge is alignment. Different departments often have conflicting priorities — for example, sales might prioritize customer acquisition while customer service focuses on resolution times. Achieving alignment across the organization is key to making AI adoption successful.
Maurice, you’ve nailed the key point — it’s not about adopting AI for the sake of ”innovation”; it’s about strategic focus and execution. Once leadership defines the core priorities, the choice of AI solutions becomes much clearer and more effective. Thanks so much for such detailed and thoughtful insights, Ricardo
Hi Shep, I really appreciate your thoughtful feedback — your insights always carry weight in the CX field. You’re absolutely right about the importance of balance. While AI is transforming customer service, we’re far from a point where full automation can replace the human touch. Personalization at scale is key — and as you said, making customers feel known, remembered, and valued, even through AI-driven solutions, creates a competitive edge. That human element — whether direct or AI-fueled — remains critical for building long-term loyalty.
Funny enough, my new article out today discusses exactly that — when it’s the right time to move from an AI agent to a human. Almost the exact topic you mentioned! Here it is: https://www.eglobalis.com/orchestrating-the-switch-from-ai-agents-to-human-interaction-when-and-why-it-matters-in-cx/
Thanks so much for sharing your expertise — always (be amazing) great to hear your perspective! Ricardo
Scott, thanks you for taking the time to comment — I really appreciate your perspective! I’m glad you found the case studies insightful. It’s fascinating to see how different companies are using these technologies to enhance CX. Always great to hear your thoughts — thanks again! Ricardo
Based on a conversation earlier today, I have something to add here. We humans always want to find the red flags, the burning fires, the major issues, so we can fix them. AI does not suffer from this irrational defect. If an AI (based on analyzing all the operational, financial, profile and attitudinal data in your IT systems) determines that the best ROI on an action to improve B2B customer retention and growth (meaning NRR) is to reinforce and spend more on something that you are already doing well, the AI will tell you that clearly and simply, helping you to deprioritize fire fighting that may have low ROI.
During the conversation today, it occurred to me for the first time that teams generally don’t like us CX professionals coming into the room and telling them exclusively about what they are doing wrong, directing them to work only on Detractor issues, for example. Yes, that’s what I used to do for about half of my professional life. I realized I was completely wrong about that today. To be welcome, you have to be more balanced, perhaps even unbalanced on the positive side, directing them to spend more on the good stuff that is really effective before mentioning the bad little thingy you found too. Then close with another good thing. Psychologists call that “The compliment sandwich” and it is fabulous for getting people to act.
Thank you for your insightful feedback Susanna. I completely agree that achieving a 360-degree view of the customer is essential for B2B companies to effectively address their challenges. Leveraging AI to segment audiences based on behavior, preferences, and purchase history enables hyper-personalization, allowing businesses to better target and serve their customers. This approach not only enhances customer satisfaction but also drives business growth. Thank you again , Ricardo
What a great reflection! Maurice, you’ve highlighted a crucial aspect of human behavior in professional settings. Our tendency to focus on problems can overshadow the importance of reinforcing what’s working well. AI’s objective analysis can indeed guide us to invest more in our strengths, leading to better ROI. Your insight into balancing feedback—emphasizing positives before addressing negatives—is a valuable approach to foster team receptivity and growth. Living and learning, your perspective offers fresh insights. Thanks so much, Maurice; I always learn a lot from you. :-) Ricardo