Key takeaways :
- AI and customer experience in B2B is about execution, not branding — when order information arrives late or support fails to respond, trust erodes; AI helps regain control through real-time visibility and proactive service
- Measurable impact: companies using AI to personalise customer interactions report 15-20% higher satisfaction and 20-30% reduction in cost to serve (McKinsey)
- Three high-value applications transform B2B order management: AI-driven order tracking with proactive notifications, intelligent chatbots handling routine inquiries 24/7, and hyper-personalised pricing and recommendations per account
- Data quality is the #1 barrier — fragmented customer and order data makes AI amplify errors rather than solve them; governance and integration matter as much as technology
In B2B order management, customer experience is defined by execution. When information arrives late or support fails to respond, trust erodes.
Artificial intelligence in customer experience helps companies regain control by improving order visibility and customer support interactions. This article explores how AI and customer experience solutions strengthen processes transparency and service quality across modern B2B operations.
What is an AI customer experience?
From reactive support to adaptive experiences
An AI client experience is not defined by a single tool or process. It emerges from the ability of multiple data, systems and interactions to connect across the customer journey. The shift is straightforward: moving away from reactive support toward experiences that adapt in real time.
Instead of waiting for issues to be reported, AI anticipates intent and friction points by analysing order status, past interactions and behavioural signals. Customer service moves upstream, before frustration builds, helping improve engagement.
Research from McKinsey shows that a company applying AI to personalise customer interactions can enhance customer satisfaction by 15 to 20%, while also reducing cost to serve. The impact goes beyond speed. It affects relevance, consistency and trust.
The role of generative AI in customer interactions
An AI-driven customer experience increasingly relies on generative models. Rather than following static workflows, GenAI enables systems to explain situations, suggest next steps and adapt responses to context.
According to Bain, organisations using generative AI in customer-facing functions report improved resolution quality, not just efficiency. In B2B environments, where journeys are complex, AI customer experience is less about automation and more about enabling clearer answers and faster decisions.
Why is AI in customer experience important?
Customer experience is now a performance issue
Customer experience has become a performance issue, not a branding topic. In B2B contexts, slow responses, fragmented support and unclear information directly affect retention, revenue and cost to serve.
Coping with scale and rising expectations
AI matters because it helps teams handle volume and complexity. As interactions multiply across channels, human teams alone struggle to maintain service quality. AI absorbs routine demand, prioritises issues and supports faster decisions when time matters.
According to McKinsey, organisations using AI to orchestrate customer interactions report 15–20% higher customer satisfaction and a 20–30% reduction in cost to serve. These gains come from better timing and relevance, not automation alone.
Expectations continue to rise. Customers compare service quality to the best experience they encounter elsewhere. Bain highlights that companies using AI in customer-facing functions retain customers more effectively by resolving issues earlier and reducing friction.
Benefits of AI in customer experience
AI and customer service: speed where it actually matters
The most immediate benefit of B2B AI solutions in customer service and experience is speed, but only where it matters. Response times improve at critical moments, when customers are waiting for answers or resolution.
Rebalancing effort across customer service operations
AI helps redistribute workload across customer service teams:
- routine requests are handled automatically
- order status checks no longer consume human capacity
- complex issues are escalated earlier, with better context
According to BCG, companies deploying AI in customer service report productivity gains of 15 to 30%, alongside faster resolution times and fewer repeat inquiries.
Consistency and cost control
AI also improves consistency across channels. The same logic applies across portals, email and support interactions. Capgemini shows that organisations using AI to personalise customer interactions can increase satisfaction by up to 20%, largely through more relevant and timely responses.
By improving first-contact resolution and reducing manual effort, AI lowers cost to serve without degrading service quality. Over time, faster responses and better consistency reduce friction and free teams to focus on customer success.
Challenges of AI in customer experience
Data quality and fragmentation
AI in B2B customer experience depends on data quality. In many organisations, customer and order data remain fragmented or inconsistent. When inputs are unreliable, AI amplifies errors instead of correcting them.
Integration with existing systems
Integration is another challenge. AI must connect with order management systems, B2B customer portal and support workflows. Without alignment between technology and processes, insights remain difficult to operationalise.
Trust, governance and risk
Customer experience and loyalty remains a human domain. Teams need to trust AI recommendations and understand their limits. Lack of transparency can slow adoption, even when the technology performs well.
As AI in B2B ecommerce scales, risk management becomes critical. Errors can propagate quickly across channels. Deloitte notes that companies succeeding with AI in customer experience invest as much in governance and operating models as they do in technology itself.
In real-world B2B operations, AI improves client experience only when insights are tightly connected to execution, not when they remain isolated in analytics or support tools.
AI-driven order tracking: enhancing transparency and customer satisfaction
The role of AI in real-time order tracking
AI enables real-time order tracking by connecting order data with logistics signals as execution unfolds. AI tools for ecommerce using machine learning models continuously update order status and delivery estimates based on what is actually happening on the ground.
Instead of relying on static ETAs, order management systems adjust information in real time. Customers receive clearer updates, while support teams work with the same live data, reducing uncertainty and unnecessary follow-ups.
Benefits for customer experience
AI-driven order tracking improves customer experience in practical ways:
- proactive notifications replace reactive status checks
- customers gain earlier visibility into delays or changes
- support teams rely on a shared, real-time source of truth
- potential issues are identified before they escalate
The result is not only better transparency, but fewer moments of friction during order execution.
Case example: Amazon
Amazon’s AI-driven logistics system illustrates this approach at scale. By analysing demand signals, inventory availability and transport capacity in near real time, the system continuously updates delivery commitments.
When disruptions occur, fulfilment and routing decisions adjust automatically. For customers, this means more reliable delivery information. For operations teams, fewer last-minute interventions.
Chatbots and virtual assistants in order management
The evolution of AI-powered chatbots
AI-powered chatbots have moved beyond scripted responses. Using natural language processing and machine learning, they understand intent, context and order history.
In order management, this allows customers to interact with systems more directly. Simple questions no longer require navigating portals or contacting support. Answers are immediate and contextual.
Key functions of AI in order management
AI-driven assistants support core order-related interactions:
- order status inquiries and delivery updates
- order modifications within predefined rules
- guided returns and refund requests
- 24/7 availability for routine support
By handling these requests automatically, chatbots reduce response times while freeing human agents to focus on complex cases.
Case example: H&M
H&M uses AI-powered chatbots to support order tracking, modifications and returns. Common requests are resolved without human intervention, reducing support load while maintaining consistent response quality across channels.
Hyper-personalized B2B order management with AI
AI in B2B personalization
In B2B environments, personalisation goes beyond content. It affects pricing, product availability and ordering workflows. AI enables this by analysing customer digital profiles, purchase history and behavioural patterns at scale.
Order management systems adapt to each account’s context instead of applying uniform rules.
Benefits for B2B order management
AI-driven personalisation supports more efficient order experiences:
- pricing reflects customer agreements and demand signals
- product recommendations align with past orders and operational needs
- recurring orders are suggested before stock constraints appear
This reduces friction for customers while improving predictability for operations teams.
Case example: Salesforce Einstein AI in B2B order management
Salesforce Einstein AI analyses customer interactions and order history to support personalised recommendations and next-best actions. In B2B order management contexts, this helps align sales, service and fulfilment decisions around a shared customer view.
Artificial intelligence and customer experience: what it changes for B2B order management
For B2B organisations, this shift matters. Customer experience is no longer managed at the edge of operations. It is embedded directly into order execution, where delays, exceptions and trade-offs occur every day.
This execution layer is also where customer experience increasingly intersects with sustainability. Improving visibility, reducing rework and anticipating issues earlier are central both to service quality and to AI for sustainable supply chains strategies.
Platforms like DJUST are designed to support this approach by embedding AI capabilities directly into B2B order management. The goal is not to add another layer of tools, but to align data, workflows and customer interactions around a shared, real-time view of the customer.
https://www.bain.com/insights/ai-wont-just-cut-costs-it-will-reinvent-the-customer-experience/
https://www.bcg.com/publications/2025/how-ai-agents-opening-golden-era-customer-experience
https://www.sciencedirect.com/science/article/abs/pii/S0747563225001311
https://www.capgemini.com/insights/research-library/shaping-the-ai-enabled-customer-experience/
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