How AI will reshape B2B digital experiences by 2030

Highlights

You’ll learn how AI will power B2B digital experiences by 2030.

• Hyper-personalization: AI tailors content per user role in real-time
• Intelligent automation: Automated workflows, chatbots and lead qualification streamline processes
• Proactive engagement: Real-time analytics anticipate customer needs and fast-track sales
• Seamless omnichannel: Consistent data and digital experiences across all platforms
• Strategic insights: Predictive analytics and dynamic pricing optimize decisions

Embrace AI and opt for Contentstack’s EDGE to bring your AI strategy to life.

PwC estimates that AI will contribute up to $15.7 trillion to the global economy by 2030. Much of that will come from its enhanced productive influence on business operations. Along with the delivery of more personalized content and experiences, AI will power B2B digital transformation in the years to come.

Beyond the buzz, AI adoption has increased in recent years. But going by historical trends, the B2B sector has always been slower to adopt technology innovations, mainly due to the complexity of transactions. This is unlike the B2C sector, where transactions are quite simple—think ordering a pizza.

However, as business buyers start to demand the same experiences as they get in B2C settings, B2B businesses must leverage AI to bridge the gap.

AI technologies reshaping B2B digital experiences

A Salesforce study reveals that 82% of business buyers want the same experience as when they are buying as individuals. They feel a clear disparity between B2B and B2C experience. B2B brands must address these gaps, and here are the main AI solutions that will drive it.

Digital twins

Digital twins are virtual models of physical systems, processes or objects. They can be used to create virtual product demos and designs that customers can visualize before they make a purchase. Digital twins come in different types, as follows:

• Data twin: It can be used to blend real-time customer data, first-party data and engagement signals to simulate target audiences.
• Product twin: This digital twin can be used to visualize entire product life cycles, from concept design to full roll-out. This allows B2B firms to get real-time data on products as if they are in service.
• Systems twins: These twin models 'physical-digital' process interactions. They can be used to track customer journeys, store operations and manufacturing processes.
• Infrastructure twins: This represents the physical structures, like buildings. They are useful for managing projects, energy distribution and other related tasks.

Machine-to-machine (M2M) commerce

Building on the IoT technology, M2M allows a direct connection between digital devices. It can be deployed in field service, remote monitoring and to authenticate systems in B2B SaaS operations.

By 2030, M2M commerce will automate more B2B transactions. That is, buyers will be able to place orders without human intervention. Utility companies will deploy M2M to manage smart grid technology, and marketing teams will use it in digital signage ads.

Generative AI and LLMs

Generative AI is a type of AI that learns from large amounts of data to create new content, including text, video, images and other forms of media, based on specific prompts. A large language model (LLM) is a type of generative AI focused on creating texts. Generative AI and LLMs are already widely used, especially in content creation, but we have not scratched the surface of what they can do. 

Going forward, they will be applied even more in various business segments, for instance:

• Procurement and B2B commerce: It will be used to automate RFP generation, evaluate vendors, negotiate smart contracts, etc.
• Product development: Automated prototype generation from concept description, predictive testing and failure analysis.
• Workflow automation: This will apply to automated quality assurance and testing, smart scheduling, intelligent process automation and automated meeting summaries.

Predictive analytics

Predictive analytics allows B2B brands to anticipate customer needs based on how they interact with the business. Artificial intelligence (AI) analyzes historical data, market trends and customer behavior, enabling you to make forecasts, track inventory and recommend products and services.

Four pillars of AI-enhanced B2B digital experience by 2030

The AI-powered digital experience will be felt the most in four areas, as will be discussed below.

Hyper-personalization at scale

AI enables personalization that goes beyond basic account customization. By 2030, more B2B brands will deploy real-time data to tailor digital experiences at the individual level. For instance, if a procurement officer and technical evaluator from the same company visit a vendor's website, they will see different interfaces. While the procurement guy may view pricing models and contract terms, the technical evaluator may receive integration guides or related documents.

Intelligent automation

According to McKinsey, generative AI could enable automation of up to 70% of business. 

Activities by 2030 will add trillions of dollars to the global economy. We will see automated systems deployed to manage complex product data, maintain catalog accuracy across multiple digital channels and streamline approval workflows. It will also be used to qualify leads identified as high conversion prospects and used as chatbots for improved customer support.

Proactive customer engagement

IDC reports that by 2027, 45% of B2B lead and demand generation will transition to automated sensing and personalized engagements. AI systems will be used to analyze customer data and interactions in real-time, fast-pathing customers to transactional commerce. With that, you can identify customer needs or gaps in their journey for proactive support and faster sales via automation.

Seamless omnichannel orchestration

By 2030, AI will orchestrate consistent experiences across digital platforms, sales representatives, customer service agents and physical interactions. You can expect a seamless flow of customer data, history and preferences between touchpoints.

AI-driven business strategy

AI will need to be paired with robust tools, strategies and business models to ensure it delivers as much as it promises. 

Data consolidation

AI deployment requires clean data consolidated across the organization. This data creates a unified customer profile that gives you visibility into account activities, preferences and opportunities. Hence, tools like Contentstack’s real-time CDP will play a pivotal role in helping B2B brands ingest, harmonize and activate customer data on the go.

Buyer behavior predictions

Advanced analytics will allow B2B companies to predict buying patterns with precision. With real-time data, marketers can get signals and use AI to identify accounts ready to enter a buying cycle. That will enable them to prepare solutions based on likely needs and also work out the best way to engage them.

Dynamic pricing models

AI enables flexible pricing that accounts for market conditions, competitive positioning and the brand’s relationship with the customer. Dynamic pricing models will become standard in B2B by 2030, as prices will adjust based on inventory levels, demand patterns and customer value.

Omnichannel synchronization

AI will orchestrate omnichannel experiences, ensuring consistency across digital platforms. As B2B buyers move between channels, they will access consistent content. So, buyers can initiate contact via the web and close the purchase by email or in person. This data sync will eliminate the disjointed experiences that frustrate today’s B2B buyers.

The personalized B2B buying experience of 2030

So, what would the buying experience look like by 2030? It will be more personalized. AI already recommends products and services based on a buyer’s interaction with a brand. Beyond that, AI will recommend complete solutions based on business needs and objectives.

AI systems will be built to understand technical specifications, integration needs and terms specific to different industries. So, you can expect AI-driven virtual assistants that go beyond customer care and query resolution.

Balancing AI automation and human touchpoints

While AI will automate many aspects of the B2B buying experience, businesses must still relate to their customers on a personal level. For instance, they can automate routine inquiries but will still need to route strategic discussions or contract negotiations to human experts.

Roadmap to implement AI for B2B firms

Here is a summary of how B2B companies can implement AI to enhance digital experiences.

1. Assess digital maturity: Evaluate their current digital systems. Ensure you map customer journeys and evaluate the tech stack and data systems. Identify gaps in the digital experiences that AI can address. 
2. Build the data foundation: Prioritize creating unified customer profiles that combine information from marketing, sales, service and operations. Set up data governance policies and address issues of data security and regulatory compliance.
3. Consider the right technology stack: Evaluate B2B commerce platforms, CMS, customer data platforms and analytics. A real-time CDP and Contentstack’s EDGE help you make the most of customer data to deliver adaptive AI-powered digital experiences.

Case study: Digital experiences excellence built on Contentstack’s AI accelerator

Golfbreaks, a global brand, leveraged Contentstack’s AI to enhance its digital solutions, enabling it to target the right audiences with tailor-made content.

Golfbreaks

Golfbreaks struggled with slow content creation and publishing. This hindered their ability to respond to market opportunities. They took part in Contentstack’s AI Accelerator program and were trained on AI Assistant, Brand Kit and Automate. Now, they use AI to tailor solutions, categorize courses, vary content and market to specific segments.

The changes allowed them to decrease time-to-market by 78% and allowed them to save seven hours per week.

Simkins said. “I can say ‘I’d like to optimize nested references on our location pages’, and I know that Contentstack will provide valid and relevant ideas, and that the team has actually considered what we’re trying to achieve. It’s a breath of fresh air.” 

Read more on how Golfbreaks implemented AI to enhance time-to-market.

Overcoming challenges in AI-powered digital experiences

Given the vast amounts of data required to power AI systems, it is critical to be aware of potential issues that may arise during the process. Here are a few strategies to deal with them.

• Data privacy and security: McKinsey report shows that countries like Singapore, Brazil, China and the EU are already working towards legislation to regulate AI. As AI systems collect and analyze more customer data, B2B companies must prioritize data safety and privacy. They must improve data governance, advanced security and GDPR compliance. 
• Integration issues: Many B2B companies work with complex technology ecosystems built over decades. These legacy systems struggle to integrate new technologies. Leveraging APIs and cloud-native solutions, such as a real-time CDP and a composable DXP, is an excellent way to address this.
• Maintaining the human element: AI systems should not be designed to replace humans. Hence, businesses should prioritize their relationship with their customers. Automate repetitive tasks with AI and maintain a human touch in customer interactions.
• Technical skills gaps: B2B companies must bring their workforce up to speed on the use of AI and modern systems. They can do this through specialized training, such as Contentstack’s AI accelerator program.

FAQ section

How will AI-driven B2B digital experiences impact customer loyalty by 2030?

AI (artificial intelligence) will power personalized content and seamless experiences in B2B settings. These experiences will address the specific needs of B2B customers, enhancing loyalty.

What key technologies should B2B companies invest in?

B2B companies should invest in real-time CDP and analytics solutions, both of which provide unified customer views and enable personalized experiences.

What role will LLMs play in creating more personalized B2B customer experiences?

LLMs will enable natural language interactions and advanced personalized content creation. It will also power intelligent guidance through complex purchasing decisions.

Learn more

AI-powered digital experiences represent the future of B2B commerce. With AI, B2B companies can deliver more unique experiences, proactive customer service and intelligent automation. However, AI deployment relies on vast amounts of data that should be secure, consistent and consolidated.

With Contentstack’s EDGE, you can activate data, integrate intelligent content management and deliver adaptive digital experiences powered by AI. Contentstack’s AI accelerator program is packed with resources that enable you to discover and make the best use of generative AI within your Contentstack workflows. Talk to us to see how it works.