In March, we hosted our annual Adobe Summit conference, where we unveiled our latest innovations and discussed strategic shifts in customer experiences across industries. We’re seeing B2B brands undergoing major transformation and innovation in our B2B portfolio was one of the major themes for this year. As we look back at the evolution of B2B marketing strategies, we can identify three major inflection points along the way:

• B2B 1.0. In B2B 1.0, the customer record was digitised in a CRM where automation tools helped generate leads, which changed the way sales and marketing worked, but has proven to be somewhat messy.
• B2B 2.0. This phase focused on advancing top-of-funnel lead journeys and marketing organisations becoming account-based to better align with sales organisations.
• B2B 3.0. This next generation of B2B customer experiences focuses on creating personalised journeys with data, content and AI — with an emphasis on the full customer lifecycle.

In the shift to B2B 3.0, marketing and sales are taking a proactive approach, where they listen for signals from accounts in real time and let AI guide the next best conversation and experience for each customer. It's highly self-service and digital — where buyers have a "choose your own adventure" experience, deciding when and how to engage with a brand. They're using conversational AI to ask questions and educate themselves. B2B brands need to support this new type of interaction to build meaningful customer relationships.

Self-driving experiences.

B2B 3.0 is infused with artificial intelligence, but it doesn’t replace human relationships — it augments them. This evolution is reminiscent of how GPS technology developed. Handheld GPS seemed radical when it was invented. GPS 1.0 brought digital turn-by-turn guidance. GPS 2.0 evolved into mobile apps with real-time rerouting and arrival time predictions that adjusted for traffic. This innovation spawned billion-dollar industries like ridesharing and delivery companies — and changed the way we drive today.

Now we're experiencing GPS 3.0 — the self-driving car. The foundation of GPS 3.0 is all the driver-assist technology leading up to full self-driving capabilities. New chips, computer vision, LiDAR and data processing are coming together as a platform. It isn't just automating driving — it's completely reimagining how we think about transportation. Even for those uncomfortable with full self-driving, there are numerous innovations in the automobile creating an incredible experience for drivers.

We're at the cusp of the same kind of technological leap for B2B marketing and sales. The technical components required to let customers drive their own experiences are now available. Customer data platforms and advanced analytics serve as a system for interpreting complex buyer behaviour. They can capture and stitch together an impressive number of events and then infer which customers are in-market and what they're trying to accomplish. That data can power models for identifying the ideal customer and their intent to then generate the right content and experiences tailored just for them. This enables agile experience creation with components that quickly and efficiently adapt to customer needs.

This shift amounts to more content — carefully placed emails, relevant documents, more interactive apps and websites. If we can use these advanced systems to understand what buyers are looking for and place them in the right journey at the right time, that's a massive leap toward driving deeper relationships at scale.

Active listening data.

Natural language processing, generative AI, deep neural networks and other models are fuelling this transformation. AI is mainstream and accessible to address your most complicated problems and mundane tasks. In B2B, it's prompting us to question how we run our businesses. Choosing the right models and feeding them with a high volume of messaging and creative content — with appropriate guardrails — is how B2B powers the next era of relationship management.

In B2B, data is typically more complex than in B2C. You have "top of funnel" anonymous prospects and known customers, with each individual creating hundreds of signals over months or even years. Then you have the rest of the funnel filled with named contacts and accounts — generating thousands of signals through contracts, sales conversations, event attendance, partner interactions and more.

The data landscape looks vastly different than even a few years ago. Much of the buyer's research happens before they talk to a sales representative. If this research keeps bringing them back to your website or they attend an event or webinar, it's highly likely that you've made their shortlist. All of that first-party behaviour is rich with purchase intent, so why not build sensors to act as an active listening system connected to your data platform?

We have the technology now to process all of this data at scale. And this new generation of B2B sales and marketing will rely more on the insights from these behavioural signals, both explicit and implicit, to drive pipeline.

One area of disruption is the ideal customer profile (ICP) — a core part of most go-to-market strategies. ICPs are based on look-alike modelling, which assumes that current customers who have purchased resemble future customers who will purchase. Unfortunately, this process can still feel like guessing. An ICP strategy vastly understates the importance of being "in market."

B2B is ready for customer profiles that emit signals about propensity to purchase by flagging certain behaviours. An active customer profile should be the core of B2B 3.0 planning and account prioritisation. It has the potential to fix the outdated process of handing off spreadsheets of leads to sales by giving marketing, sales and even customer success teams a common understanding of accounts and their behaviour.

Where does AI come in? This customer behaviour data is the fuel for AI. A wide range of dynamic data is required for an AI agent to support planning, personalisation and next-best-action decisioning for an account. AI, whether an explicit agent or an implicit one, creates radical prioritisation for the front office and drastically reduces frustration for the customer.

Content assembly.

With the right data foundation in place, let's examine how experiences are created in B2B 3.0. There's a shift from search engines sorting web links to conversational AI providing answers. Similarly, a new generation of customers won't give their personal data for research whitepapers. Instead, they'll ask a conversational agent for summaries and key takeaways. Learning is becoming AI-based, but that doesn't mean content is useless — quite the opposite.

B2B companies must reimagine content production in B2B 3.0, designing assets specifically for AI consumption scenarios. One approach is breaking assets into modular fragments so an end-customer's AI can communicate with your AI agent to assemble research in real-time. This modular approach to creating content differs from the finished, polished assets marketing and sales typically create. Hero assets will still need to be created — the human touch and personal creativity will still finish content in many cases — but most variations will be AI-generated and AI-assembled.

Volume is an important factor in the future of customer experiences. The scale of content required for buyer research has increased exponentially. Building presentations, ads, reports, product documentation or onboarding videos manually will only slow down customer access to information. The opportunity here is for AI agents to use core creative assets and fragments to refine messaging and tone, making these variations more intimate.

Imagine an experience creation agent as a member of the account team, pulling together just the relevant materials for a meeting on the fly or making information available to a customer precisely when they need it. Your sales team might be able to make this work for your largest accounts where you're willing to invest in a human-intensive, white-glove approach. But how do you do this if you want to expand beyond your top 20 to 50 accounts without exponentially increasing your costs? You need to be where the customer is going and where they already are.

Customer-first journey orchestration.

No matter how large your customer base is, your relationship with customers should always feel personal. Generic messaging and campaigns don't support that feeling. If you've worked in a B2B setting before, you understand the complexity in these relationships. Your larger accounts might have 50 to 100 different people engaging with you, researching for over a year and interacting across different channels. Even if you only have hundreds of customers, the number of interactions is always in the thousands.

Given this complexity, curating unique journeys for each individual and account requires an incredible amount of digital orchestration. You must consider unique content for different roles and titles within the company — and whether they're practitioners or decision-makers. You need to create step-by-step paths for each persona.

For your mid-market and SMB segments one sales rep or customer success manager might be responsible for 100 accounts, making it impossible for them to prioritise effectively.

This is a perfect use case for agentic orchestration. Feed the AI all that data, your library of experiences, your goals — and it maps out the right journey for each persona. It dynamically routes and reroutes customers toward more education or a human interaction based on implicit or explicit behaviour. In B2B 3.0, when visitors come to your site, marketing, sales and customer success will be wrapped into one digital concierge that can help with top of funnel discovery, engage in meaningful conversations and qualify each person.

This type of agentic journey for different buyers is the magic of B2B 3.0. It's personalised relationships at scale. AI agents continue to learn from interactions and optimise models. This will affect how marketing and even sales create experiences.

Like every industry, B2B has entered a new era with AI — and it's quickly changing the customer lifecycle.

Adobe for B2B 3.0.

B2B 3.0 is reshaping how marketing, sales and customer success teams work together — with AI, content and customer data at the core.

Adobe solutions for B2B go-to-market orchestration are purpose-built to power this transformation. We connect data, content and journeys into a seamless system that helps teams engage buyers at every stage — from first touch to renewal and expansion.

Our platform helps you:

• Understand buying intent by interpreting signals across accounts, leads and buying groups.
• Deliver personalised content at scale — optimised for AI consumption, localisation and role-specific engagement.
• Orchestrate journeys across long, complex B2B cycles with the agility to adapt in real time.

Adobe is leading the shift in customer experience, from web and mobile to social and now AI. Across these platforms, the pillars remain the same — complete data, engaging content and personalised journeys. Our solutions help your team build stronger customer relationships, grow pipelines and drive more profitable experiences.

Learn more about Adobe for B2B go-to-market orchestration.

Sunil Menon is senior director, strategy & product marketing for Adobe Experience Cloud. In this role, Menon manages a team of product marketers working closely with cross-functional teams to further scale product and go-to-market strategy, messaging and positioning for products focused on customer journey management, campaign orchestration and real-time personalisation for both B2B and B2C businesses.

Menon joined Adobe in 2017 and over the past 6 years has worked in product management, strategy and product marketing leadership roles. He has over 24 years of experience working in various product management, general management and product marketing roles.  He joined Adobe from OpenText where he led product management and strategy for the digital experience portfolio.

Menon holds engineering degrees from the Indian Institute of Technology, Madras and Kansas State University and is a co-inventor of US Patent US20180181556 - SYSTEMS AND METHODS FOR CONVERSION OF WEB CONTENT INTO REUSABLE TEMPLATES AND COMPONENTS.

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