AI in B2B sales: How smart assistants transform work processes

Artificial intelligence is rapidly changing how companies in B2B sales work with software, make decisions, and grow. But where does the hype end, and where does real value begin?

Especially in combination with modern CPQ software solutions (Configure–Price–Quote), one thing becomes clear: companies that do not adopt AI will sooner or later fall behind. “Innovation leaps no longer happen annually, but quarterly,” says Simone Schatto from the international consulting company Roland Berger. Even today, AI supports process and quote optimization, helping companies successfully meet the challenges of B2B sales.

What to expect in this article

AI in B2B sales: Between hype and reality

Guided Selling in the CPQ as a key use case for AI in B2B sales

How does AI enhance the Customer Journey?

Augmented Intelligence Assistants: Support rather than replacement

Success strategies for introducing AI to B2B sales

Summary: AI and CPQ: Indiespensable for the sales of the future

AI in B2B sales: Between hype and reality

The term “artificial intelligence” currently shapes many discussions around digitalization and sales software. “AI has now arrived in B2B sales, and consequently expectations are high,” confirms Simone Schatto, Director Consumer Goods, Retail & Agriculture, Sales & Marketing, at the renowned consulting firm Roland Berger.

Recent studies show that companies expect AI to significantly increase process efficiency, improve margins, and boost closing rates. At the same time, AI is ideally expected to help companies stay ahead in an increasingly competitive market environment. “AI does not replace sales, but it will change tasks. For sales employees, it is therefore important to engage with AI,” says Thomas Riegler, speaker at the VDMA Software and Digitalization trade association. Riegler makes it clear that AI is not intended to replace human sales employees. Routine tasks such as data analysis, lead scoring, or standard communication will gradually be automated. Riegler also emphasizes: “AI is not magic; it has to be trained properly.” The technology only delivers real value when companies use it purposefully, feed it with high-quality data, and train their employees accordingly. “Companies should provide their employees with the necessary know-how in order not to fall behind.”

At the same time, many B2B sales teams are currently facing growing challenges: increasing quote complexity, high price pressure, rising customer expectations, long sales cycles, and in particular demographic change with a declining availability of skilled workers. “Valuable knowledge held by experienced employees can only be preserved with the right methods, such as expert interviews or debriefing workshops, and thus made available for future generations,” Riegler explains. In this context, AI offers an important lever for securing knowledge and effectively relieving employees.

Guided Selling in the CPQ as a key use case for AI in B2B sales

In B2B sales, the focus is not on abstract visions, but above all on the question: How can AI provide concrete support? How can processes be designed to be customer-centric, time killers consistently reduced, and quotation and sales processes deliberately streamlined and aligned around the customer?

Many companies are looking for realistic entry points and often find them in CPQ software that centrally integrates configuration, pricing, and quotation processes. “In the area of quotation management and contract design, CPQ in combination with AI is one of the most exciting trend topics currently attracting companies’ interest,” reports Simone Schatto from Roland Berger.
Modern CPQ solutions bring structure to complex sales processes and support the quotation process with AI: They combine product knowledge, pricing data, and customer information in one place, creating a reliable foundation for error-free quote creation. AI supports the process by providing intelligent decision support within a Guided Selling approach. For example, AI can automatically compare a quote with previous quotes while it is being created.

Based on this, the AI can suggest the ideal or the best available options. Special requests can not only be implemented more quickly, but also approved more easily internally by the departments responsible. At the same time, AI helps create cover letters for quotations that are precise and customer-centric and can be presented to the customer digitally.
The advantage: Using so-called “digital quotes,” customers can enter questions directly and easily within the quote itself and receive responses from the sales employee or the specialist departments in the same place.

How does AI enhance the Customer Journey?

AI does not only add value during the quotation phase; it unfolds its potential across the entire customer lifecycle from the initial approach through to after-sales service. Here are some concrete use cases:

Lead generation phase:

AI supports the personalization of communication, identifies interests, prioritizes leads, and helps determine the optimal timing for initial contact. In the Customer CPQ area—digital end-customer configurators that support customers in product selection, configuration, pricing, and quote creation—AI-based features are used more and more. These help customers in the early stages of product discovery, for example through needs-based recommendations. This enables customers to configure their desired product online themselves, at least at a basic level depending on complexity and individual requirements—an approach that, as Simone Schatto reports, is already appreciated by buyers in the B2B sector today.

Quote phase (CPQ):

During quote creation, artificial intelligence provides context-based recommendations as part of Guided Selling, shows sales employees variants and price impacts in real time, significantly simplifies configuration, and simultaneously ensures error-free results.

Service phase:

More use cases arise in the service domain: By analyzing feedback and service requests, AI will increasingly be able to identify valid sentiment, prioritize issues, and support service teams in deriving targeted measures to improve customer satisfaction.

In this way, AI accompanies the customer journey in a learning, transparent, and comprehensible manner. It enables employees in the respective teams to make data-driven decisions, design processes more efficiently, and strengthen customer loyalty in the long term.

Augmented Intelligence Assistants: Support rather than replacement

Augmented Intelligence Assistants (AIA®) stand for the responsible and practical use of AI in sales. Their purpose is not to replace employees, but to simplify decision-making through context-aware knowledge, real-time analytics, and easy-to-understand recommendations.

These thinking AI assistants analyze data, reveal relationships, and provide actionable insights at exactly the right moment. They operate seamlessly in the background of the CPQ interface, ensuring that information appears precisely where it is needed within the process.

This keeps humans firmly in the decision-making role, while artificial intelligence enhances transparency, speed, and the overall quality of decisions.

1. Ask the right questions: Before introducing AI solutions, companies should clarify which processes are to be supported, where friction currently occurs, and at which points AI can truly add value. What matters most is not the technology itself, but its concrete benefit for the organization, employees, and customers.
2. Take change management into consideration:AI changes ways of working, role models, and decision-making processes. Employees must be involved early, trained, and empowered to use new assistant systems with confidence. Transparency, traceability, and trust are key prerequisites for acceptance.
3. Build a solid technological foundation:
   A robust IT architecture, clearly defined responsibilities, and integrated systems form the basis for reliable AI applications. Equally important is a step-by-step approach along concrete use cases, rather than a large-scale rollout.
4. Ensure data quality, data protection, and digital sovereignty:
   For AI to be used safely and responsibly in sales, it requires clean, structured, and consistent data. Data protection and data ownership are not downstream IT issues, but central strategic success factors. Companies must be able to clearly understand at all times which data is used for what purpose, where it is stored, and how it is protected. Digital sovereignty through fair.digital solutions builds trust; both internally among employees and externally with customers.

Conclusion: AI and CPQ: Indiespensable for the sales of the future

Many companies have already embarked on this journey and successfully established AI-supported sales processes. Especially in combination with modern CPQ systems, it becomes clear that AI is not an end in itself, but a genuine competitive advantage.

“The future of B2B sales will become even more digital and at the same time more personal through AI,” predicts Simone Schatto. “The use of AI will noticeably change the role of sales employees in the coming years. On the one hand, it will create more freedom by taking over many of today’s transactional tasks. On the other hand, personal interaction will gain importance: building real customer relationships, proactively developing solutions, and shaping the future of collaboration through dialogue.” Thomas Riegler from the VDMA shares this view: “There will be some tasks that will no longer need to be carried out in the future; instead, there will be time for other activities on which one can focus,” says Riegler.

In the future, AI will very likely act even more autonomously: it will recognize patterns more quickly, make more targeted recommendations for suitable product combinations in cross-selling and after-sales, or independently take over parts of pricing. Yet one thing remains unchanged despite AI: “Humans will remain in the driver’s seat for the foreseeable future,” Simone Schatto is convinced. The best results emerge when human experience and machine intelligence work together in synergy.