Generative AI is forcing a reevaluation of commercial strategy

It is not the technology itself, but its application that determines its value

While the consumer sector has been benefiting from data-driven marketing and automated customer interactions for years, the B2B sector—and the business services industry in particular—remains strikingly conservative. But the pressure to digitize is mounting. Business customers’ expectations are changing, sales cycles are getting shorter, and the need to streamline processes is greater than ever.

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Generative artificial intelligence (gen AI) now appears to be impacting the commercial processes of business service providers as well. Not as a buzzword or an experimental gimmick, but as a concrete catalyst for lead generation, customer management, and sales. Yet adoption remains limited for the time being. According to McKinsey’s latest B2B Pulse Survey, only 19% of companies are actually using gen AI in their commercial processes. A nearly equally large group (23%) is in the process of implementing it, while the majority is mainly observing.

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Digital maturity is lagging behind

The B2B market for business services has traditionally been relationship-driven. It features account managers with long-standing client relationships, manual RFP processes, and a sales structure in which technology has long played only a supporting role.

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But customer behavior is undergoing a fundamental shift. Research, comparison, and even purchasing are increasingly taking place digitally. Even in the B2B sector, “self-service” is the new normal. Customers expect immediate information, transparency regarding price and availability, and personalized communication. For service providers, this means their commercial infrastructure must be redesigned to be scalable, digital, and data-driven.

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And that's exactly where generative AI comes into play.

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From Seller to Data Consumer

Generative AI is often associated with text generation or chatbots, but its true power lies in linking data to business decision-making. McKinsey identified seven use cases that deliver immediate value in B2B environments:

1. Next-best opportunity: automatic prioritization of the most promising leads based on internal and external data.
2. Next-best action, prescriptive recommendations for the next step in the customer journey: follow up, call, or nurture.
3. Meeting support, automatically generated meeting preparation materials, including customer information and meeting objectives.
4. Proposal Responder: Streamlining the proposal process through automated draft responses to RFPs.
5. Smart pricing, dynamic price negotiation based on customer segmentation and bargaining power.
6. Research assistant, real-time customer and competitor analysis to inform sales and marketing.
7. Smart coaching, analysis of sales calls for personalized feedback and targeted training.

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These applications are not just a pipe dream. They are already being used in sectors such as manufacturing, insurance, and professional services. The results are tangible: shorter sales cycles, higher conversion rates, and more informed customer interactions.

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But… there’s no silver bullet

Still, caution is warranted. Gen AI offers many possibilities, but it also has limitations:

• The technology requires well-structured and integrated data. Many organizations lack that foundation.
• There is a risk of “hallucinations”: factual inaccuracies that are convincingly presented as truths.
• Sales teams need time to adopt new technology. Without training and change management, the impact will not materialize.
• Integration with existing CRM, ERP, and marketing platforms requires technical leadership and vision.

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In short: anyone who starts using AI without a strategy is more likely to increase complexity than productivity.

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Buy off-the-shelf, build something unique

An important consideration for executive teams is striking the right balance between “buy” and “build.” Many generative AI applications, such as transcription, summarization, and text generation, are available through off-the-shelf solutions. For more strategic applications, such as proactive lead scoring or customer coaching, it pays to develop a proprietary model that aligns with the company’s specific market position.

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The pace of implementation is also a factor. Rapid experimentation with MVPs can help build internal buy-in. But without a long-term vision—including data governance, architecture, and training—AI will remain an isolated project.

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(Gen) AI is not optional, but strategic

The use of AI is not a matter of “if,” but of “when” and “how.” Business service providers that invest in commercial innovation now will create a structural competitive advantage—not by automating everything, but by empowering people with data, insights, and digital tools.

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For those who take business seriously, AI isn't just hype—it's becoming more and more of a necessity.

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