Generative AI forces recalibration of commercial strategy

Not the technology itself, but the application determines the value

While the consumer sector has been benefiting from data-driven marketing and automated customer interactions for years, the B2B sector, especially business services, remains remarkably conservative. But the pressure to digitize is increasing. Business customers' expectations are changing, sales cycles are getting shorter, and the need to set up processes more efficiently is greater than ever.

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Generative artificial intelligence (gen AI) now seems to be hitting the commercial processes of business service providers. Not as a buzzword or experimental toy, but as a concrete accelerator of lead generation, customer management and sales. Still, adoption is limited for now. According to McKinsey's latest B2B Pulse Survey, only 19% of companies actually use gene AI in commercial processes. An almost equally large group (23%) is in the process of implementation, while the majority are mostly observing.

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Digital maturity continues to lag behind

The B2B business services market is traditionally relational. Account managers with long-term customer relationships, manual RFP processes, and a commercial structure in which technology has long played only a supporting role.

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But customer behavior is changing fundamentally. Orientation, comparison and even purchase are increasingly taking place digitally. Even within B2B, "self-service" is the new normal. Customers expect instant information, transparency about price and availability, and personalized communication. For service providers, this means that their commercial infrastructure must be redesigned, scalable, digital and data-driven.

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And that is precisely where generative AI comes in.

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From vendor to data consumer

Generative AI is often associated with text generation or chatbots, but its real power lies in connecting data to commercial 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 process: follow up, call or nurture.
3. Meeting support, automatically generated call preparation including customer information and call goals.
4. Proposal responder, acceleration of proposal processes through automatic draft responses to RFPs.
5. Smart pricing, dynamic price negotiation based on customer segmentation and bargaining power.
6. Research assistant, real-time customer and competitive analysis as input for sales and marketing.
7. Smart coaching, sales call analysis for personalized feedback and targeted training.

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

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But ... no silver bullet

Still, caution is called for. Gene AI offers many possibilities, but it also has limitations:

• Technology requires well-structured and integrated data. Many organizations lack that foundation.
• There is risk of "hallucinations": factual inaccuracies convincingly presented as truths.
• Commercial teams need time to adopt new technology. Without training and change management, the impact fails to materialize.
• Integration with existing CRM, ERP and marketing platforms requires technical direction and vision.

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In short, those who embark on AI without a strategy are more likely to increase complexity than productivity.

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Buy standard, build the distinctive

An important consideration for executives is the 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 to match the specific market position.

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The pace of implementation is also a factor. Rapid experimentation with MVPs can ensure internal support. But without a long-term vision, including data governance, architecture and training, AI remains an isolated project.

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(Gen) AI is not optional, but strategic

Deploying AI is not a choice of if, but when and how. Business service providers who invest in commercial innovation now are creating 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 commerce seriously, AI is not hype, but increasingly a necessity.

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