生成式AI助力B2B业务盈利增长研究报告：七大用例

Gen AI can enhance profitable B2B sales growth. Seven use cases showhow B2B leaders can maximize benefits and drive sustainable impact witha tailored gen AI strategy. by Alexander Dierks and Richelle Deveauwith Siamak Sarvari and Sonia Joseph Griffin B2B leadersare accustomed to using technology to help them achieve profitable growth. Latelythey’ve been looking at a technology that hasthe potential to accelerate sales transformationsacross the entire seller journey—gen AI. Gen AI can helpdrive outsized, profitable growthbyboosting revenue generation, increasing sales productivity, and streamlining internal processes.These leaders believe the potential is great. According toMcKinsey’s latest B2B Pulse SurveyofB2B decision-makers, 19 percent of respondents are already implementing gen AI use casesfor B2B buying and selling, and another 23 percent are in the process of doing so. That’s promising. However, the flip side is that most B2B leaders have yet to embrace gen AI oreven engage with it. A few leaders tell us they are unsure where the benefits would come from andwhether the business impact justifies the investment. Some feel overwhelmed by the abundance ofideas and seek advice on what to prioritize. In this article, we explore seven compelling use cases across the deal cycle by analyzing gen AIdeployments and their impact on sales ROI and customer experience (exhibit).1These use cases canimprove effectiveness and efficiency and start delivering near-immediate impact. We also examineactual deployments by leading organizations. Finally, we suggest key considerations that can helporganizations establish a gen AI implementation strategy that aligns with their goals and desires todrive profitable growth in sales. Exhibit<Gen AI B2B sales>Exhibit <1> of <1> Gen AI can affect the entire B2B deal cycle. Potential gen AI impact in use cases across B2B deal cycle, nonexhaustive Chapter 1. Next-best opportunity B2B sellers often struggle with oversimplified rules, manual customer research, a lack of dataintegration, or inadequate training on sales tools. AI can help lead them to their “next-bestopportunity.” It can process multiple disparate data sources to prioritize possibilities. Gen AI canparse significant amounts of unstructured data (for example, PDFs, flat files, or photographs)to provide advanced recommendations and instructions. Gen AI can also synthesize relevantinformation about leads onto a consolidated battlecard, allowing sellers to chase their next-bestopportunity based on clear, critical information. This use case can significantly accelerate the time-consuming process of conducting accountresearch, mapping relationships, and identifying additional stakeholders. Gen AI modules can betrained to answer questions by mining a variety of sources, such as news articles, company reports,and transaction data. The resulting outputs can be integrated directly into a company’s customerrelationship management (CRM) to help sellers prioritize customers and opportunities. Businesses that deal with a large number of products and leads are most excited about thisuse case. Inthe B2B Pulse Survey, B2B commercial leaders in construction materials, shipping,chemicals, or petrochemicals companies—where leads are often generated and managedmanually—were disproportionately more enthusiastic about this use case compared with others.2 example, field sellers would drive a vehiclearound a city or town to visually identifynew construction project locations. Toaddress the issue, the company firstbuilt an AI engine that used both internaland external data sources to scoreand prioritize existing opportunities,and to identify targeted productrecommendations. It then used gen AI toextract insights about upcoming capital projects from unstructured public data (inthis case, construction permits), identifyingnew opportunities and improvingprioritization on existing ones. Finally, itleveraged gen AI to personalize outreach atscale. This resulted in more than $1 billionworth of new opportunities (increasingtheir pipeline by 10 percent), and more thandoubling click-through rates in the firstfiscal year. CASE STUDY Gen AI in the field:Supercharging outreach A distributor of industrial materialswas looking to boost growth but facedchallenges identifying and acting onopportunities. The process could becumbersome and time-intensive. For Chapter 2. Next-best action Even when opportunities are prioritized based on engagement and intent data, some salesorganizations struggle to know what steps are needed to take advantage of opportunities thatrequire immediate engagement. Gen AI and machine learning can improve guidance to sellers on the “next-best action” to take,such as whether to place a lead in a low-engagement nurturing segment for a later month or in thequeue for a top-priority marketing campaign. Gen AI can also categorize leads by channel actions,such as identifying who to invite to a webinar or who may benefit from