Generative AI helps B2B organizations grow profitably by improving revenue generation, sales productivity, and internal efficiency across the full deal cycle. From the text, there are seven main ways it does this: 1. **Finding and prioritizing the next-best opportunities** Gen AI can process large volumes of structured and unstructured data (for example, CRM data, PDFs, permits, photos, news, company reports) to surface and score the most promising accounts and deals. It consolidates insights into “battlecards” so sellers can quickly see key stakeholders, history, and recommended products. • In McKinsey’s Global B2B Pulse Survey, **19%** of B2B decision-makers are already implementing gen AI use cases for buying and selling, and another **23%** are in the process. • Industries with many products and manually managed leads (construction materials, shipping, chemicals, petrochemicals) are especially interested in this use case. **Example:** A distributor of industrial materials combined an AI scoring engine with gen AI that mined construction permits and then personalized outreach at scale. The result: more than **$1 billion** in new opportunities and a **10%** increase in pipeline, plus more than double the click-through rates in year one. 2. **Guiding sellers with next-best actions** Even when leads are prioritized, teams often struggle with what to do next. Gen AI and machine learning can recommend specific actions: whether to nurture a lead, move it into a priority campaign, invite someone to a webinar, or trigger one-to-one outreach. It can also generate personalized email or voicemail scripts based on signals like churn risk. • This “next-best action” use case is particularly attractive in tech services, durable equipment, and insurance, where sellers have many options to expand accounts. **Example:** An enterprise equipment manufacturer used AI to clean sales data, predict maintenance schedules, and generate prioritized upsell and cross-sell opportunities embedded in CRM. A virtual assistant sent hyper-personalized emails and routed hot leads back to sellers. The company increased its pipeline from new and existing customers by more than **20% of total revenue**. 3. **Improving meeting preparation and seller productivity** Gen AI can synthesize information from service tickets, transaction data, financials, and past interactions into concise meeting prep notes. It can draft talking points and objection-handling scripts so sellers spend less time preparing and more time with customers. • This is especially valuable in industries with long sales cycles and large deal values (for example, aerospace and defense, oil and gas refining, energy distribution), where more than **40%** of surveyed leaders are excited about meeting support. **Example:** A materials company integrated more than 20 data sources into a gen AI tool that produced meeting notes summarizing financials, goals, historic sales, prior actions, preferences, and stakeholders. The tool was built in seven weeks with input from 30+ sellers and freed up more than **10%** of time for the target seller group. 4. **Streamlining RFP responses** Responding to complex RFPs is often slow and manual. Gen AI can search through thousands of pages of internal and external documents, generate draft responses, and surface competitive benchmarks and proof points. • Around **40%** of biopharma leaders and **30%** of healthcare leaders in the survey are highly interested in gen-AI-enabled RFP responders. **Example:** A healthcare managed care organization fed gen AI with historical RFP responses and public contract data. The tool cut the time to assess competitor capabilities by **60–80%**, while improving the quality and competitiveness of proposals. 5. **Enabling smarter, more dynamic pricing** AI and gen AI can microsegment customers, estimate willingness to pay, and support negotiation with data-backed guidance. They can also automate price administration and approval workflows. • Smart pricing is a priority in sectors where pricing strongly affects profitability and products are less differentiated (paper and packaging, energy distribution, shipping). **Example:** A B2B services company built an AI-based pricing model using hundreds of customer and deal parameters. Deals were scored in an app that suggested discount ranges and fed into CRM approvals. The company saw a **10% uplift in earnings**, not by simply raising prices, but by optimizing discounts in line with strategic goals. 6. **Acting as a smart research assistant in live selling** Gen AI can quickly pull and synthesize information from corporate sites, annual reports, earnings calls, emails, and internal systems, including during live calls. This helps sellers respond with more relevant insights and value propositions. • This “smart research assistant” use case has the highest average interest in the survey, with **27%** of respondents excited about it. **Example:** A global industrials company built an AI-enabled growth engine that mapped existing and potential customers across more than ten data sources and prioritized them by share of wallet and potential. An AI agent then helped tailor value propositions versus competitors. The result: **40% higher conversion rates** and **30% faster lead execution**. 7. **Providing data-driven coaching and performance insights** Gen AI can analyze call transcripts and other interactions to identify behaviors linked to better outcomes (for example, empathy markers, structure, offer positioning). It then generates personalized coaching recommendations for each seller. • Service industries with consistent sales pitches, such as B2B insurance, show strong interest; **35%** of insurance leaders are enthusiastic about smart coaching. **Example:** A telecom company used gen AI to analyze call-center conversations and feed insights into a coaching engine. Personalized coaching after each call led to a **7-point increase in customer satisfaction** and a **20% reduction in training costs**. Taken together, these use cases show that gen AI is not just a point solution. It can reshape the end-to-end B2B sales journey—from prospecting and pricing to coaching and RFPs—while delivering measurable gains in pipeline, conversion, earnings, and customer satisfaction.

A practical starting point is to focus on a small number of high-impact, lower-risk use cases that align with your current sales pain points and data maturity. The text highlights several patterns you can use to prioritize. 1. **Anchor on your biggest bottlenecks in the seller journey** Map your end-to-end sales process and identify where time, effort, or value is being lost. Common starting points include: • **Lead and opportunity management** – If your teams struggle to find and prioritize good opportunities, consider next-best opportunity and next-best action engines. These use your existing CRM, transaction data, and external data to surface and rank leads, then recommend what to do next. • **Meeting preparation and research** – If sellers spend too much time preparing for meetings or researching accounts, a gen-AI-powered meeting assistant or smart research assistant can quickly synthesize data into prep notes and talking points. • **RFP response** – If your business depends on complex, infrequent RFPs, a gen AI RFP responder can significantly reduce manual research and drafting time. • **Pricing and discounting** – If you see wide discount variance and margin leakage, smart pricing models and negotiation support can help standardize and optimize pricing decisions. 2. **Match use cases to your industry context** The survey data in the text suggests that some use cases are especially relevant by sector: • **High-product-volume, manually managed leads** (construction materials, shipping, chemicals, petrochemicals): start with next-best opportunity and lead scoring. • **Tech services, durable equipment, insurance**: next-best action engines that guide expansion and cross-sell are particularly valuable. • **Aerospace and defense, oil and gas refining, energy distribution**: meeting support tools can free up significant seller time in long, complex deal cycles. • **Life sciences and healthcare**: RFP responders can help manage complex, regulated, data-heavy proposals. • **Paper and packaging, energy distribution, shipping**: smart pricing is a strong lever for profitability. • **Insurance and other service industries with consistent pitches**: smart coaching tools can systematically improve seller performance. 3. **Leverage existing data and tools first** Many of the examples in the text show companies starting by connecting gen AI to data they already have: • A materials company integrated more than **20** existing data sources into a meeting prep tool. • A managed care organization used historical RFP responses and public contract records to build its RFP assistant. • A B2B services company used hundreds of existing customer and deal parameters to build its pricing model. Rather than waiting for a perfect data environment, start with the data that is most complete and most closely tied to revenue (for example, CRM, transaction history, service tickets). Then iterate. 4. **Design for quick wins with clear metrics** The case studies show that early deployments can deliver measurable results within months when they are scoped tightly and tied to specific KPIs: • Pipeline growth (for example, **10%** pipeline uplift and **$1 billion** in new opportunities for the industrial distributor). • Conversion and speed (for example, **40%** higher conversion and **30%** faster lead execution for the industrials company). • Productivity (for example, more than **10%** of seller time freed up at the materials company; **60–80%** faster competitor assessment for the MCO). • Financial outcomes (for example, **10%** earnings uplift from smart pricing). • Customer experience (for example, **7-point** increase in customer satisfaction at the telecom company). Define a small set of metrics for each pilot (such as time saved per seller per week, pipeline uplift, or margin improvement) and track them from day one. 5. **Combine gen AI with analytical AI and human judgment** The text emphasizes that gen AI is usually deployed alongside analytical AI and machine learning. For example: • An AI engine scores and prioritizes opportunities; gen AI then mines unstructured data and personalizes outreach. • Analytical models predict maintenance schedules; gen AI drafts personalized emails and routes responses. • Pricing models score deals; gen AI helps explain the rationale and support negotiations. This combination helps you avoid overreliance on a single technology and keeps humans in the loop for decisions that affect customers and pricing. 6. **Plan for scale and governance from the start** While the text focuses on use cases and impact, it also notes that organizations need a gen AI implementation strategy that aligns with their goals and risk appetite. In practice, that means: • Clarifying where AI will augment versus automate seller activities. • Setting guardrails for data privacy, regulatory compliance, and brand voice in customer communications. • Building feedback loops so sellers and managers can refine prompts, models, and workflows over time. By starting with a few targeted use cases that match your industry, data, and pain points—and by measuring impact clearly—you can build confidence, demonstrate value, and then expand gen AI across the broader B2B sales journey.

The text provides multiple case studies with clear, quantified outcomes across pipeline growth, productivity, pricing, and customer experience. Here are the main results. 1. **Pipeline growth and opportunity creation** • **Industrial materials distributor** – Built an AI engine to score and prioritize opportunities and used gen AI to mine construction permits and personalize outreach. – Outcome: more than **$1 billion** in new opportunities and a **10%** increase in sales pipeline in the first fiscal year, plus more than **2x** click-through rates. • **Enterprise equipment manufacturer (aftermarket and services)** – Deployed a lead-generation engine and next-best-action model, with a virtual assistant sending hyper-personalized emails and routing hot leads. – Outcome: pipeline from new and existing customers increased by more than **20% of total revenue**. • **Global industrials company (market research and hunting)** – Used an AI-enabled growth engine to map and prioritize existing and new customers, and an AI agent to articulate tailored value propositions. – Outcome: **40% higher conversion rates** and **30% faster lead execution** once fully implemented. 2. **Seller productivity and time savings** • **Materials company (meeting preparation)** – Implemented a gen AI tool that integrated more than **20** data sources and produced meeting prep notes summarizing financials, goals, historic sales, prior actions, preferences, and stakeholders. – Outcome: more than **10%** of time freed up for the target seller group, allowing more time with customers. • **Healthcare managed care organization (RFP responses)** – Fed gen AI with historical RFP responses and public contract records to generate competitive intelligence and synthesize key requirements. – Outcome: **60–80%** reduction in time required to assess competitors’ capabilities, while improving the quality and strategic positioning of proposals. 3. **Pricing and margin improvement** • **B2B services company (smart pricing and discount control)** – Built AI models using hundreds of customer and deal parameters, with separate models for new deals and renewals. Deals were scored in an app that suggested discount ranges and fed into CRM approvals. – Outcome: **10% uplift in earnings**. – The improvement came from optimizing discounts—raising prices where the data supported it and allowing lower prices where needed—rather than simply increasing prices across the board. 4. **Customer experience and capability building** • **Telecom company (call-center coaching)** – Used gen AI to analyze call transcripts, identify competence markers (such as empathy), and feed insights into a coaching engine that provided personalized suggestions after each call. – Outcome: **7-point increase** in customer satisfaction scores and a **20% reduction** in training costs. 5. **Interest and adoption signals from the market** Beyond individual case studies, the survey data in the text shows that adoption is underway and interest is broad-based: • **19%** of B2B decision-makers are already implementing gen AI use cases for buying and selling, and another **23%** are in the process. • **27%** of respondents are excited about smart research assistants. • More than **40%** of respondents in aerospace and defense, oil and gas refining, and energy distribution are excited about meeting support tools. • Around **40%** of biopharma leaders and **30%** of healthcare leaders are highly interested in gen-AI-enabled RFP responders. • **35%** of B2B insurance leaders are enthusiastic about smart coaching use cases. Taken together, these results show that when gen AI is applied to specific B2B sales challenges—such as opportunity identification, meeting prep, RFPs, pricing, and coaching—organizations can see meaningful gains in pipeline, conversion, earnings, seller productivity, and customer satisfaction within months, not years.