B2B leaders are accustomed to using technology to help them achieve profitable growth. Lately they’ve been looking at a technology that has the potential to accelerate sales transformations across the entire seller journey—gen AI. Gen AI can help drive outsized, profitable growth by boosting revenue generation, increasing sales productivity, and streamlining internal processes. These leaders believe the potential is great. According to McKinsey’s latest B2B Pulse Survey of B2B decision-makers, 19 percent of respondents are already implementing gen AI use cases for 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 or even engage with it. A few leaders tell us they are unsure where the benefits would come from and whether the business impact justifies the investment. Some feel overwhelmed by the abundance of ideas and seek advice on what to prioritize.
In this article, we explore seven compelling use cases across the deal cycle by analyzing gen AI deployments and their impact on sales ROI and customer experience (exhibit).1 These use cases can improve effectiveness and efficiency and start delivering near-immediate impact. We also examine actual deployments by leading organizations. Finally, we suggest key considerations that can help organizations establish a gen AI implementation strategy that aligns with their goals and desires to drive profitable growth in sales.
1. Next-best opportunity
B2B sellers often struggle with oversimplified rules, manual customer research, a lack of data integration, or inadequate training on sales tools. AI can help lead them to their “next-best opportunity.” It can process multiple disparate data sources to prioritize possibilities. Gen AI can parse significant amounts of unstructured data (for example, PDFs, flat files, or photographs) to provide advanced recommendations and instructions. Gen AI can also synthesize relevant information about leads onto a consolidated battlecard, allowing sellers to chase their next-best opportunity based on clear, critical information.
This use case can significantly accelerate the time-consuming process of conducting account research, mapping relationships, and identifying additional stakeholders. Gen AI modules can be trained 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 customer relationship management (CRM) to help sellers prioritize customers and opportunities.
Businesses that deal with a large number of products and leads are most excited about this use case. In the B2B Pulse Survey, B2B commercial leaders in construction materials, shipping, chemicals, or petrochemicals companies—where leads are often generated and managed manually—were disproportionately more enthusiastic about this use case compared with others.2
Gen AI in the field: Supercharging outreach
2. Next-best action
Even when opportunities are prioritized based on engagement and intent data, some sales organizations struggle to know what steps are needed to take advantage of opportunities that require 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 the queue 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 immediate one-to-one interaction. Gen AI can even personalize outreach, such as suggesting email or voicemail scripts based on churn risk.
In the B2B Pulse Survey, next-best action stands out as one of the most exciting use cases in industries such as tech services, durable equipment, and insurance, where sellers are faced with a relatively large set of options to expand accounts and advance opportunities.3
Gen AI in the field: Accelerating aftermarket and services sales
3. Meeting support
Since sellers struggle with lots of complex information, preparing for key client meetings can be a time-intensive process. Gen AI and other types of automation can save sellers time and improve conversations. The technologies can synthesize critical information from multiple sources (such as service tickets or transaction data) and provide relevant insights in an easy-to-consume format. A large language model (LLM) can even draft talking points and responses to objections for more efficient preparation without sacrificing conversation quality.
Meeting support does not have to take a long time to deploy. There are readily available gen-AI-enabled tools that are relatively industry-agnostic, can reference meaningful sources across a wide array of industries, and can be customized easily with off-the-shelf solutions.
The meeting support use case tends to generate the most excitement among industries with long sales cycles, numerous meetings, and large deal values, where the savings on administrative time can be significant. For example, more than 40 percent of B2B Pulse Survey respondents in aerospace and defense, oil and gas refining, and energy distribution indicated they are excited about this use case.4
Gen AI in the field: Driving sales productivity
4. Request for proposal responses
Responding to requests for proposals (RFPs) can be a time sink. But gen AI can improve the efficiency and accuracy of RFP responses, reduce response times, and manage internal tracking. Gen AI helps drive consistency and improve the customer experience as multiple functional teams give input on how to respond to an RFP.
This use case is exciting for leaders across a wide variety of industries, with particular interest from life sciences companies, which frequently handle highly complex, regulated, and data-intensive RFP responses that normally require extensive manual efforts to address. Roughly 40 percent of biopharmaceutical leaders and 30 percent of healthcare leaders responding to the B2B Pulse Survey were extremely excited about the potential for a gen-AI-enabled RFP responder.5
Gen AI in the field: Streamlining responses
A healthcare managed care organization (MCO) transformed how it responded to RFPs by adopting gen AI. While doing research to draft responses, its sales teams frequently had to sift through hundreds of documents, each with thousands of pages. In an industry where an RFP may only be issued once every three to four years for each market, the stakes were high. Intense competition demanded responses that highlighted financial robustness and specific capabilities that outshone those of competitors. Any misstep could result in a lost contract worth billions of dollars annually.
The introduction of a gen AI tool marked a paradigm shift. By feeding gen AI with unstructured data from the MCO’s historical responses—along with information from publicly available contract records—the sales team could generate competitive intelligence in mere seconds. This tool provided instant access to relevant innovations and competitor benchmarks, enabling more strategic and informed decision-making during the drafting process. For example, the gen AI tool could instantly synthesize customer expectations of response times to provider voicemails, call-center hours of operation, and the time taken to secure prior authorization, critical details that previously required extensive manual research. Since the introduction of the tool, the MCO was able to cut the time required to assess competitors’ capabilities by 60 to 80 percent. The insights generated strengthened its proposal in response to a competitive RFP. The tool enabled increased efficiency and bolstered the MCO’s competitive edge in an information-rich industry where every RFP counts.
5. Smart pricing
The impact of AI on pricing can be huge. Many B2B industries rely predominantly on basic analytics and commercial acumen of the sales team. AI creates the opportunity for significant innovation. It allows B2B players to tailor models that have mostly been used only in high-paced B2C industries (for instance, online retail). The result is new opportunities for first movers and new risks for laggards.
There are several predominant applications of AI and gen AI for smart pricing. One is in AI-led price setting, in which the microsegmentation of customers allows for an assessment of customers’ willingness to pay and to buy at a given price point. Additional applications include gen-AI-enabled negotiation support and pricing administration. Companies have started to use gen AI to analyze publicly available data and interactions with customers and track the effectiveness and performance of negotiations, as well as create tailored arguments. This also equips sellers with a score and rationale for how much negotiation power they have. Gen AI is also proving effective in the use of automation in price administration, including system updates and approval workflows.
In the B2B Pulse Survey, smart pricing was prioritized by respondents in industries where pricing has a significant impact on profitability, and products have less differentiation and variability (for example, paper and packaging, energy distribution, and shipping).6
Gen AI in the field: Dynamic deal scoring
Many B2B organizations list for their customers prices that are later discounted in negotiations to close deals. This leads to a wide variance in actual final prices. Some variance makes sense, but discounts from differences in sales rep negotiation skills or sales history, for example, may be unnecessary.
One B2B services company aimed to rein in its discount variance and tighten its pricing model. Using an AI tool, it created a pricing structure based on hundreds of customer and deal parameters with separate models for new deals and renewals. This was packaged for the sales team in an intuitive app where their deals were analyzed and scored, providing them with a range of desirable discounting options. This, in turn, fed into an approval workflow in the CRM, giving instant visibility into how good a deal really was. Finally, the insights from the AI model were used to train the sales teams. Those insights revealed what drove wanted and unwanted discount variance, equipping sales teams with guidance on where to hold their ground or where to give in during negotiations.
As a result of using AI for smart pricing, the company saw a 10 percent uplift in earnings. Notably, the pricing solution was not solely about increasing price. Rather, it focused on optimization, guiding teams toward higher prices where possible while allowing for lower prices where necessary. This kind of nuance allows companies to actively steer pricing toward their strategic objectives, whether that’s margin, volume, or a balanced combination.
6. Smart research assistant
High-performing B2B sellers spend considerable time researching customers, prospects, and products. Pulling together insights from corporate websites, annual reports, and earnings calls, as well as emails and internal data, takes significant time. This can be especially cumbersome for sellers who are trying to engage a customer on a live call while struggling to quickly locate, digest, and synthesize relevant information. This customer interaction has been transformed by gen AI, which can assist sellers with quick fact-finding during calls. As a result, sellers are sharper and more insightful, which improves the overall experience.
Respondents to the B2B Pulse Survey showed the highest average interest in the smart research assistant use case, with 27 percent saying they were excited about its prospects.7
Gen AI in the field: Driving seller productivity
7. Smart coach
Given the length and complexity of some B2B sales processes and deal cycles, it’s often challenging for sales managers and leaders to effectively benchmark seller performance. Gen AI can analyze seller performance across all customer interactions to provide managers with a comprehensive view of performance and recommend targeted coaching based on seller-specific needs. It can also provide personalized performance insights directly to sellers to allow for personal development and growth.
B2B Pulse Survey respondents in service industries that use relatively consistent sales pitches are the most eager to help their sellers with gen-AI-enabled smart coaches. For example, 35 percent of leaders in the B2B insurance space indicated they are enthusiastic about the smart coach use case.8
Gen AI in the field: Improving call-center sales
These seven case studies reveal the potential of gen AI to transform the end-to-end sales journey. Industry leaders are excited about these use cases, but they are even more interested in the next wave of innovation, agentic AI. With limited human intervention, agents can reason, interpret, and make autonomous decisions for an activity or workflow. Consider next-best-action use cases: Agentic AI will not only identify specific actions (such as classifying a lead as medium priority, requiring one to two warm-up outreach emails) but will actually execute the action by automatically reaching out to a prospect, evaluating their interest, and responding back (for example, by sending a message that reads, “We noticed you were interested in a specific product, so we wanted to provide more detail”). AI agents can also nurture a relationship with a sales prospect through multiple communications about potential actions, such as setting up a meeting between the customer and seller. AI agents are so powerful that they have the potential to bring all the seven use cases to the next level.
Five key lessons for deploying gen AI in B2B sales
The seven case studies illustrate how companies can leverage AI to fundamentally rewire their sales capability for outsize, profitable growth. Effective deployment of gen AI is crucial for success. Whether starting their first pilot or scaling initial efforts, any company looking to achieve lasting change across their sales organization should consider these five lessons.
Start with the problem, not the technology
The decision to use gen AI or any other technology should be guided by specific business considerations. For B2B sales, the primary consideration should be identifying where this technology can propel outsize, profitable growth. Companies can start by pinpointing core business challenges, such as lead acquisition, servicing important accounts, or managing services more effectively. Then they can determine the use cases that will deliver the most value. Once priorities are clear, B2B leaders can decide whether these needs are best met by technologies such as rule-based automation, machine learning, AI, or gen AI.
In some cases, sales organizations may not need to pivot to gen AI, especially if foundational processes such as order management or lead routing are still manual. When error tolerance is very low, simple automation with direct links to the source might be a sufficient and more reliable approach, avoiding the potential risks of gen AI hallucinations. The key to designing and developing the best solutions is a clear understanding of the business problem. Only then can a sales leader evaluate whether gen AI is the right choice for their needs.
Keep the seller at the center
To get the most value from a gen AI solution, it’s imperative to ensure that its design is focused on users’ needs. B2B organizations can start by evaluating current sales processes to find ways to free up sellers’ time or provide valuable insights to sellers when they need them most. It also means digging into the customer’s journey. Sellers who can use the right insights and efficiencies to create more moments of customer delight will be more excited to use the solution.
Commercial leaders can ask themselves the following questions to ensure that gen AI solutions are seller-centric:
- Impactful: Is the solution something sellers care about? Will it have meaningful impact?
- Clear: Is the output easy to understand?
- Understandable: Can sellers easily explain outputs to customers?
- Prescriptive: Are outputs clearly linked to specific actions for sellers?
- Reliable: Will sellers trust outputs and find information consistent and accurate?
If the answer to any of these five questions is no, it’s worth revisiting the solution’s design—including critical features, data sources, analytics outputs, or how information is presented. On the other hand, positive answers to these questions will make it more likely for the gen AI use case to be enthusiastically adopted by sellers.
Buy the easy stuff and build for competitive advantage
It’s no surprise that most organizations don’t build entire gen AI capabilities on their own from scratch. Even when they opt to build for a specific use case, a significant portion of the functionality (such as an LLM) often comes from publicly available, off-the-shelf solutions that can be fine-tuned. In this sense, a “build” approach is more accurately described as “buy plus build.”
To decide between a “buy” or a “buy-plus-build” strategy, it’s important to set clear commercial priorities for high-impact gen AI use cases that can give your sales organization an edge. For lower-complexity use cases with largely standard functionality (such as summarizing meeting transcripts), leading organizations often opt to buy and deploy a ready-made gen AI solution quickly. For high-value use cases with the potential for unique performance and competitive advantage (such as delivering the right offer at the right time), it’s better to take a buy-plus-build path where investments in targeted development beyond out-of-the-box functionality can enable greater impact. Making the right choices on when to buy versus when to invest in building custom solutions for strategic advantage can set leaders apart from the competition.
Balance immediate impact and lasting capabilities with a clear AI strategy
As commercial leaders start to deploy gen AI use cases in B2B sales, it’s essential to establish and maintain a clear vision of the overall commercial tech stack and the enterprise AI strategy and architecture. Inconsistent architectures can lead to wasted efforts, incompatible solutions, and increased costs. By ensuring alignment from the start, organizations can prevent fragmentation from disparate development efforts, and integrate various AI initiatives seamlessly, maximizing their value.
Leading organizations can blueprint efforts in a matter of weeks, allowing them to swiftly develop effective gen AI use cases while maintaining a cohesive framework. They do so by carefully scoping minimal viable products (MVPs) and leveraging partners when they don’t have the right people internally. Early successes act as lighthouses to encourage excitement, mobilize the organization, and secure support and resources for scaled implementation.
While quick wins are important, they should not come at the expense of foundational capabilities. Investing in the right technological infrastructure is vital for long-term success. This includes robust data management and governance, comprehensive data processing capabilities, and a modernized tech stack. Equally important is talent. Ambitious organizations cultivate teams with the skills to build, maintain, and enhance gen AI functionalities over time. This involves hiring the right talent and continuously upskilling employees to foster a culture of innovation and adaptability. By striking the right balance between near-term impact and long-term capabilities, organizations can ensure their gen AI journey is both effective and sustainable.
Invest in seller adoption from the get-go
Commercial leaders are often eager to implement new gen AI solutions to boost performance. However, getting sales teams to adopt these solutions sustainably and at scale can be more challenging than launching the technology. A seller-centric design and an experimentation mindset that leads to first MVPs are a good start, but leaders need to invest time and effort to maximize adoption and produce real impact. When deploying AI solutions in sales, it’s crucial to take an agile approach to development, including an iterative process with frequent test-and-learn cycles rooted in seller feedback and continuous refinement within tightly linked business and tech teams.
Effective deployment also requires careful change management, a practice that’s far too often overlooked. Leading organizations use a variety of strategies to prepare and support sellers for new AI solutions. These include frequent communications and setting clear expectations, using seller champions and sounding boards, providing training sessions and recognizing success stories, and employing thoughtful use of new solutions by sales leaders. Incentivizing sellers who experiment with AI and celebrating catching errors as part of innovation can foster a culture of continuous improvement.
Finally, AI centers of excellence can help accelerate adoption and scale gen AI to more use cases. These centers can prioritize resources, centralize funding, ensure proper change management, and drive responsible use.
While many B2B sales organizations are still in the early stages of technological development, leading companies are already scaling their gen AI capabilities. Commercial leaders whose companies are experiencing higher growth tend to be more enthusiastic about gen AI and are implementing multiple use cases to transform their growth strategies and seller journeys. Gen AI can empower teams by providing better insights, driving higher conversion rates, and boosting productivity. Autonomous agents may deliver even more impact. With the right growth strategy and go-to-market model in mind, and the willingness to turn interest in gen AI into action, B2B leaders can unlock a future with enormous potential.
