Artificial intelligence (AI) and machine learning (ML) continue to push the boundaries of what is possible in marketing and sales. And now, with the ongoing step-change evolution of generative AI (gen AI), we’re seeing the use of open-source platforms penetrating to the sales frontlines, along with rising investment by sales-tech players in gen AI innovations. Given the accelerating complexity and speed of doing business in a digital-first world, these technologies are becoming essential tools.
Inevitably, this will impact how you operate—and how you connect with and serve your customers. In fact, it’s probably already doing so. Forward-thinking C-suite leaders are considering how to adjust to this new landscape. Here, we outline the marketing and sales opportunities (and risks) in this dynamic field and suggest productive paths forward.
Our research suggests that a fifth of current sales-team functions could be automated.
How AI is reshaping marketing and sales
AI is poised to disrupt marketing and sales in every sector. This is the result of shifts in consumer sentiment alongside rapid technological change.
Omnichannel is table stakes
Across industries, engagement models are changing: today’s customers want everything, everywhere, and all the time. While they still desire an even mix of traditional, remote, and self-service channels (including face-to-face, inside sales, and e-commerce), we see continued growth in customer preference for online ordering and reordering.
Winning companies—those increasing their market share by at least 10 percent annually—tend to utilize advanced sales technology; build hybrid sales teams and capabilities; tailor strategies for third-party and company-owned marketplaces; achieve e-commerce excellence across the entire funnel; and deliver hyper-personalization (unique messages for individual decision makers based on their needs, profile, behaviors, and interactions—both past and predictive).
Step changes are occurring in digitization and automation
What is generative AI?
Many of us are already familiar with online AI chatbots and image generators, using them to create convincing pictures and text at astonishing speed. This is the great power of generative AI, or gen AI: it utilizes algorithms to generate new content—writing, images, or audio—from training data.
To do this, gen AI uses deep-learning models called foundation models (FMs). FMs are pre-trained on massive datasets and the algorithms they support are adaptable to a wide variety of downstream tasks, including content generation. Gen AI can be trained, for example, to predict the next word in a string of words and can generalize that ability to multiple text-generation tasks, such as writing articles, jokes, or code.
In contrast, “traditional” AI is trained on a single task with human supervision, using data specific to that task; it can be fine-tuned to reach high precision, but must be retrained for each new use case. Thus gen AI represents an enormous step change in power, sophistication, and utility—and a fundamental shift in our relationship to artificial intelligence.
AI technology is evolving at pace. It is becoming increasingly easy and less costly to implement, while offering ever-accelerating complexity and speed that far exceeds human capacity. Our research suggests that a fifth of current sales-team functions could be automated. In addition, new frontiers are opening with the rise of gen AI (see sidebar “What is generative AI?”). Furthermore, venture capital investment in AI has grown 13-fold over the last ten years.1Nestor Maslej et al., “The AI Index 2023 annual report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, April 2023. This has led to an explosion of “usable” data (data that can be used to formulate insights and suggest tangible actions) and accessible technology (such as increased computation power and open-source algorithms). Vast, and growing, amounts of data are now available for foundation-model training, and since 2012 there’s been a millionfold increase in computation capacity—doubling every three to four months.2Cliff Saran, “Stanford University finds that AI is outpacing Moore’s Law,” Computer Weekly, December 12, 2019; Risto Miikkulainen, “Creative AI through evolutionary computation: Principles and examples,” SN Computer Science, 2(3): 163, March 23, 2001.
Would you like to learn more about our ?
Would you like to learn more about our ?
What does gen AI mean for marketing and sales?
The rise of AI, and particularly gen AI, has potential for impact in three areas of marketing and sales: customer experience (CX), growth, and productivity.
For example, in CX, hyper-personalized content and offerings can be based on individual customer behavior, persona, and purchase history. Growth can be accelerated by leveraging AI to jumpstart top-line performance, giving sales teams the right analytics and customer insights to capture demand. Additionally, AI can boost sales effectiveness and performance by offloading and automating many mundane sales activities, freeing up capacity to spend more time with customers and prospective customers (while reducing cost to serve). In all these actions, personalization is key. AI coupled with company-specific data and context has enabled consumer insights at the most granular level, allowing B2C lever personalization through targeted marketing and sales offerings. Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.
Bringing gen AI to life in the customer journey
There are many gen AI-specific use cases across the customer journey that can drive impact:
A gen AI sales use case: Dynamic audience targeting and segmentation
Gen AI can combine and analyze large amounts of data—such as demographic information, existing customer data, and market trends—to identify additional audience segments. Its algorithms then enable businesses to create personalized outreach content, easily and at scale.
Instead of spending time researching and creating audience segments, a marketer can leverage gen AI’s algorithms to identify segments with unique traits that may have been overlooked in existing customer data. Without knowing every detail about these segments, they can then ask a gen AI tool to draft automatically tailored content such as social media posts and landing pages. Once these have been refined and reviewed, the marketer and a sales leader can use gen AI to generate further content such as outreach templates for a matching sales campaign to reach prospects.
Embracing these techniques will require some openness to change. Organizations will require a comprehensive and aggregated dataset (such as an operational data lake that pulls in disparate sources) to train a gen AI model that can generate relevant audience segments and content. Once trained, the model can be operationalized within commercial systems to streamline workflows while being continuously refined by agile processes.
Lastly, the commercial organizational structure and operating model may need to be adjusted to ensure appropriate levels of risk oversight are in place and performance assessments align to the new ways of working.
- At the top of the funnel, gen AI surpasses traditional AI-driven lead identification and targeting that uses web scraping and simple prioritization. Gen AI’s advanced algorithms can leverage patterns in customer and market data to segment and target relevant audiences. With these capabilities, businesses can efficiently analyze and identify high-quality leads, leading to more effective, tailored lead-activation campaigns (see sidebar “A gen AI sales use case: Dynamic audience targeting and segmentation”).
Additionally, gen AI can optimize marketing strategies through A/B testing of various elements such as page layouts, ad copy, and SEO strategies, leveraging predictive analytics and data-driven recommendations to ensure maximum return on investment. These actions can continue through the customer journey, with gen AI automating lead-nurturing campaigns based on evolving customer patterns.
- Within the sales motion, gen AI goes beyond initial sales-team engagement, providing continuous critical support throughout the entire sales process, from proposal to deal closure.
With its ability to analyze customer behavior, preferences, and demographics, gen AI can generate personalized content and messaging. From the beginning, it can assist with hyper-personalized follow-up emails at scale and contextual chatbot support. It can also act as a 24/7 virtual assistant for each team member, offering tailored recommendations, reminders, and feedback, resulting in higher engagement and conversion rates.
As the deal progresses, gen AI can provide real-time negotiation guidance and predictive insights based on comprehensive analysis of historical transaction data, customer behavior, and competitive pricing.
- There are many gen AI use cases after the customer signs on the dotted line, including onboarding and retention. When a new customer joins, gen AI can provide a warm welcome with personalized training content, highlighting relevant best practices. A chatbot functionality can provide immediate answers to customer questions and enhance training materials for future customers.
Gen AI can also offer sales leadership with real-time next-step recommendations and continuous churn modeling based on usage trends and customer behavior. Additionally, dynamic customer-journey mapping can be utilized to identify critical touchpoints and drive customer engagement.
This revolutionary approach is transforming the landscape of marketing and sales, driving greater effectiveness and customer engagement from the very start of the customer journey.
Winning tomorrow’s car buyers using artificial intelligence in marketing and sales
Read the report
Commercial leaders are optimistic—and reaping benefits
We asked a group of commercial leaders to provide their perspective on use cases and the role of gen AI in marketing and sales more broadly. Notably, we found cautious optimism across the board: respondents anticipated at least moderate impact from each use case we suggested. In particular, these players are most enthusiastic about use cases in the early stages of the customer journey lead identification, marketing optimization, and personalized outreach (Exhibit 1).
These top three use cases are all focused on prospecting and lead generation, where we’re witnessing significant early momentum. This comes as no surprise, considering the vast amount of data on prospective customers available for analysis and the historical challenge of personalizing initial marketing outreach at scale.
Various players are already deploying gen AI use cases, but this is undoubtedly only scratching the surface. Our research found that 90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years (Exhibit 2).
Our research found that 90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years.
Overall, the most effective companies are prioritizing and deploying advanced sales tech, building hybrid teams, and enabling hyper-personalization. And they’re maximizing their use of e-commerce and third-party marketplaces through analytics and AI. At successful companies, we’ve found:
- There is a clearly defined AI vision and strategy.
- More than 20 percent of digital budgets are invested in AI-related technologies.
- Teams of data scientists are employed to run algorithms to inform rapid pricing strategy and optimize marketing and sales.
- Strategists are looking to the future and outlining simple gen AI use cases.
Such trailblazers are already realizing the potential of gen AI to elevate their operations.
Our research indicates that players that invest in AI are seeing a revenue uplift of 3 to 15 percent and a sales ROI uplift of 10 to 20 percent.
Anticipating and mitigating risks in gen AI
While the business case for artificial intelligence is compelling, the rate of change in AI technology is astonishingly fast—and not without risk. When commercial leaders were asked about the greatest barriers limiting their organization’s adoption of AI technologies, internal and external risk were at the top of the list.
From IP infringement to data privacy and security, there are a number of issues that require thoughtful mitigation strategies and governance. The need for human oversight and accountability is clear, and may require the creation of new roles and capabilities to fully capitalize on opportunities ahead.
In addition to immediate actions, leaders can start thinking strategically about how to invest in AI commercial excellence for the long term. It will be important to identify which use cases are table stakes, and which can help you differentiate your position in the market. Then prioritize based on impact and feasibility.
The AI landscape is evolving very quickly, and winners today may not be viable tomorrow. Small start-ups are great innovators but may not be able to scale as needed or produce sales-focused use cases that meet your needs. Test and iterate with different players, but pursue partnerships strategically based on sales-related innovation, rate of innovation versus time to market, and ability to scale.
AI is changing at breakneck speed, and while it’s hard to predict the course of this revolutionary tech, it’s sure to play a key role in future marketing and sales. Leaders in the field are succeeding by turning to gen AI to maximize their operations, taking advantage of advances in personalization and internal sales excellence. How will your industry react?
Richelle Deveau is a partner in McKinsey’s Southern California office, Sonia Joseph Griffin is an associate partner in the Atlanta office, where Steve Reis is a senior partner.
The authors wish to thank Michelle Court-Reuss, Will Godfrey, Russell Groves, Maxim Lampe, Siamak Sarvari, and Zach Stone for their contributions to this article.
Explore a career with us