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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 incremental development of generative AI (gen AI), we are seeing the use of open source platforms penetrate the sales front, along with increasing investment by sales technology 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.
This will inevitably affect how you work – and how you connect with and serve your customers. In fact, it probably already does. Forward-thinking C-suite leaders are considering how to adapt to this new landscape. Here we outline the marketing and sales opportunities (and risks) in this dynamic field and suggest productive ways 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 all sectors. This is the result of changes in consumer sentiment along with rapid technological changes.
Omnichannel is table stakes
Across industries, engagement models are changing: today's customers want everything, everywhere, all the time. While they still want 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 re-ordering.
Winning companies—those that increase their market share by at least 10 percent annually—tend to use advanced sales technology; building 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 to individual decision makers based on their needs, profile, behavior and interactions – both past and predictive).
Step changes are taking place 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 compelling images and text at astonishing speed. This is the great power of generative AI or gen AI: it uses 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 can be adapted to a wide variety of downstream tasks, including content generation. For example, Gen AI can be trained to predict the next word in a sequence of words and can generalize this ability to more 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 achieve high precision, but must be retrained for each new use case. Gen AI thus represents a huge leap in power, sophistication and utility – and a fundamental shift in our relationship with artificial intelligence.
AI technology is developing in step. It is becoming increasingly easier and cheaper 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 advent of generative AI (see sidebar "What is generative AI?"). Furthermore, venture capital investment in artificial intelligence has grown 13 times over the past 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 “actionable” data (data that can be used to formulate insights and suggest tangible actions) and available technology (such as increased computing power and open source algorithms). Large and growing amounts of data are now available for basic model training, and since 2012 there has been a million-fold increase in computational capacity—a doubling every three to four months.2Cliff Saran, "Stanford University Finds AI Exceeds Moore's Law," Computer Weekly, 12 Dec. 2019; Risto Miikkulainen, "Creative artificial intelligence through evolutionary computation: principles and examples," SN Computer Science, 2(3): 163, March 23, 2001.
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What does gen AI mean for marketing and sales?
The rise of AI, and especially gen AI, has the potential to impact three areas of marketing and sales: customer experience (CX), growth and productivity.
For example, in CX, hyper-personalized content and offers can be based on individual customer behavior, personality and purchase history. Growth can be accelerated by leveraging artificial intelligence to drive top-line performance, giving sales teams the right analytics and customer insights to capture demand. In addition, AI can increase sales efficiency and performance by offloading and automating many mundane sales activities, freeing up capacity to spend more time with customers and prospects (while reducing the cost to serve). In all these actions, personalization is key. AI combined with company-specific data and context has enabled consumer insights at the most granular level, enabling B2C personalization through targeted marketing and sales offers. Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.
Brings AI to life in the customer journey
There are many gen AI-specific use cases across the customer journey that can create an impact:
A gen AI sales application: 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 characteristics that may have been overlooked in existing customer data. Without knowing all the details of these segments, they can then ask a gen AI tool to automatically prepare tailored content, such as social media posts and landing pages. Once these have been refined and reviewed, the marketer and a sales manager can use gen AI to generate additional 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 the model has been trained, it can be operationalized in commercial systems to streamline workflows while being continuously refined by agile processes.
Finally, the commercial organizational structure and operating model may need to be adjusted to ensure that appropriate levels of risk oversight are in place and that performance reviews align with the new ways of working.
- At the top of the funnel, gen AI outperforms traditional AI-powered lead identification and targeting that uses web-scraping and simple prioritization. Gen AI's advanced algorithms canexploit patterns in customer and market data to segment and target relevant target groups. With these capabilities, companies can effectively analyze and identify high-quality leads, leading to more effective, tailored lead activation campaigns (see sidebar “A Gen AI Sales Application: Dynamic Audience Targeting and Segmentation”).
In addition, gen AI can optimize marketing strategies through A/B testing of various elements such as page layout, ad copy and SEO strategies, leveraging predictive analytics and data-driven recommendations to ensure maximum return on investment. These actions can continue throughout the customer journey, where gen AI automates lead nurturing campaigns based on evolving customer patterns.
- In the sales movement, gen AI goes beyond initial engagement with the sales team and provides continuous critical support throughout the sales process, from proposal to deal closing.
With its ability to analyze customer behavior, preferences and demographics, gen AI can generate personalized content and messages. From the beginning it can help withhyper-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 deliverreal-time negotiation guidance and predictive insightbased on extensive 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 delivera warm welcome with personal training content, highlighting relevant best practice. A chatbot functionality can provide immediate answers to customer questions and improve 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. other than thatdynamic customer journey mapping can be utilizedto identify critical touch points and drive customer engagement.
This revolutionary approach is transforming the marketing and sales landscape, driving greater efficiency and customer engagement right from the start of the customer journey.
Winning tomorrow's car buyers using artificial intelligence in marketing and sales
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Commercial leaders are optimistic - and reaping the benefits
We asked a group of commercial leaders to give their perspective on use cases and gen AI's role in marketing and sales more broadly. In particular, we found cautious optimism across the board: respondents expected at least moderate impact from every use case we proposed. In particular, these actors are most enthusiastic about use cases in the early stages of the customer journey's prospect identification, marketing optimization and personal outreach (Appendix 1).
1
These top three use cases are all focused on prospecting and lead generation, where we are witnessing significant early momentum. This comes as no surprise given the vast amount of data about potential customers available for analysis and the historical challenge of personalizing the initial marketing response at scale.
Various players are already implementing gen AI use cases, but this is arguably only scratching the surface. Our research found that 90 percent of commercial leaders expect to use gen AI solutions “often” over the next two years (Exhibit 2).
2
Our research found that 90 percent of commercial leaders expect to use gen AI solutions “often” over the next two years.
Overall, the most effective companies prioritize and implement advanced sales technology, build hybrid teams and enable hyper-personalization. And they maximize their use of e-commerce and third-party marketplaces through analytics and artificial intelligence. In successful companies, we have 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 look to the future and outline simple gen AI use cases.
Such pioneers are already realizing the potential of AI to elevate their operations.
Our research shows that players who invest in AI experience a 3 to 15 percent increase in revenue and a 10 to 20 percent increase in sales ROI.
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 biggest barriers limiting their organization's adoption of AI technologies, internal and external risks topped the list.
From IP infringement to data protection and security, there are a number of issues that require thoughtful mitigation strategies and management. The need for human oversight and accountability is clear and may require the creation of new roles and capabilities to fully exploit the opportunities ahead.
Beyond immediate actions, managers can begin to think strategically about how to invest in commercial AI for the long term. It will be important to identify which use cases are table games and which can help you differentiate your position in the market. Priorities are then made based on effect and feasibility.
The AI landscape is evolving very quickly, and winners today may not be viable tomorrow. Small startups 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, speed of innovation versus time-to-market, and ability to scale.
AI is changing at breakneck speed, and while it is difficult to predict the course of this revolutionary technology, it will certainly play a key role in future marketing and sales. Leaders in the field are succeeding by turning to gen AI to maximize their operations by taking advantage of advances in personalization and internal sales quality. How will your industry respond?
Richelle Deveauis a partner in McKinsey's Southern California office,Sonia Joseph Griffinis an associate partner in the Atlanta office, whereSteve Reisis 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.
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