(11 pages)
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).
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).
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
Search Openings
FAQs
What is the difference between AI and generative AI? ›
Generative AI produces new content, chat responses, designs, synthetic data or deepfakes. Traditional AI, on the other hand, has focused on detecting patterns, making decisions, honing analytics, classifying data and detecting fraud.
What is generative AI for sales teams? ›Generative AI can analyze customer feedback, reviews, and social media comments to gauge sentiment and identify potential issues or opportunities. This information can help sales teams address customer concerns, capitalize on positive feedback, and tailor their sales messages to resonate with their target audience.
How does AI affect sales and marketing? ›Leveraging AI in marketing and sales has become increasingly important in today's fast-paced business environment. For example, AI algorithms can analyze vast amounts of customer data and provide insights into buying patterns, helping companies to personalize their marketing campaigns and increase conversions.
What are examples of generative AI? ›Generative AI is used in any algorithm/model that utilizes AI to output a brand new attribute. Right now, the most prominent examples are ChatGPT and DALL-E. Another example is MusicLM, Google's unreleased AI text-to-music generator. An additional contender (which hasn't performed as well as ChatGPT) is Google's Bard.
What are the benefits of using generative AI? ›Generative AI has many incredible use cases and benefits throughout key industries, such as healthcare and marketing. It has the potential in the short-term and long-term to improve efficiency, reduce costs, boost creativity, and more.
How does generative AI affect sales? ›Generative AI for sales has a lot of pros. It can help you write knockout pitches and emails, make the discovery process pop, and analyze a slew of data in seconds. In other words, it's a tool that can handle the busywork so you can sell more efficiently.
How are companies using generative AI? ›This rapidly evolving artificial intelligence field has the potential to help organizations quickly generate content, improve customer service and develop new products. People use generative AI models to conduct searches, create art, compose essays and make conversation -- polite and otherwise.
What is generative AI summary? ›Generative artificial intelligence or generative AI is a type of artificial intelligence (AI) system capable of generating text, images, or other media in response to prompts. Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics.
What are the benefits of AI in sales? ›AI in sales can be used to help manage and predict customer behavior, identify cross-selling and upselling opportunities, automate repetitive tasks, and improve forecasting accuracy. Ultimately, the goal of AI in sales is to boost efficiency and effectiveness while reducing costs.
What is an example of using AI in marketing? ›Starbucks is one example of a brand using its loyalty card and mobile app to collect and analyze customer data. They announced plans for personalization back in 2016. Since then, they've built quite the app experience. It records purchases, including where they are made and at what time of day.
How is sales and marketing field benefiting from AI? ›
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.
What are the benefits of using AI in marketing? ›- 1) Customer Behaviour is Now More Predictable. ...
- 2) Customer Engagements are Analyzed Better. ...
- 3) Ads Can Now Target Specific Audiences. ...
- 4) Marketing Can Now Be Automated. ...
- 5) Customer Relationships are Now Better Fostered. ...
- 6) Marketing Contents are Optimized.
AI in marketing has many uses. In addition to fine-tuning content, it can learn customer preferences and offer them relevant recommendations. It can predict customer spending patterns, perform market research, and assist customers in real-time when live agents are unavailable.
What are the problems with AI in sales? ›Problem: AI is a sophisticated algorithm that requires special skills to implement and use it. Thus, sales teams need to be augmented with specialized knowledge in data management, software optimization, and integration. Otherwise, AI tools can be used incorrectly and thus provide little value.
How is generative AI being used today? ›Generative AI uses machine learning algorithms to analyze large amounts of data, “learn” from it and develop new content from what it gleans. This process can be used to create everything from news articles to stock photography.
Why is generative AI so popular? ›Generative AI tools reduce the money and time needed for content creation, thereby boosting productivity and profitability. The rise of generative AI also breeds innovation, paving the way for new business models and applications.
Why is it called generative AI? ›A generative model can take what it has learned from the examples it's been shown and create something entirely new based on that information. Hence the word “generative!” Large language models (LLMs) are one type of generative AI since they generate novel combinations of text in the form of natural-sounding language.
What are the competitive advantages of generative AI? ›Generative AI can automate many time-consuming tasks, such as content creation, which can help you save time and resources. With the help of generative AI, your marketing team can produce more content, faster, and with less manual effort. Personalization is critical in modern marketing.
How will generative AI change the world? ›AI Will Help Create and Run Websites and Web Apps
There are already tools available that can help you build websites without coding, and generative AI will take this capability to the next level. In the near future, AI will not only help you create websites and web apps, but also run them on autopilot.
Such potent personalized content risks making generative-AI-based social media particularly addictive, leading to anxiety, depression and sleep disorders by displacement of exercise, sleep and real-time socialization.
How generative AI will change marketing? ›
Generative AI can expand a marketer's value — and make work more engaging — by giving them the ability to do more quality work faster and shift their mental energy and time to tackle the strategic work that machines can't duplicate.
How does AI increase sales? ›Generative AI can reverse administrative creep, for example, by helping salespeople write emails, respond to proposal requests, organize notes, and automatically update CRM data. Enhancing salespeople's customer interactions. The use of AI in sales has been progressing of late.
What is the value of the generative AI market? ›[385 Pages Report] The market for generative AI is anticipated to increase from USD 11.3 billion in 2023 to USD 51.8 billion by 2028, at a CAGR of 35.6% over the course of the forecast period.
How does generative AI affect business? ›Generative AI solutions allow businesses to streamline mundane tasks, generate legal documents and contracts, identify potential areas of legal risk or dispute, and provide valuable insights into case law.
What is the most popular generative AIS? ›- GPT-4.
- ChatGPT.
- AlphaCode.
- GitHub Copilot.
- Bard.
- Cohere Generate.
- Claude.
- Synthesia.
AI-powered forecasting models can analyze vast amounts of data and identify patterns that humans may miss, leading to more accurate predictions. This can help businesses make better decisions and avoid costly mistakes.
What are 3 advantages of AI? ›- AI drives down the time taken to perform a task. ...
- AI enables the execution of hitherto complex tasks without significant cost outlays.
- AI operates 24x7 without interruption or breaks and has no downtime.
- AI augments the capabilities of differently abled individuals.
AI can also automate and streamline the sales forecasting process, reducing human bias, errors, and manual work. AI can also provide insights and recommendations to help sales teams optimize their strategies, actions, and outcomes.
What is AI marketing strategy? ›AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in digital marketing efforts where speed is essential.
What are 3 different examples of AI doing things today? ›- Manufacturing robots.
- Self-driving cars.
- Smart assistants.
- Healthcare management.
- Automated financial investing.
- Virtual travel booking agent.
- Social media monitoring.
- Marketing chatbots.
What are three real life example of AI? ›
Apple's Siri, Google Now, Amazon's Alexa, and Microsoft's Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.
How will AI impact the future of marketing? ›AI is revolutionizing how marketers approach digital campaigns, from leveraging predictive analytics in order to improve customer segmentation to using machine learning algorithms to optimize ad targeting.
What is the impact of AI on digital marketing? ›Chatbots: AI-powered chatbots can provide personalized marketing messages and customer recommendations based on their past behavior and preferences. This can help digital marketers create a tailored customer experience.
Which marketing behavior can benefit from AI? ›Real-time personalization
Personalization begins with understanding your customer's preferences and then providing them with a solution accordingly. AI helps you gather demographics, location, and buying history, helping you personalize your marketing campaigns in real-time.
With the help of AI, marketers can analyze customer data and behaviour, segment their target audience, and personalize their marketing efforts. This can lead to better engagement, higher conversion rates, and increased customer loyalty.
What are the weaknesses of AI in business? ›High Costs
It requires plenty of time and resources and can cost a huge deal of money. AI also needs to operate on the latest hardware and software to stay updated and meet the latest requirements, thus making it quite costly.
- Healthcare.
- Wildlife Conservation.
- Learning and Training.
- Transportation.
- Hiring.
- Renewable Energy Sector.
- Research and Development.
- Logistics and Operations.
The 3 types of AI are artificial superintelligence, general or strong AI, narrow or weak AI.
Is machine learning the same as generative AI? ›Unlike traditional machine learning algorithms that are programmed to make predictions based on a given set of data, generative AI algorithms are designed to create new data. This could be in the form of images, text, music, or even entire video clips.
What is generative system in AI? ›Generative AI is a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data.
What are the different types of AI? ›
- Purely Reactive. These machines do not have any memory or data to work with, specializing in just one field of work. ...
- Limited Memory. ...
- Theory of Mind. ...
- Self-Aware. ...
- Machine Learning. ...
- Deep Learning. ...
- Input Layer. ...
- Hidden Layer.
The components of AI include Machine Learning, Natural Language Processing, Computer Vision, Robotics, and Expert Systems. These components enable machines to learn, understand, and interact with the world around them in ways that were previously impossible.
What are the 4 elements of AI? ›- Data Element.
- Algorithms Element.
- Platforms Element.
- Integration Element.
- Reactive machines. Reactive machines are AI systems that have no memory and are task specific, meaning that an input always delivers the same output. ...
- Limited memory. The next type of AI in its evolution is limited memory. ...
- Theory of mind. ...
- Self-awareness.
Already, generative AI examples are found in industries ranging from healthcare to manufacturing to finance to marketing. Additionally, examples of generative AI tools are also growing, as developers work to evolve the original technology to create new software.
What are the limitations of generative AI? ›- Cost. Generative AI can be expensive to implement. ...
- Time. Generative AI can take a long time to train and deploy. ...
- Data Quality. ...
- Overfitting. ...
- Explainability. ...
- Ethical Concerns. ...
- Mitigating the Disadvantages of Generative AI. ...
- Conclusion.
This rapidly evolving artificial intelligence field has the potential to help organizations quickly generate content, improve customer service and develop new products. People use generative AI models to conduct searches, create art, compose essays and make conversation -- polite and otherwise.
What are the risks of generative AI in business? ›Generative AI models can amplify ethical and bias-related risks that originate from the data used to train the model. The ability of generative AI to produce entirely new content that may be offensive, harmful, or biased can increase these risks many fold.
What is unique about generative AI? ›A generative model can take what it has learned from the examples it's been shown and create something entirely new based on that information. Hence the word “generative!” Large language models (LLMs) are one type of generative AI since they generate novel combinations of text in the form of natural-sounding language.
What are the 5 ideas of AI? ›In this fun one-hour class, students will learn about the Five Big Ideas in AI (Perception, Representation & Reasoning, Learning, Human-AI Interaction, and Societal Impact) through discussions and games.
Which programming language is used for AI? ›
Python and Java are both languages that are widely used for AI. The choice between the programming languages depends on how you plan to implement AI. For example, in the case of data analysis, you would probably go with Python.