By Alexander Chiejina
Generative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions, McKinsey stated, adding, “We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be.
“While generative AI is an exciting and rapidly advancing technology, the other applications of AI continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modelling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve,” the report added.
According to the McKinsey report, generative AI has the potential to revolutionise the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills, adding that the technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language.
"Generative AI–fueled chatbots can give immediate and personalised responses to complex customer inquiries regardless of the language or location of the customer. By improving the quality and effectiveness of interactions via automated channels, generative AI could automate responses to a higher percentage of customer inquiries, enabling customer care teams to take on inquiries that can only be resolved by a human agent," the report explained.
Resolution during initial contact. Generative AI can instantly retrieve data a company has on a specific customer, which can help a human customer service representative more successfully answer questions and resolve issues during an initial interaction.
Reduced response time. Generative AI can cut the time a human sales representative spends responding to a customer by providing assistance in real time and recommending next steps.
Because of its ability to rapidly process data on customers and their browsing histories, the technology can identify product suggestions and deals tailored to customer preferences. Additionally, generative AI can enhance quality assurance and coaching by gathering insights from customer conversations, determining what could be done better, and coaching agents.
Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalised messages tailored to individual customer interests, preferences, and behaviours, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.
Potential operational benefits from using generative AI for marketing includes significantly reducing the time required for ideation and content drafting, saving valuable time and effort. It can also facilitate consistency across different pieces of content, ensuring a uniform brand voice, writing style, and format. Team members can collaborate via generative AI, which can integrate their ideas into a single cohesive piece. This would allow teams to significantly enhance personalization of marketing messages aimed at different customer segments, geographies, and demographics.
Mass email campaigns can be instantly translated into as many languages as needed, with different imagery and messaging depending on the audience. Generative AI’s ability to produce content with varying specifications could increase customer value, attraction, conversion, and retention over a lifetime and at a scale beyond what is currently possible through traditional techniques.
Generative AI could help marketing functions overcome the challenges of unstructured, inconsistent, and disconnected data — f\or example, from different databases — by interpreting abstract data sources such as text, image, and varying structures. It can help marketers better use data such as territory performance, synthesised customer feedback, and customer behaviour to generate data-informed marketing strategies such as targeted customer profiles and channel recommendations. Such tools could identify and synthesise trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.
Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders.
- Generative AI
- Banking industry: $200bn to $340bn annually
- Retail/consumer packaged goods: $400bn to $660bn
- High tech: $460bn, especially software engineering
- General retail: $390bn (marketing and sales influence)
Generative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions, McKinsey stated, adding, “We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be.
“While generative AI is an exciting and rapidly advancing technology, the other applications of AI continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modelling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve,” the report added.
According to the McKinsey report, generative AI has the potential to revolutionise the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills, adding that the technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language.
"Generative AI–fueled chatbots can give immediate and personalised responses to complex customer inquiries regardless of the language or location of the customer. By improving the quality and effectiveness of interactions via automated channels, generative AI could automate responses to a higher percentage of customer inquiries, enabling customer care teams to take on inquiries that can only be resolved by a human agent," the report explained.
Resolution during initial contact. Generative AI can instantly retrieve data a company has on a specific customer, which can help a human customer service representative more successfully answer questions and resolve issues during an initial interaction.
Reduced response time. Generative AI can cut the time a human sales representative spends responding to a customer by providing assistance in real time and recommending next steps.
Because of its ability to rapidly process data on customers and their browsing histories, the technology can identify product suggestions and deals tailored to customer preferences. Additionally, generative AI can enhance quality assurance and coaching by gathering insights from customer conversations, determining what could be done better, and coaching agents.
Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalised messages tailored to individual customer interests, preferences, and behaviours, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.
Potential operational benefits from using generative AI for marketing includes significantly reducing the time required for ideation and content drafting, saving valuable time and effort. It can also facilitate consistency across different pieces of content, ensuring a uniform brand voice, writing style, and format. Team members can collaborate via generative AI, which can integrate their ideas into a single cohesive piece. This would allow teams to significantly enhance personalization of marketing messages aimed at different customer segments, geographies, and demographics.
Mass email campaigns can be instantly translated into as many languages as needed, with different imagery and messaging depending on the audience. Generative AI’s ability to produce content with varying specifications could increase customer value, attraction, conversion, and retention over a lifetime and at a scale beyond what is currently possible through traditional techniques.
Generative AI could help marketing functions overcome the challenges of unstructured, inconsistent, and disconnected data — f\or example, from different databases — by interpreting abstract data sources such as text, image, and varying structures. It can help marketers better use data such as territory performance, synthesised customer feedback, and customer behaviour to generate data-informed marketing strategies such as targeted customer profiles and channel recommendations. Such tools could identify and synthesise trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.
Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders.