By Alexander Chiejina
- 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 Artificial Intelligence (AI) is poised to unleash the next wave of productivity globally with AI permeating human lives incrementally, through everything, from the tech powering smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers, says McKinsey in a June 2023 Report titled, “The Economic Potential of Generative AI: The Next Productivity Frontier”.
As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based programme developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.
Generative AI applications such as ChatGPT, GitHub, Copilot, Stable Diffusion, and others have captured the imagination of people around the world, thanks to their broad utility — almost anyone can use them to communicate and create — and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganisation and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.
“Generative AI has the potential to generate $4.4 trillion in value across industries – $460 billion in high tech (especially software engineering) and $390 billion in retail with predominant factors such as marketing and sales influencing these figures. Four business functions account for approximately 75% of the total annual value of AI: Customer operations; Marketing and sales; Software engineering; including Research and development,” the report explained.
The report continued: “AI is expected to match median human performance and reach top-quartile human performance earlier than expected. While automation is increasing, the total percentage of hours that could be automated by integrating technologies that exist today has increased from 50% to 60–70%. The technical potential curve is steep because of the acceleration in generative AI’s natural-language capabilities,” the report added.
The speed at which generative AI technology is developing is not making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute — the length of the average novel — compared with roughly 9,000 tokens when it was introduced in March 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products.
To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. Generative AI as applications are typically built using foundation models. Foundation models are part of ‘deep learning’, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.
Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarise, answer questions, and draft new content, among other tasks.
Generative AI is projected to have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.
Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.
The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in previous estimates, McKinsey noted.
Excitement over Generative AI is palpable, and early pilots are compelling. But the report acknowledges that a full realisation of the technology’s benefits will take time, noting that leaders in business and society still have considerable challenges to address. And the report says these challenges include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.
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.
