Generative AI has the potential to change the anatomy of work by empowering individual workers and automating tasks that impact daily productivity levels.
This is what emerges from the McKinsey research “The economic potential of generative AI: The next productivity frontier” based on the analysis of 63 use cases on 16 business functions in the customer care, marketing, sales, software development, R&D fields, covering the 75% of total annual value derived from Generative AI applications.
Already in 2012, the McKinsey Global Institute (MGI) estimated that “knowledge workers” spend about a fifth of their time (equivalent to an entire day of the working week) in research and information gathering activities.
Passing these tasks to generative AI would greatly increase efficiency and productivity at work.
Generative AI is capable of driving value across the entire organization, revolutionizing internal information management systems.
However, it is advisable to take care to integrate the tool in a supervised environment, in which it is possible to verify that the content generated by the LLMs is compliant with the position of the company, for example.
As indicated by McKinsey, mathematical models trained on publicly available data are not sufficient to protect Companies against potential cases of plagiarism, copyright infringement and intellectual property rights.
Generative AI is exposed to bias risk and the generation of limited or distorted information.
For this reason, human supervision becomes an integral part of the strategy for adopting new technologies.
How generative AI brings value to the company: use caseS
Customer operations
In the Customer Operations field, AI improves the user experience and agent productivity by promoting digital self-service and helping to increase operator skills.
McKinsey research finds that in a company with 5,000 customer care agents, the application of generative AI increased problem resolution by 14% and reduced the time spent on case management by 9%.
A few examples:
Customer Self Service
The integration of LLMs in chatbot solutions allows for a personalized response to even complex requests, increasing the quality and efficiency of interactions through automated channels. It is estimated that AI can help reduce contact requests to agents by 50%.
RESOLUTION TIME
Automation systems respond in real time to customer requests and allow you to instantly retrieve data on a specific customer, reducing the time for operators to handle requests.
PERSONALIZED INTERACTIONS
The technology identifies product suggestions and personalized offers on the individual customer, through the collection and analysis of data obtained from previous conversations.
McKinsey estimates that applying generative AI to customer service functions could increase productivity between 30 and 45 percent.
sales and Marketing
Generative AI has rapidly taken hold in marketing and sales functions, where content generation and communication personalization are driving forces. Technology can help create personalized messages based on specific interests, preferences and behaviors of individual customers, as well as, perform tasks such as producing articles, presentations, social media posts and product descriptions, etc.
content creation
Generative AI significantly reduces the time it takes to ideate and create new content in different writing styles and formats. It contributes to the personalization of marketing content, speeds up translations into any language, produces content with specific objectives (conversions, attraction, awareness, engagement, retention, etc.)
Enhanced use of data
Generative AI supports the analysis and interpretation of unstructured data (e.g. videos and images), coming, for example, from different databases. It can assist in the analysis of customer feedback and behavior to generate data to support marketing strategies targeted on the profile of individual customers.
SEO optimization
Generative AI supports SEO-optimized content generation activities (for example, structuring page titles, image tags and URLs) by increasing conversion opportunities and optimizing costs.
products suggestion and personalized search
With generative AI it is possible to customize the suggestion and search for products through multimodal inputs through texts, images, speech, as well as the ability to detect the user profile.
For example, technology can use individual consumer preferences, behavior patterns, and purchase history to suggest relevant products and generate personalized product descriptions.
increase of sales opportunities
Generative AI could identify and prioritize sales leads
based on the user profile and structured and unstructured data, suggesting actions to improve customer engagement at each touchpoint, increasing conversion opportunities.
lead development
Generative AI supports lead development activities by synthesizing relevant information on consumers’ interests and profiles for sales agents, suggesting suitable points for up-selling actions and scripts on which to engage the sales conversation.
AI can also be used for sales follow-up and passive nurturing of leads automatically, until customers are ready for direct interaction with a human sales agent.
McKinsey estimates that generative AI could increase marketing function productivity by 5 to 15% and sales productivity by 3 to 5%.
Software engineering
In Software Engineering, generative AI can be used to produce code starting from a natural language prompt.
The technology can also be applied in the analysis, cleaning and labeling of large volumes of data, in the design of systems and in testing and maintenance activities (for example, using insights into system logs and performance data for diagnosing problems, suggesting fixes, and predicting areas for improvement).
According to McKinsey the direct impact of AI on software engineering productivity
it could vary from 20 to 45%.
Product R&D
In research and development, generative AI could increase productivity between 10 and 15 percent.
For example, the life sciences and chemical industries have started using generative AI applied to basic models in their research and “generative design” activities.
augmented design
Generative AI can help product designers reduce costs by selecting materials more efficiently, helping to optimize logistics and manufacturing costs.
Improved testing and product quality
The use of generative AI in design can support the production of a higher quality product, resulting in an increased potential for market appeal.
Generative AI can also accelerate testing through its ability to develop test scenarios and user profiles.
Conclusions
Technology has changed the anatomy of work for decades. Over the years, machines have helped to endow workers with “superpowers”: for example, in the industrial age, machines have enabled workers to perform physical tasks beyond their abilities. More recently, computers have allowed knowledge workers to perform calculations that would have been impossible to do manually.
Technology increases labor productivity through the automation of tasks performed by the individual.
According to McKinsey, generative AI will have greater impact on cognitive work and in particular on activities involving decision-making, realization and collaboration activities, which previously had a lower potential for automation.
As a result, many of the work activities that involve communicating, supervising,
documentation and interaction with people have the potential to be automated by generative AI, accelerating the transformation of work into professions, in education and technology, for example.
However, the era of Generative AI has just begun and it will still be some time before we can fully grasp the benefits offered by these new technologies. Organizations and companies have some challenges to face, relating to risk management, the development of new skills, as well as, organizational and cultural adaptation.
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