How to use data to fuel generative AI

Rather than fearing the looming changes, understanding how this technology can augment human efforts is key. This requires learning how to effectively prompt the technology, which will make humans increasingly valuable and in demand. Undoubtedly, roles will change, and some will disappear; however, new AI-driven jobs will emerge, including strategists, data analysts, content curators, training managers and ethicists. Every marketer should explore the tools and learn how to augment their work in the quest to improve the productivity and effectiveness of marketing.

From our work with retail and CPG companies, we have seen this type of software-centric culture boost customer satisfaction rates by as much as 40 percentage points. Technology-inspired operating models also help push organizations’ performance closer to that of software players on other metrics. For instance, in one major North American retail brand, we saw this shift lead to a 60 percent improvement in time to market, from idea inception to software delivery, for such offerings as new app features. While generative AI technology and its supporting ecosystem are still evolving, it is already quite clear that applications offer the most significant value-creation opportunities. Those who can harness niche—or, even better, proprietary—data in fine-tuning foundation models for their applications can expect to achieve the greatest differentiation and competitive advantage.

Generative AI could propel higher productivity growth

These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce.

• Could decrease by 1.6 million jobs, in addition to losses of 830,000 for retail salespersons, 710,000 for administrative assistants, and 630,000 for cashiers.
• Generative AI has the potential to increase US labor productivity by 0.5 to 0.9 percentage
  points annually through 2030 in a midpoint adoption scenario.
• For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design.
• While ChatGPT is focused on text, other AI systems from major platforms can generate images, video, and audio.

The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

by MIT Technology Review Insights

The next time you walk onto a showroom floor, think about the ability of a copilot to hear your conversation with the salesperson and guide both of you. We’re talking about creating a very tailored experience that addresses your needs as a consumer. Before, it was all about taking a camera crew and a few Mercedes cars out to Death Valley and taking some pictures. So marketing campaigns will be targeted to individual consumers based on their preferences and experiences. The report, which looks at the economic potential of generative AI, says it could add between $2.6 to $4.4 trillion to the global economy through “63 generative AI use cases spanning 16 business functions,” which is roughly the same amount as the UK’s GDP in 2021. While there are a few smaller players in the mix, the design and production of these specialized AI processors is concentrated.

Product managers help drive tech development by setting the strategy, road map, and feature definitions while serving as a liaison among consumers, business, data/engineering, and design teams. For the sector to fulfill its ambition of becoming true software innovators, that reality has to change. Given the versatility of a foundation model, companies can use the same one to implement multiple business use cases, something rarely achieved using earlier deep learning models. A foundation model that has incorporated information about a company’s products could potentially be used both for answering customers’ questions and for supporting engineers in developing updated versions of the products.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Beyond hiring top tech talent, companies can invest in the latest technologies that drive productivity. For example, generative-AI-based automation services have already proved to reduce engineering workload and speed up time Yakov Livshits to market. A March 2023 McKinsey experiment with GitHub Copilot showed that, in teams working on e-commerce platforms, AI tools resulted in overall performance gains of 25 to 50 percent for lower- to medium-complexity tasks.

In the near future, we expect applications that target specific industries and functions will provide more value than those that are more general. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories.

Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower.5Pitchbook. This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

Application builders may amass this data from in-depth knowledge of an industry or customer needs. For example, consider Harvey, the generative AI application created to answer legal questions. Harvey’s developers fed legal data sets into OpenAI’s GPT-3 and tested different prompts to enable the tuned model Yakov Livshits to generate legal documents that were far better than those that the original foundation model could create. This company’s customer support representatives handle hundreds of inbound inquiries a day. The company decided to introduce a generative AI customer-service bot to handle most customer requests.

It is becoming even more urgent to solve occupational and geographic mismatches and connect workers with the training they need to land jobs with better prospects. The fact that workers have been willing to pivot and change career paths, while a tighter labor market encouraged companies to hire from broader applicant pools, gives cause for optimism—but not complacency. For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed.

The sheer speed with which generative AI (gen AI) has entered organizations has taken leaders by surprise. It took years for mainstream AI to reach some degree of maturity; in 2022, adoption had more than doubled since 2017, but the proportion of organizations using AI has plateaued between 50 and 60 percent for the past few years. Given the technology’s breakneck pace of evolution and adoption, companies can’t predict just where it will take them, but its business uses already abound.