Generative AI landscape: Potential future trends
Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great. Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9). As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities.
For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. We estimate that applying genrative ai generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. 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.
Jim and Mike on Data Privacy and TikTok
Generative AI has already been used to design drugs for various uses within months, offering pharma significant opportunities to reduce both the costs and timeline of drug discovery. Still, AI innovations are generally accelerating, creating numerous use cases for generative AI in various industries, including the following five. Generative AI can explore many possible designs of an object to find the right or most suitable match. It not only augments and accelerates design in many fields, it also has the potential to “invent” novel designs or objects that humans may have missed otherwise. While it’s easy to fall into the trap of seeing OpenAI as the sole gatekeeper of this technology — and ChatGPT as the go-to generative AI tool — this fortunately is far from the case.
- In theory at least, this will increase worker productivity, but it also challenges conventional thinking about the need for humans to take the lead on developing strategy.
- Based on the report, some of the most in-demand jobs will include AI and machine-learning specialists, business-intelligence analysts, information-security analysts, FinTech engineers, and data analysts and scientists.
- Heinz, for example, used an image of a ketchup bottle with a label similar to Heinz’s to argue that “This is what ‘ketchup’ looks like to AI.” Of course, it meant only that the model was trained on a relatively large number of Heinz ketchup bottle photos.
- Your insights and experiences are invaluable in helping us shape the future of generative AI at SAP.
Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, genrative ai including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. Although these are also only just beginning to emerge, fine-tuning publicly available, general-purpose LLMs on your own data could form a foundation for developing incredibly useful information retrieval tools. These could be used, for example, on product information, content, or internal documentation.
What are the challenges of Generative AI?
Todd Johnson, managing director at digital transformation consultancy Nexer Group, predicted generative AI will help drive the creation of natural language interfaces (NLIs) that are more intuitive and easier to use. “NLIs enable users to communicate with computer systems using natural language instead of programming languages or syntax,” he explained. For example, in a supply chain context, generative AI could provide an audio interface for workers in a warehouse distribution center. Workers could interact with the NLI through a headset connected to a manufacturer’s ERP system to navigate a packed warehouse, find specific items, and reorder materials and supplies.
Nestle used an AI-enhanced version of a Vermeer painting to help sell one of its yogurt brands. Stitch Fix, the clothing company that already uses AI to recommend specific clothing to customers, is experimenting with DALL-E 2 to create visualizations of clothing based on requested customer preferences for color, fabric, and style. Overall, it provides a good illustration of the potential value of these AI models for businesses. They threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. This is not the “artificial general intelligence” that humans have long dreamed of and feared, but it may look that way to casual observers.
Is this the start of artificial general intelligence (AGI)?
Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. Large language models (LLMs), like ChatGPT, showcase the potential for new technologies, like transformers. Hybrid models combine the benefits of LLMs with symbolic AI’s accurate and controllable narratives.
There are many fundamental trainings anyone can invest in to better understand emerging technologies and how they’re changing businesses. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods.
In this future work landscape reshaped by generative AI, we’re seeing a significant shift from task-oriented to outcome-oriented models, turning traditional work processes on their heads. This transformation has profound implications for the nature of work, the skills required and the overall employee experience. Such a process calls for keen industry insights, an understanding of human psychology and an awareness of the competitive landscape. A new wave of technology is ushering in a transformative era marked by generative AI that’s taking center stage across diverse professional landscapes. Generative AI is a dynamic, intelligent entity that feeds on the vast knowledge reservoir of the digital cosmos, producing custom-made, innovative solutions to cater to specific needs. By 2025, more than half of all software engineering leader role descriptions will explicitly require oversight of generative AI, the consultancy estimates.
Similarly, researchers could speed up projects by relying on automation tools to sort and synthesize large data sets. In fact, the occupational categories most exposed to generative AI could continue to add jobs through 2030 (Exhibit 4), although its adoption may slow their rate of growth. And even as automation takes hold, investment and structural drivers will support employment. The biggest impact for knowledge workers that we can state with certainty is that generative AI is likely to significantly change their mix of work activities. The majority of these shifts came from people leaving jobs in food services, customer service and sales, office support, and production work (such as manufacturing). At the same time, managerial and professional roles plus transportation services collectively added close to four million jobs from 2019 to 2022.