McKinsey: The economic potential of generative AI

But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue.

This report focuses on generative AI’s economic impact and how it is reshaping work, benefits banking and retail sectors, and automates up to 70% of tasks. We take a first look at where business value could accrue and the potential impacts on the workforce. ORBIS is powered by ESPAS, the European Strategy and Policy Analysis System, a unique inter-institutional project aimed at strengthening the EU’s efforts in the crucial area of forward planning. Global economic growth was slower from 2012 to 2022 than in the two preceding decades.8Global economic prospects, World Bank, January 2023.

US policymakers, regulators discuss future of AI regulation at AIGG

By integrating GenAI with information and operational technology systems, organizations can tap the power of tacit knowledge as a formalized capability. Acquired over time through contextualized experience and skills development, an organization’s tacit knowledge is generally concentrated among a few key individuals who hold that knowledge primarily in their heads and not in any readily codifiable form for access. When the input of these more knowledgeable individuals is needed to solve complex business problems or to make numerous business decisions, they tend to become the choking points. GenAI has essentially handed over the keys to the knowledge kingdom, enabling businesses to discover and harness enterprise knowledge at unprecedented speed and scale for tremendous value creation.

Then there is the question of drift; data and models can drift independently or interdependently. Data sources may change, and the trends in what the data is revealing may also require the model in turn to adapt its algorithms. Vector indexes won’t be the headline, and neither will gen AI-business intelligence integration. Database vendors will expand on their generic vector index offerings today with a greater variety of optimized choices, and they will incorporate orchestration that allows gen AI queries to be enriched with tabular, BI-style results. Scarcity of graphics processing units has gotten to the point where enterprises can only gain access through long-term, one-to three-year commitments for silicon for just-in-case capacity that is only likely to be about 20% utilized on average. Going meta here, we could see AI enabling an aftermarket of unused GPU cycles emerging for AI jobs.

Canada rolls out generative AI code of practice

While this emergence is still largely aspirational, the approach has significantly advanced deep learning and convolutional neural networks. DeepMind’s AlphaGo is a notable example, demonstrating remarkable success in areas such as strategic gameplay. It involves constructing a vast knowledge base where symbols represent the physical world’s various elements. These symbols and logic networks, comprising if-then statements, are manipulated to mimic human cognitive processes, especially higher-level thinking. The symbolic approach, theoretically, is adept at performing advanced logic and thought processes. A quintessential example of this approach is the CYC project, initiated by Douglas Lenat of Cycorp in the 1980s, which aimed to develop a comprehensive AI system with a broad knowledge base and a powerful logic system.

Most firms that operate globally will be cognisant that an increasing number of nations are in the process or have already introduced dedicated tech regulations catering to respective countries. In such times, for any business operating globally, it may become a significant operating expense to individually comply with every nation’s needs. As a result, data localisation will become common for most businesses next year, instead of upcoming regulations and the general course that the World Wide Web has taken. A large part of the growth in industrial tech adoption, as well as the advent of connected and autonomous cars, will see a steadily increasing need for eSIMs across all sorts of devices. ESIMs are essentially the virtual counterparts of a physical SIM card and are imperative for anything to connect to a network wirelessly. Having eSIMs embedded into networking hardware will be the obvious way forward as both consumer and enterprise technologies become increasingly connected.

Written by Intelligent Enterprise Institute

Any company or individual who doesn’t effectively use AI might be less productive — and less competitive — than those who do. Adapting to new technology requires custom learning experiences based on the different skills, knowledge, and attitudes of a diverse workforce. “We’ve observed developments happening 50% faster,” she states, emphasizing AI’s role in enhancing productivity and innovation. She pointed out the approach in Australia, where there’s a significant emphasis on diversifying the tech field. Napier believes it’s essential for governments in all regions to collaborate in solving the challenges of increasing the number of developers and people skilled in coding. In AI-enabled security tools, GitHub views AI as an augmentation capability, not a replacement for human expertise.

North Korea, a state that has used its cyber capabilities for everything from counterfeiting to hacks of governments and companies alike may see those capabilities enhanced by AI. Generative AI is also rapidly decreasing the cost and increasing the quality of “deep fakes” and other tools for dis- and misinformation. Non-state actors, including extremist groups and criminal enterprises, will likely find new ways to exploit AI for their own ends in ways that have clear geopolitical and commercial externalities. India’s past technology successes, with a focus on lowering costs, democratizing access, and on small and medium-sized businesses, may provide a playbook for its next chapter of technological innovation. In the last few years, the country has become a hub not only for labor and outsourcing, but also for entrepreneurship and innovation.

But before long, the way we live and the nature of global politics will be shaped by generative AI. Will the problems that generative AI creates be solved by the very same technology, or will they prove to be intractable? The relative nascency of this new node of AI innovation and the steep trajectory of improvement in generative AI models make these questions more momentous, and difficult to answer. For one, there is a risk that models trained with publicly available data may infringe copyrights. Second, virtual “try-on” applications could produce distorted representations of certain demographic groups due to limited or biased training data. Therefore, a significant amount of human supervision is required for conceptual and strategic thinking tailored to the needs of the specific organization.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.