Generative AI: From Idea to Pilot, and Pilot to Scale, how do you get started?

The generation phase is typically considered a variation phase, during which firms are concerned with simply generating as much creative input as possible (Girotra et al., 2010). In the next stage, a selection process starts, which is aimed at selecting the idea with the highest chance of gaining market acceptance once it is introduced in the market (Girotra et al., 2010). The latest research on GenAI suggests that this technology can offer firms several ways to conduct market research.

But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential.

These correct answers are provided in forms of “labels” or “annotations,” which require human involvement in labor-intensive tasks. The significant cost of annotation severely restricts the volume of data available for model training, limiting the ability to generalize effectively to novel settings (Bommasani et al., 2021). While the world has only just begun to scratch the surface of potential uses for generative AI, it’s easy to

see how businesses can benefit by applying it to their operations. Consider how generative AI might change

the key areas of customer interactions, sales and marketing, software engineering, and research and

development. 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.

Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on. While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge.

Artificial intelligence can solve many problems that humans can’t, such as traffic congestion, parking shortages, and long commutes. Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that don’t yet exist. Self-driving vehicles are powered by generative AI, enabling them to navigate roads and make real-time decisions.

If workers are supported in learning new skills and, in some cases, changing occupations, stronger global GDP growth could translate to a more sustainable, inclusive world. Furthermore, general purpose technologies like AI are likely to experience a lag between their initial adoption and observable improvements in productivity. However, as these technological and organizational complements are gradually implemented, the productivity benefits of AI begin to materialize, marked by an upward trajectory in the J-curve. Generative AI has the potential to increase labor productivity globally by 0.1–0.6 percent annually until 2040. In Finland, the productivity increase is slightly higher, roughly 0.2–0.7 percent annually. When combined with other technologies, work automation can boost annual productivity growth globally by 0.2–3.3 percentage points, and in Finland the increase can be even more substantial, approximately 0.7–3.6 percentage points annually.

The hype around generative AI has reached a fever pitch in recent months and for good reason as the industry has the potential to add $4.4 trillion to the global economy annually, a new McKinsey report argues. Generative artificial intelligence (GenAI) has the potential to be a significant strategic economic lever for businesses across sectors. In assessing the potential economic impact of GenAI from a productivity perspective, it is worthwhile to consider the TFP dynamics observed during the ICT revolution. Looking back at history, TFP was a driving force behind the acceleration in US labor productivity growth that took place during the ICT revolution of the late 1990s. Beginning in the mid-1990s, output per hour began to grow rapidly, reversing the productivity growth slowdown of the 1980s.

Discover how leaders are using technology to move their business forward and strengthen ongoing digital maturity. The reality is that scalability and dependability require extensive training, experimentation and process development – not unlike the careful nurturing infants need. To help marketers apply GenAI effectively, we provide in Table 1 a summary of studies that investigate GenAI’s emergent capabilities that are most closely related to innovation. These capabilities include idea generation, divergent thinking, analogical thinking, and inductive reasoning, which are all traditionally considered prerequisites for creativity (Dahl & Moreau, 2002; Vartanian et al., 2003). Additionally, our interviews reveal that an increasing number of managers rely on GenAI, or wish to, for decision-making support.

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. 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. The study proposes a balanced approach and flexible guidelines for incorporating generative artificial intelligence (AI) into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy.

Advantages and Disadvantages of Generative AI

For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. The system, trained on millions of examples of successful and unsuccessful conversations, provided suggestions that the agents could use, adapt, or reject. The tool was rolled out in phases, creating quasi-experimental evidence on its causal effects. Due to breakthrough developments in generative artificial intelligence (AI), the global AI market could soon rake in between $17.1 and $25.6 trillion annually, according to a new report by consulting company McKinsey. Generative AI has a significant impact across all industry sectors, especially high tech, banking, and life sciences.

Researchers at Stanford, for example, trained a relatively small model, PubMedGPT 2.75B, on biomedical abstracts and found that it could answer medical questions significantly better than a generalist model the same size. Their work suggests that smaller, domain-specialized models may be the right choice when domain-specific performance is important. Transformers, introduced by Google in 2017 in a landmark paper “Attention Is All You Need,” combined the encoder-decoder architecture with a text-processing mechanism called attention to change how language models were trained. An encoder converts raw unannotated text into representations known as embeddings; the decoder takes these embeddings together with previous outputs of the model, and successively predicts each word in a sentence. As technology continues to advance, we can expect AI to be more deeply integrated into the day-to-day operations of financial institutions in the next decade, improving overall business efficiency, optimizing customer experience, and creating more business value. Early adoption and effective use of these technologies by bankers will set them apart from the competition and create new business opportunities.

NRI Australia and GPT Strategic work with companies to transform ideas into scalable realities, especially with emerging technologies. Join us for a fireside chat where we’ll dive deep into Generative AI – sharing lessons learned, uncovering the benefits, and navigating the pitfalls. While the tech industry works on making training efficient and lowering power consumption, there are still ways that LLMs can drive energy savings. Sarah the economic potential of generative ai Burnett, a technology evangelist, tells ITPro that if a company deploying an LLM can enable an office of dozens of workers to reduce their laptop usage by 30 minutes a day, then this could help offset their LLM’s net energy consumption. Although building your own LLM may involve higher upfront costs, the investment is likely to pay off in the long run because it can be highly customizable and trained to your specific needs.

The Future of Finance: Analysis of the Potential and Applications of generative AI

On the other hand, creating novel content is a subjective task and creativity is widely regarded as a quintessential human capability (Koivisto & Grassini, 2023). Thus, prior studies suggest a negative response towards GenAI use (Castelo et al., 2019; Morewedge, 2022). In light of such conflicting predictions emerging from the literature, further research is needed to investigate consumer responses to a firm’s use https://chat.openai.com/ of GenAI and how the disclosure of GenAI-generated content differs from that generated by other types of AI. Given our focus on GenAI’s capability to create new content, we propose research opportunities related to how firms can harness the innovative potential of GenAI throughout the firm innovation process. However, developing generative AI models requires a lot of computing power, which can be expensive.

A group from Stanford recently tried to “distill” the capabilities of OpenAI’s large language model, GPT-3.5, into its Alpaca chatbot, built on a much smaller model. The researchers asked GPT-3.5 to generate thousands of paired instructions and responses, and through instruction-tuning, used this AI-generated data to infuse Alpaca with ChatGPT-like conversational skills. Since then, a herd of similar models with names like Vicuna and Dolly have landed on the internet. Efforts to mitigate the tension between owners of copyright in the training data and AI developers have emerged, mostly involving modifications to generative model training or inference to reduce the likelihood of generating infringing outputs [35, 4, 33].

Generative AI can help retailers with inventory management and customer service which are both cost concerns for store owners. It can also help retailers innovate, reduce spending, and focus on developing new products and systems. Generative AI is improving operations and ensuring that employees are following the proper steps. It can also enhance performance visibility across business units by integrating disparate data sources. AI algorithms detect fraud and identify investment opportunities in the financial industry. Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations.

In contrast, the Shapley value method accounts for the incremental impact of incorporating a data source alongside all possible combinations of other sources. This comprehensive approach effectively captures the intricate dynamics among data sources, offering a more accurate and fair assessment of each contribution. Andrew Binns writes about how organizations inspire and frustrate efforts for innovation and change.

Scaling laws allow AI researchers to make reasoned guesses about how large models will perform before investing in the massive computing resources it takes to train them. Most recently, human supervision is shaping generative models by aligning their behavior with ours. Alignment refers to the idea that we can shape a generative model’s responses so that they better align with what we want to see.

Generative AI models – the risks and potential rewards in business

A May survey of 1,700 developers across Stack Overflow’s community found that 76% are using AI code assistants or plan to use them in the near future. However, many of these developers admitted that their AI code assistants struggled with context, complexity, and obscurity within code. Lucidworks’ second annual survey on generative AI, published in June, found that only one in four of more than 1,000 executives surveyed had successfully launched generative AI initiatives in the past year.

Table 1 indicates that current models have limited reasoning capabilities with respect to making causal inferences. Computer scientists attribute hallucinations and these limitations to the absence of physical data in most GenAI models, which constrains their understanding of the world (Webb et al., 2023; Zečević et al., 2023). Indeed, most studies conducted so far have focused on LLMs trained exclusively on text-based inputs, which lack embodiment, sensory stimuli, or grounded experience that are crucial for human decision-making (McClelland et al., 2020). However, the emergence of multimodal models like GPT-4 V(ision) (i.e., capable of processing text, image, sound, and other sensory data) may pave the way for GenAI to develop a more integrated understanding of the world (McClelland et al., 2020; Webb et al., 2023). Thus, we note that GenAI is rapidly evolving and that these limitations could potentially be addressed in the future. In technical terms, we can define GenAI as deep neural networks, pre-trained on large amounts of data to create a foundation model, which is then fine-tuned to produce new content by following human instructions (Bommasani et al., 2021).

And, as we saw in the first installment of our article series, it could also take time for the productivity benefits of GenAI to materialize. There has generally been a delay between the inception of paradigm-shifting technologies and their diffusion across the economy. But the faster speed of GenAI diffusion could mean that the boost to economic activity could be felt more quickly – that is, in the next three to five years. McKinsey has found that gen AI could substantially increase labor productivity across the economy. To reap the benefits of this productivity boost, however, workers whose jobs are affected will need to shift to other work activities that allow them to at least match their 2022 productivity levels.

Targeted content encourages people to share it with like-minded individuals and build loyalty and trust toward a company. Gathering and analyzing employee performance data is a time-consuming and tiresome procedure. Empowering AI to perform these tasks can free up time and allow HR professionals to focus on other activities. The algorithm can monitor everyone’s performance, provide feedback, notice skill gaps, and advise on development opportunities. The sectors that are said to experience the biggest chances and benefits are healthcare, finance, transportation, manufacturing, entertainment, big techs, and retail.

In Accenture’s Technology Vision 2023 for Biopharma, we explore how 4 tech trends are shaping new and different ways of operating, collaborating and innovating. But it took a decade longer than the first generation of enthusiasts anticipated,

during which time necessary infrastructure was built or invented and people adapted their behavior to the

new medium’s possibilities. This time, though, many neural net researchers stayed the course, including Hinton, Bengio, and LeCun.

Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. A report by McKinsey & Company found that AI could automate up to 45% of the tasks performed by retail, hospitality, and healthcare workers. This could lead to job displacement but the report also noted that it doesn’t necessarily mean that AI will automate a job just because it can.

On the other hand, an off-the-shelf LLM has been built for a wide range of tasks, so there may come a point when it no longer suits your needs and you have to invest in a new one. For those companies that have had success with generative AI, governance has become a bigger priority than both revenue growth and cost reduction. Generative AI and large language models have been progressing at a dizzying pace, with new models, architectures, and innovations appearing almost daily.

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. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. While generative AI tools are able to carry out simple and repetitive coding tasks, more complex requests require human intervention to oversee them and to correct any mistakes.

Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. 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. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials.

With transformers, you could train one model on a massive amount of data and then adapt it to multiple tasks by fine-tuning it on a small amount of labeled task-specific data. Through fill-in-the-blank guessing games, the encoder learns how words and sentences relate to each other, building up a powerful representation of language without anyone having to label parts of speech and other grammatical features. Transformers, in fact, can be pre-trained at the outset without a particular task in mind. Once these powerful representations are learned, the models can later be specialized — with much less data — to perform a given task. They are built out of blocks of encoders and decoders, an architecture that also underpins today’s large language models.

A main challenge in applying the framework of SRS lies in its substantial computational cost. The evaluation of the utility functions on different combinations of data sources requires retraining the model multiple times. In some applications where the number of copyright owners is small, the computational challenge might not be as severe as it seems. Indeed, we envision that this contract-based framework works best when the entire copyrighted data is partitioned among a handful of copyright owners so that each source has enough data to impact the training outcome.

• Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013.
• However, the efficiency property of the Shapley value [34], which ensures the sum of Shapley values equals the grand coalition’s utility, loses meaning when considering ratios.
• Employed correctly, they can create a significant competitive advantage at each step of the value chain.
• Across the high-tech industry, the technology could deliver value of up to $240–460 billion annually, as technology speeds up and makes software development more efficient.

We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. 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. 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 (Exhibit 1). 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.

Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. GenAI systems are expected to permeate wide segments of business operations in coming years with significant implications for a wide range of activities, such as customer support, marketing and sales, business operations and software programming. As GenAI technologies gain traction, labor productivity will likely rise through direct labor efficiency gains but also through the enhancement of organizations and business processes.

Variational autoencoders added the critical ability to not just reconstruct data, but to output variations on the original data. It is important to note that although spending in the “other” category is particularly prominent, it is reasonable to have a higher proportion of spending because the category covers many types of businesses. At the same time, it also reflects the active investment and application of AI technology in all walks of life around the world, indicating that the use of AI will be further expanded in more fields in the future. In today’s fast-changing financial environment, generative AI is gradually becoming an important tool for financial professionals. With the explosive growth of data volumes, traditional manual processing methods can no longer meet the demand for efficiency and accuracy in banking. AI high performers are much more likely than others to use AI in product and service development.

Generative models can also inadvertently ingest information that’s personal or copyrighted in their training data and output it later, creating unique challenges for privacy and intellectual property laws. Solving these issues is an open area of research, and something we covered in our next blog post. A simple approach utilizes similarity scores between training data and generated content as a valuation metric [40]. For example, [8] extends the TRAK framework [27] to generative models, and [41] further introduced empirical approaches to improving the performance of [8].

Prominent models include generative adversarial networks, or GANs; variational autoencoders, or VAEs; diffusion models; and transformer-based models. Generative AI can’t have genuinely new ideas that haven’t been previously expressed in its training data or

at least extrapolated from that data. Generative AI requires human

oversight and is only at its best in human-AI collaborations. The generative AI story started 80 years ago with the math of a teenage runaway and became a viral sensation

late last year with the release of ChatGPT. Innovation in generative AI is accelerating rapidly, as

businesses across all sizes and industries experiment with and invest in its capabilities. But along with

its abilities to greatly enhance work and life, generative AI brings great risks, ranging from job loss to,

if you believe the doomsayers, the potential for human extinction.

Across the banking industry, for example, 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. It’s why many of the big tech giants have previously agreed to information sharing and the transparent and public reporting capabilities of their AI models. However, generative AI can be difficult to govern because there’s no universal definition and regulation over the technology is likely to spark years of legal headaches. Several research groups have shown that smaller models trained on more domain-specific data can often outperform larger, general-purpose models.

Where business value lies

As the temperature increases, the model becomes more random, leading to more diverse and creative output. Third, users can employ top_p sampling to restrict the model’s selection to a subset of tokens (the nucleus), rather than considering all possible tokens. For instance, setting a top_p value to 0.2 means that the model will only select from those tokens that represent the top 20% of the probability mass for the next token. Given these technical nuances of GenAI, we identify avenues for future research, both at the consumer and firm level. In sum, training foundation models is a highly resource-intensive process that demands substantial computational power and can take months to complete. For instance, it is estimated that the cost of training GPT-4 is over $100 million (Korinek, 2023).

In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts. We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here. These findings are a reminder that while generative AI will bring great opportunities to some, it also brings real-world threats to many others.

These partnerships demonstrate how companies can drive efficiency and innovation by building on their strengths and leveraging each other’s capabilities. There are several ways to milk the technology, suggest INSEAD professors in this INSEAD Explains video series. One is to carry out experiments with the aim of improving performance as well as learning GenAI technologies and applications. Finally, organisations should ensure they have the right talent and infrastructure to harness GenAI’s power. This argument suggests that the true potential of GenAI is represented by the combination of different LLMs, not an individual model. As such, we warn scholars intending to compare human performance with that of GenAI that a fair comparison should always require assembling different foundation models.

For example, knowledge workers with a Bachelor’s degree or higher will likely face similar levels of job insecurity to those without college degrees and high school diplomas. This stands in contrast to previous technological shifts which placed lower-skilled workers at greater risk of losing out. Unfortunately, despite the manufacturing sector benefiting massively from AI-aided automation, this new wave of advancement is only expected to contribute around $100 billion annually to the sector.

The second way generative AI can deliver major economic impact is by accelerating the process of scientific and educational discovery. That might include reducing the cost of research—the technology’s capabilities to interrogate vast data sets, for example, can help develop and test hypotheses quickly and more cost-efficiently. Given generative AI’s ability to provide outputs in a variety of formats—text, images, video, audio, computer code, and synthetic data—Asia is likely to see an explosion of new content. “While innovation will continue to need a human spark, generative AI can play a role in supporting the creative process,” says Ahmed Mazhari, president of Microsoft Asia. 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.

The use of Gen AI in finance is expected to increase global gross domestic product (GDP) by 7% or nearly $7 trillion. Gen AI is a good fit with finance because its strength is dealing with vast amounts of data and this is precisely what finance relies on to function. As a technology that is democratized—one that doesn’t simply exist in a faraway lab or tech community in Silicon Valley, for instance—generative AI lowers the barriers to participation. But this also entails a profound workforce shift, changing the processes of production within the economy and, in turn, the types of tasks that are undertaken and the skills needed to succeed.

Software developers collaborating with generative AI can streamline and speed up processes at every step,

from planning to maintenance. During the initial creation phase, generative AI tools can analyze and

organize large amounts of data and suggest multiple program configurations. Once coding begins, AI can test

and troubleshoot code, identify errors, run diagnostics, and suggest fixes—both before and after launch. Thompson notes that because so many enterprise software projects incorporate multiple programming languages

and disciplines, he and other software engineers have used AI to educate themselves in unfamiliar areas far

faster than they previously could. He has also used generative AI tools to explain unfamiliar code and

identify specific issues.

Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot.

First, several papers have offered a research map for the applications of AI in marketing (Davenport et al., 2020; Huang & Rust, 2021; Puntoni et al., 2021). However, GenAI is a different type of AI that is poised to transform marketing in a completely different Chat GPT way. Recently, Huang and Rust (2023) have analyzed how GenAI can be used to move the customer along the customer care journey. Complementing their perspective, we analyze the impact of GenAI on a different yet fundamental activity for firms (i.e., innovation).

In this light, semivalues [7], which are a generalization of the Shapley value that drop the efficiency axiom, could provide a viable alternative. Future work could aim to establish axiomatic justifications to identify the most suitable solution concepts within the semivalue class for royalty distribution in this context. Ironically, Goldman Sachs are the ones bringing common sense analysis to the AI debate with an excellent report that punctures some of the hype of generative AI. The problem that Goldman Sachs highlight, resting on Acemoglu’s research, is that the sums for AI’s great impact on productivity just don’t add up. Recent research by MIT professor Daron Acemoglu suggests that total productivity gains of AI could be as little as 0.53% over 10 years. You can foun additiona information about ai customer service and artificial intelligence and NLP. He describes these gains as “nontrivial, but modest.” This is something of an understatement when compared with the $25 trillion-dollar economic impact estimated by McKinsey.

Notably, [6] proposed a revenue-sharing mechanism for AI-generated music based on TRAK, which is closely related to our work. However, the LOO scores neglect the high-order training data interactions, which may result in undesirable attribution scores (see Appendix B for detailed discussion). Rather than restricting AI developers’ use of copyrighted data, we propose establishing a mutually beneficial revenue-sharing agreement between AI developers and copyright owners. However, a major challenge in developing

a revenue-sharing model for generative AI, in contrast to conventional cases of sharing between digital platforms and independent content creators [6], lies in the complexity of training generative models on diverse data sources. This results in the “black-box” nature of model training and content generation, making the traditional, straightforward pro rata methods unsuitable [21].

Tom Stein, chairman and chief brand officer at B2B marketing agency Stein IAS, says every marketing agency,

including his, is exploring such opportunities at high speed. One of the main fears professionals have regarding generative AI is that it may cost people their jobs. They may also replace humans in positions requiring analyzing and gathering technical data.

Across the high-tech industry, the technology could deliver value of up to $240–460 billion annually, as technology speeds up and makes software development more efficient. The banking sector can benefit up to $200–340 billion annually if the use cases are fully implemented. In addition, the retail and the consumer goods industry could collectively benefit by up to $400–660 billion a year.

Therefore, we also focus on capabilities related to reasoning, such as causal reasoning, logical reasoning on new cases, and making causal inferences. Integrating classical and generative AI in supply chain design enhances resilience, agility, and sustainability. It focuses on patients and customers, using deep knowledge and data integration to improve plans and capacity. Life sciences companies need to adopt these intelligent technologies and embrace this paradigm shift to remain competitive.