PRESSR: CalypsoAI partners with Deloitte Middle East to unleash the power of Generative AI

Like existing forms of artificial intelligence, generative AI is a disruptive force for humans. In the near term, CEOs need to work with their leadership teams as well as HR leaders to determine how this transformation should unfold within their organizations—redefining employees’ roles and responsibilities and adjusting operating models accordingly. CEOs should be mindful that leaders secure buy-in with their teams so that levels of stakeholder interest and engagement are kept high as initiatives and use cases are identified. Generative AI is a powerful tool that can transform how organizations operate, with particular impact in certain business domains within the value chain (for example, marketing for a retailer or operations for a manufacturer). The ease of deploying generative AI can tempt organizations to apply it to sporadic use cases across the business.

It is important to have a perspective on the family of use cases by domain that will have the most transformative potential across business functions. Organizations are reimagining the target state enabled by generative AI working in sync with other traditional AI applications, along with new ways of working that may not have been possible before. To that end, we recommend convening a cross-functional group of the company’s leaders (for example, representing data science, engineering, legal, cybersecurity, marketing, design, and other business functions). Such a group can not only help identify and prioritize the highest-value use cases but also enable coordinated and safe implementation across the organization.

But under the right conditions, generative AI has the power to eliminate the compromise between agility and scale. As research accelerates and becomes more and more proprietary, and as the algorithms become increasingly complex, it will be challenging to keep up with state-of-the-art models. Data scientists will need special training, advanced skills, and deep expertise to understand how the models work—their capabilities, limitations, and utility for new business use cases.

The bank decided to build a solution that accesses a foundation model through an API. The solution scans documents and can quickly provide synthesized answers to questions posed by RMs. Additional layers around the foundation model are built to streamline the user experience, integrate the tool with company systems, and apply risk and compliance controls.

Increasingly staying ahead of competitors requires that enterprises leverage leading-edge technology for competitive advantage. The excitement around generative AI is palpable, and C-suite executives rightfully want to move ahead with thoughtful and intentional speed. We hope this article offers business leaders a balanced introduction into the promising world of generative AI. Generative AI refers to artificial intelligence algorithms that enable using existing content like text, audio files, or images to create new plausible content. In other words, it allows computers to abstract the underlying pattern related to the input, and then use that to generate similar content. Our BCG responsible AI consulting team helps organizations execute an strategic approach to responsible AI through a tailored program based on five pillars.

Product R&D: Reducing research and design time, improving simulation and testing

Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. 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. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10).

However, as more and more businesses sign up with Palantir, that’s likely to change. As Palantir CEO Alex Karp has put it, “Obviously, our performance in U.S. commercial is extraordinary, some would say bombastic.” According to a PricewaterhouseCoopers study, AI could contribute almost $16 trillion to the global economy by 2030. That means the investment opportunity extends beyond Nvidia (NVDA 3.09%) and other big technology companies. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). 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.

Customer-relationship-management systems will suggest ways to interact with customers. Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to generate content. Foundation models can also be used for non-generative purposes (for example, classifying user sentiment as negative or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to generative AI in this article, we include all foundation model use cases.

Do we have the necessary capabilities?

Small and large operators report similar views on where to prioritize, focusing on customer service and IT in similar measure, suggesting the possibility of new competitive pressures emerging for incumbents (Exhibit 4). 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. The time to act is now.11The research, analysis, and writing in this report was entirely done by humans.

For businesses and individuals alike, the union of generative AI and no-code tools offers not just convenience but a promise of a more democratized and vibrant digital future. Keep in mind we are still using early generations of generative AI and foundation models. As later generations are developed, more functionality will be available, which will require increased responsibility and oversight on our part. This will especially be true in the future when AI becomes integrated into our healthcare, social and personal systems.

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Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. 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.

Large players that want to remain independent while using the latest AI technology will need to build strong internal tech teams. CEOs play a critical role in understanding the nuances of generative AI and its future impact on their organization. Generative AI presents a transformative opportunity for organizations to gain a competitive edge, drive innovation and promote business growth.

Google has been working with AI for over a decade; Lenovo began investing billions in AI starting in 2017; and IBM has also been building and using foundation models and generative AI to create sophisticated pharmaceutical and medical research for many years. The use of generative AI coding tools may result in code with vulnerabilities or bugs, posing risks to software quality and security. On average data analysts spend a lot less of their productive time on model development and analysis than they should. GenAI can lessen the amount of productive time spent on laborious tasks by helping with data classification, segmentation and enrichment. Hands-on experience in the boardroom can build familiarity with the technology and appreciation of its value and risks. Moreover, because generative AI can improve decision making, it would be remiss of boards not to explore its potential to help them perform their duties to the best of their ability.

Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Incorporating no-code platforms into your tech stack involves educating staff on the no-code vision, selecting the right platform based on your needs and requirements, and measuring the impact on your team’s performance and business outcomes.

Implementing Gen AI applications into everyday operations, while exercising caution, can be beneficial to leapfrog competition. Predictive Analytics and NLP  NLP systems can provide real-time analytics, sentiment analytics, and enhanced personalization for user experience. When predictive analytics and NLP technologies are integrated, they provide valuable insights by analyzing textual data to identify patterns, trends and make future predictions.

The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023. Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves.

Many of these practices are now enabled or optimized by supporting software (tools that help to standardize, streamline, or automate tasks). Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set. The first example is banking, with an estimated total value per industry of $200 billion to $340 billion, and a value potential increase of 9–15% of operating profits based on average profitability of selected industries in the 2020–22 period. The third example is pharma and medical products, with an estimated total value per industry of $60 billion–$110 billion, and a value potential increase of 15–25% of operating profits based on average profitability of selected industries in the 2020–22 period.

Generative AI enables organizations to leverage data in a way that was not possible before and streamline operations, scale organizations and gain a competitive edge in a much more efficient manner. Through personalization and creative ways to engage with both data and content, generative AI is becoming instrumental in breaking down organizational silos. You can foun additiona information about ai customer service and artificial intelligence and NLP. For example, if the use case is relatively straightforward and can be supported by an off-the-shelf foundation model, a generalist may be able to lead the effort with the help of a data and software engineer.

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. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. 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. Implementing robust security measures and ensuring user data privacy are fundamental responsibilities for CEOs. I would also like to point out that accessing supercomputing resources from anywhere using an internet connection is a valuable feature of this service.

Such power can be ingrained into your organization because these solutions are based on open interfaces. Companies gain a competitive advantage by deploying cutting-edge AI systems to drive innovation, optimize operations and enhance customer experiences. By adopting generative AI solutions and utilizing them effectively, CEOs can position their companies as industry leaders now and in the future. Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that question the status quo and spark change. BCG delivers solutions through leading-edge management consulting, technology and design, and corporate and digital ventures.

If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. 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. Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations.

Leaders should consider leveraging cleaning techniques such as named entity recognition to remove person, place, and organization names. As LLMs mature, solutions to protect sensitive information will also gain sophistication—and CEOs should regularly update their security protocols what every ceo should know about generative ai and policies. Generative AI lacks a credible truth function, meaning that it doesn’t know when information is factually incorrect. The implications of this characteristic, also referred to as “hallucination,” can range from humorous foibles to damaging or dangerous errors.

By leveraging these models, we empower teams to create irresistible product experiences with unparalleled precision and efficiency. In addition to hiring the right talent, companies will want to train and educate their existing workforces. Prompt-based conversational user interfaces can make generative AI applications easy to use. But users still need to optimize their prompts, understand the technology’s limitations, and know where and when they can acceptably integrate the application into their workflows. Leadership should provide clear guidelines on the use of generative AI tools and offer ongoing education and training to keep employees apprised of their risks.

• We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place.
• Overall, the integration of Generative AI in business operations holds the promise of increased productivity, creativity, and competitiveness in the marketplace.
• Incorporating no-code platforms into your tech stack involves educating staff on the no-code vision, selecting the right platform based on your needs and requirements, and measuring the impact on your team’s performance and business outcomes.

Moreover, finding appropriate talent and obtaining quality data remain significant challenges for telcos, although confidence about solving these rose among surveyed leaders this year as compared to last. Because gen AI democratizes access to powerful capabilities, any telco—a small operator or large incumbent—can reshape customer expectations and its organizational efficiency. In doing so, they can potentially narrow previously unassailable competitive advantages and overturn long-standing barriers to growth. Those at the forefront of this movement stand to position themselves to regain growth faster and capture a more significant share of the nearly $100 billion in incremental value (Exhibit 2). That is in addition to the $140 billion to $180 billion in productivity gains that gen AI will create in the industry above what could be unlocked by traditional AI.

CEOs need to optimize and utilize innovative GenAI technology to streamline business. The first option is to review the board’s composition and adjust it as necessary to ensure sufficient technological expertise is available. In the past, when companies have struggled to find technology experts with the broader business expertise required of a board member, some have obtained additional support by setting up technology advisory boards that include generative AI experts. However, generative AI will likely have an impact on every aspect of a company’s operations—risk, remuneration, talent, cybersecurity, finance, and strategy, for example.

The development cost comes mostly from the user interface build and integrations, which require time from a data scientist, a machine learning engineer or data engineer, a designer, and a front-end developer. Costs depend on the model choice and third-party vendor fees, team size, and time to minimum viable product. However, because of the way current foundation models work, they aren’t naturally suited to all applications. For example, large language models can be prone to “hallucination,” or answering questions with plausible but untrue assertions (see sidebar “Using generative AI responsibly”). Additionally, the underlying reasoning or sources for a response are not always provided.

Such accessibility doesn’t just empower individual innovators but also redefines the operational frameworks of businesses, especially at an enterprise level. The democratization brought about by generative AI and no-code development is reshaping how core enterprise solutions are approached and deployed. Combining generative AI with no-code tools is like giving everyone a chance to be a tech innovator. Just like how the internet and mobile apps changed how we use technology, mixing generative AI with no-code can turn everyday users into app builders. As developing software becomes easier, people from all walks of life, even without tech skills, can now make their own digital tools.

The trained model added value by predicting which drug candidates might lead to favorable outcomes and by improving the ability to accurately identify relevant cell features for drug discovery. This can lead to more efficient and effective drug discovery processes, not only improving time to value but also reducing the number of inaccurate, misleading, or failed analyses. In this example, a large corporate bank wants to use generative AI to improve the productivity of relationship managers (RMs). RMs spend considerable time reviewing large documents, such as annual reports and transcripts of earnings calls, to stay informed about a client’s situation and priorities.