What is the future of Generative AI?

For instance, providing contact center agents with an understanding of a client’s history would streamline problem resolution, boost productivity and increase customer satisfaction. One option for data privacy is to store the full model on premises or on a dedicated server. (BLOOM, an open-source model from Hugging Face’s BigScience group, is the size of GPT-3 but only requires roughly 512 gigabytes of storage.) This may limit the ability to use state-of-the-art solutions, however. Beyond sharing proprietary data, there are other data concerns when using LLMs, including protecting personally identifiable information.

As Generative AI holds transformative potential, companies must assess their readiness to embrace this technology fully. At Digital Wave Technology, we support businesses in evaluating their technical expertise, data architecture, operating model, and risk management processes to ensure seamless and successful integration of GenAI into their workflows. Our team provides white-glove support to retailers, brands, and CPG companies in addition to expert insights on the future of AI in retail. Generative AI, driven by foundation models, offers transformative potential, as seen in scenarios like real-time sales call support. The immediate value lies in integrating generative AI into everyday tools used by knowledge workers, promising substantial productivity gains. The foundation models powering generative AI have cracked the code on language complexity, allowing machines to learn context, infer intent, and showcase independent creativity.

Each is suitable for different use cases and has its own costs, requiring leaders to develop not only a clear vision and strategy for which use cases to pursue, but also how. Only one-third of surveyed telco leaders say they buy products off-the-shelf, suggesting that many telcos continue to embrace a do-it-yourself model. This move is likely to slow innovation and distract talent from more differentiating use cases, as it has in the past with other technologies. However, despite the magnitude of the opportunity and the level of interest (and need), our survey found few that follow the kind of holistic approach required to succeed at scale. Only about one-third of telco leaders said they have a capability-building plan for employees on gen AI or are investing in change management efforts—two core building blocks for building a culture of innovation and the test-and-learn mindset.

But the technology still poses real risks, leaving companies caught between fear of getting left behind—which implies a need to rapidly integrate generative AI into their businesses—and an equal fear of getting things wrong. The question becomes how to unlock the value of generative AI while also managing its risks. Recent progress in computing power, data storage, and algorithms has spurred the development of more sophisticated AI systems, enabling the rise of generative AI models like ChatGPT, GitHub Copilot, and Stable Diffusion​​. These models are not only transforming the way we interact with technology but also redefining the capabilities of machines in understanding and creating complex content.

This combination is anticipated to facilitate a faster and more intuitive application development process. Traditional no-code platforms provide non-developers with drag-and-drop tools, in turn democratizing the application development process. Generative AI, when incorporated, can further streamline this process by converting user-provided requirements into application templates or frameworks.

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Development costs involve building the user interface and integrations, requiring expertise from a data scientist, machine learning engineer, designer, and front-end developer. Ongoing expenses include software maintenance and API usage costs, varying based on model choice, vendor fees, team size, and time to minimum viable product. The generative AI ecosystem is evolving to support the technology’s training and application. Specialized hardware provides essential computing power, and cloud platforms facilitate access to this hardware. MLOps and model hub providers offer tools and technologies for adapting and deploying foundation models in end-user applications. Numerous companies are entering the market, providing applications built on foundation models for specific tasks, like assisting customers with service issues.

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). The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain.

GenAI models can bypass these issues by uncovering new data dimensions and correlations for consideration. It can also perceive new patterns that might not be detectable by traditional data analysis techniques. As a CEO, understanding this technology and the value it can add to your business is becoming more pertinent in recent times. Both questions involve cultural issues that boards should consider prompting their management teams to examine. Depending on what they find, reformulating a company’s culture could prove to be an urgent task.

Retail Industry

Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development. The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month. You can foun additiona information about ai customer service and artificial intelligence and NLP. In March 2023 alone, there were six major steps forward, including new customer relationship management solutions and support for the financial services industry. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities.

When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. Global economic growth was slower from 2012 to 2022 than in the two preceding decades.8Global economic prospects, World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth. An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery.

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. 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.

It’s a labor-intensive process that requires extensive trial and error and research into private and public documentation. At this company, a shortage of skilled software engineers has led to a large backlog of requests for features and bug fixes. DME is a leader in professional services with uninterrupted presence in the Middle East since 1926 with 26 offices in 15 countries and around 5,900 partners, directors and staff.

Effective integration of generative AI into business processes requires strategic planning. This includes a disciplined approach to data management, ensuring the availability of quality data to train AI models. Companies also need to adapt their operating models and governance structures to effectively leverage generative AI technologies​​.

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. 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.

The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. In today’s dynamic business landscape, continuous learning and adaptation are vital for CEOs to steer their companies successfully.

Within Marketing and Sales

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. Generative AI, a powerful technology, raises important ethical considerations in its application. The creation of content by AI prompts the responsibility to ensure that its use aligns with ethical principles. As this technology can generate text, images, and even human like content, there’s a need to safeguard against misuse, such as spreading misinformation or generating harmful content.

CEOs will want to design their teams and processes to mitigate those risks from the start—not only to meet fast-evolving regulatory requirements but also to protect their business and earn consumers’ digital trust. The future of generative AI in business is marked by continuous evolution and growth. Companies need to stay ahead of emerging trends and technologies to maintain a competitive edge. Adopting generative AI demands significant infrastructural and architectural considerations.

The off-the-shelf solution integrates with existing coding software, allowing engineers to write code descriptions in natural language. While more experienced engineers benefit most, the tool cannot replace human expertise, and risks include potential vulnerabilities in AI-generated code. Costs are relatively low, with fixed-fee subscriptions ranging from $10 to $30 per user per month. Implementation involves minimal workflow and policy changes, overseen by a small cross-functional team.

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. 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. 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.

The phased implementation involves internal piloting, learning from employee feedback, and gradually shifting toward customer-facing use cases with human oversight. Generative AI frees up service representatives for higher-value inquiries, enhancing efficiency, job satisfaction, service standards, and customer satisfaction. Significant investments in software, cloud infrastructure, tech talent, and internal coordination are required for this transformative use case. Fine-tuning foundation models costs 2-3 times more than building software layers on top of an API, encompassing talent and third-party cloud computing or API costs.

Gen AI represents just a small piece of the value potential from AI

Leaders therefore need to encourage all employees, especially coders, to retain a healthy skepticism of AI-generated content. Company policy should dictate that employees only use data they fully understand and that all content generated by AI is thoroughly reviewed by data owners. Generative AI applications (such as Bing Chat) have already started implementing the ability to reference source data, and this function can be expanded to identify data owners. Neural networks are designed to mimic the human brain’s interconnected network of neurons, while deep learning refers to the training of neural networks with multiple layers to learn complex representations. Generative AI, on the other hand, can generate data samples based on existing patterns, which can enable organizations to make better predictions—even in the absence of large datasets. Let’s explore how our solutions align with the key points from the article, and how Digital Wave empowers businesses to harness the true potential of GenAI.

Lenovo is correct in its assumption that more processing power will be needed at the edge. Growth in emerging technologies such as LLMs or other AI-enhanced technologies such as computer vision applications will continue to require more and more processing power for real-time inferencing by edge devices. One of the main advantages of HPE GreenLake for LLM is its ability to scale supercomputing resources up or down on demand without any architectural limitations.

This can be particularly useful for handling large amounts of data, a major consideration for AI models. Additionally, while most cloud companies charge significant fees for data egress, HPE GreenLake for LLM has no data egress fees. Here is how a few large companies use AI to enhance features, products, services and workflow offerings.

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. For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5).

Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools what every ceo should know about generative ai can review code to identify defects and inefficiencies in computing. Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design.

For example, the European telco started by assigning three data scientists to monitor their handful of deployed models and plans to expand the team as more models are deployed. A significant portion of implemented gen AI solutions can be adapted and reused in multiple use cases. A gen AI chatbot developed to improve agent productivity, for example, can be repurposed with additional fine-tuning or data to answer frequently asked questions by new employees or provide IT support. An off-the-shelf content generation system for drafting sales proposals may also streamline the development of marketing and business plans.

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 could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. HPE GreenLake for LLMs is provided through a partnership with Aleph Alpha, a German AI startup that provides users with an LLM for use cases that require text and image processing and analysis.

What are CEOs saying about generative AI?

But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.

These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”).

AI models in the future will probably train on more data as AI gets more intelligent and more capable. Snowflake is scooping up business from top corporations, including 691 of the Forbes Global 2,000. Thanks to a usage-based billing model, its 131% net revenue retention signals that customers spend more on the platform over time. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great.

Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies.

• Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence.
• This shift underscores the importance of training employees to work effectively alongside AI systems​​.
• Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021.
• Creatio, a global vendor of one platform to automate industry workflows and CRM with no-code.
• Project Helix’s on-premises approach also provides better infrastructure and operations control and management, yielding a higher ROI.
• For instance, the blueprint should include a framework for determining which large language models to use and when (commercial or open-source models for example, or those that support hybrid workloads).

The sudden rise of gen AI has brought the dream of the AI-native telco significantly closer to becoming a reality. With it comes the opportunity for telcos to reverse their recent stagnant fortunes and usher in a new era of growth and innovation. To answer the call of gen AI, telcos will need to quickly adopt a culture of innovation and experimentation enabled by the core building blocks shared in this article, one they have previously struggled to build and maintain. With the technology moving so rapidly, those operators that embrace it now are likeliest to create a significant lead that will be difficult for others to follow. One of gen AI’s superpowers is its ability to uncover connections in seemingly unrelated data sets, which has implications for how organizations choose to collect and measure data, and how they manage it to ensure responsible use. This blend of optimism and restraint highlights the critical juncture the industry faces.

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. As a result, companies can stand up applications and realize their benefits much faster. Most telco leaders we surveyed1The online survey was in the field from November 9, 2023, to December 6, 2023, and garnered responses from 130 telco operators in North America, Latin America, Europe, Europe, Africa, Asia, and the Middle East. Say they are developing gen AI solutions that range from pilots to full-scale deployments, and leading telcos such as AT&T, SK Telecom, and Vodafone have made much-publicized early gen AI commitments and launched trials. Some telcos around the world have started to experience significant double-digit percentage impact from this technology.

MLOps and model hub providers offer the tools, technologies, and practices an organization needs to adapt a foundation model and deploy it within its end-user applications. Many companies are entering the market to offer applications built on top of foundation models that enable them to perform a specific task, such as helping a company’s customers with service issues. All of this is possible because generative AI chatbots are powered by foundation models, which contain expansive neural networks trained on vast quantities of unstructured, unlabeled data in a variety of formats, such as text and audio. For instance, the blueprint should include a framework for determining which large language models to use and when (commercial or open-source models for example, or those that support hybrid workloads).

With many employees already using the technology in their personal lives, organizations will need to consider how to help them learn to apply the technology in a professional context, upskilling and reskilling staff at scale. Such work can be made easier using gen AI, for example to develop and deliver customized and adaptive training programs, and even to onboard employees. The industry has struggled these last ten-plus years to achieve the potential of “traditional” AI, given the complexity and legacy processes involved.