Companies must stop GenAI experiments and launch long-term strategies

Generative AI (GenAI) is quickly becoming an integral part of our lives and a powerful force for creating value within organizations. The technology’s potential to accelerate innovation and improve efficiency and productivity extends to nearly every function in every industry. There are many opportunities for businesses to integrate it into their digital transformation, but also to consider the role it will play in their organization’s operating model.

However, because the technology is still in its infancy, companies are struggling to best leverage it to drive business value. The long-term impact of GenAI is not always fully understood, raising questions about the risks for businesses. With so many potential applications, it is difficult to determine the best approach to maximize investment in AI. Additionally, teams may be given limited resources to explore GenAI in the face of this caution, even when faced with pressure to quickly execute GenAI applications through proofs of concept.

Ultimately, the companies that are moving fastest in the field of newcomer generation AI are not necessarily doing it well, just as those that appear slower to get started may actually be building the best foundations and safeguards for success by being better informed about potential risks and recognizing the need for careful governance and planning. Developing a detailed newcomer generation AI strategy is a step that can be missed in the race to anticipate, and being an early mover for competitive advantage must be weighed against the potential repercussions.

Addressing these challenges requires careful consideration of the most practical and strategic use cases for AI and how they align with long-term business objectives. Building on these robust and defined use cases, a GenAI strategy must establish the most relevant applications for the business, profile the tangible value that can be extracted, and ensure that the right people, processes, governance, and technologies are in place to scale GenAI investments while mitigating risk. Ultimately, the organization’s long-term operating model will need to be revisited to truly realize the potential of this technology.

Despite the hype, companies approach AI with caution

Our recent industry research reveals that GenAI technology is on the agenda for 96% of organizations globally, but a significant proportion (39%) are taking a “wait and see” approach to adoption. This caution could be explained by the significant challenges associated with deploying GenAI applications, including data governance, security, and privacy. Many organizations are also still exploring what the technology can do for their business and which use cases to focus on, with legitimate concerns about return on investment (ROI).

Our research found that GenAI has the greatest potential in IT, sales and customer service, and marketing, with the high-tech sector leading the way in adoption. GenAI’s ability to synthesize massive amounts of data can help upskill employees, improving their productivity and performance.

Chatbots are currently the most widely used GenAI application (83%), with a wide variety of applications ranging from improving customer experience, to personalized AI assistants for consumers and staff, to enhancing sales teams with a product and offer knowledge tool. 75% of business leaders are looking to use the technology to create more advanced data applications, and a similar proportion (71%) are using AI for text summarization and search (70%). These use cases for the technology are being adopted across many industries.

Beyond Basic Productivity: From 3D Modeling to Digital Twins and the Metaverse

GenAI technology can transform the customer experience, making it more efficient, personalized, and engaging. Beyond the limited idea of ​​what a chatbot once was, some retailers are experimenting with using GenAI to create visualizations of clothing based on customer preferences for color, fabric, and style. For example, at Capgemini in the UK, we are working with organizations such as Heathrow Airport to implement GenAI solutions for the passenger experience, supporting its operations with faster, more comprehensive, and more responsive customer service for its nearly 80 million passengers each year.

In the aerospace, manufacturing, and defense industries, GenAI is used to improve product design through 3D modeling. By using GenAI models, engineers and designers can optimize the design process and create innovative structures for aircraft, spacecraft, and defense systems. This approach helps produce highly efficient and aerodynamically optimized components, improving the overall performance of the final product and reducing costs.

Another interesting application of GenAI, when combined with 3D modeling, spatial computing, and workflow automation, is that it can be used to create unique virtual experiences. Chipmaker Nvidia is using GenAI in its Omniverse platform to create exciting virtual worlds in the metaverse. This technology can be used to create 3D-related media for entertainment and product demonstrations, and to build digital twins or virtual replicas of products, factories, and infrastructure.

The “commercial metaverse” allows manufacturers to replicate what they can do on a physical production line in a virtual environment, allowing them to run simulations and virtually test product changes before applying them to a physical product. GenAI, combined with digital twins, can also help develop new materials by modeling the solution and its components, simulating it with a digital twin, and making adjustments to create the optimal product.

Advancing industrial innovation through simulations and synthetic data

Another interesting use case for GenAI is its ability to analyze massive amounts of data to create test scenarios, simulations, and even synthetic data. Automakers are already leveraging this capability to improve the development of autonomous vehicles by generating and testing safety and performance scenarios, customizing vehicle features, and improving predictive maintenance. In the energy and utilities sectors, the technology can also be used to track and predict energy consumption, while in the pharmaceutical industry, it can significantly accelerate drug discovery.

GenAI can learn complex relationships in original datasets and produce synthetic data that more reliably reflects these unique patterns. Synthetic data is particularly valuable for organizations that store large, complex datasets or are highly regulated, such as the energy, utilities, or financial services industries. This approach allows AI algorithms to store relationships and patterns in the data without having to record individual-level information, ensuring data privacy and enhancing security.

A strategy-driven approach to success

There’s no point waiting: According to a Capgemini Investment Trends study from January 2024, 88% of global companies surveyed plan to focus on AI, including GenAI, in the next 12 to 18 months. To make the most of all these opportunities, companies must integrate and evolve GenAI into their organizational strategy and operations, as early adopters have much to gain. But before experimenting with the technology, it’s essential to establish a GenAI strategy. This will require focusing on AI use cases that align with their strategic priorities and building trust and accountability into their AI systems. It’s also essential to take a human-centric approach to AI deployment, incorporating human oversight and user feedback, and upskilling employees to ensure everyone can get the most out of the technology. In a rapidly changing environment, an effective GenAI strategy can confidently move a business from initial applications to maturity and unlock a wealth of opportunities.

Steven Webb is the Director of Technology and Innovation at Capgemini UK.