Enterprises must stop GenAI experiments and start long-term strategies

Generative AI (GenAI) is rapidly becoming an integral part of our lives and a powerful force for driving value within organisations. The potential of the technology to accelerate innovation and improve efficiency and productivity extends to nearly all functions across all industries. There are extensive opportunities for businesses to factor it into their digital transformation, but also to consider the part it will play within the operating model of their organisation.

However, as the technology is still in its relative infancy, businesses are struggling with how best to exploit it to drive business value. GenAI’s long-term impact is not always fully understood, posing a question of risk to businesses. With so many potential applications, determining the best approach to maximise AI investment is difficult. Moreover, teams may be provided with limited resources to explore GenAI in the face of this caution, even when facing pressure to execute GenAI applications at speed via proof of concepts.

Ultimately, the businesses that are moving the fastest on GenAI are not necessarily doing it right, just as those that appear slower off the mark may actually be building the best foundations and guardrails for success, through being better informed of the potential risks, and recognising the need for governance and careful planning. Devising a detailed GenAI strategy is a step that may be missed in the race to look ahead of the game, and being an early mover to gain competitive advantage must be balanced with the potential repercussions.

Tackling these challenges requires careful consideration of the most practical and strategically important use cases of AI, and how they align to long-term business goals. Building on these strong, defined use cases, a GenAI strategy should 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 available to scale GenAI investments while mitigating risks. Ultimately, the long-term operating model of the organisation will need to be reviewed to truly realise the potential of this technology.

Despite the hype, businesses are approaching AI with caution

Our recent industry-focused research revealed that GenAI is on the boardroom agenda at 96% of organisations globally, but a significant proportion of them (39%) are taking a “wait-and watch” approach to adoption. This caution could be explained by the significant challenges around rolling-out GenAI applications, including governance, security, and data privacy. Many organisations are also still exploring the possibilities of the technology for their business and the best uses cases to focus on, with legitimate concerns over return on investment (ROI).

 Our research revealed that GenAI has the greatest potential within IT, sales and customer service, and marketing functions, with the high-tech sector leading the way in adoption. The ability of GenAI to synthesise huge amounts of data can help augment employee skills, improving their productivity and performance.

Chatbots are currently the most widely used GenAI application (83%), with a huge diversity of applications from customer experience augmentation, to personalised AI assistants for consumers and staff, or enhancing sales teams through a product and offers knowledge tool. 75% of business leaders are looking to use the technology for building more advanced data applications and a similar proportion (71%) are using AI for text summarisation and search (70%). These uses cases of the technology are being adopted across multiple business sectors.

Beyond basic productivity: from 3D modelling to digital twins and the metaverse

GenAI technology can transform the customer experience, making it more efficient, personalised, and engaging. Going beyond the limited idea of what a chatbot once was, some retailers are experimenting with using GenAI to create visualisations of clothing based on requested customer preferences for colour, fabric, and style. For example, at Capgemini in the UK we are working with organisations such as Heathrow Airport to implement GenAI solutions for passenger experience, supporting its operations with faster, more comprehensive and sensitive customer service for its almost 80 million passengers each year.

In the aerospace, manufacturing and defence industries, GenAI is being used to enhance product design via 3D modelling. By employing GenAI models, engineers and designers can optimise the design process, creating innovative structures for aircraft, spacecraft, and defence systems. This approach enables the production of highly efficient, and aerodynamically optimised components, improving the overall performance of the final product and reducing costs.

Another compelling application of GenAI, when combined with 3D modelling, spatial computing, and workflow automation, is that it can be used to create unique virtual experiences. The chip manufacturer Nvidia uses GenAI within 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 ‘business metaverse’ allows manufacturers to replicate what they can do on a physical production line in an virtual environment, enabling them to run simulations and test product changes virtually before they are applied to a physical product. GenAI, combined with digital twins, can also help develop new materials by modelling the solution and its components, simulating it with a digital twin, and making the needed adjustments to create the optimal product.

Advancing industry innovation with simulations and synthetic data

Another powerful use case with GenAI lies in its potential to analyse huge amounts of data to create testing scenarios, simulations, and even synthetic data. This is already exploited by automotive companies to enhance autonomous vehicle development by generating and testing scenarios for safety and performance, customising vehicle features, and improving predictive maintenance. In the energy and utilities sectors, the technology can also be used to track and predict energy utilisation, while in the pharmaceutical industry it can significantly accelerate drug discovery.

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

 A strategy-first approach for success

The “wait and watch” will only hold so long – Capgemini's Investment Trends research from January 2024 shows 88% of the global organisations profiled plan to focus on AI including GenAI within the next 12–18 months. To be able to make the most of all these opportunities, businesses need to move to integrate and scale GenAI in their organisational strategy and operations, as early movers do stand to gain considerably. But before experimenting with the technology, it is essential to establish a GenAI strategy. This will require focusing on AI use cases that align to their strategic priorities and embedding trust and responsibility into their AI systems. It is also critical to adopt a human-centric approach to AI deployment, embedding human oversight and user feedback, and upskilling employees to ensure everyone can make the most of the technology. In a fast-moving environment, an effective GenAI strategy can take a business from initial applications to maturity with confidence, and open up a wealth of opportunities.

Steven Webb is the chief technology & innovation officer at Capgemini UK.