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Deliver an unprecedented commerce experience with generative AI

Jennifer Marchand & James Housteau
18 Mar 2024

While it is still early to pinpoint the full trajectory of generative AI, it is too powerful to be downplayed or ignored. AI itself has been a revolutionary technology, and the recent breakthrough means its prevalence in business will only escalate.

Many companies are now scrambling to incorporate generative AI models and scale them across the enterprise. For businesses facing consumers, the incentive is clear: use generative AI to drive higher value and revenue with an enhanced commerce experience.

While we know that Generative AI is a top agenda item in boardrooms, in our recent Capgemini Research Institute report, ‘Harnessing the value of Generative AI’, we found that 76% of retail organizations believe the benefits of generative AI outweigh the risks – and 62% have established dedicated teams and budgets for generative AI.

Technology has reinvented the way people engage with products and services, but we have only scratched the surface of what’s possible. Generative AI will allow us to go deeper – transforming the business-customer relationship as we know it and unlocking a new level of maturity and engagement.

Generative AI is only transformative if used effectively

Suppose that 50 percent or more of all customer contact is handled by AI. This is increasingly likely considering the current trend. In fact, 47% of organizations use or plan to use generative AI across sales and customer service (e.g., optimizing support chatbots/self-service). Much of our interactions today are already managed by AI; we need only consider the ads we see on our devices to realize it operates everywhere.

On this scale, it will be crucial to ensure AI runs effectively, or it will achieve no benefit. For example, if an email is not adequately personalized, it may easily be perceived as spam. If a support chatbot cannot properly comprehend an issue, regardless of complexity, it must pass it to a human, introducing friction into the process.

Generative AI can transform the customer experience, but only if it becomes an improvement over what consumers are used to. It cannot ride on the novelty factor alone. For instance, consider Siri and Alexa: innovative systems powered by voice recognition that surprised us with their capabilities. Today, their features are considered common and only meaningful in a small set of use cases.

Redefining efficiency and customer engagement

Combine the support capabilities of those tools with a large language model (LLM) founded upon generative AI, however, and you will get something truly new and transformative. That is what it brings to commerce by breaking conventional limits and amplifying possibilities. For a business, this allows for a better yield from interactions and a greater reach across channels with minimal infrastructure. For consumers, it enables an unprecedented product or service experience.

Imagine walking into a retail superstore, with your wireless earbuds on and phone in hand. By using a store app configurated with a LLM, you will have your own personal assistant providing directions and tailored recommendations. Whether you need low-calorie, gluten-free products or clothing suggestions for a cocktail party, you will have a virtual expert guiding you in a streamlined, next-generation shopping experience.

The engagement and efficiency this creates makes it a clear winner over the traditional method. Walking in circles looking for products or doing a manual browser search is a loss of valuable time and a clunky experience.

Technology shines where it drives meaningful improvement. And with 70 percent of consumers already looking at generative AI tools for product or service recommendations, its potential in this space is truly bright.

How to thrive with generative AI as it matures

The first step for many companies will be improving the quality and accuracy of their data. Powering generative AI tools with poor data is like having a race car fitted with an old engine from a beater vehicle – rendering it ineffective and unreliable, especially compared to the competition. A robust data foundation is therefore essential for getting tangible value from any use case of generative AI.

There is also a common, preconceived notion that implementing these tools only requires a one-time effort. It demands a regular commitment, especially because the technology is rapidly evolving. A continuous engineering feedback loop enables development teams to constantly scrutinize the generative models and their underlying parts with a focus on making the user experience consistently better.

Companies that invest in these areas will be positioned to thrive in the era of generative AI when the technology matures.

The human element remains critical

Corporations need people behind these systems with the right blend of skills, knowledge, and experience, all aligned to the brand’s values, ethics, and overall mission. While AI will generally displace – rather than replace – some human roles, people will be fundamental to the success of these tools. It is also far more useful to dedicate time and attention into learning how to wield them effectively than worrying excessively about any potential negative impact they may have on work.

At the individual level, leveraging generative AI to code, validate, or drive creativity empowers people to become better workers and exponentially more productive. There are nearly endless problems that need to be solved and, with this potent technology, employees will be able to tackle more of them – making themselves greater assets to the business while contributing to its growth and success.

Capgemini at Google Cloud Next 2024

Google Cloud Next brings together a diverse mix of developers, decision makers, and cloud enthusiasts with a shared vision for a better business future through technology. As a Luminary Sponsor, Capgemini is committed to elevating the event experience with opportunities to boost learning and engagement and get fresh insight into today’s riveting topics – including generative AI.

Whether the aim is empowering businesses or their people to unlock the power of generative AI, Capgemini is at the forefront of this revolution. Our continuous work in this growing domain means we are equipped to help our partners capitalize on this unique technology and engineer use cases for enhanced and unprecedented customer experiences.

Come by our booth and let’s discuss the possibilities in the world of Generative AI, Cloud, Data/AI, and Software Engineering. Or reach out to us – we would love to hear your perspective on how we can get ready for what comes next.


Jennifer Marchand

Enterprise Architect Director and GCP CoE Leader, Capgemini/Americas
Jennifer leads the Google Cloud COE for Capgemini Americas, with a focus on solutions and investments for the CPRS, TMT, and MALS MUs, and supporting pre-sales across all MUs. She has been with Capgemini for 18 years focusing on cloud transformation since 2015. She works closely with accounts to bring solutions to our clients around GenAI, AI/ML on VertexAI and Cortex, Data Estate Modernization on Big Query, SAP on Google Cloud, Application Modernization & Edge, and Call Center Transformation and Conversational AI. She leverages the broader Capgemini ecosystem across AIE, Invent, ER&D, I&D, C&CA, and CIS to shape cloud and transformation programs focusing on business outcomes.

James Housteau

IT Transformation Director | I and D Global Practice, Google Cloud GenAI COE
Over two decades in the tech world, and every day feels like a new beginning. I’ve been privileged to dive deep into the universe of data, transforming raw information into actionable insights for B2C giants in retail, e-commerce, and consumer packaged goods sectors. Currently pioneering the application of Generative AI at Capgemini, I believe in the unlimited potential this frontier holds for businesses.