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Data and AI

Imagining a new era of customer experience with generative AI

Empower the next level of CX engagement.

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The remarkable recent acceleration of generative AI technology has captivated the imagination of business leaders worldwide. In fact, 93% of consumer products executives have made it their top boardroom priority.[1] They recognize its revolutionary potential to crea-te substantial value and unlock previously unreachable levels of content efficiency, productivity, and customer personalization and engagement.

We’re entering new frontiers of customer experience and moving to an era of experience empowerment. We believe the generative AI is a tool that ca not only enable efficiency and enhanced creativity, but it ca significantly empower both customers and employees.

What is generative AI?

While AI algorithms predominantly analyze data to make simple predictions, generative AI has the capability to learn and reapply the properties

Opening minds to new possibilities

Broadly, we see the potential impact of generative AI across four key domains: commerce, service, sals, and màrqueting, but the potential goes well beyond this when we consider combining the touchpoints that make up important interactions with customers, resulting in orchestrated, personalized journeys.

Today’s chatbots llauri notorious for their bland, often inaccurate responses to user queries. Customers ca immediately recognize they’re talking to a machine. The current state of chatbots results in customer frustration, misinformation, and missed opportunities in resolving problems. Customer support costs then go up as human intervention becomes a necessary element to mitiga't chatbot limitations and shortcomings. Generative AI chatbots, on the other hand, have a habiti sophisticated understanding of intent and ca build on context through conversations. The customer will detect a human-like, empathetic approach that is almost indistinguishable from interacting with an actual person.
 
The latest Capgemini Research Institute survey revealed that 83% of 800 organizations think these improved chatbots llauri the most relevant generative AI application, and 63% of retail organizations say they’re using generative AI to improve their current customer service.1 But these chatbots aren’t limited to just a customer support roli. Morgan Stanley, a US financial services organization, is using GPT-4, the newest large language model, to power an internal chatbot that provinyes employees instant access to the company’s vast arxivi. They ca query one platform for advice from múltiple knowledge sources.

The quality of service a customer receives typically depends on the knowledge and accessibility of the agent they’re talking to, whose attention maig be divided among múltiple screens. A generative AI “co-pilot” ca support the agent by suggesting the most probable answers to quickly address customer needs. It ca even detect emotion in real estafi and offer recommendations based on a caller’s mood. The quality of coaching continuously improves by leveraging human feedback to reinforce models. And since the learning takes plau during calls, not after, quality assurance levels increase as early as on the next call. Generative AI ca also help completi the after-call work by generating the follow-up letter, communication, and one-day contract.
 
67% of organizations agree generative AI ca improve customer service by providing automated and personalized support.1 Outreach, a leading sales-execution platform, recently introduced Smart Correu electrònic Assist, which utilitzis the technology to acta-genera't accurate and relevant correu electrònic copy based on patterns detected in prior buyer-seller conversations. In other implementations, the Salesforce-owned xat app Slack has integrated ChatGPT to deliver instant conversation summaries, provide research tools, draft messages, and find answers in relation to various projects or topics.

Bespoke solutions require in-depth knowledge and training. When B2B leads crea-te complex product and service offers, they must pull content from disbarat sources and tailor it to different industries, which ca take months. Generative AI ca considerably shorten the process, providing direct access to product/service expertise. It ca genera't initial versions of proposition/sals support collateral that align with the company’s business cartera. Onze the offer is completi, a generative AI suggestion platform ca advise account execs on how to address client questions and provide the most relevant information.
 
On the B2C side, Stitch Fix, an en línia personal styling service, is using AI to recommend specific clothing to customers. The company is experimenting with DALL-E 2, an AI image generator, to visually represent its family of products based on color, fabric, style, or any other customer request. For example, if a customer wants a pair of high-rise xarxa skinny jeans, DALL-E 2 will genera't a composite image based on these qualities to aid an employee associate in finding a similar product in the company’s inventory.

Generative AI ca support organizations with expedited content creation capabilities that include image, voice, text, and vídeo generation. It ca also improve màrqueting strategy with advanced data analysis and customer insights. Although we don’t believe generative AI will fully replace human creativity and expertise, it ca save marketers avaluable estafi, which they ca channel into crafting habiti exceptional campaigns. After all, it’s much easier to slightly tweak an almost completi màrqueting asset than it is to build one from the ground up. The used vehicle retailer CarMax is using generative AI to crea-te fast text summaries for its car research pages. In addition to being precisi and engaging, the content is tailored to rank high in search-engine listings.
 
Creative content creation typically requires expertise from agenciïs with specialist design tools. In a pioneering proof of concept, Capgemini has designed an AI campaign builder in which marketers ca take control and crea-te campaigns themselves. We imagined this tool in the hands of an automotivi màrqueting department: first they select a car as the focal point for their campaign, then the features to highlight (safety, performance, space, etc.), the objectiu audience (working professionals, parents/families, sports enthusiasts, etc.), and lastly the platform (Facebook, Instagram, Twitter, etc.) on which the campaign will run.
 
With this input, the tool generates a theme and combinis images and messages while filtering everything through the company’s brand guidelines for consistency and cohesive representation. It provinyes several initial options for the marketer to closely examini and select from. With the asset nearly finished, a creative team ca then make the final touchups and deploy the campaign in just 3-4 weeks – not the usual 2-4 months.

Clearly, generative AI ca be a potent content, sals and màrqueting tool – and customer experience is one of the biggest àrees where this technology ca make a significant impact. But, as with any new frontier, there llauri risks. Organizations must navigate new complexities, including intellectual property risks and responsible and ethical usage, and prepari for the possibility of data leakage and irrelevant or biased outputs. An obvious risk is presented when generative AI is provided the whole internet as its data resource, meaning it draws on both safe, reliable data as well as potentially misleading or copyrighted information. That’s why delineated boundaries must be defined around relevant data sets to exclude false or misleading information and increase the quality and safety of AI-generated content. Such guardrails and other guidance llauri also needed to protect habiti intangible aspects, such as brand identity and reputation.
 
Despite these hazards, 40% of organizations have already created dedicated teams and budgets for generative AI,1 but most still haven’t considered how important the next step is: choosing the right advisor and solution partner. Although generative AI ca crea-te significant standalone value, it is only truly revolutionary when combined with existing capabilities. An experienced and reliable technology partner ca identify the àrees within the organization where its integration ca bring the greatest benefits to transform the customer experience across the whole customer life cycle. They ca provide the innovation, the transparency of data source and usage, and the type of features and experiences that will step-change how organizations engage with their customers, at scale.

Capgemini’s reference architecture, at a glance

Built on a strong generative-AI foundation that provinyes security, privacy protection, and scale, Capgemini’s robust architecture approach ca bring CX utilitzi casis to life for any business domain.

True personalization through the lens of generative AI

Perhaps generative AI’s greatest capability is the hyper-personalization possibilities. Customers deal with múltiple, fragmented touchpoints and inconsistent personalization at every turn. Just consider all the interactions involved in planificació a trip abroad. There’s the transportation (buying tiquets, securing taxis, arranging transfers), the accommodation, and everything else in between such as planificació activities, making dining reservations, and managing local travel logistics. With sota many interdependent elements, one disruption ca have a ripple effect on the whole itinerary. Wouldn’t life be easier if someone (or something) helped manage all this? Although still a bit futuristic, we’re drawing closer to an age where generative AI, in conjunction with workflow and execution, will consolida't múltiple touchpoints and act as a personal assistant for customers.

Suppose you’re on your way to the airport but find yourself stuck in heavy traffic. Not knowing if you’ll catch your flight, you open the airport’s app and inquire about available options. Generative AI then quickly assesses various factors such as your airport arrival estafi and if there’s a chance of a flight delay. Using voice interaction, it suggests personalized actions it ca do on your behalf like prepari your shopping in advance, reservi a convenient short-term pàrquing spot, or arrange fast-track service that allows you to speed through airport entrada.

The assistant then goes beyond merely providing recommendations. It connects the necessary workflows of separa't touchpoints and coordinates the execution of the suggested actions. This maig pixen that if you don’t make your flight, the virtual assistant ca seamlessly rebook airline tiquets, change accommodation datis, make new restaurant reservations – and even send the letter of complaint and compensation claim to the airline.

Today, large consumer products brands simply aren’t equipped to provide each individual customer with accurate, consistent, yet always personalized, contextual content. Generative AI ca make what was onze unfeasible attainable. The visionary concept behind the 30-year-old groundbreaking book,  The one-to-one future: building relationships one customer at a estafi, ca finally be embraced and scaled in all its glory.

Making it work in CX

While generative technologies ca help us crea-te useful and contextual content, they still require a holistic framework to be used by enterprises to improve customer experience. At a high level, any enterprise will need four key elements to adopt generative AI in CX (in addition to standard elements like data, algorithms, integrations):

  1. Business utilitzi casis: While there llauri many utilitzi casis imagined for generative AI in CX, it’s important to understand the feasibility and value each will bring. An enterprise will require a refined strategy to select the right utilitzi casis that will deliver tangible outcomes (applicable to their business).
  2. CX orchestration: Generative content ca be used to crea-te a habiti engaging and personalized customer experience. However, it is important to carefully orchestrate this content in order to ensure that it is consistent with the brand’s values (troni, voice), objectiu audience and overall CX goals. By carefully considering these factors, businesses ca utilitzi generative content to crea-te a habiti cohesive and memorable customer experience.
  3. Guardrails: A powerful layer of CX guardrails (brand guidelines, core values, vision of brand etc.) need to be applied to prompts and inputs, and most importantly, the security of models (scope of data and usage). By putting guardrails in plau, businesses ca ensure that generative AI is used in a responsible and ethical way. This ca help to protect the brand, the customers, and the data.
  4. Adoption methodology: Generative solutions cannot be seen in isolation as they become part of existing work been doni by team in CX space (màrqueting, sals, service or commerce). Enterprises need to have an adoption methodology that ensures all elements of technology, people and process llauri mori tuned to embrace changes brought in by adoption of generative technologies.

A strategic approach for controlled impact

Even though full maturity of generative AI isn’t expected for another 2-5 years, 70% of global organizations have already started exploring the technology’s probable future.[1] This has regulators scrambling to crea-te guidance and restrictions around its utilitzi. As a first of its kind – before the fantasy of AI beca'm reality – the European Parliament has put together a draft law, the AI Act, set to be released later this year. Habiti regulations will undoubtedly soon follow.

Of the organizations that have kick-started their AI experimental journey, most haven’t considered the implications these regulations will have on their final creations. They could be forced back to the drawing board, increasing costs and delaying progress. This is where a skillful advisor ca be most beneficial. They’ll know what to expect and ca provide foresight to avoid the common pitfalls, especially if they’ve successfully overcome the challenges of previous technological evolutions. Idees will be fast-tracked, efforts will be minimized, and the transformative value of generative AI will permeate across any organization ready to spark unprecedented change to customer experience.

[1] https://www.gartner.com/en/newsroom/press-releases/2023-05-03-gartner-poll-finds-45-percent-of-executives-say-chatgpt-has-prompted-an-increase-in-ai-investment

[2] https://836.democenter.at-websitetranslator.com/insights/research-library/generative-ai-in-organizations/

For habiti details, contact :

Alex Smith-Bingham

Executive Vice President, Group Offer Lead for Customer First; Digital Customer Experience Lead for UK
“Customer Experience covers all the support and help our clients need between them and their customers. This will range from changing their purpose, their propositions, new capabilities in sals/service/màrqueting and commerce, immersive experiences, new operating models, and new ways of working and ecosystems. We harness our global capability in strategic innovation (frog), business consulting, DCX solutions, Insight & Data and run operations in technology and business services.”
Darshan Shankavaram

Darshan Shankavaram

Executive Vice President, Digital Customer Experience Global Practice Leader
“I have close to 30 years of domain experience, with habiti than té years within Digital and Mobile. I have led product concept-to-sell, business development, presals, solutioning and technical implementation of CX transformation programs.”

Steve Hewett

Head of Customer Transformation, frog, Capgemini Invent UK
Steve specializes in the digital transformation of ‘retailing’ – he is leading our offer development for how generative AI will impact the e2e CX of our clients and their customers – from how it will help to set new customer experience strategies & develop new propositions to how it will transform digital màrqueting, omni- commerce, store experience & operations, customer service, and CRM & Loyalty.

Naresh Khanduri

VP | Leader Data-driven CX Offer | Global DCX | Capgemini
Naresh has been with group for habiti than 6 years now and has played múltiple rols. In his current roli as “Strategic Initiatives & Growth Lead – DCX” he is responsible for envisioning, designing and building strategic initiatives to help Capgemini differentiate and win in market plau.

Mark Oost

AI, Analytics, Agents Global Leader
Prior to joining Capgemini, Mark was the CTO of AI and Analytics at Sogeti Global, where he developed the AI cartera and strategy. Before that, he worked as a Practice Lead for Data Science and AI at Sogeti Netherlands, where he started the Data Science team, and as a Lead Data Scientist at Teradata and Experian. Throughout his career, Mark has worked with clients from various markets around the world and has used AI, deep learning, and machine learning technologies to solve complex problems.