The sport of rugby is reinventing itself – with increasingly diverse practices and players from all walks of life. And technology further encourages this trend towards inclusion.

With the French Rugby Federation indicating there were almost 26,500 registered female rugby players in 2022, one thing is certain: women are getting involved. The growth rate of female participation is evidence of this – just between 2019 and 2021, the number went up by 22%. The organizational infrastructure supporting the women’s game is also expanding. From Gravelines to Ajaccio and Brest to Montbéliard, France now boasts over 430 women’s rugby clubs.

Since its origins, rugby has largely been the domain of a narrow social group. But for several years now, the sport has been undergoing a process of democratization, adding more participants with diverse backgrounds.

This significant transformation can be explained by a series of developments that affect both the social aspect of the sport, which is more open to diversity, and how it is played.

Rugby has taken on many different forms. Alongside the well-established rugby union, rugby league, and rugby sevens varieties, there are now new forms of play, such as wheelchair rugby and non-contact variants like flag rugby, touch rugby, beach rugby, and rugby à 5. Rugby à 5, a touch variety, has even been listed as a “health sport” in the latest edition of Médicosport-Santé, a dictionary used by doctors in France to prescribe sports as a medical treatment.

These new varieties of the sport open up participation to a larger number of players, including people with disabilities and those seeking to play outside of the traditional club-based, competitive, contact-sport format.

Increasing media coverage of women’s competition

The growing popularity of rugby in France is not only due to the new varieties that make it more accessible. It can also be attributed to the popularity of the women’s national team, widely followed by fans, and commented on in the press and on social media.

For example, on April 29, 2023, over 58,000 spectators turned out at Twickenham Stadium in London for the final of the 2023 Women’s Six Nations Championship, which pitted England against France – a new attendance record.

The media coverage of competitions and players, thanks in particular to interviews conducted by female journalists specializing in the sport, helps to create role models and career paths that are relatable to all women.

Breaking down stereotypes

International bodies have also played a key role in opening up the sport. In 2003, the International Rugby Board introduced the principle of “zero tolerance” for all forms of discrimination, on and off the field. Supported by the member federations, the fight against exclusion continues to make rugby a welcoming sport. Initiatives are also taking place to encourage the representation of women in the refereeing corps, in managerial and coaching positions, and in management positions within individual governing bodies.

There is still a long way to go, and we must not let up in our efforts to overcome entrenched prejudices, but the movement towards inclusion has begun in earnest and we can look to the future with optimism as rugby continues to integrate and promote the values of tolerance, solidarity, respect, sharing, and fair play.

It’s a sport resolutely turned towards others, with diversity at its core. No team can be competitive if it consists of players who are all alike.

Like in a company, each position on a rugby team requires different yet complementary skills. It’s the right combination of these specific skills that creates a team chemistry that results in success.

Diversity and inclusion are also sources of innovation as the wide spectrum of talent allows us to embrace different styles of play, feed off different approaches, and help teams and the sport as a whole to improve. This type of evolution is precisely what will contribute to making rugby competitive and interesting well into the future.

Rugby: A school of life

As a means of promoting humility and solidarity, rugby teaches us to work better together and interact more effectively as a team. It develops communication and leadership skills and also forges players’ personalities by confronting them with defeat and teaching them how to get back up with the collective support of the group to keep striving for more.

The qualities promoted by rugby are strengths for the personal and professional development of each individual who participates. Capgemini embraces this message as a Global Partner of World Rugby’s Women in Rugby program. As part of this initiative, the Group partnered with World Rugby to develop the Women in Rugby Leadership Program, which aims to identify and support current and future generations of female rugby leaders around the world to bring about greater parity in the sport. Each year, 12 female scholarship holders receive access to the best learning programs at Capgemini University.

Rugby has come a long way from its days as an exclusive sport for a small pool of participants. Embracing novel variations, becoming more accessible to people with disabilities, and encouraging gender parity, the sport has expanded its reach far beyond its traditional demographic. With continued initiatives aimed at diversity and inclusion, rugby can continue to increase its impact on the world as an experience that ultimately contributes to improved well-being, a sense of community, and personal fulfilment for everyone.

Combining the power of AI with edge computing, Edge AI brings intelligence and decision-making capabilities directly to the edge of the network, enabling faster, more efficient, and privacy-preserving applications. However, this comes with a new set of challenges, most notably how to optimize AI models so that they can run on these low powered edge devices.

In this blog, we explore what Edge AI is, how to use model compression techniques to address hardware limitations and some current and future applications of the approach.

Understanding Edge AI

Edge AI refers to the deployment of AI algorithms and models directly on edge devices, such as smartphones, Internet of Things (IoT) devices, and smart sensors, rather than relying on cloud-based data centers.

This decentralized approach eliminates the need to send all data to the cloud for processing, making AI-driven applications more responsive, sustainable, and privacy-friendly. This approach also makes solutions more tolerant to slow or patchy connectivity.

Key Benefits of Edge AI

Privacy and Security: Edge AI helps safeguard user privacy by processing data locally, without sending it to external servers. This approach ensures that sensitive information, like facial images or biometric data, remains on the device and isn’t exposed during data transmission.

Sustainability: By processing data on the edge, Edge AI reduces the amount of raw data that needs to be sent to the cloud. Only relevant and processed information is transmitted, reducing the need to transmit and store the raw data, avoiding the associated costs and energy impact. Compressing models ensures they are efficient and use a minimal amount of energy to run. 

Real-time Decision Making: With Edge AI, devices can make intelligent decisions locally, without relying on cloud connectivity. This is particularly valuable for applications that need rapid responses or do not have assured network connections.

The need for Model Compression

Edge devices are, however, typically much slower than their data center counterparts, often needing to meet restrictive power and thermal constraints. Traditional deep learning models often contain millions or even billions of parameters, which require substantial computational power and memory to process.

Edge AI models, therefore, must be optimized to run well on the target hardware. Model compression is an excellent way to do this. Model compression describes a set of techniques aimed at reducing the size of deep learning models, without compromising their performance.

Model Compression techniques

There are multiple techniques that can be employed individually or in combination to reduce model size and computational complexity. Some examples are:

• Pruning: Pruning involves removing unnecessary connections and nodes from the neural network. This reduces the size of the network and required computation at run time, though there is a limit to the amount a network can be pruned before the impact on results becomes significant.
• Quantization: Quantization reduces the precision of model parameters, typically from 32-bit floating-point numbers to lower bit representations (often 8-bit). This reduces memory and computation requirements, without significantly compromising accuracy.
• Hardware optimization: Models can be tuned to take advantage of specific hardware features that accelerate inferencing, such as graphics processing units (GPUs) and tensor processing units (TPUs).

Note: If you also need to preserve data privacy while training AI models, you need to look at approaches like Federated Learning, as covered here.

Applications of Edge AI

• Healthcare: Wearable health devices can utilize Edge AI to analyze and interpret real-time health data, alerting users or healthcare providers to potential issues, while ensuring potentially sensitive patient data remains on device.
• Industrial IoT: In manufacturing and industrial settings, Edge AI can power predictive maintenance algorithms, optimizing production processes and reducing downtime.
• Agriculture: Edge AI-powered sensors in agriculture can monitor crop health, optimize irrigation schedules, and detect anomalies, without requiring constant internet access.

Edge AI represents a transformative shift in AI deployment, empowering devices at the edge of the network with intelligence and decision-making capabilities. By harnessing the advantages of edge computing, Edge AI overcomes the limitations of traditional cloud-based AI and opens up a world of possibilities for real-time, privacy-preserving, and efficient applications.

Looking to the future, the growth of 5G networks and advancements in hardware capabilities will further propel Edge AI’s adoption. The combination of low-latency connectivity and powerful edge devices will unlock new possibilities for AI applications in areas we’ve only begun to explore.

Integration in an Artificial Intelligence entity based enterprise

The development of a modular and scalable aiPaaS based architecture will play a significant role in managing the complexities of integrating AIEs. By breaking down these complexities into manageable components, a streamlined workflow design process will be created. This approach will allow for increased collaboration between different teams and skill levels, encompassing both human and AI-driven participants. The flexibility inherent in this architecture will foster a more efficient and cohesive design environment, adaptable to various needs and objectives.

Automation and machine learning will also be integral to the transformation of the AIE integration development process. Utilizing AI-driven automation tools will not only simplify the process but also make it more accessible to a broader range of developers. Machine learning algorithms will further enhance this accessibility by aiding in identifying patterns, making predictions, and generating work products. These advanced technologies will guide the development process, bringing forth a new level of intelligence and adaptability that aligns with the rapidly evolving demands of the industry and allowing human developers to do what they do best – making judgements about the optimal solutions.

The emergence of natural language low-code and no-code platforms will mark another significant advancement, particularly in the realm of AI-based integration. These platforms, capable of understanding natural language directions, will enable those without extensive technical expertise to actively participate in integration development. The result will be a democratization of the integration design and development process, allowing for greater inclusivity. By expanding the range of contributors, these platforms will foster innovation and diversity of thought, reflecting a more holistic approach to technological advancement. The combination of these three elements—modular architecture, AI-driven automation, and natural language based low-code/no-code platforms—will offer a compelling vision for the future of aiPaaS, one that is both inclusive and innovative.

Specific to Salesforce

In the contemporary technological landscape, the utilization of AI Integration Platforms as a Service (aiPaaS) is growing, with a robust market including players such as Mulesoft, Informatica, and Boomi. These products and services offer a variety of tools that simplify and accelerate the delivery of integrations. As these platforms evolve to aiPaaS, they can be expected to take natural language direction and require far less manual configuration and custom coding than today’s platforms. The transformation from traditional methods to AI-driven platforms represents a significant shift in how integrations will be designed and developed, heralding a more efficient and user-friendly era.

Alongside these advanced platforms, the collaboration between AI Assistants and human developers will become an essential aspect of integration development. AI Assistants will work hand-in-hand with human developers, providing real-time prediction, guidance and feedback, and automated configuration and code production. Humans will complement this technical prowess with contextual understanding, creativity, and strategic thinking—qualities humans will use to form a symbiotic relationship with AI capabilities. Together, they will work as a team when engaging aiPaas platforms to build integrations, combining the best of human judgement and AI prediction and production.

The concept of continuous and just-in-time learning and adaptation adds another layer of sophistication to this new model of development. AI Assistants will likely possess the ability to learn and adapt from previous integration experiences, continuously improving and streamlining future integration tasks. This continuous learning process enables a dynamic and responsive approach to development, where AI systems not only execute tasks but also grow and evolve with each experience, leading to a perpetually enhancing and adapting system.

The convergence of these factors—aiPaaS utilization, human-AI collaboration, and continuous learning—paints a promising picture for the future of integration development. This multifaceted approach combines technological innovation with human creativity and ethical responsibility, forming a comprehensive and forward-thinking model that will define the next generation of integration development and delivery.

The role of developers

In the realm of integration development, human developers will continue to play a crucial role in strategic planning and decision-making. Their expertise and insight into the broader business context are essential in crafting strategies and making key decisions that align with both business goals and program impacts beyond just technology. While automation and AI-driven tools can offer efficiency and precision, the human capacity to understand and act upon complex business dynamics remains vital. Humans’ ability to navigate the multifaceted landscape of organizational needs, politics, and market opportunities will ensure that delivered features align with organization objectives.

In addition to their strategic roles, human developers also bring an irreplaceable creative and empathetic approach to problem-solving. While AI can handle complex computations and process large data sets with remarkable speed, it cannot replicate the human ability to think creatively and apply empathetic judgement. Human developers possess the innate ability to see beyond the data, considering the subtleties of human behavior, emotions, and relationships. This creative problem-solving skill is a powerful asset in designing solutions that are not only technically sound but also resonate with end-users and stakeholders.

Monitoring and oversight will remain firmly in the human domain. Human oversight ensures that the integration adheres to ethical standards and societal values and aligns with the unique business culture and customer needs. In an increasingly automated world, the importance of ethical consideration, cultural alignment, and a deep understanding of customer requirements cannot be overstated. Human developers act as stewards, maintaining the integrity of the system by ensuring that it reflects the values and needs of the people it serves.

Together, these three elements—strategic planning, creative problem-solving, and human oversight—highlight the enduring importance of human involvement in aiPaaS integration development. They underscore the idea that while technology continues to advance, the human touch remains indispensable. It is this harmonious interplay between human ingenuity and technological prowess that promises to drive innovation, efficiency, and success in the future of integration development.

Actions for developers to prepare

In the rapidly evolving aiPaaS landscape, developers must embrace new technologies and methodologies to remain at the forefront of their field. This includes becoming familiar with AI-driven automation tools, machine learning, and other emerging technologies that are transforming the way integrations are developed and delivered. Understanding how these cutting-edge technologies can be utilized within platforms like Salesforce will be vital. The ability to harness these tools to enhance efficiency, drive innovation, and meet unique business needs will position developers as key players in the digital transformation journey.

Investing in continuous learning is another essential step for developers to stay competitive and relevant. Keeping abreast of changes in regulations, best practices, and technological advancements will require a commitment to ongoing education. Pursuing certifications, attending workshops, and participating in conferences will keep skills up-to-date and ensure that developers are well-equipped to adapt to the ever-changing environment. This investment in learning will not only nurture professional growth but also foster a culture of curiosity, agility, and excellence.

Monitoring the development of aiPaaS platforms will be an integral part of this ongoing learning process. Gaining proficiency in these platforms will broaden the scope of development opportunities and allow for quicker and more agile integration within Salesforce. As aiPaaS platforms continue to mature and become more pervasive, they will redefine how integrations are conceived and implemented. Understanding these platforms and becoming adept at leveraging their capabilities will enable developers to deliver more innovative and responsive solutions.

Collaboration skills will also be paramount in the future landscape of integration development. The emerging paradigm involves close collaboration between humans and AI, where AI assistants augment human abilities rather than replace them. Developing the ability to work synergistically with AI assistants and human colleagues alike will be a valuable asset. Cultivating these collaboration skills will not only enhance individual effectiveness but also contribute to a more cohesive and innovative development ecosystem.

Finally, focusing on strategic and creative problem-solving skills will distinguish successful developers in an increasingly automated world. While certain tasks may become automated, the ability to strategize, creatively problem-solve, and think outside of the box will remain uniquely human. These skills will define the role of developers as visionaries and innovators, empowering them to drive change, inspire others, and create solutions that resonate with both business objectives and human needs.

Together, these five areas of focus form a roadmap for developers to navigate the exciting and complex world of modern integration development. Embracing new technologies, investing in continuous learning, understanding aiPaaS platforms, cultivating collaboration skills, and nurturing strategic and creative thinking will equip developers to thrive in this dynamic environment. These strategies align perfectly with a future where technology and humanity converge, creating a rich tapestry of possibilities and progress.

Conclusion

The evolving landscape of aiPaaS within Salesforce represents both challenges and opportunities. Salesforce developers should view this as a chance to grow and contribute uniquely to the organization’s goals. By embracing new technologies, investing in continuous learning, and honing both technical and collaborative skills, Salesforce developers can position themselves at the forefront of this exciting era of technological advancement. This preparation will enable them to continue to be vital contributors to their organizations’ success in an increasingly interconnected and dynamic world.

I recently started working at Capgemini, having spent the last 6 months at home with my 1.5-year-old child. During this time, virtually all major technology companies launched their own version of generative AI or services related to it. While I was immersed in cleaning the highchair and diaper changing- the Twitter-verse (or should I say “X-verse”) was abuzz with hashtags like #ML, #AI, #GPT and #LLM. For a solution architect like me, the world looks very different from what it was just a year ago.

While the technology landscape hasn’t changed that much in a year, the dawn of generative AI has been a major eye-opener in some respects. The promise of AI in a marketing context is enticing: the ability to generate personalized experiences with speed and scale. Even though the learning curve may seem steep, the reality is that you don’t have to be a data scientist to understand the business implications of AI. Let’s dive a bit deeper and I’ll explain why.

How to train an AI?

A common misconception about AI models is that you always need to have lots of data to use one. Large language models (LLM) like GPT-4 do initially require vast amounts of data and training but that essentially constitutes as product development. An LLM requires human effort to “teach” it to weed out inaccurate and false answers to user prompts. After sufficient training, the model can answer most user queries with decent accuracy. At this stage, it can be deployed and made publicly accessible. These pre-trained models can be implemented to specific business needs in a couple of ways.

The first method is via fine-tuning the model. Instead of using huge computational power and human working hours to train a model from scratch, you take a commercially available LLM and train it further for a specific business application. The model will likely provide decent answers to user prompts right from the start, owing to the model’s extensive pretraining. However, human supervision is required to flag all biases, misconceptions and falsehoods that surface. Over time, the model becomes more attuned to its new environment, and its accuracy improves.

The alternative to supervised training is to use what is called in-context learning (ICL). Instead of humans supervising the model and giving it feedback, the pretrained model compares its output to its context, making predictions based on past information present in where the model operates in. A typical use case is deploying an LLM to a CRM environment, such as is the case with Salesforce’s Einstein GPT. The model looks for past records and uses that to provide more accurate answers to user queries. This method of training is attractive in that it has a greatly reduced need for human effort.

The inescapable truth of data quality

Whichever method you choose to train an LLM, you always run into the same conclusion: data quality is key. Bringing us back to the marketing context: what does good quality data mean? Marketing is unique in that it relies heavily on both external and internal data sources. Marketing deals with customer demographics; interests and preferences; website engagement; product or service usage; contact information; and purchase history. With the variety of data sources, there is also a greater risk of data management issues, such as:

• Duplicate leads
• Contradicting marketing permissions
• Outdated contact information
• False or spam contacts
• Mismatched marketing engagements

Identifying and fixing these issues should be your first plan of action if you plan on leveraging AI in your marketing. Failure to do so could lead to escalating already existing problems. For instance, if you provide the LLM bad data as context, you end up with biased, inaccurate, or simply false suggestions.

In the case of personalization, this can cause prospects to receive wrong versions of dynamic content. As for segmentation, bad data fed to the model can cause triggered automations to target the wrong people. The worst thing is that this kind of AI malfunction will go unnoticed until a customer complains about it. And then it will be too late. Bad data will undermine all efforts to incorporate AI into marketing.

Avoid pitfalls with a clear strategy

There are plenty of what I call “AI nihilists” out there. A common criticism is that LLMs will always produce biased, limited, or flawed results. There are others who believe LLMs’ potential to be fundamentally out of reach for most businesses. I’ve never found this sort of attitude particularly helpful. We saw the same thing with Electric Vehicles, and now we’re seeing an unprecedented surge in both demand and production of electric cars. Which side do you choose: the progressives or the laggards?

Assuming you chose the former, you are looking for a way to combat the problems I’ve outlined in this post. The best way to do this is by shifting perspective to a strategic level.

You need what I’d call a marketing data strategy. It sits between the company marketing strategy and data strategy. Essentially, we want to explore how data and AI relate to marketing strategy building blocks, e.g.:

• Segmentation (How do we identify our key segments? Which data sources do we need?
• Targeting (Which life events or milestones do we look for?)
• Positioning (How do we determine customer pain points? How do we know if our marketing is successful?)

The above are illustrative examples; your marketing strategy is your own, and there is no need to reinvent it. Ideally, you want to leverage what you already have as much as possible. There are, however, certain areas that you want to include in your marketing data strategy regardless of your chosen format. Here are my suggestions:

Marketing Data Strategy:

– Data Sources and integration
– Compliance
– Consent and preferences
– Personalization
– Reporting and analytics
– Storage, maintenance and retention
– Targeting and triggering

A marketing data strategy lays out rules and policies that intend to help organizations to develop their marketing technology stack, incorporate new data sources, build new marketing workflows, or adopt new processes. It provides a bedrock that you can anchor decisions on, and it ensures that you consider all relevant questions before making major data-related decisions. As is the case with any strategy, this too is a living document; don’t let it fall into disrepair – include regular checkpoints to assess and update the strategy.

Final thoughts

We’ve outlined the possibilities and risks associated with the use of LLM in marketing. By now, you should have a better understanding of what it takes to adopt an LLM in a business context. I’d like to add that LLM adoption requires a significant investment of company resources. To work properly, an LLM may require a Customer Data Platform, data lake, data management platform, analytics platform, or a combination of these. In addition, you need to hire or allocate specialists to supervise and develop your organization’s AI capabilities. Finally, I strongly recommend including a Chief Data Officer role for any business seeking to tap into AI. With top-level sponsorship, any such initiative has much better odds at succeeding.

Catch up on my session at Dreamforce where I explored this topic even further

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With more than 100 million users and 160 billion website visits in the month of June alone, generative AI tools like ChatGPT are becoming the new “go-to” when it comes to seeking product or service recommendations – a point confirmed by our recent generative AI survey.

As the excitement and appetite among consumers for generative AI-enabled services mounts, executives across industries are considering the commercial implications for this technology and how it can be used to solve problems, personalize at scale, and increase productivity. But as companies roll out new use cases every day (think personalized content creation, next-gen chat bots, sales augmentation tools, etc.) and platforms like Salesforce introduce powerful new capabilities using this technology, it can be difficult to know where and how to leverage generative AI to create a real impact.

To be successful, companies need to ask the right questions to ensure each generative AI initiative is designed to strengthen and enhance the CX (Customer Experience) and drive value for the customer and the business.

5 key questions to ensure your gen AI initiative is set up for long-term success

1. What value does this use case bring to the existing CX?

The possibilities for generative AI are endless – so companies need to focus less on the capabilities of the technology and more on the purpose it serves for the user and value it brings to the business. Having a clear sense of the expected impact is absolutely crucial for determining where to focus limited resources, as well as how to measure the value of the program over time.  

2. Is the data being used clean, timely, accurate and complete?

The success of a generative AI initiative rests in large part on the strength of the data being used to inform it. When it comes to initiatives within Salesforce, this includes use of both structured enterprise data, as well as unstructured data, such as information gleaned from knowledge articles, emails, social media posts and more. To that end, companies may need to adapt and mature their data capability to ensure teams have access to the data they need and can identify gaps within their process. This is crucial for ensuring content produced by generative AI tools is accurate and non-biased.   

3. Is generative content consistent with the brand’s values, relevant to the audience and aligned with overarching CX goals?

Just because companies can create content quickly with the help of generative AI, doesn’t mean they should. The rules and best practices of content creation apply to generative content. This means that content produced by generative AI-enabled tools, including those offered by Salesforce, must still be useful and relevant for the user and integrated within the brand’s existing content ecosystem. 

4. What guardrails are in place to ensure generative AI is being used safely, securely, and ethically?

Generative AI is a rapidly evolving technology; regulations that dictate how it can be used safely and securely are still in development. Even if an organization is leveraging capabilities offered by a platform like Salesforce, it’s incumbent upon them to build a powerful layer of CX guardrails (including brand guidelines, core values, vision of brand, etc.) that can be applied to prompts and inputs, as well as the security of models (scope of data and usage). Further, as regulations are introduced, it is important that companies can quickly adapt and evolve their generative AI strategy to ensure they remain compliant.

5. How does the use of generative AI affect other areas of the business beyond the CX?

Generative solutions do not operate in silos and therefore should not be viewed in isolation. Likewise, companies need to remember that delivering a strong, engaging CX requires collaboration and integration across the entire enterprise. As such, when implementing this technology, enterprises need to have an adoption methodology that ensures all elements of technology, people and processes are adapted to manage the ripple effect of generative AI use cases across the entire CX and throughout the business.

Exploring the impact of data and emerging technologies across the entire CX

Generative AI can be a powerful content, sales, and marketing tool – and customer experience is one of the biggest areas where this technology can make a significant impact. However, to tap its full potential and scale its use, companies must combine this technology with existing capabilities and integrate it across the entire customer life cycle. This includes platforms like Salesforce, which play an integral part in sales, service, marketing, and commerce efforts that support the customer experience.

However, managing the complexity of this new technology and the intricacy of its integration is no simple task. While generative AI may be a relatively new concept for many companies, Capgemini is a recognized leader in the field. We have been working with clients on AI topics for several years, helping companies identify the areas within the organization where its application and integration can bring the greatest benefits to transform the customer experience.

For example, we are currently working with Heathrow Airport to enhance their CX journey with generative AI. As part of this process, we leveraged our recently launched Generative AI for Customer Experience offer to help the organization implement cutting-edge eCommerce and other service solutions to provide faster, more personalized customer service and revamp the passenger experience.

Kickstart your generative AI journey with Capgemini

For companies that are ready to begin experimenting with generative AI within the CX, Capgemini can help accelerate their journey. Our dedicated generative AI practice helps companies rapidly scale this capability, solutioning and delivery, while our Generative AI Lab enables them to follow the evolution of the technology and how it can be deployed in their industry.

Ready to get started? Our expert team is here to answer all your questions about how generative AI can be used to revolutionize the CX in Salesforce and beyond. Get in touch with me using the options below.

When we think about building personalized, differentiated experiences, data and AI are probably the first things to come to mind, especially when it comes to digital experiences. And perhaps with good reason. Data (to inform and personalize) and AI (to automate and scale) are essential for creating engaging and impactful experiences.

But data and AI on their own cannot make a great experience. These are assets – the building blocks of every relevant and engaging interaction. To truly deliver a great experience, companies first need to focus on their foundation.

As Capgemini gets ready for Dreamforce 2023, I’d love to explore the real enablers of every great experience: the digital foundation and the integration layer.

The integration layer: Where loyalty is won and lost

Data and AI may be enjoying their moment in the spotlight, but the unsung hero of every successful experience is the integration layer. Often underestimated or even overlooked, integration is the backbone and the intelligent network between the customer experience and primary functions of the business and its partner ecosystem.

For example, a retailer with a strong integration layer can connect every single interaction across the customer journey to ensure a truly great experience through personalization, convenience, and value-added services. This includes:

• Serving a personalized ad for relevant products based on a combination of enterprise and experience data
• Tracking inventory levels at the closest store and proactively alerting the customer when a product sells out
• Offering an alternative purchase option, such as reserving the product for pick up at a different store or purchasing through an online channel
• Enabling one-click purchases and payment
• Reviewing supply chain data to confirm the delivery timing
• Requesting the desired delivery method from the customer
• Connecting with the third-party delivery provider to confirm the service
• Providing real-time updates to the customer during the delivery window
• Requesting feedback on the delivery experience from the customer
• Inviting the customer to leave a review of their purchase
• Suggesting a relevant companion product to the customer’s order and restarting the process

When executed correctly, this entire process feels seamless and effortless to the customer. However, it is, of course, the result of significant effort that cuts deep into, and across, an organization. Leveraging a ready-to-use capability like MuleSoft enables companies to focus efforts on delivering the value-add to the customers, as opposed to the configuration and operation of the integration platform.

It’s also important to keep in mind that this process isn’t just the result of data – it also results in new data. This information can be used to develop insights that will enable the brand to rapidly assess and evolve their offers, respond to external forces of disruption, and develop and launch new products and services based on the real needs and preferences of their customers. However, to effectively capitalize on this new information, organizations must have the digital foundation and integration capabilities, such as those offered through MuleSoft, to take those disparate pieces of information and turn them into actionable insights quickly and effectively.

Integration: The common denominator for building agility, enabling transformation, and driving impact

The integration layer is the common denominator across the success of almost every business goal:

1. Having a robust integration layer enables agility and enables companies to quickly develop, launch and scale different products and services, as well as respond to disruptions as they arise;
2. It is the foundation for digital transformation, uniting all aspects of the customer lifecycle and making that data available across the enterprise; and
3. It’s the key that unlocks business value by enabling teams to use data in a way that creates differentiated and personalized experiences that are scalable.

To ignore integration is to deny your organization the opportunity to adapt, innovate, and grow. Explore our Salesforce partnership to find out more or reach out to me directly using the options below.