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Driving change: Inclusion as a key ESG agenda

Sreeram Yegappan
Sreeram Yegappan
16 Mar 2023

ESG transformation is the current buzzword across all industries, and Financial Services (FS) play a bigger role than most in building this sustainable future. FS is one of the key pillars of any economy – the intermediary that channels capital into low-carbon activities and finances key transition activities to achieve Paris 2050 goals.

However, FS still lags on the ‘S’ of ESG!

In sustainability discussions, the ‘environmental’ aspect is often at the forefront, leaving a lot to be desired on the ‘social’ front. FS employs 7.6 million individuals in the United States alone. So, naturally, the social practices of this sector are looked up to, giving it the opportunity to be the leader and changemaker. One such area that presents this opportunity is DEI- Diversity Equity and Inclusion and a subset of that is ‘Gender diversity’.

Despite efforts to improve the representation of women in leadership and management positions, the industry still has a long way to achieving gender parity. It brings us back to the debate of equality vs equity in an unequal society. Treating everyone the same only ensures that the marginalised continue losing out. Upliftment strategies are required to address imbalances by actively seeking and promoting qualified candidates from underrepresented groups.

Diversity needs to be more than just numbers and ticking a checkbox

Gender representation at entry-level roles is relatively balanced in this sector. However, as women progress in their careers, the gap widens significantly. The pipeline to the top is leaky: the gender ratio starts at 47% for support staff and goes down to 23% for executive level. Within FS, a dismal 5% of CEO positions were held by women in 2021.

financial services representation

European banks paint the same picture, with zero women CEOs appointed by the top 30 banks in Europe, despite almost half of these companies replacing CEOs in the same time frame.

Hiring women just for ticking a checkbox hampers the progress on the much-needed sociocultural shift. Merely advising women to be more assertive and “put themselves out there” undermines the mismatch between how women are often perceived, and the qualities often associated with a leader. Subtle biases prevent the required “learning cycle” which helps mould leaders.

The women that do reach leadership positions are often expected to do multiple hats of uplifting other women and providing them emotional support. In an environment where there are already very few women in these roles, this burden called the “hidden cost” is unevenly shared, unrecognised, and largely uncompensated for.

Beyond mere societal duty, multiple other factors make diversity initiatives essential

In addition to being an ethical social responsibility, diversity is crucial to multiple stakeholders of a financial services company – employees, investors & customers.

diversity drivers in financial services

Visa was awarded the title of ‘Best employer for Women’ by Forbes Magazine in 2018 in lieu of its efforts in creating a workplace with equal opportunities and pay, through programs like “Return to Work”. A similar initiative by Capgemini, “She said Yes”, aims at encouraging former employees to come back after gaining external experience. Another program called “CAPtivate” is Capgemini’s Career initiative to support female professionals who want to transition back into the workforce. Inclusive policies like these enable companies to create a supportive environment and initiate conversations.

For investors, diversity can bring a fresh perspective to the investment process, leading to a wider range of opportunities, and better risk management. A European Central Bank study found that banks with more than 37% female directors had around 10% lower lending volumes to companies with higher pollution rates. Sodexo, a food and facilities service provider, found that with optimal gender balance, their employee engagement increased by 4 percentage points, gross profit increased by 23% and brand image strengthened by 5 percentage points.

Additionally, a diverse employee base reflects the diverse customer base, helping the company to better understand what its customers need. This leads to better customer experiences, greater customer satisfaction, and increased customer loyalty.

Being prepared for a more accountable and responsible future: vital for organisation survival

Regulatory authorities are also pushing companies to reveal statistics and initiatives. Companies listed on Tokyo Stock Exchange are obligated to publish their diversity policies and goals. The UK’s Financial Conduct Authority requires listed companies to disclose performance data against diversity targets including 40% female board representation.

To sum up, it is vital for companies in FS to inculcate gender diversity in their long-term growth strategy. There are multiple factors at play and the gender conversation has been around for a while. We have identified three broad considerations to keep in mind as we formulate our strategy and solutions.

diversity-growth-strategy

The goal is to keep in mind the need for a shift in the organisation’s culture and this is possible only through involving all stakeholders. Recognise and identify areas for improvement, create a supportive environment and set clear collaborative targets that provide the much-needed flexibility.

Future growth requires constant innovation. Therefore, invest in your diverse workforce to attract and retain the best talent who collectively work towards company goals in an environment that supports their success.

Meet our experts

Sreeram Yegappan

Sreeram Yegappan

VP, Account Executive

Devshree Narware

Engagement Partner
Shubham

Shubham

Manager, ELITE Leadership 2022

Chiara Ribeiro

Manager, ELITE Leadership 2022

    Our experiences with XOps in life sciences

    Gerard Kerr
    17 March 2023

    We are increasingly seeing the need for XOps amongst our life sciences clients to ensure a smooth transition of novel solutions from R&D to production.

    The life sciences industry has long been driven by the need to develop and exploit solutions as quickly as possible, leading to it being very much at the forefront of technological innovation. The COVID-19 pandemic has further highlighted the need for the ability to adapt quickly – in diagnostics, drug discovery, clinical trials, and in scaling up manufacturing faster than ever before.

    Technological innovation within life sciences

    XOps is about the smooth delivery of technological innovations into sustainable and flexible production environments.  Some examples of technological innovation in life sciences are:

    However, as the amount of data flowing into R&D teams continues to rise, there’s a risk of potential discoveries such as these going unseen. Technological solutions like AI are very helpful, but they’re not the whole solution. For that, we need to look at the foundations of the way R&D teams are organized.

    Building an XOps organization

    At Hybrid Intelligence, we provide staff with experience encompassing several specializations. We hire staff with scientific academic backgrounds. We then train them in important disciplines, such as manipulating and analyzing data, modeling, software engineering, and architecture. Many staff come with foundational aspects from academia, however we professionalize and imbue best practices, and we encourage staff to share their experiences and skills with their colleagues.

    This approach to staff development forms the foundation for our XOps organization. We can also build an XOps team using a collection of specialists with a strong culture of collaboration and communication. When we form our XOps teams, we ensure to balance the team skills appropriately depending on the solution. We will include a mix of the relevant domain and technical experts.

    Our experience in life sciences

    We recently established an XOps team in one of our clients’ drug discovery organizations, to support a portfolio of innovative tools that have certain specialist purposes. Uses ranged from user-friendly ways to run models on an HPC used to find new proteins with similar attributes to others, to an automated way to identify systematic errors in lab results. We are working with various machine learning models, a graph database, a data pipeline step to ingest from other databases, and a lot of software with very specific UX needs.

    Our XOps team is made up of several data engineers, software developers, some HPC specialists, and a small number of data scientists. We also chose team members based on their experience in biochemistry. The team is coordinated through an XOps manager, who has broad experience in all the areas mentioned previously.

    When working, we involve representatives from each of the main functional areas that make up our team in all discussions. We find that we can not only provide a much better service, but solution reliability is increased, time to resolution of issues is lowered, and we generate more ideas as a team as to how to improve the tools. These have impacts on our end client that help improves the efficiency and effectiveness of their work.

    I have also experienced the opposite – where there was not an XOps team in place, but in fact many disparate teams handling the maintenance of each component of the solution that aligned to their functional area. In these instances, I observed shirking of responsibilities, finger pointing regarding issues, and an inability to get tasks done quickly.

    Conclusion

    The situations I have outlined are currently bringing benefits to my clients. In the future, as emerging technologies mature and converge, solutions will grow increasingly complex. If XOps is not necessary for your team right now, anyone adequately managing the wave of new innovation will find it increasingly important in the future.

    Author

    Gerard Kerr

    XOps Manager and Technical Consultant, Capgemini Engineering
    Gerard leads our specialist engineering and R&D operations services team in the US. He helps his clients govern, maintain, and evolve their data driven solutions and models. Gerard’s focus is enabling digital transformation, data driven innovation, and protecting his clients’ investments.

      Everyday AI: Next-generation self-service analytics

      Bridget Shea
      15th March 2023

      Organizations have been using the term “self-service analytics” for nearly a decade now, but for many companies, it’s not a source of value generation. But businesses cannot afford to waste time or money-spinning up or supporting a self-service analytics program that people aren’t actually using or that’s not generating tangible value. Self-service analytics in the age of AI needs to be about truly enabling people to ask infinite questions of their data and then empowering them to find or build trusted answers on their own.

      Imagine the smoothest self-service analytics experience possible at your organization today. It probably goes something like this:

      • A business user looks at someone else’s data product (such as a dashboard) built to answer some specific set of questions.
      • Business user asks a question that is within the realm of possibility to answer with this data product (e.g., “How did sales perform last quarter?” or “How did my marketing campaign perform?”).
      • The business user gets a trusted answer without having to ask anyone in the middle, such as IT or a data team, for help.

      The future of self-service analytics is about empowering people

      But what happens if this business user wants to ask new questions that are outside the realm of what that dashboard was built for? For example, the marketing manager sees her campaign did not perform well and wants to understand who she should target for her next campaign, potentially even with a score predicting who will be most likely to open her emails. Or a supply-chain manager has identified a pattern of shortages but doesn’t have the tools to dig in and get more visibility to address the problem. Most likely, every new question or business challenge is a new ask to a team to build a data product (dashboard) that provides answers from which they can self-service.

      It’s easy to say that the future of self-service analytics is about moving from descriptive to predictive (and even prescriptive) analytics. But it’s more than that. It’s about empowering people, especially business users, to ask questions about their data and find or build trusted answers on their own, whether that means building a dashboard or a machine-learning model for themselves. This is where the term “citizen data scientists” comes into play and why, in the future, the concepts of self-service analytics and citizen data scientist will become somewhat intertwined.

      Ultimately, putting the full power of data in the hands of the people involved in the day-to-day business (we call this Everyday AI) is what will move self-service analytics from providing answers to providing impact and, with it, value. Yet, with great power comes great responsibility, so the key in the coming years will be for leaders to provide the framework that allows for this fundamental transformation.

      Building self-service analytics for impact

      A world in which data is accessible and anyone can build data or AI projects and solutions to answer business questions might sound scary. To be honest, without the right tools, technology, and processes, it is scary and can devolve quickly into data chaos.

      Seeing that risk, it’s critical in this new world of self-service analytics that the initiative:

      • Doesn’t exist in a vacuum. When businesspeople have the data, their questions for IT come up a level and can be more impactful. For example, how can I automate what I’ve built so I don’t have to update it every week with new data?
      • Is built on trust. Leaders need to trust employees’ ability to use data in a self-service context. Business users working on self-service analytics need to trust the data that they’re working with. Managers and executives alike need to trust the insights delivered from self-service analytics projects. If just one of these layers is missing, it doesn’t work.
      • Has the proper governance built-in, complete with appropriate guardrails. This can be as simple as proper permissions management at the dataset or the project level, but it goes all the way up to the macro level. How are data and models being used? Who is monitoring this to lower the overall risk to the organization?

      For example, Dataiku customer GE Aviation implemented its own version of a self-service system that allows it to use real-time data at scale to make better and faster decisions throughout the organization. Engineering uses data from these tools to redesign parts and build jet engines more efficiently, the supply chain team uses it to get better data insights into its shop floors and streamline supply-chain processes, finance uses it to understand key metrics and more.

      At its core, its self-service program equips everyone (with proper access rights) with the ability to discover and use data, prepare that data, and create a data product, including developing predictive models within Dataiku. At the same time, it also ensures projects pass a set of checks, balances, and governance measures.

      Next-generation self-service analytics technology

      There are people in the business who have the ambition to go on their own data journey and will do it if the points of friction are reduced and they are enabled to do so. This is the essence of the next generation of self-service analytics and, as previously discussed, of citizen data science.

      The idea behind the next generation of self-service analytics isn’t that individuals can do and build whatever they want with data (which would lead to data chaos). It’s about empowering people, and choosing the right technology is an important milestone. The right technology should connect doers with data by bringing people of diverse skill sets together to work with data in a common ground.

      Ultimately, it should be second nature for anyone in the business to produce new insights and to work with data in a way that is easily reusable. Individuals should benefit from the expertise of the many as timely new data products are created and maintained across the whole enterprise. That’s where the value lies.

      “Self-service program equips everyone with the ability to discover and use data, prepare that data, and create a data product, including developing predictive models.”

      INNOVATION TAKEAWAYS

      EMPOWER THE PEOPLE

      The future of self-service analytics in the age of AI is intertwined with the idea of citizen data science – both are about truly empowering people.

      PROVIDE TRUST AND INDEPENDENCE

      Business people must be able to ask questions about their data and find or build trusted answers on their own.

      ALL ABOUT THE TOOLING

      The right tools and technology are critical to enabling people while also maintaining the appropriate level of governance and control.

      Interesting read?

      Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and Dataiku. Find all previous Waves here.

      Author:

      Bridget Shea

      Chief Customer Officer, Dataiku
      Bridget Shea, based in New York City, has proved to be a long-time fixture of the city’s tech scene. She has worked closely with the Dataiku leadership team for several years in an advisory role, bringing her deep data science and analytics expertise.

        Employing a data-first approach to connected products with a cloud-native platform

        Arne Rossmann
        15th March 2023

        Connected products increase revenue and create smoother customer experiences, but only with the correct use of data amid data-explosion in the current connected world.

        Data generated throughout a connected product’s lifecycle can be used to introduce new business models, launch new intelligent services, monetize data assets, and help redesign future iterations of the product with market relevancy in focus.

        This data comes from quality control checks, simulations, and validation assessments during the manufacturing process. And, once the products are launched into the market, data becomes available on how consumers are using these products.

        However, in order to leverage data for connected products effectively and to become fully data-driven, organizations need the right data platform.

        A data-first approach

        When it comes to managing data, many organizations face challenges due to limitations imposed by their legacy systems. These systems frequently compartmentalize data in silos, which results in disparate methods of handling data. This means organizations lose out on valuable insights into their business operations and the complete product lifecycle, including customer experience.

        IDEA (Industrialized Data & AI Engineering Acceleration) by Capgemini simplifies the complex process of data management for organizations and provides a streamlined approach to collecting, governing, and managing data on cloud.

        By leveraging the capabilities of IDEA by Capgemini, organizations can gain actionable insights into their operations through a focused analysis of data at every stage of a device’s journey and a comprehensive view of the product’s end-to-end lifecycle. This holistic approach not only delivers meaningful insights but also results in a substantial increase in efficiency, with a ~40% acceleration in time to value in data and AI estate modernization on cloud.

        A jump start for data platforms

        The conventional method for establishing a data platform typically takes a minimum of three to four months. This includes researching and assembling the infrastructure components and integrating them with different services.

        However, with this solution, a secure infrastructure on cloud can be established and an interactive user interface can be activated within four to five weeks, allowing you to quickly begin obtaining valuable data insights by configuring the data pipeline, combining and processing data, data governance, and finding meaning from them using AI-driven insights.

        Simple flexibility

        The solution simplifies the complex process of building an efficient data platform. With a modular design and platform-as-code, users of IDEA have the flexibility to customize and change components based on customer needs.

        IDEA by Capgemini’s user interface facilitates the effortless integration of elements through its intuitive, low-code approach. This enables the customization of components by a wider range of users, including those with limited technical expertise – not just highly skilled data engineers.

        The implementation of a single, streamlined user interface that integrates all data components and considers all elements of the product lifecycle ensures that the insights generated from the data can be reliably trusted.

        Accelerating value

        Capgemini’s IDEA solution significantly reduces the time required for modernizing a data estate and enables organizations to adopt AI and analytics across the enterprise for accelerated business value.

        To discover more about the potential of IDEA by Capgemini – and learn how it can help you use data to gain a competitive advantage – visit our webpage.

        Author:

        Arne Rossmann

        Chief Architect Data & AI for Intelligent Industry
        As a part of the Data & AI for Intelligent Industry team working as a Chief Architect, I support our clients by giving them advice and guidance on the architectures for Data & AI Platforms within the domains of Digital Manufacturing, Digital Twin, Intelligent Supply Chain, Connected Products and 5G & Edge, and this across all our sectors. The main goal of my work is to enable our clients on their journey towards data-powered enterprises to leverage the value lying within their data and by sharing them across the company and with the outside network of suppliers and partners.

        Anupam Srivastava

        Director, Insights & Data, CapgeminiOffer leader – Data & AI for Connected Products, Privacy Preserving AI
        As a next gen technology professional in cloud, AI & data engineering Knowledge graph, federated learning my focus is on innovation across industry leveraging technology. Experience in building high-performance teams and delivering high-end technology solutions using emergent technologies leveraging experience in product development, presales, and architecture.

          Rethink to reinvent

          Diederik Vieleers
          14 Mar 2023

          What manufacturing organizations can do to build resilience in an increasingly uncertain world

          The pandemic has been a great disruptor, remolding the business environment in every sector. As we progress into 2023, we are witnessing fresh dynamics in the market at an unforeseen velocity and is painting a new picture on how the world is shaping up—one that is much different from what we would have expected a couple of years earlier. Last year, we witnessed new geopolitical challenges unfolding within Europe that showed how heavily dependent we are on other markets for natural resources and raw materials. The manufacturing sector has been no exception and is experiencing the aftershocks of these uncertainties.

          At Capgemini, we have identified key trends that will shape the future of manufacturing and suggest what businesses can do to stay resilient in today’s volatile and uncertain environment.

          The future of the industry is intelligent: The era of intelligent products and services will significantly shape how businesses operate, and early adopters will generate significant competitor advantage and business value through interconnected data-driven products and processes. Traditionally, manufacturers focused their strategy on products as objects. Today however, Intelligent Industry has added a new dimension by shifting the focus towards a more customer-centric approach that provides tailored services to end-customers. Organizations that deploy business models based on lifecycle services will grow faster and be more resilient in rapidly changing markets. Organizations must discover the power of data to envision new products and services, improve supply chains, create new customer experiences, deliver new sources of value, and achieve new outcomes with intelligent industry.

          Supply chain transformation to lower dependencies: The current geopolitical situation has exposed the disadvantages of overreliance on other markets. Most supply chains were negatively impacted from the lack of semi-conductors during the peak of the pandemic, and shortage of natural resources due to geopolitical challenges within Europe. To de-risk the vulnerability of global models and generate a real competitive advantage, manufacturers will need to reassess and optimize their supply chains to diminish reliance on other markets and associated risks. A supply chain that is focusing today on data-driven networks and advanced digital transformation capabilities to streamline logistics and prioritize resilience will successfully survive tomorrow. Some measures that supply chain managers should consider are diversifying their suppliers, exploring different transport partners (and processes), and sourcing alternative raw materials (or alternative sources). They should aim to redesign their supply chains and be as compact and localized as possible to minimize cost and carbon footprint. 

          Automation as a catalyst to become future proof: In order to overcome the current challenges, organizations should invest in bringing production processes closer to home and relocating factories to closer neighborhoods. To address forthcoming human resource limitations such as lack of access to low-cost labor countries or skill and talent, organizations should consider automating operations and productions. This will require strong integration between IT (Information Technology) and OT (Operations Technology) within target architectures to make connected processes and products autonomous., This fundamental investment will open new data-driven opportunities bringing costs down and making the unit efficient and safe in the long run.

          Sustainability at the heart of business transformation: Regardless of the maturity stage, most organizations are now transitioning towards zero-waste production and are becoming carbon conscious as it has a direct correlation with better profitability. Therefore, organizations need to focus on product design that will increase lifespan and build a zero-waste supply chain. Organizations that recycle, refurbish, or remanufacture their products to increase the average lifespan have already seen the benefits of reducing waste, bringing down costs, and, most importantly, lowering their carbon footprint. Ways to extend products’ lifespan include design for easy disaggregation, repairability, and reusability; hardening products’ reliability and endurance, ensuring maintenance is in place, and, once the product lifespan is over, efficiently decommissioning it. Apart from making supply chains compact, organizations need to identify areas of energy wastage, unnecessary carbon emissions, and heat expenditure and take steps to lower them. In the Netherlands we are already seeing a positive shift towards hydrogen-based plants. Manufacturers who already have a transformation plan in place or are in the process of designing one to migrate from traditional gas-based energy sources to hydrogen-based sources, will be a step ahead in the path to future readiness.

          Co-creation and data-sharing to retain competitive advantage: With compliance having a key role in stimulating organizations’ sustainability goals, we see a lot of players across the industry working together. To ensure compliance towards their sustainability goals and effective assessment of their carbon footprint, organizations will need to have access to all data that can be made available to them, and as a result work with all partners across their supply chain, share respective data, and beyond. More importantly, working together and co-creating plans of action will be critical in creating value for organizations.

          The industry is continuously evolving. Disruption, game changers, and re-inventions are constant part of this evolution, and it is important for organizations to reinvent themselves through an outside-in perspective, specifically from their customers’ point of view. Ways to build effective autonomous operations lie in a sound and good architecture, integration of OT and IT, and the skill to use digital twins effectively. Another key factor lies in smart data management, in terms of quality as well as quantity. Organizations need to inspire confidence and trust amongst their partners in the supply chain to bring their products to the market in a cost-effective and sustainable way. To face these fundamental changes and future-proof themselves, businesses will have to rethink their product design, rebuild their operations, and create supply chains based on effective data management and sustainability and reuse by design.

          Author: Diederik Vieleers, VP, Head of Complex Manufacturing and Technology

          Author

          Diederik Vieleers

          VP I Head of Complex Manufacturing and Technology, Capgemini Netherlands
          Experienced market leader with a demonstrated wide history of working in the IT and services industry, focused on innovation and creating business opportunities.

            Should I stay or should I go? – my 2 cents on current and future leaders in Digital Process Automation 

            Gustaf Soderlund
            15 March 2023

            The State of Digital Process Automation

            Digital process automation (DPA) technology has been instrumental as the engine for digital transformation through digitizing and automating manual and paper-based process. As such, the DPA space has been a great place to be for many years, but it is becoming more crowded with more and more vendors. Current DPA leaders (like Pega, Appian and to some extent IBM and Microsoft) are under heavy attack from several fronts. We see suppliers entering DPA from other areas, like Salesforce (from sales and marketing) and even more so, ServiceNow (from ITSM and employee services). Lowcode (only) suppliers, like Outsystem and Mendix, are also trying to enter this space with basic workflow and business rules capabilities, but to a much lower cost. As some sort of RPA capabilities are nowadays integrated in the DPA platforms, even typical RPA suppliers (like UI Path and BluePrism) are starting to step up the value chain into IPA/DPA as analysts are deeming traditional RPA dead. But, as the analyst firm Horses for Sources cleverly points out: “What isn’t dead is the fact that RPA created the path…to a much bigger market that’s evolving”. What is also clear is that the DPA market is growing and that existing vendors (and newcomers) will want to capitalize on this, but the competitive landscape will get more fierce and tough going forward.

            Focus Areas for Current and Future Leaders in Digital Process Automation

            So, where should leaders keep and increase their focus to keep the edge and where should challengers extend their capabilities to become future leaders?

            As a connected experience becomes more and more important, digital process automation platforms need to be able to orchestrate timely and contextual content across the journey using both historical and real-time stream processing data. While the central DPA platform would manage the processes for the business, a well-connected process management platform will be able to define and control process behavior dynamically based on recommendations. As organizations increasingly invest in cloud solutions, it is imperative that DPA solutions continue to build, not only their own AI capabilities, but also the ability to adopt to AI services extended by various leading hyper scalers. 

            Considerations for Prospective Clients of Digital Process Automation

            So, what does this mean for you as a prospective client? I’ve summarized three points to consider: 

            Vendor Selection for Digital Process Automation

            So, which vendor should you pick? Of course, the typical consulting response – ‘it depends’ – is relevant here. But the slightly longer response is, the more complex and regulatory needs you have, the more it makes sense to pay the premium price for some of the leading platforms. If your need is neither complex nor regulatory, you can safely opt for one of the more lightweight or even the low code-only solutions. You could do this analysis yourself, but it helps involving a trusted advisor doing this work that have the ability, the experience and can stay objective towards the vendors throughout this process.

            Planning and Execution

            Always start with the business need, the ToBe or the Target Operating Model – and then work backwards, to ensure you generate real business value (more on this in a coming blog post). Focus more on what you want to achieve (fitment analysis) than which platform is cheapest or comes with most bells and whistles. Many DPA implementations are transformative in their nature, which means you need to spend some time to really plan and prepare the implementation (else you should ‘prepare to fail’). And, when you take this plan to execution, you want an SI that’s prepared to have some skin in the game and take ownership beyond resourcing. Having the right partner can be the difference between a transformative change and a failed IT project.

            Managing Change and Culture in Digital Process Automation

            Don’t underestimate the cost of change, both from a technical point of view and a cultural point of view. Changing an enterprise workflow system can be a very big and costly affair, so that kind of decision shouldn’t be taken lightly and only if it’s supported by a very strong (and challenged) business case. From a cultural perspective, having a structured organizational change management approach can be vital.

            At Capgemini, we’ve been doing digital process automation or IBPMS for the last 20+ years. You can always reach out to us if you want more detailed advice on this subject!

            Author

            Gustaf Soderlund

            Global VP Public Sector Sweden, Nordics
            Gustaf has many years of experience selling, delivering, and leading business process and customer engagement solutions in a variety of industries, including banking and insurance Gustaf currently leads Pega globally and is the Augmented Services leader for Financial Services.

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              What we learned at MWC Barcelona 2023

              Capgemini
              Capgemini
              14 Mar 2023

              Mobile World Congress 2023 (MWC 2023), the biggest telecom festival of the world was organized by GSMA in Barcelona a few days ago. This four-day event gave important directions to the global telecom industry on how to effectively monetize their network investments and overcome any future challenges.

              From Private 5G networks and Artificial Intelligence (AI) to Metaverse and immersive experiences, this event provided glimpses of a world connected with 5G. We also learnt the future telco networks are going to be more sustainable, energy efficient, and mostly open and cloud native. Overall, the event left a strong impression on the world that telco industry is now gearing up for next levels of growth and ready to assume a bigger role in the global ICT ecosystem.

              Here are our key takeaways from MWC 2023.

              • Industry players are coming together to deploy private 5G networks
              • Having sustainable, energy efficient networks is becoming critical for CSPs
              • New hybrid cloud solutions for telcos are coming from hyperscalers
              • Open RAN continued to find its industry sweet spot through collaborations
              • GSMA’s ‘Open Gateway’ initiative gathered MNOs’ support
              • Telefónica is gradually creating an ecosystem for Metaverse applications

              Industry players are coming together to deploy private 5G networks

              CTOs and CIOs from large enterprises were present at the event, and they were more than curious to understand the benefits of having private wireless networks. CXOs wanted to have dependable, unified, and simplified connectivity at their premises for better safety, security, and productivity of their assets. Telcos, NEPs, chipmakers, hyperscalers and their IT & system integrator partners came together to show to the enterprises that it is possible to have an exceptionally reliable connectivity by deploying private 5G networks.

              Cisco and NTT have partnered to bring managed private 5G solutions that can be integrated with enterprises’ pre-existing LAN/WAN and Cloud infrastructures. Deutsche Telekom (DT) also partnered with Microsoft on private 5G, specifically to target underserved small and medium enterprise (SME) businesses. To transform industrial automation systems through virtual connectivity, Schneider Electric, Qualcomm, and Capgemini announced collaboration to create a tailored, end-to-end 5G private network solution, which can be deployed across industrial and logistical sites.

              There remains a huge growth potential for CSPs and other industry players in the private 5G space across enterprise segments, as this market is catching up fast due to rapid industry automation. Furthermore, Capgemini is also supporting the automotive industry’s fast transformation by embracing 5G intelligent connectivity, computer vision sensors, edge, and cloud services to enable cars to become ‘mobility software platforms’ for assisted driving and road security.

              Having sustainable, energy-efficient networks is becoming critical for CSPs

              Apart from fulfilling their net zero commitments, there is a grown demand among telcos for energy efficient networks due to rampant energy shortages and rising inflationary pressures. The interest from many spheres of the industry is coming to overcome these challenges through AI and virtualization. During the event, Qualcomm and Mavenir introduced a new OpenBeam massive MIMO Active Antenna Unit based on Qualcomm QRU100 5G RAN Platform. The solution uses AI to reduce total cost of ownership (TCO) of 5G deployments, increase energy efficiency and raise network performance.

              Intel also launched its 4th Gen Intel Xeon Scalable processors with Intel vRAN Boost, which can deliver capacity gains and power savings for telcos. CSPs are also embracing such energy efficient / Intelligent RAN solutions. For example, Singtel announced the deployment of Ericsson’s Cell Sleep Mode function to conserve energy at its mobile base stations. Going forward, we expect more telcos to demand energy efficient solutions to safeguard their margins while also sticking to their net zero commitments.

              New hybrid-cloud solutions for telcos are coming from hyperscalers

              During the event, we saw hyperscalers widening their product portfolios for the telco industry to reduce any friction to the cloudification of networks and IT infrastructures. Microsoft launched ‘Azure Operator Nexus,’ a hybrid cloud platform that allows CSPs to run their carrier-grade workloads both on-premises and on Azure. Google Cloud has also launched several new products that help CSPs build, deploy, and operate hybrid, cloud-native networks, collect & manage network data, and improve CX using AI. On the similar lines, VMware highlighted the deployment of its Telco Cloud platform by the global CSPs, with product advancements and an expanded partner ecosystem. 

              Some CSPs are embracing the Cloud, not only to fulfill their own virtualization or modernization goals, but also to leverage the Cloud for transforming themselves into industry solution providers. For example, Swisscom has partnered with Ericsson to explore hybrid cloud use cases with Amazon Web Services (AWS). Swisscom has also started a Proof-of-Concept (PoC) trial with Ericsson 5G Core applications running on AWS, to explore how the use cases support the needs of telcos.

              Open RAN continued to find its industry sweet spot through collaborations

              At the event, we witnessed a growing industry consensus that collaboration is the key for developing a conducive Open RAN ecosystem. For example, NTT DOCOMO announced that it is cooperating with KT Corp, Vodafone, Smart Communications, DISH Wireless, and Singtel on various Open RAN initiatives, while Supermicro announced that it is working with Capgemini and Intel to achieve real-world performance gains for Open RAN. Deutsche Telekom (DT) also reinforced its commitment to Open RAN with plans for commercial brownfield deployments and selecting Mavenir for a multi-vendor Europe deployment including massive MIMO starting in 2023.

              GSMA’s ‘Open Gateway’ initiative gathered MNOs’ support

              There was a strong support from GSMA to the open-source community. GSMA announced an industry-wide initiative named ‘Open Gateway,’ providing a framework of network APIs to give universal access to CSPs’ networks to developers. Launched with the support of 21 MNOs, this move represented a major shift in telcos’ design and delivery services. Hyperscalers AWS and Azure also backed the project. With a growing industry support to Open APIs, we are expecting the future telco networks to become more open and disaggregated, offering better monetization opportunities to telcos.

              Telefónica is gradually creating an ecosystem for Metaverse applications

              We saw an industry wide interest in digital twins, AR/VR, and immersive experiences throughout the event, making it clear that the industry has started to take Metaverse seriously. Telefónica was quick to take advantage of this trend and displayed their propositions for the Metaverse. The CSP presented a demo on ‘Making Smart Industry happen,’ where it simulated an industrial process and corrected errors in industrial parts with support from 5G, edge, data, and machine learning. Telefónica also partnered with KDDI and collaborated with Sturfee (a tech co building Visual Positioning System) and Mawari (a provider of Cloud Rendering and Streaming technologies) to create 5G MEC powered XR Digital Twin Store. This project demonstrated application of XR technologies to create a sense of co-presence and togetherness. Like Telefónica, we are expecting a further industry participation in developing Metaverse applications, for both B2C and B2B segments.

              Conclusion

              After strong CAPEX on 5G in 2022, telcos are increasingly seeking network monetization opportunities, especially from the enterprise segment. CSPs are now more focused on addressing a growing demand for private 5G networks among enterprises and showing a greater willingness to collaborate with other industry players to deliver most effective private wireless solutions. There is also an increased telcos’ interest in having more open, disaggregated networks so they could establish themselves as platform providers or marketplaces for B2B and developers’ communities. Apart from monetization, telcos have also increased their focus on cost saving by improving their network and operating efficiency. CSPs are investing more in sustainable, energy efficient networks as well as speeding up their cloud transformation to save future outlays. Thus, going forward we are expecting CSPs to become smarter, asset light organizations, with more agility and an innovation mindset to play a bigger role beyond connectivity.

              TelcoInsights is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders.

              Author

              Vaibhav Mahajan

              Telco Intelligence Expert at Capgemini Technology Services
              Vaibhav is a Program Manager in Capgemini’s Global Telecom Industry Platform. He has robust sector expertise in telecoms, helping clients develop strategic & prospective vision with actionable industry insights.

                Retention instead of tension by using the full potential of HR Cloud

                Dr. Sandra Duesing
                13 Mar 2023
                capgemini-invent

                “80% of new employees decide whether or not to stay with a company within the first six months of being hired”

                Source: SAP SuccessFactors Onboarding product brochure

                As mentioned in our previous blog article: Create the decisive factor for employees to stay by leveraging HR Cloud technologies, the optimal employee-centric Onboarding journey consists of three phases: pre-boarding, acclimation, and belonging and performance excellence. In this article, we take a closer look at the third and final phase of the Onboarding journey: belonging and performance excellence. This phase starts after the employee’s first month in the company and ends six months after the start date. The aim of this phase is the full integration of the employee – especially in an organizational and social way. This enables the employee to act independently and reach a high level of performance.

                Admin and technological layer

                In relation to admin and technological aspects, the focus is on ensuring employees can independently operate relevant technical tools and use them effectively. Thus, the employee should also have a good overview of the functionalities of the relevant tools and be able to assess their proper use. This ensures that the employee is fully technically onboarded and can focus on professional development.

                Professional and content-related layer

                In the belonging and performance excellence phase, employees develop professional skills that enable them to work independently on professional tasks. Additionally, employees acquire expertise and embark on a steep learning curve with relevant training courses and projects that support professional development. These aspects lead directly to the realization of career development goals employees bring with them when they join the company.

                HR Cloud technologies can provide a great deal of support here. For example, HR Cloud vendors, such as SAP SuccessFactors, foster the completion of training participation by tracking the current status in a dashboard (see illustration 1). This enables employees to meet career, personal development, and administrative needs.

                Illustration 1: Continuous performance management

                Organizational and cultural layer

                Important components of the belonging and performance phase take place primarily at the organizational and cultural level. This includes the introduction to corporate goals and strategies and the beginning of the identification with corporate values. These actions are undertaken to enable employees to act independently as brand ambassadors.

                Additionally, it is essential to accompany the employee during this phase and to hold regular feedback conversations: “how are things going at the moment?” On the one hand, this shows appreciation. But on the other hand, it is possible to trigger the internalization of the company’s values and goals by the new employee, too.

                HR Cloud technologies can also provide a great deal of support here, since the “continuous performance management” fosters and drives regular feedback and coaching check-ins with the lead (see illustration 1). Thus, it allows new employees to experience a positive feedback culture. Regular feedback conversations, in which employees act as feedback receivers, but also as feedback providers, make an important contribution at this stage. It is important to the development of individual employees and their overall Onboarding experience.

                Social and network layer

                On a social level, the aim is to achieve full team integration. For example, this can be achieved through teambuilding measures, using activities, exercises, and methods that strengthen the sense of belonging between colleagues. To build and expand their own professional network, opportunities to participate in networking events should also be offered. These events can have a thematic hanger or be tied to social groups (e.g., women’s events).

                How to optimize the employee experience in Onboarding

                In this series of four blogs, we covered the optimal employee-centric Onboarding journey, consisting of three phases: pre-boarding, acclimation, and belonging and performance excellence. The articles highlighted how HR Cloud technologies optimize the employee experience during all three Onboarding phases, making a significant contribution to the integration and enablement of employees in a new organization. As outlined, the use of HR cloud systems, such as Workday, SAP SuccessFactors, Oracle, and ServiceNow are indispensable in the context of Onboarding and pays off in the following ways:

                • Optimized employee experience during the Onboarding process to make the employee feel welcome, appreciated, and informed
                • Aligned activities across all Onboarding experience layers during each Onboarding phase, which significantly contribute to employee enablement
                • Easier, more comfortable, and efficient Onboarding tool handling despite complex Onboarding processes and the involvement of several departments

                By leveraging HR cloud technologies, organizations can provide an optimized employee experience and enablement in turn leading to increased employee performance, productivity, and retention.

                Our authors

                Dr. Sandra Duesing

                Vice President in Workforce & Organization and the Global Head of Reinventing HR | Capgemini Invent
                As Capability Lead Workforce & Organization at Capgemini Invent with a dedicated focus on Experience Excellence in HR & HR IT, I am passionate to re-imagine work & unlock underlying human potential to drive digital transformation journeys for business and society successfully.

                Svenja Stegemann

                Senior Consultant in Workforce & Organization | Capgemini Invent

                Anne Geiter

                Consultant in Workforce & Organization | Capgemini Invent

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                  Is your supply chain ‘intelligent’ enough to handle future disruptions?

                  Tim Bridges
                  10 Mar 2023

                  The world in which consumer products (CP) businesses operate is changing quickly. While unpredictable global events of the past few years have impacted daily operations, leading to labor shortages, higher inflation and commodity costs, there has also been a shift in consumer expectations.

                  The bar for effective customer experience has been slowly rising, and now the new normal is fast delivery of goods, anywhere, at any time.

                  The latest report from the Capgemini Research Institute, which surveyed 1,000 companies across four sectors including CP businesses, found that over half of the organizations acknowledge that their supply chains have altered significantly over the past two years. Yet only one in five organizations feels equipped to handle the disruption. Just 11% of organizations feel prepared to meet shorter fulfilment timelines for consumers and only 9% believe they are well-equipped to offer personalized products and services.

                  So, what does this mean for businesses? It’s time for consumer product organizations to completely rethink their business models. Supply chain transformation is needed, not only to withstand these ongoing challenges and stay ahead of the curve but also to become capable of meeting changing consumer demands. To future-proof an organization, there is a need to digitize and connect the entire business in a way that’s completely different to anything the industry has seen before. 

                  The key to survival is enabling an intelligent supply chain. Such a data-driven, technology-enabled, scalable, and sustainable supply chain network can help businesses drive improved customer loyalty, create more business value and meet sustainability goals.

                  Enabling intelligent supply networks

                  Our research indicates that businesses across sectors understand the need to rethink their supply chain. They plan to increase investments by 17% over the next three years to transform their supply chains.

                  It would be prudent for these investments to flow into a combination of different technologies that can break down operational silos and create greater efficiencies. For instance, Internet of Things (IoT) devices can provide analytics for predictive maintenance in factories to lower costs and reduce disruption. While AI algorithms are increasingly taking the place of human planners to create touchless planning models.

                  Nike, for example, has completely transformed its supply network by using data to fine-tune operations, with a focus on inventory and demand sensing. Demand sensing produces precise, short-term forecasts of customer demand on a daily, and potentially hourly basis, which allows retailers such as Nike to accurately determine customer behavior and adapt their operations accordingly.

                  A new kind of demand planning

                  Historically, consumer demand data has been the bedrock for forecasting annual demand spikes. However, it has little bearing on what might happen in the future – something that is particularly important in today’s volatile business and economic landscape.

                  Consumer product organizations can become far more agile in anticipating short-term demand spikes in real-time through data, analytics, and AI. Take Unilever as an example, which utilizes one-click AI-augmented forecasting to analyze demand patterns while also gaining insights from consumers through e-commerce and social media channels.

                  By employing near real-time monitoring of consumer sentiment in categories (such as end-of-week analysis), and One-Click AI Sales Forecasting, businesses can predict rising and falling demand in the short, mid and long term. This type of data-backed forecasting means businesses can ramp up or down production to accommodate fluctuating demand.

                  Moving towards Intelligent Industry

                  Having insight into demand is one thing, but how can organizations fulfil that demand or, perhaps more importantly in today’s climate, decrease the volume of production when demand falls? The most effective model is a unified approach where supply and demand insights are integrated with connected manufacturing.

                  This is where Intelligent Industry plays a vital role. Near real-time visibility into production lines and related elements that are enabled by the cloud and digital factories, which are capable of monitoring and self-driving performance and maintenance – this is Intelligent Industry. Some of the key benefits of the Intelligent Industry are reduced labor dependencies and improved forecast accuracies through increased digitization.

                  However, it’s important to remember that greater digitization does not equal full automation, and the human element remains overwhelmingly relevant. The Capgemini Research Institute found that 80% of organizations that were most successful at digitalizing their industrial system first addressed skills shortages by upskilling existing employees and recruiting talent with the required digital skillsets. The real value is realized when organizations balance investment in smart factories with programs to upskill their workforce so they can utilize smart factories effectively.

                  Intelligent Industry for a sustainable future

                  Sustainability continues to be top of mind for conscientious consumers, and they want to see brands take action to reduce their environmental impact through transparent sustainability initiatives. With supply chains accounting for over 90% of an organization’s greenhouse gas emissions, prioritizing sustainability is not just important, it is critical.

                  However, through the adoption of sustainable practices across the value chain plus real-time tracking systems to monitor performance, businesses can better get a read on where there are sustainability gains to be made across the supply chain. Only with the visibility to identify where sustainability gaps exist can businesses hope to fix them.  

                  The challenges are many for CP businesses today – risk-proofing supply chains, meeting consumer demands, creating sustainable products, ensuring fair trade and overall decreasing their environmental impact. Reassessing their existing business model and implementing an intelligent supply network is imperative to surviving future disruption.

                  The opportunities presented by an intelligent supply chain are clear and it’s encouraging to see that those that have laid the foundation for this are already reaping the benefits.

                  Connected manufacturing links digital technology with the engineering of complex devices, vehicles, and equipment, to develop and deliver a more intelligent manufacturing experience for businesses

                  About Author

                  Tim Bridges

                  Global Head of Consumer Products & Retail
                  Tim Bridges leads Capgemini’s Global Sectors and the Consumer Products, Retail, Distribution (CPRD) global sector practice, a portfolio that includes major global retail, fashion, restaurant, consumer products, transportation, and distribution brands such as McDonald’s, Coca-Cola, Meijer, Office Depot, Domino’s, and Unilever.

                    Improving mobile application development with hybrid application frameworks

                    Gurjit Singh Butalia
                    9 Mar 2023
                    capgemini-engineering

                    Native apps are created explicitly for one platform, in the programming language directly supported by a particular device, and can take full advantage of that device’s capabilities. Web apps, on the other hand, resemble native apps but are, in fact, mobile-optimized websites.

                    Hybrid apps are a mixture of native and web technologies. A hybrid approach to application development will consider both native and web capabilities to provide an optimal, cost-effective solution for cross-platform compatibility. As such, hybrid application frameworks offer a number of benefits to mobile application development projects, allowing project teams to save time, reduce costs, and take advantage of web programming expertise.  

                    Comparing native and hybrid mobile applications

                    Native mobile apps are developed with consideration of a specific mobile operating system. For example, engineering teams use the Kotlin programming language – which interoperates with Java – for developing apps for Android devices, while Swift and Objective-C are used to develop apps for iOS devices.

                    There are many features specific to the device hardware, such as GPS, camera, and fingerprint recognition which, when used directly by native apps, deliver fast performance and are very reliable. However, the development cost is high because each supported platform requires a separate mobile app to be developed.

                    Hybrid applications, however, are typically written in HTML5 and JavaScript, or with the help of cross-platform frameworks like React Native, Ionic, Flutter, and Xamarin, and are developed with features available in both native and web apps. Web apps can be wrapped in a mobile app, for instance, and hybrid apps can leverage built-in device capabilities.

                    Available both in each platform’s respective store, or as progressive web app solutions, hybrid apps are generally used to reduce time to market for an application on various platforms with responsive design. And, as apps developed for Android and iOS share the same codebase, hybrid apps require less maintenance than their native counterparts. That said, since hybrid applications run inside the mobile app shell, they won’t perform as quickly as native apps.

                    Choosing the right development framework

                    A team’s choice of mobile development framework will depend on the type and complexity of the app they’re building.

                    Here, then, are a few suggestions for deciding whether to use a native or hybrid development framework:

                    • Customer-facing applications, such as for ecommerce, can be expected to be complex with a long lifetime. They’ll need to target the two leading platforms – iOS and Android – and will need to perform as well as possible to receive positive ratings in the app stores.
                    • Apps developed for internal use, such as audit tools, are also expected to be long-lived. They’ll be complex in term of business logic, but they may not require the immediate support of new OS features.

                    Other apps might have a device strategy that dictates that they’re only developed on a single platform. For example:

                    • Some event apps may only be used during the lifetime of the event, meaning they have a very short duration. Since they’re aimed at consumers, they need to be on both Android and iOS, and can be developed through suitable hybrid frameworks for rapid development and used for a short period.
                    • A native framework is recommended for building complex mobile applications with a long set of dependencies, such as games, which require animation, or health apps that interact with sensors in real time.
                    • A native stack is also recommended for building consumer-facing applications with a long lifetime. Teams can experiment by building partial features in cross-platform tools. LinkedIn and Facebook, for example, have been experimenting with React Native.

                    In summary, engineering teams can choose native deployment when they need to define a clear upgrade path, support features of new OS releases, and achieve the highest possible performance. Cross-platform, hybrid frameworks can be selected when the solution has a reasonable level of complexity, code reusability, low staffing requirements, and an aggressive time to market.

                    A simple solution

                    Applications created on a native platform can provide a good user experience, and are a better choice when an app has low latency use cases, and requires support for the latest OS features, such as upgrades, migration, and security.

                    The value of hybrid applications, however, is that they help developers write the code once and deploy it to different platforms, allowing them to launch their mobile applications quickly and reduce maintenance costs.

                    As such, a hybrid approach offers a simple solution when faced with time-to-market constraints, a small budget, or a lack of native app development experience. There are various hybrid frameworks available that teams can adapt, based on their experience. Indeed, they have become increasingly essential in today’s rapidly changing environment, where companies need to innovate quickly, and when there is a lack of resources and expertise in native app development.

                    Author

                    Gurjit Singh Butalia

                    Senior Architect, Software and Digital, Capgemini Engineering
                    Gurjit has over 24 years of experience in embedded, web, and mobile applications, DevOps, and AWS cloud architecture and development. He has worked in pre-sales consulting and as a solution architect in the software and digital team.