Join us for this interesting and insightful conversation with, Marjorie Bordes, Vice President, Group Cyber Defense Operations, where she explains the importance of diversity in cybersecurity, how Capgemini’s values foster a positive and supportive work environment, and how she finds the cybersecurity profession both fascinating and fulfilling.

1. Tell us about your role. What does a day in your life at Capgemini look like? 

Every day is a new day depending on new rising threats, major attacks on the market and/or incidents impacting our clients that need the involvement of my Cyber Defense team. Part of my role is to enable and empower my teams to do what they do best: protect Capgemini against potential external and internal cyber threats. Together, we are facing daily complex situations to solve; we challenge each other on the best way to deal with them and adapt our posture in the context of constantly evolving threats. On top of that, I’m excited to contribute to our global digital transformation, demonstrating that cyber is not something that we need to be weighed down by, but part of the solution to accelerate a client’s business and secure their future.  

2. What makes you proud to work at Capgemini? 

I’m proud to work at Capgemini because everything is possible, as long as you have a good idea, you are able to put it into practice, develop it, and deliver it. This entrepreneurial spirit is really present and absolutely amazing. It allows us to be an active player in the future we want!

3. How are you working towards the future you want? 

By concentrating our efforts on the production of high-quality cyber threat intelligence which structures our threat detection capabilities, guides our offensive security programs, and helps our customers to adapt on a day-to-day basis their cybersecurity posture.   

4. What difference does it make to have diversity in the cyber leadership? (The value of diversity in the cyber leadership) 

Diversity is very key in cyber because it allows us to break down certainties, cross viewpoints, experiences, and sensitivities. It’s an undeniable asset for understanding the complexity of our business. I’m already looking forward to continuing to enrich our team with very different profiles and backgrounds, and I’m proud to start the journey to be able to welcome neurodiverse profiles very soon.  

5. What advice would you give to someone joining Capgemini Cybersecurity? 

Enjoy your new journey among a community of very skilled experts. Connect with the people all across the Globe, understand their respective missions and how you’ll work with them, and learn from all of them as well. You’ll discover how your uniqueness will serve/support the Capgemini One Team.

Finally, I never asked myself if I was able to do something or not because I’m a woman.  I have spent my career working in professional environments that historically have been dominated by male colleagues. I have never seen this as a barrier, I have always been driven by the skills and experience I can offer.  Being a woman has never stopped me at all.  

If you are looking for a role in cybersecurity at Capgemini, please visit our career page

Generative AI is the pinnacle of data science. It will boost profits, reduce costs, and help you expand into new markets. To take full advantage of generative AI’s capabilities, train your models on all your data.

The world is being transformed by AI-assisted medicine, education, scientific research, law, and more. Today, researchers at the University of Toronto use generative AI to model proteins that don’t exist in nature; pharmaceutical giant Bayer now uses generative AI to accelerate the process of drug discovery; and education provider Khan Academy has developed an AI chatbot/tutor, Khanmigo, to personalize learning. And with each passing day, the list of AI use cases across all industries only continues to grow.

According to the Capgemini Research Institute, nearly all (96 percent) of executives cite generative AI as a hot topic of discussion in their respective boardrooms. Generative AI is not just used as an aid to surface information the way a search engine does; with generative AI, organizations can combine their proprietary data with foundation models that have been pre-trained on a broad base of public data to create a sustainable competitive advantage.

Generative AI then becomes the most knowledgeable entity within your organization.

However, as with all analytics, generative AI is only as good as its data. To fully leverage AI, an organization needs a solid data foundation and organizational norms that facilitate responsible and effective use of data.

Data readiness for generative AI depends on two key elements:

1. The ability to move and integrate data from databases, applications, and other sources in an automated, reliable, cost-effective, and secure manner
2. Knowing, protecting, and accessing data through data governance

Automated data pipeline platforms, like Fivetran, allow enterprises to capture all of their data, irrespective of the source platform. These automated tools reduce the friction and overhead required to maintain the flow of data to continuously train generative AI applications.

OPERATIONALIZING GENERATIVE AI

To operationalize generative AI effectively, organizations must establish a solid foundation of automated, reliable, and well-governed data operations. Generative AI requires a modern and scalable data infrastructure that can continuously integrate and centralize data from a variety of sources, including both structured and semi-structured data.

However, as businesses start to operationalize generative AI, they may encounter a number of challenges.

• Data quality and preparation: Generative AI models are only as good as the data they are trained on. It is important to ensure that the data is high-quality, clean, and well-organized. This includes identifying any potential biases in the data that may distort the outputs of any model trained on it.
• Security and governance: Security and governance in the context of generative AI concern masking sensitive information, controlling data residency, controlling and monitoring access, and being able to track the provenance and lineage of data models.
• User experience: It is important to design user interfaces for your model that make it easy for people to interact with your models.
• Scalability: It is important to choose a generative AI platform that can scale to meet your needs at a reasonable cost.

Generative AI models are trained on massive datasets of text, code, images, or other media. Foundation models, which are off-the-shelf generative AI models that are pre-trained on large volumes of (usually public) data, may be specialized by industry or use case. Choosing the right foundation model can have a significant impact on performance and capabilities. For example, a foundation model that specializes in code generation will do so in a more comprehensive and informative way than a model that is trained on a general dataset of text. Other specialties of foundation models may include sentiment analysis, geospatial analysis, image generation, audio generation, and so on.

While you can easily make use of pre-trained, publicly available AI models, your data is a unique asset that differentiates your organization from the competition. To make the most of it, you must additionally supply foundation models with your business’s unique context.

With access to your organization’s accumulated data, a properly tuned generative AI model can become the most knowledgeable member of your organization, assisting with analytics, customer assistance, sales and marketing, software engineering, and even product ideation.

The Fivetran product team leverages generative AI and natural language processing technologies to develop Fivetran Lite Connectors in a fraction of the time of Fivetran’s standard connectors, while ensuring the same high quality, data integrity, and security customers expect from Fivetran.

In addition, several notable organizations have already found practical ways to use generative AI. Global commercial real estate and investment management company JLL recently rolled out a proprietary large language model that employees access through a natural language interface, quickly answering questions about topics such as an office building’s leasing terms. Similarly, the motor club in the US, AAA, now uses generative AI to help agents quickly answer questions from customers. Of the 100 tech companies profiled in the Forbes Cloud 100, more than half use generative AI.

According to Carrie Tharp, VP Strategic Industries, Google Cloud, “Generative AI opens up a new avenue, allowing people to think differently about how business works. Whereas AI and ML were more about productivity and efficiency – doing things smarter and faster than before – now it’sabout ‘I can do it completely differently than before.’”

Until enterprises get the data right, the nirvana of asking generative AI app-specific and contextual organizational questions in a “Siri-like” way will remain elusive. Get the data right, and it opens up possibilities for all analytics workloads, including generative AI and LLMs.

To make full use of an ever-expanding roster of powerful foundation models, you must first ensure the integrity, accessibility and governance of your own data. Your journey into generative AI and the innovation and change it can bring will be fueled by high-quality, usable, trusted data built on automated, self-healing pipelines.

“GENERATIVE AI APPLICATIONS ARE ONLY AS GOOD AS THE DATA THAT POWERS THEM.”

INNOVATION TAKEAWAYS

OPERATIONALIZE GENERATIVE AI

Operationalization begins with centralizing data and modernizing the data stack to include all available data.

AUTOMATED DATA ACCESS

By automating data pipelines, enterprises can focus on improving data models and algorithms to accelerate the efficacy and ROI of investing in a generative AI application.

CREATE AN UNFAIR ADVANTAGE

Generative AI trained on your data will provide insights and guidance driven by your data, creating a unique competitive advantage that cannot be replicated.

Interesting read? Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 7 features 16 such fascinating articles, crafted by leading experts from Capgemini, and partners like Aible, the Green Software Foundation, and Fivetran. Discover groundbreaking advancements in data-powered innovation, explore the broader applications of AI beyond language models, and learn how data and AI can contribute to creating a more sustainable planet and society.  Find all previous Waves here.

Over the last few years, we have been witnessing a strong adoption of artificial intelligence and machine learning (AI/ML) across industries with a wide variety of applications. Use cases range from cost reduction via automation to the generation of additional business via the introduction of AI-infused products and services. The launch of the generative AI (GenAI) application ChatGPT by OpenAI in November 2022 only accelerated AI adoption. At present, many of the tech giants including the leading cloud platform vendors like Google, Microsoft, and Amazon have strong GenAI offerings along with those from many smaller vendors and open-source platforms.

In short, GenAI is an AI discipline where the AI foundation models (FMs) are trained on vast amounts of multimodal data (i.e., text, image, audio, video, terabytes of data, trillions of parameters). With proper user requests on the input, FMs can generate a large variety of multimodal synthesized outputs. Large language models (LLMs) are a subclass of FMs specializing in text. An added benefit of GenAI is its highly superior natural language processing (NLP) capabilities, in many cases using multimodal input/output, making it a great and not-realized-before technology for human-computer interfaces. This is one of the key reasons for the heightened interest in GenAI.

GenAI, with applicability in virtually all industries, can significantly improve many of the day-to-day operations of a business as well as help launch new business capabilities. While some GenAI-based autonomous products like certain types of text, image, and audio/video processing are emerging, many of the enterprise-grade usage scenarios that are currently in focus involve GenAI-based digital assistants to humans.

These assistants can help chatbots (and copilots):

• Respond to open-ended questions in a more human-like manner
• Improve overall customer experience
• Detect features and anomalies in images and transactions
• Help with code writing and testing
• Expand work automation
• Improve a wide range of document processing
• Make cognitive and semantic content searches more efficient and effective
• Provide advanced analytics to assess what-if scenarios
• Assist in creative content generation.

Typical metrics for business growth are revenue increase and healthy profitability. Productivity, innovation, and time-to-market are the key enablers of business growth. Depending on the situation, the discipline of GenAI can positively impact some or all of these enablers. A recent McKinsey study [1] estimates that GenAI-enhanced productivity and innovation could add between $2.6 and $4.4 trillion to the global economy annually and identified that around 75% of the value delivered would fall under four use case categories:

• Customer operations
• Marketing and sales
• Software engineering
• R&D

An early 2023 Capgemini Research Institute report [2] that explored a wide variety of industry use cases and surveyed nearly a thousand executives shows the broad applicability of GenAI and high ROI expectations from GenAI adoption. Of course, to realize significant business growth benefits, GenAI-based applications need to be functionally completed using additional application components besides the GenAI piece and need to be scalable, reliable, and integrated with other enterprise systems as necessary.

Example: A GenAI-enhanced multimodal and omnichannel B2C commerce application

Figure 1. Modular and component-based architecture for “Casey” – A GenAI-powered virtual retail assistant

We, at Capgemini, recently developed a virtual retail assistant, named “Casey” to accept orders and drive the order-to-cash process for partner stores (see figure 1). Casey is voice-activated and GenAI-enabled. Capgemini solution accelerator components power,[3] Google/GCP, and Soul Machines. For the end-to-end application, we layered a ‘digital human’ with conversational AI and cloud-native headless commerce APIs,[3] all pre-integrated for conversational commercial kiosks. It serves as a store-in-store order kiosk allowing the partner stores to maximize their channel reach with minimal investment. Casey is a business growth enabler – it opens a new revenue channel where it is easy to market innovative offers and whose cost does not grow rapidly with business growth, i.e., highly productive, and the solution construction allows for fast time-to-market implementation. Casey’s solution architecture is modular which has enabled us to use this as a basis for many other digital channel use cases in a variety of industries, for example, grocery, general retail, call center, telco, and automotive.

As this example illustrates, to build GenAI-powered applications that cover full customer journeys thus yielding tangible business value, we need to either combine several other application components and technologies or integrate the GenAI parts into otherwise functionally complete existing applications in case suitable ones are available

Creating enterprise-grade GenAI-based apps: Key considerations

To build a GenAI-based enterprise-grade application delivering substantial business growth, we need to consider:

• Opportunity formulation. Identification of the right business-relevant opportunities with realistic ROI projections is a critical success factor (CSF) for eventual success with GenAI-based applications. Especially as companies embark on GenAI adoption, it can reduce the risk of failure if GenAI is used to augment existing activities and processes. For example, the addition of GenAI into an existing customer churn prediction algorithm could process unstructured data like call recordings from customer interactions and customer reviews, capture additional insights like ‘sentiment’, specific store or product issues, competition strengths and weaknesses, possible new product bundles, and suggest appropriate ‘white glove’ treatments to reduce churn. As another example, GenAI could assist in a customer’s product exploration by improving existing user interfaces with visuals and helpful hints and by simplifying the actual purchase action by making the supporting processes more transparent.
• Solution design. One of the first considerations in drafting a GenAI-based solution strategy is to recognize that GenAI-powered interactions with customers or end users can produce actions that may not follow strict workflows, i.e., the complete application needs to have the flexibility to appropriately react to more free-flowing human-GenAI conversations. If the solution is built from scratch such flexibilities can be developed from the ground up which, of course, means a larger development burden. Cloud-first development and use of pre-built components (such as Capgemini’s Digital Cloud Platform [3]) can significantly reduce this burden. If the GenAI components are incorporated in an existing solution then the existing solution most likely will have to be refactored for proper integration of the new and the old including changing/upgrading some of the functionalities of the older components, for example, from batch processing to real-time response, etc. The choice of the appropriate GenAI tool/platform and the availability of data required for the proper functioning of the solution are also key considerations.
• Customer/employee experience and data orchestration. The value of GenAI in chatbots (and copilots) is the level of personalization and context an unscripted conversation can provide to a customer and employee. To retain this value, an enterprise must think through how to orchestrate various interaction points (or digital teammates) for consistency, as well as share interaction and customer data so the next conversation at a different interaction point is able to pick up the conversation where that customer left off last time. These chatbots are also a tool to empower employees to assist customers more broadly, where previously, an employee used to rely on what she knew at that moment now has access to comprehensive and granular data on-demand. Enterprises must also consider an orchestration layer to connect the various GenAI initiatives and data.
• Scale-out. GenAI is still an emerging technology; hence, it is advisable to start small, prove concrete business value, and then scale out to realize the target business benefits. However, in GenAI use cases where technical feasibility has already been proven elsewhere and a realistic business case for the solution is deemed positive, it can be worth the time and effort to create solution architectures with possible scale-out in mind. Such architectures would consider solution performance under production workload, availability and disaster recovery, security and data privacy, identity and access management, error handling, development and run cost optimization, and sustainable development practices. In the scale-out phase, a cloud-based solution approach is often superior and should be duly considered. Some of the GenAI-specific considerations are enterprise data foundations and trust (solid source of truth for customers, vendors, products, promotions, knowledge base, etc.), LLM selection, LLM lifecycle management, prompt version control across environment tiers, UX design for free-flowing conversations, balancing intent-based and generative-based interactions, incorporation of human-in-the-loop, response feedback loop, cost monitoring and optimization, technical debt management, and responsible AI governance.
• Measure and improve. Adequate measurement of solution performance is essential to understanding the current maturity of the solution and possible future enhancements; thus, measurement mechanisms should be built into the solution as first-class citizens. As such, high-level KPIs from traditional solutions can be reused in GenAI-powered solutions, for example, reduction in churn rate, increase in revenue per customer, efficiency in anomaly detection, and the like. However, it would be insightful to also add some metrics related to the model and system quality, and the performance of the GenAI components (see, for example, a summary of relevant metrics in [4]) which could include response error rate, range of input over which response accuracy stays acceptable, system latency, throughput, and run cost.
• Learn and grow. Capturing and sharing experiences as the solutions are developed and rolled out – and learning from them – is extremely valuable for fast-developing technologies like GenAI. Some design documentation, decisions taken along with the rationales, and stakeholder and end-user feedback are good ways to capture experiences from which lessons learned can be derived. This process would help in improving the solution over time as well as increase the organizational maturity to take on higher value (and potentially higher complexity) GenAI-based projects down the line. Over time, defining a robust set of build patterns across use cases would be helpful for asset reuse, solution management, and acceleration of new use case implementations.

Concluding remarks:

Done right, GenAI has tremendous power to push most enterprises forward with healthier business growth and higher market competitiveness. As a productivity enabler, GenAI is expected to accelerate automation by ten years with nearly half of the current tasks having been automated by the end of this decade.[1] Not to be left behind, enterprises should focus both on identifying what GenAI-powered applications are the most valuable for them as well as acquiring, either in-house or via partners, adequate skills to understand the ‘what’ and the ‘how’ of GenAI. In the early stages of GenAI maturity, spot solutions can bring quick wins while as the maturity grows, incorporation of GenAI in broader and across enterprise value chains should be considered for reaching higher benefit goals – and this will take some foundational investment in data, UX strategy, integration strategy, and building a GenAI platform.

References:

[1] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

[2] https://www.capgemini.com/in-en/insights/research-library/generative-ai-in-organizations/

[3] https://www.capgemini.com/us-en/solutions/digital-cloud-platform-for-retail/

[4] https://cloud.google.com/transform/kpis-for-gen-ai-why-measuring-your-new-ai-is-essential-to-its-success

Traditional BI and AutoML platforms enable self-service access to mountains of high-fidelity data, but they fail to deliver actionable insights to drive better business outcomes.

Enter autonomous analytics, such as Aible, which can surface anomalous KPIs and trends and the key drivers as actionable insights. They complement popular BI tools to guide analysts to swift, precise insights. For decades, firms have struggled to make BI work to drive business outcomes. Today, end users have access to more data than ever before but not the actionable insights necessary to help steer the business to the best possible outcomes. As Herbert Simon, Nobel Prize and Turing Award winner, noted back in 1971, “Wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it. “In 2013, Clayton Christensen (of Innovator’s Dilemma fame) et al. wrote in the Harvard Business Review article “Consulting on the Cusp of Disruption” that, “The big data company BeyondCore can automatically evaluate vast amounts of data, identify statistically relevant insights, and present them through an animated briefing, rendering the junior analyst role obsolete. “BeyondCore eventually became Salesforce Einstein Discovery and inspired the modern augmented analytics wave. The team behind BeyondCore has now started Aible, which takes autonomous analytics to the next level by marrying it with generative AI.

GENERATIVE AI-POWERED INSIGHTS DRAMATICALLY FASTER, WITH MINIMAL COST

Aible’s patented technology automatically explores millions of cuts of data in minutes, at a fraction of the cost of legacy of industries have published case studies showing the scalability, cost-efficiency, and speed of the Aible platform. A Google blog post entitled Aible’s serverless journey to challenge the cost vs. performance paradigm explains how Aible delivered such analytics efficiencies on BigQuery, but such efficiencies can be expected on any platform.

As an illustration of this capability, consider data for an outbound call-center that makes calls to offer co-branded credit cards to prospects. Aible enables autonomous analytics to use transaction data, enriched with raw operational data such as agent attributes like education, experience, call quality, and scores, to understand how and to precisely what extent they drive KPIs like conversion rates and offer a focused view into what can be done to address opportunities for improvement.

For example, it is helpful to combine agent attributes in the credit card call center illustration above into agent segments that can be used as engineered features in the analysis to better understand the precise extent to which cohorts drive outcomes. Aible automatically generates and evaluates such cohorts to determine the “net effect” of each combination on the KPI of interest. The most significant drivers of the tracked KPIs are reflected in a circular Sankey visualization, which shows the net effects of all variables at a glance. Alternatively, for business users, Aible can auto-generate traditional dashboards with the key charts organized in order of their impact on the KPI.

AUGMENT BI PLATFORMS

Autonomous analytics platforms can work in standalone mode but work best as complements to popular BI platforms. Aible automatically evaluates raw and engineered data, determines key insights, and auto-generates the KPI driver view. It can even export native BI tool dashboards, to be embedded into yet other BI tools such as Tableau and Power BI. In this design, we retain all the capabilities of the BI platform, with Aible’s analytics engine helping us find the key statistically-sound insights behind the scenes. This also suggests a new way for working for analysts and business leaders. The BI platform should be configured to monitor KPIs and alert analysts and/or business leaders about key insights related to the KPIs with additional information on which of the patterns surfaced are credible. The analysts can use these reports as starting points to pull further data. When used in this manner, the Aible engine can be thought of as driving BI for enterprise performance analytics. The BI platform should also be leveraged when analysts are looking to get reports in broader contexts than understanding KPI drivers.

“AUTONOMOUS ANALYTICS PLATFORMS CAN WORK IN STANDALONE MODE BUT WORK BEST AS COMPLEMENTS TO POPULAR BI PLATFORMS.”

DATA GRANULARITY IS ESSENTIAL

To get the most out of autonomous analytics platforms, the transaction data must be at the most granular level possible and then tagged with all raw and engineered attributes, such as from interaction and segmentation analysis, that make sense for that level. The autonomous analytics system can also auto-generate engineered features. Aible uses this data to generate key insights from the data and enable users to ask business questions such as “How can I improve sales to Gen Z customers?” instead of just analytical questions that must be translatable to SQL.

The engine will monitor KPIs, identify significant trends and shifts in the KPIs, and highlight statistically credible alerts. It also generates visuals to explain the single or multi-variate patterns in a matter suited to the user persona – from circular Sankey charts and mind maps for expert analysts, to dynamic dashboards and generative AI storytelling for business users.

The Aible AI engine provides a list of drivers in a circular Sankey chart, with any overlap clearly indicated. The same view also provides ordered lists of drivers and corresponding charts showing the exact impact of each on tracked KPIs. In addition to this, the AI engine also provides a view into the behavior shift and population shift for each driver for period-to-period results, where the rate effect reflects change attributable to the average change in the value of a cohort, whereas the mix effect reflects the change in the proportion of that cohort.

Aible includes a generative AI platform that uses foundation models such as PaLM 2 and GPT-4 to allow users to ask the Aible engine questions about the drivers and their precise extent of impact behind KPIs in plain English; these generate a well-articulated response, also in plain English. Such a system gives everyone at firms the power to interrogate the engine about business questions related to the KPI and the drivers behind KPIs or KPI changes.

A properly implemented platform can provide insights into which customer segments and/or employee segments are driving observed KPIs as applicable. Aible provides the necessary guardrails for enterprises to securely scale insights from generative AI responses with its ability to automatically doublecheck the output to reduce hallucinations (where generative AI creates inaccurate facts).

Aible can deliver insights in near real-time, allowing firms to respond to market threats and opportunities much faster, and in a very surgical fashion to optimize outcomes.

INNOVATION TAKEAWAYS

AI FIRST

An AI first approach automatically analyzes raw data across millions of variable combinates – group-by and drill-down charts– in a matter of minutes and costing cents.

THE ART OF STORY TELLING

Generative storytelling automatically highlights key insights in the data while double-checking the generative AI for hallucinations.

HAVE IT YOUR WAY

Insights can be consumed in multiple ways, from conversational interfaces to dashboards and mind maps.

Interesting read?

Capgemini’s Innovation publication,Data-powered Innovation Review | Wave 7 features 16 such fascinating articles, crafted by leading experts from Capgemini, and partners like Aible, the Green Software Foundation,and Fivetran. Discover groundbreaking advancements in data-powered innovation, explore the broader applications of AI beyond language models, and learn how data and AI can contribute to creating a more sustainable planet and society. Find all previous Waves here.

Could NVIDIA’s AI and GPU dominance be at risk?

Have you heard about #LPUs, or Language Processing Units yet? This new kid on the block is 10x faster, 90% less latency, minimal energy vs. Nvidia GPUs. What does this mean for #ai‘s #genAI future?

I explore this massive shift in my latest article. Discover how Groq could redefine AI hardware efficiency and challenge the current giant.

Capgemini’s award-winning “Innovation Awareness Week” initiative is driving an inclusive culture of learning through innovation across all levels of its organization.

Innovation is the lifeblood that breathes excitement, relevance, and change across every area of business.

In its simple definition, innovation culture refers to the deliberate cultivation and support of an environment that encourages and nurtures the generation, development, and implementation of new ideas, processes, products, or services. It involves creating conditions that stimulate creativity, experimentation, and collaboration among individuals and organizations, leading to the generation of innovative solutions and advancements.

In a world grappling with environmental challenges, sustainable innovation leadership has emerged as a pivotal approach to addressing pressing global issues while fostering growth. Integrating innovation into creative sustainable strategies is becoming increasingly necessary for businesses, organizations, and governments worldwide.

Innovation is everyone’s superpower

According to the Capgemini Research Institute, open innovation is crucial to navigating current and future business challenges, with 71% of organizations planning to increase investment in the next two years.

But to do this effectively, organizations need to start spreading the message that innovation is everyone’s superpower – that innovation is the secret sauce that can unlock the recipe for creativity, serendipity, and success.

Educating the workforce on ways to harness the innovative power of emerging technologies in a safe, controlled environment ensures they are ready for the future and whatever else the market might throw at them. It also enables people to share their learning experiences across every level of the organization.

Fostering a culture of innovation drives positive change

But how can an organization make this a reality across its business?

At Capgemini, we recently organized “Innovation Awareness Week” – a series of webinars designed to ignite the creative spark and empower our people to thrive in today’s ever-changing market.

Led by expert speakers, thought leaders, experienced innovators, and young professionals from across Capgemini and externally, these webinars inspired and equipped our people with the necessary strategies to foster a culture of innovation.

The webinars covered various topics including the role of storytelling in bridging the gap between people and technology, the importance of understanding the business reality, and delivering real value for the client. They also emphasized that innovation can also be the outcome of evolution and that a growth mindset requires people to be constantly challenged.

Award-winning, innovative HR

“Innovation Awareness Week” has helped Capgemini revolutionize how we approach challenges, envision possibilities, and drive innovation within the organization. This is enabling our people to be more proactive and innovative in helping our clients to transform their businesses and bring about positive change.

And it’s for these reasons that we’re extremely proud that Innovation Awareness Week has recently won a 2023 HRO Today Award in the “Best-in-Class: Innovation” category. With a record 175 nominations this year, this award clearly demonstrates Capgemini as being a leader among an elite group of exceptional HR teams.

Initiatives such as Innovation Awareness Week strengthen our intellectual capital by encouraging teams to bring our mission to life and drive results for our customers.

To learn how Capgemini’s Intelligent Learning Operations can help you deliver a personalized, connected, and continuous learning journey, contact: preeti.chopra@capgemini.com

While telecoms networks were largely dependent on manual systems and processes before they had software-driven rules and automation built in, today, new innovations are set to drive tangible business benefits across the telecoms industry. Today, by leveraging machine learning (ML) and data and analytics, networks have the autonomy to take certain low risk, rules-based actions. However, telco networks look set to evolve once more, as the promise of a ‘self-serving’, ‘self-fulfilling’, and ‘self-assuring’, or autonomous networks draws closer.

The increasing demand for improved customer experience is placing pressure on telcos and their network resources. Despite investments in 5G and fiber broadband improving the reach and quality that networks provide, the increasing number of mobiles and devices connecting to networks, and the subsequent rise in volume of data means that telco networks are now more complex than ever. Essentially, the management of networks is increasingly going beyond the reach of manual operations. This is where autonomous networks can drive tangible business value for telcos, providing a springboard to improved customer service, as well as more efficient and more sustainable networks.

Investment in autonomous networks is on the rise

According to the latest research by the Capgemini Research Institute, telcos are expected to invest $87 million on average in autonomous networks over the next five years. [KA1] 

Today, according to TM Forum’s taxonomy of autonomous networks, the majority (84%) of telcos have either a level-1 or level-2 autonomous network. We know from our research that this is set to increase, with 61% of telcos aiming for at least level-3 autonomy over the next five years. Currently, Europe leads the way in overall network maturity, with over half (51%) of telcos at level 2 network autonomy, although North America has the greatest proportion of telcos at level 3, at 14%. Despite the clear eagerness showcased by telcos for greater network autonomy, the majority of the use cases are still at proof-of-concept stage. Of those surveyed, adaptive/dynamic network policies for changing conditions are the most popular use case at proof-of-concept stage (46%), followed slice optimization and SLA assurance in RAN/ORAN (40%).

Subverting the challenges

Despite the innovative use cases that autonomous networks can deliver, there remains considerable barriers to adoption. Chief among these is cultural issues, with just over half (51%) of telcos citing that employees don’t have the right mindset to undertake such a shift. Although, when you consider that just 17% of telcos have a well-defined autonomous networks transformation strategy, and fewer than one in five organizations have appointed a dedicated leader, it’s no surprise that cultural issues persist. However, the barriers to adoption aren’t limited to just cultural issues. In fact, we found that issues around technological maturity is slowing down telcos’ autonomous network journeys. Our survey found that 48% of telcos flagged technology integration was a noteworthy issue. Additionally, 33% and 25% of telcos flagged that technology maturity and the lack of skills among the internal workforce as significant barriers to adoption.

Gen AI and sustainability benefits front of mind

Over the past year, generative AI has risen from emerging technology status to the center of the boardroom conversation. All industries alike are assessing their operations to understand where they can integrate the technology, and telecoms is no different. According to our survey, three in five telcos are exploring generative AI for autonomous networks, and one in ten has implemented generative AI for networks at partial scale. Generative AI strikes that crucial balance for telcos as a cost reducer, and efficiency driver. We know from our research that the most popular use cases are complex event processing and dynamic bandwidth and path selection. On a more granular level, generative AI can assist telcos with translation, fraud resolution and model training.

However, it’s not just generative AI-related benefits that telcos are reaping and in fact, those telcos that are moving faster on their autonomous network journeys, are realizing the benefits. In fact, in just the past two years, telcos have on average achieved a 20% improvement in operational efficiency and 18% reduction in OPEX through autonomous networks. The survey also finds that telcos are expected to invest $87 million in autonomous networks over the next five years, but that this would amount to $150 million – $300 million in OPEX savings. And the benefits aren’t limited to simply cost savings, with the sustainability benefits front of mind for many telcos.

Today, it’s crucial that businesses have sustainability built into their core. Energy accounts for 30-40% of telco OPEX, with the Radio Access Network (RAN) accounting for 80% of network energy consumption. And those who transition to a higher level of autonomous network can expect a reduction of somewhere between 7.5%-15% reduction in their networks carbon emission. For instance, Telefónica Group has successfully reduced its energy consumption by 7.2% between 215 and 2022. While this initial number may seem low, when you consider that their network traffic has increased seven-fold over the same period, they have reduced their overall emissions by 51% over the period. As generative AI continues to cement itself as a key innovation across the telecoms landscape, we’re going to see these results improve much quicker.  

Accelerating the transition

As aforementioned, just 17% of telcos have a comprehensive autonomous networks strategy in place. Those who have this strategy in place can expect to realize the benefits of autonomous networks much sooner. With this in mind, I wanted to take a moment to explain what a comprehensive autonomous network strategy consists of.

• Strategy & roadmap: Here it’s critical that telcos establish the business case early on, so that they can secure the necessary finance and build a strategy that simultaneously resonates at both a global and local level.
• People: With innovations comes the need for new skillsets. Telcos should work to bridge the skills gap in areas such as AI by upskilling and reskilling the current workforce. As I mentioned earlier, the cultural shift presents one of the biggest barriers. By reorganizing systems, processes and tools, telcos can guide their organizations through to a new, more efficient operating model.
• Technology: Technology integration issues were high on the agenda of telco executives. To combat this, they should ensure they have an end-to-end view of their data landscape and leverage the cloud for virtualization where possible. Atop of this, telcos should invest time into establishing robust data-governance and data-management frameworks.
• Pace of transformation: As always, the pace of transformation depends on the maturity of the technology. For instance, beginner telcos should consider which network domains and use cases to prioritize, whereas those midway through their journey should double down on investment and focus on scaling.
• Innovation: Open and disaggregated networks open the door to new innovative use cases. Telcos should experiment with emerging technologies such as generative AI, metaverse and digital twins to ensure they enhance network efficiency.

The operating model of networks is going through a generational shift, from one managed by human operators, to an autonomous one whereby AI and data take center stage. While this shift requires significant investment, telcos should welcome it with open arms. Autonomous networks provide strike that all important balance of reducing costs, increasing efficiency, and contributing to a more sustainable future.

We look forward to meeting you at the Capgemini booth (2K21) in Hall 2 at MWC Barcelona from February 26th to February 29th