The semiconductor industry drives every industry in the world, yet the semiconductor value chain is complex as components travel more than 25,000 miles and cross 70 borders before completion.

During COVID-19, the semiconductor industry got a big boost due to changing consumer habits – driven by remote work, distance learning, gaming, and online shopping – which increased the demand for consumer electronic devices. This unprecedented growth in semiconductor demand resulted in increased supply chain complexities, forcing companies to deploy makeshift solutions across their supply chain and manufacturing processes to handle the various challenges.

Then Gen AI happened

The launch of Chat GPT in late 2022 propelled the technology industry to take a closer look at generative AI, a broad field of artificial intelligence, thus bringing Gen AI into the zeitgeist. Truth be told, like many industries, Artificial Intelligence (AI) and Machine Learning (ML) have always been used in Semi, but the availability of large language models (LLMs) and foundational models encouraged companies to quickly realign their technology roadmaps to include generative AI for enterprise use. At most companies, the initial focus was first, to find ways to improve customer experience via marketing content creation, and second, to deploy touchless solutions to respond to customer queries efficiently and accurately. But as generative AI gained significant visibility and popularity, it became clear to consumers and enterprises from all industries, including the semiconductor industry, that its potential is much deeper and wider.

We see generative AI is poised to influence the entire value chain of the semiconductor industry. On one hand, it will uncover applications that will drive up growth in chip demand. On the other hand, it will drive up demand for generative AI enabled chips for processing specialized applications. As more and more companies are launching new ‘AI enabled chips’, it is expected that Gen AI will also change the way companies do business with process and task automation and intelligence along the entire value chain. We call this “Simulated Futures” because it brings a whole new ease and intelligence to design and simulation at every turn.

The substantial benefits

The adoption of Gen AI promises substantial benefits for semiconductor value chain companies, including streamlined design workflows, accelerated exploration of design alternatives or layout optimization, efficient bug tracking, predictive analytics for manufacturing optimization, simulating manufacturing and supply chain challenges, improved equipment utilization, and yield improvement. These advancements translate into tangible improvements in Time-to-Market (TTM), cost reduction, and overall product quality, offering a competitive edge in the market.

At the same time, integrating Gen AI offers semiconductor companies numerous Go-to-Market (GTM) benefits, including competitive differentiation, automated product specs documentation, innovation opportunities, and the creation of valuable IP assets through collaborative ventures. By leveraging Gen AI across the enterprise, companies can enhance productivity, profitability, and operational efficiency, thereby solidifying their position in the industry.

The immense opportunities in the horizon

In this multi-part thought leadership series, we will talk about ways Gen AI is revolutionizing the semiconductor value chain. We’ll explore the comprehensive integration of AI throughout the silicon design workflow, from conception to high-volume manufacturing. and look at how it underscores the critical collaboration between AI specialists and semiconductor Subject Matter Experts (SMEs) in realizing its full potential. We’ll explore how the future can now be simulated.

Read further on how we will share what we already see today (Now technology) and predict the near future (Near technology) on ways semiconductor ecosystem companies can leverage Gen AI to develop intelligent products and services, operations, innovation, and customer experience.

Our next article will focus on How Gen AI will Revolutionize the Chip Design Process.

It has been estimated that application maintenance costs account for more than 90% of the total cost of application development, as compared to 50% a couple of decades ago.

The cost of software and application maintenance has reached an all-time high as the result of an increasing demand for business agility. To meet the demand, organizations are constantly deploying new features across an increasingly complex landscape of distributed computing, cloud, micro-architectures, and on-demand services amongst others. This reactive maintenance works well when the stakes aren’t so high, but a complex system requires a measured approach to maintenance.

Preventive maintenance takes a more defensive, proactive approach to averting potential outages through implementing various checkpoints across processes to assess the systems’ quality. However, sometimes this morphs into an over-regulated, ultra-defensive system riddled with tollgates before a problem gets solved, which defeats the purpose of being able to respond to issues in a timely fashion.

To avoid the bureaucratic bottleneck, many enterprises turn to an AIOps platform for anomaly detection to predict and detect failures and automate recovery actions with minimal engineer intervention. However, enterprises struggle to derive value from AIOps platforms due to the massive amounts of data needed to make accurate predictions, making AIOps a more theoretical approach to maintenance.

Only 12% of companies adopting AIOps use it as part of their day-to-day operations – nearly 40% don’t use it at all.

To evolve IT systems into the hyper-efficient, self-healing systems that can run without becoming a maintenance nightmare, we need a different approach.

Insight-guided predictive maintenance

Automating maintenance is the ideal – a self-healing system that requires little outside input saves person-hours and operating budgets, but it can be amplified with a Site Reliability Engineering (SRE)-driven approach to AIOps.

SRE uses a framework to align business key performance indicators (KPIs) with value stream flow metrics, which establishes service-level indicators (SLIs) and service-level objectives (SLOs). These SLIs and SLOs are indicators of what a business expects to deliver for the best customer experience, otherwise known as service-level agreements (SLAs).

When the SRE parameters are combined with AIOps, teams can analyze only the data streams deemed necessary according to the established SLIs and SLOs. A unified observability dashboard provides a holistic view of the current state of apps, infrastructure, and data, helping to identify potential problem scenarios. This SRE data analysis, augmented by Generative AI capabilities, precedes any decisions on automating a solution. If automation is found to provide value, the team will build predictive models specifically for those scenarios. Depending on the maturity of a system, these models can also automatically trigger the appropriate corrective measures, preventing the problem scenarios from actually occurring.

We, at Capgemini, have implemented a version of this solution for a client in the retail industry, using an SRE-driven approach to AIOps across their SAP landscape. Events were tracked using infrastructure monitoring tools such as the SAP Solution Manager (SolMan) to derive correlations across time, and scenarios were created using a configuration management database. The scenarios were studied by subject matter experts to uncover insights and triage situations before they escalated. AIOps auto-resolved some issues based on relevance, applicability, and solution availability.

The solution resulted in a 96% reduction in the number of day-to-day alerts that required monitoring team interference. In addition, zero alerts from manual errors were missed, resulting in an improved quality of service.

If you’d like to know more about an SRE-driven approach to application maintenance services, visit us here, or you can contact me directly.

The role of a Site Reliability engineer consists of taking a holistic look at the entire IT application landscape of an enterprise from an end user’s perspective. They ensure that the individual systems involved in fulfilling an end user’s business requirement are doing so effectively, so that the end-user can accomplish their task with minimal problems. 

To meet this expectation, a site reliability engineer is typically expected to have a broad knowledge of the entire spectrum of IT systems, while combining a deep awareness of technical infrastructure, operating systems, and computer networking with an attention to higher level service level objectives (SLOs).

Site Reliability engineers need to focus on solving problems by building software components and features in ways that prevent problems from reoccurring, or at least make them less painful to overcome. Because of this, it’s often recommended that the Site Reliability engineers come from a software engineering background with an awareness of operations, rather than the other way around. 

Some technical skills that a Site Reliability engineer should possess include data analysis and visualization, non-functional testing and chaos engineering, and architectural oversight and governance. Experience with Agile, incident management, DevOps, and release management are also big plusses, as well as infrastructure, both cloud and software. 

Of course, with the recent advances in the field of AI, it is not uncommon to expect Site Reliability engineers to design intelligent IT estates that can leverage the power of generative AI (GenAI) to enhance the reasoning and decision-making capabilities of self-healing systems, adding this new dimension to a Site Reliability engineer’s tech skills repertoire.

Additionally, soft skills such as problem-solving, teamwork, working under pressure, and strong written and verbal communication are keys to success. 

You may think that this makes a Site Reliability engineer appear like someone straight out of a Marvel movie casting call. But remember, it’s not necessary to have all these skills in one individual. After all, even Marvel resorts to the collective efforts of superhero teams like the X-Men or the Avengers, who together help meet the goal of saving the planet.

If you’re wondering what Site Reliability Engineering (SRE) is, you’ve probably skipped our first post of this series. In this article, we will look at how SRE differs from DevOps. 

If you’re wondering what Site Reliability Engineering (SRE) is, you’ve probably skipped our first post of this series. In this article, we will look at how SRE differs from DevOps. 

There is a significant overlap between the two concepts. Both tend to address the silos between development and operations teams. In terms of practices followed, there are a lot of parallels. However, the approach and objectives are quite different.

• Agility first vs. reliability first: 
  The main goal of DevOps is to increase business agility – how do we release new features faster? How do I get my defect fixes in production sooner? This is largely done through cross-pollinating development and operations teams and aligning their processes to mutual goals. These goals are met through processes and automated pipelines that move code faster to production and staging environments. 
  The objective of SRE is to ensure that while business agility is pursued, it doesn’t come at the cost of overall reliability of the system. This is typically achieved using a separate central team. 

• Failure tolerance levels: 
  DevOps looks to ensure there are no failures, while SRE accepts that failures are inevitable. Instead, SRE focuses more on ensuring the continued availability of core business services with minimal impact through chaos engineering and destructive testing practices. 

We have seen several cases recently, where, despite complete DevOps implementation, companies continue to bleed millions of dollars when their core systems go down – SRE will help plug that gap.

Another aspect worth mentioning is the scope of SRE. While the DevOps concept focuses on bridging the gap between development and operations teams, SRE extends that by also focusing on architecture and business. This ensures system resiliency is built in by design to react to and recover from unexpected disruptions.

Next, let’s talk about AIOps and SRE. To find out if there are any differences and what those differences may be, read our next blog post here.

In simple terms, reliability is defined as the probability of success. However, in the application world, reliability is talked about in terms of availability and measured in the context of the frequency of failures.

Reliability is important as it can help build or lose confidence in a product and an organization’s brand reputation.

In the current IT landscape, which is complex, multi-layered, and cloud-based, the traditional approach to preventing system failures doesn’t quite work.  

With so many moving parts, there are bound to be disruptions that result in failures. This requires a change in mindset to expect failures, and to build systems that are resilient to these failures. Site reliability Engineering (SRE), also known as service reliability engineering, is the approach you need to anticipate and recover from failures.

SRE applies a software engineering mindset to system administration. As a software engineer, you look at the business requirements and develop the system aligned to those requirements. Likewise, a Site Reliability engineer needs to look at how each disruption can affect the business requirement and then find a solution for it accordingly.

An Agile-focused, product-driven approach and IT/OT integration have been key drivers for the growing demand for SRE today. 

SRE began at Google around 2003 as a method to ensure Google’s website remained “as available as possible.” The team responsible for site availability applied software engineering concepts to system administration methods, which later formed the basic tenets of SRE, as described in an online book published by Google.

Like most enterprise constructs, businesses don’t need to mimic the same methods used by Google. While we need to assess these practices in the context of the enterprise, there are certain basic tenets of SRE that must be followed: 

• Agree upon a set of service-level indicators (SLIs) and service-level objectives (SLOs) to understand the targets and measures
• Accept failure as normal and manage an “error budget” that is used to strike a balance between system updates and system stability
• Understand that the site reliability engineers are neither part of the development team nor the operations team. It needs a separate central team that takes the end-to-end across apps, infra, backend, frontend, middleware, etc.
• Automate processes. A key objective of SRE is to “reduce toil.”

Does this sound familiar? A bit like DevOps, perhaps? Then click here to read our next post on how SRE is different from DevOps.

In the ever-evolving financial landscape, personalization is no longer a luxury but a necessity for organizations seeking to retain and attract customers. Delivering the right message, tailored to individual needs and preferences, and at the optimal time and channel, is the crux of this journey. It’s about transcending generic offerings and forging genuine connections with clients. As per the 2023 report on the State of Personalization Maturity in Financial Services from Dynamic Yield by Mastercard, 86% of FIs stated that personalization is a clear, visible priority for the firm and its digital strategy, with 92% planning to invest further in the practice.

While the destination is clear—enhanced customer satisfaction, loyalty, and business growth—the path towards achieving this is intricate. It demands a sophisticated incorporation of innovative technology, intense data analytics, and an intelligent understanding of customer behavior and preferences. Banks embarking on this journey must equip themselves with the right tools and knowledge to navigate this complex terrain.

Setting the course: charting the path with personalization in financial services

Personalization is the guiding light for banks towards a future where customer-centricity is paramount, and this journey can help across multiple avenues:

• Enhanced Customer Experience: Personalization helps craft experiences that resonate with the client’s financial goals, challenges, and aspirations.
• Unlocking Revenue Opportunities:  Clients who prefer the personal touch are more likely to engage with additional offerings, presenting lucrative cross-selling and up-selling opportunities.
• Competitive Differentiation: In a crowded market, personalization can help be a differentiating factor, drawing in clients who seek a truly tailored experience.
• Enhanced Operational Efficiency: Effective personalization helps to streamline processes, eliminate inefficiencies, and allocate resources with precision.
• Risk Mitigation: Tailoring products and services to align with an individual’s risk profile allows banks to fine-tune their risk assessment and mitigation strategies.

Implementation of these personalization initiatives rely heavily on innovative technology and advanced analytical skills, and Business Process Management (BPM) platforms are a great way to start your journey towards precision personalization. These platforms provide the necessary framework to seamlessly integrate personalization into every facet of customer interaction.

Embarking on the journey: BPM platforms as your GPS for personalized experiences

As banks prepare for this transformative journey, BPM platforms serve as the indispensable toolkit that help chart the way towards personalized client experiences through many key features:

• Advanced Analytics: These tools analyze customer data and interactions to identify patterns and preferences, informing more personalized service delivery.
• Customer Journey Mapping: BPM platforms allow businesses to create detailed customer journey maps, identifying key touchpoints for personalization.
• Rule-Based Decisioning: Businesses can automate personalized responses or actions through rules, based on specific customer behaviors or attributes.
• Integration Capabilities: The ability to integrate with CRM systems & other digital platforms helps ensure that data is effectively utilized for personalization.

It is evident that BPM platforms emerge as the trusted GPS as we navigate the intricate terrain of personalized experiences. Banks can use BPM platforms in many ways throughout their personalization journey:

• Automation of Personalized Workflows: BPM platforms empower banks to create tailored workflows that respond to specific customer triggers, ensuring timely and relevant interactions.
• Consistency Across Channels: Whether a client prefers online banking or branch visits, BPM platforms ensure a consistent and personalized experience at every touchpoint.
• Data Integration and Analysis: By aggregating data from various sources, BPM platforms provide a comprehensive view of the client, enabling tailored solutions and targeted communication.
• Dynamic Process Adaptation: As client needs evolve, BPM platforms allow for real-time adaptation of processes, ensuring that personalization remains dynamic and responsive.

A remarkable example of BPM platform implementation is Wells Fargo which employed the Pega Customer Decision Hub, enhancing personalization through real-time modeling and adaptive machine learning for tailored interactions. This helped boost customer engagement rates by 3-10x and increasing conversion rates across channels.

Reaching the destination: the transformational impact of BPM-driven personalized customer experiences

The impact of effectively implemented BPM-enabled personalization is overwhelming. Businesses can expect to see:

• Increased Customer Engagement: Clients are more likely to engage with banks that demonstrate a genuine understanding of their needs and goals.
• Enhanced Customer Satisfaction and Loyalty: Personalized experiences create a sense of loyalty and trust, leading to long-term client relationships.
• Higher Conversion Rates: Personalization can lead to more effective marketing and sales strategies, resulting in higher conversion rates and increased revenues.
• Operational Efficiency: Automation streamlines processes, reduces manual effort, and frees up valuable resources for strategic initiatives.
• Competitive Differentiation: In a crowded market, personalization can be the key differentiator that sets a bank apart.

This journey of precision personalization is not without its challenges. Although BPM platforms are the right way forward, choosing the right platform can be the differentiating factor in how clients resonate with the banks and drive positive engagement.  Capgemini takes pride in partnering with industry leaders that can help banks achieve precision personalization and ultimately, achieve lasting success in the ever-evolving financial landscape.

Join the Capgemini experience at PegaWorld iNspire 2024. Visit our booth #22 and explore a world where intelligent connections unlock personalized journeys. 

Governments harness spatial computing for enhanced decison-making

In today’s fast-paced digital era, governments worldwide constantly seek innovative ways to streamline operations, enhance public services, and make informed decisions. One groundbreaking technology paving the way for this transformation is spatial computing. By leveraging highly accurate virtual environments, governments can unlock many benefits, revolutionizing how they plan, manage, and engage with their constituents.

At its core, spatial computing integrates the physical and digital worlds, providing immersive, interactive experiences that mirror reality. Whether simulating urban landscapes, modeling infrastructure projects, or analyzing complex data sets, this technology offers unparalleled insights and opportunities for governments at all levels.

One notable example of this transformative power is the partnership between Capgemini and Unity, two industry leaders at the forefront of spatial computing innovation. Together, they have created a remarkable digital twin for the Orlando region, showcasing the immense potential of this technology.

In Orlando’s regional digital twin, Capgemini’s expertise in digital transformation and Unity’s cutting-edge 3D visualization capabilities have converged to create a virtual replica of the city and its surroundings. This digital twin isn’t just a static model; it’s a dynamic, data-rich environment that enables real-time simulations, scenario planning, and predictive analytics.

So, what are the benefits of governments tapping into the possibilities offered by highly accurate virtual environments like the Orlando regional digital twin?

Firstly, enhanced decision-making becomes a reality. Policymakers can gain deeper insights into various scenarios and their potential outcomes by visualizing complex data in a spatial context. Whether it’s optimizing traffic flow, planning for natural disasters, or assessing the impact of new development projects, governments can make more informed decisions that benefit their constituents.

Secondly, improved collaboration and stakeholder engagement are fostered. Virtual environments provide a common platform where diverse stakeholders can come together, visualize concepts, and co-create solutions. This fosters transparency, fosters inclusivity, and ensures that decisions are made with the input of all relevant parties.

Thirdly, virtual simulations enable governments to realize significant cost and time savings through early intervention. By simulating projects in a virtual environment, potential issues can be identified and addressed before they become costly problems. Whether it’s identifying design flaws, optimizing resource allocation, or minimizing construction delays, the benefits of early intervention are manifold, instilling confidence in the effectiveness of this technology.

In conclusion, spatial computing represents a paradigm shift in how governments operate and engage with their communities. Governments can unlock new possibilities for innovation, efficiency, and collaboration by harnessing the power of highly accurate virtual environments. The partnership between Capgemini and Unity, exemplified by the Orlando regional digital twin, serves as a testament to the transformative impact of this technology. As we look to the future, the possibilities are limitless, and governments worldwide stand to reap the benefits of embracing spatial computing in their decision-making processes. In Orlando’s regional digital twin, Capgemini’s expertise in digital transformation and Unity’s cutting-edge 3D visualization capabilities have converged to create a virtual replica of the city and its surroundings. This digital twin isn’t just a static model; it’s a dynamic, data-rich environment that enables real-time simulations, scenario planning, and predictive analytics.