A tool-agnostic approach to process optimization helps identify opportunities, quantify value, and enhance processes without dependence on specific tools

Although accelerators to process optimization like automation and artificial intelligence typically catch the eye, our framework focuses on a tool-agnostic approach to opportunity identification, value quantification, and process improvement. With that as the backbone, such tools as process mining, robotic process automation (RPA), and AI can help to rapidly drive value. By following the most effective steps to actionable process transformation, organizations can get the best results from their process improvement framework and chart a path to maximum value.

This blog drills down into our introductory viewpoint on process engineering by highlighting our approach, how to identify the right process improvements, and how to measure value. For more context on process engineering challenges, strategy, potential projects, and adoption, please check out our Introduction to Process Engineering article.

Our high-level approach to process optimization

Process improvement has been an objective for many companies for decades. We follow a cutting-edge, tool-agnostic approach across six major steps:

• Identify: We analyze root causes, identify (sub) process inefficiencies, and pinpoint high manual effort. This initial identification can occur in a traditional approach through root cause analyses and value identification workshops but can also be bolstered by data-driven process mining analyses. It is critical in this stage to take your potentially long list of ideas and conduct a high-level value assessment and prioritization to zero in on a more actionable short list.
• Define: We map inefficient process steps, manual effort, and system breaks within the identified process area. Once again, this definition can take a more traditional approach to process performance management and focus on interviews or be streamlined by process mining dashboards and process diagrams.
• Assess: We quantify and validate the financial impact of identified optimization measured in order to justify the implementation effort. It is critical in this stage to identify: (1) the quantified magnitude of process improvement, (2) the right solution in order to achieve the measured improvement, and (3) the potential cost of implementing the solution. Using the three outputs, we create a business case for the idea.
• Prioritize: Using simple value and effort levers, we compare created business cases in order to prioritize the ideas and ultimately determine approval of the idea.
• Implement: Once the idea is approved, we implement the optimization by using the defined solution design.
• Steer: Following implementation, we monitor the realized value of the solution and compare with the committed value. Additionally, we revisit the adoption plan and sustain value by continuously tracking and reviewing the progress of intended improvements.

Identifying the right process improvement opportunities and high-impact solutions

During our “Identify” to “Assess” phases, we qualify a potential process improvement by employing a proven improvement framework to categorize the type of improvement needed:

1. Focus on elimination of the activities driving unnecessary work effort (i.e., duplicated processes and rework).
2. Leverage best practices to implement a standard process across all domains and groups.
3. Optimize the configuration of backend IT systems (i.e., process gaps leading to users acting outside systems of record).
4. Non-invasive technology to drive further automation (i.e., making enhancements in existing SAP systems to improve particular processes).
5. Achieve further benefits realization by applying robotics on top of or replacing existing processes or systems (i.e., RPA bots, OCR/ICR, machine learning, generative AI (Gen AI), etc.).

This framework is intended to build in complexity – a given improvement idea can incorporate one or many of these types of improvements. It is important to analyze the true root causes of process inefficiencies to understand which aspects of process improvement to deploy.

Upon identifying the right categories of improvements needed, it is essential that organizations convert these ideas into tangible solutions. Prior to implementation, we need to consider solution design, potential technologies needed and their architecture, and the overall business case for the solution which will justify its implementation. In the next section, we discuss our approach to measuring value.

Realizing value from process optimization

There are many areas of the organization where process optimization tools can generate major value. While some areas, such as finance and procurement, are well understood, others are starting to get more attention. Moreover, there are several ways to measure value. Many organizations focus on reducing costs, but equally as beneficial is the concept of cost avoidance, where process improvements can lead to reallocation of time to more value-adding activity. As shown below, we simplify the types of value into three buckets: revenue enhancement, expense reduction, and working capital improvement.

Kickstarting your journey to process optimization success

In our experience across a wide variety of use cases, it is common for business teams to focus on labor productivity as the main target. It is interesting to note that when analyzing the magnitude of improvement across these value types, we have actually seen a significantly higher potential for value in expense reduction and working capital improvement. Of course, every use case is unique, so it will be important to evaluate all of these levers while creating your business case.

The expected value of successfully improving processes helps to prioritize and sequence the multiple use cases considered. It also facilitates justification of capacity allocation across the different teams that need to come together to deliver this work. And ultimately, it clarifies who in the organization needs to approve and recognize the value to call its realization completed. We have had great success adopting this approach across a variety of business process domains and industries to the tune of multi-million-dollar improvement potential. We identify these improvements rapidly, with a sustained approach to iterate and foster likewise in perpetuity. We encourage you to unlock your process optimization potential by focusing in on your non-value-adding activities and overall process inefficiencies. We have the solidified approach and track record of success to kick start a collaborative journey on your path to value in process optimization.

Generative AI (Gen AI) revolutionizes process optimization. We explore mass adoption, rapid implementation, and the role of Centers of Excellence (CoEs)

Gen AI solutions are profoundly transformative, captivating industries with its potential to revolutionize how businesses operate. This is primarily due to its ability to streamline processes and enhance efficiency. However, despite the excitement, AI process optimization has faced significant practical application challenges. Historical attempts at process improvement, from re-engineering to robotic process automation (RPA) have often fallen short of expectations. As organizations stand at the cusp of a new technological era, the critical question remains: Can Gen AI finally fulfill the long-awaited promise of process optimization? As part of our value proposition, Gen AI strategy and solutions for business growth, we answer that question and more in the below blog post.

Exploring the hype

Gen AI solutions have rapidly emerged as leading drivers of the adoption of technological innovations, with 96% of executives in our recent Capgemini Research Institute survey citing it as ‘a hot topic of discussion in their respective boardrooms’ (Oost et al 2023: 9). In addition, Capgemini Research Institute concluded that, of executives surveyed, over half (59%) say ‘their leadership became strong advocates for Gen AI after only six months of the technology having hit the mainstream’ (Ibid: 10). This high level of interest highlights Gen AI as probably the fastest new technology to attract such strong senior-executive interest. It gives non-technical users the ability to interact with very advanced and powerful AI models, generating widespread excitement and driving fast adoption of this transformative tool across various functions and industries. But how widespread is the adoption of sound Gen AI strategies.

Despite the significant excitement and investment around Gen AI, the practical application of AI for process optimization and other enterprise-level activities has been limited. While promoted as a revolutionary optimization tool, Gen AI solutions yet to fully realize their potential in this domain. Many large corporations are motivated by its potential to streamline processes and enhance efficiency as ‘60%’ of surveyed executives believe that ‘generative AI will completely revolutionize the way we work’ (Ibid: 16). But so far, the results have fallen short of the promise.

What surveyed executives say about Gen AI (Capgemini Research Institute)

surveyed executives say Gen AI is a hot topic in boardrooms	say leadership strongly advocates Gen AI's use	say Gen AI will revolutionize the way we work

The key reason is that the journey from experimentation to adoption and realization has been filled with challenges, ranging from adoption/change management challenges to ethical concerns and technical hurdles. For example, worries regarding data privacy and algorithmic bias have cast a shadow over the widespread adoption of Gen AI in process engineering. Additionally, the complexities of integrating Gen AI into existing workflows have proven to be real barriers for organizations seeking to harness its capabilities effectively.

With a combined 50 years of assisting clients by streamlining operations and process optimization, we, your authors, have time and again seen that many of these efforts have not lived up to their lofty expectations. This goes all the way back to the use of process re-engineering, adoption of workflow tools, RPA, process mining, and now AI. Our strong belief is that with this latest wave of AI and Gen AI, we have now reached an inflection point where a lot of the prior barriers to scaled adoption will be overcome and the promise of process optimization will finally become much more a reality in the short term. AI for process improvement is becoming a game-changer. Let’s take a look at some key aspects of this new way to focus on achieving process optimization outcomes.

The inflection point

With the potential to unlock unprecedented efficiency and innovation, the strategic use of Gen AI is not only an opportunity but a necessity for organizations aiming to remain competitive. This train has left the station and is only picking up speed, and each day that companies wait, they will fall further behind their more advanced competitors, emphasizing the widespread adoption and integration of this technology into business operations. Process optimization using machine learning is yielding impressive results.

Currently, the focus in process optimization revolves around leveraging process mining and RPA tools with some Gen AI embedded, enhancing existing systems with automation and predictive analytics. However, the trajectory is ready to shift as Gen AI quickly matures and deployment for break-through improvement takes center stage. This transition signals a recalibration of workforce dynamics, with mundane tasks increasingly automated and Gen AI acting as companion to human resources, enabling them to focus on higher-value activities.

The evolving role of CoEs exemplifies this shift, increasingly prioritizing value realization and sharing/scaling of solutions over previous technology deployment focus to maximize the benefits of process optimization powered by Gen AI. This new focus includes:

• Generate and Prioritize Demand: CoEs have to identify the most valuable use cases to work on to deliver material improvements and generate organizational momentum.
• Lead Development: CoEs will lead the development of new methodologies and frameworks for integrating Gen AI into existing processes, ensuring optimal utilization and performance.
• Facilitate Collaboration: CoEs will facilitate sharing of successes and cross-functional collaboration, bridging the gap between data scientists, engineers, and domain experts to drive innovation and problem-solving.
• Drive Training and Upskilling: CoEs will lead training and upskilling initiatives to equip employees with the necessary skills and knowledge to leverage Gen AI tools and technologies.

This shift requires a reevaluation of skillsets within CoEs, with more emphasis on building expertise in data analysis, algorithm development, and process optimization. Additionally, CoEs will play a pivotal role in fostering a culture of innovation and continuous improvement, driving the next evolution of automation and process engineering practices in alignment with the capabilities of Gen AI. By embracing a methodology-centric approach, rather than a technology-centric one, CoEs will be key in unlocking the full potential of Gen AI and driving organizational success in the era of process engineering driven transformation. Companies and industries at the vanguard of this transformation are embracing a methodology-centric approach to drive adoption and utilization of Gen AI.

How to get on the accelerated path to success

As organizations embark on their journey towards leveraging Gen AI for process optimization and excellence, scaling up poses an ever-increasing challenge. Achieving success at scale requires a strategic approach that encompasses both technological capabilities and organizational readiness. The path to success begins with strategic planning, effective execution, and a clear understanding of the organization’s overall objectives and how Gen AI can support these. Some key steps include the following:

Success stories

It is evident that the path to successful Gen AI integration has to be both strategic and ambitious to drive real impact across the organization. However, successful implementation requires more than just following a set of instructions. It demands a collaborative effort, drawing upon the expertise and insights of both internal stakeholders and external partners. This collaborative principle has been exemplified in recent work, where Capgemini seamlessly merged diverse perspectives and competencies to achieve transformative outcomes, as shown below:

Global Media: Capgemini worked with a global media and entertainment company on a digital people project, leveraging a Gen AI-enabled chat-engine. This resulted in an increase in cart size, revenue, net-promoter score (NPS), brand awareness, and a decrease in operating costs. A notable business outcome, according to a Capgemini Research Institute study on Gen AI, was the increase in engagement, with digital users reporting the system as easier to use (38%), more engaging (85%), and more effective than chatbot equivalents (92%).

Automotive: Capgemini successfully addressed this major automotive manufacturer’s challenge of manually comparing supplier documents by implementing an AI-driven automation solution. By leveraging its expertise in AI implementation projects and cloud technology, we delivered a PoC funded through a strategic partnership. As a result, our client experienced a significant reduction in time spent on document comparisons, enabling employees to focus on higher-value tasks while mitigating the risk of delays in this important verification process.

Global bank: Capgemini collaborated with an international bank to address complex information usage challenges by developing a customized Gen AI-driven Q&A system. Leveraging strategic partnerships, Capgemini optimized and deployed an advanced AI solution tailored to the client’s needs, ensuring confidentiality. The implemented solution significantly reduced search time for client-specific content, resulting in substantial cost savings and paving the way for future Gen AI applications across business lines.

Transportation: Capgemini played a pivotal role in a high-profile project aimed at transforming how passengers access information in the context of the 2024 Olympic and Paralympic Games in Paris, France. Our expertise in complex program steering, change management, and innovative technologies enabled the team to follow a radical approach to automation, resulting in a Gen AI-based multilingual passenger information tool developed in less than two months. This successful track record, responsiveness, and delivery team enthusiasm were key factors in Capgemini’s selection as the partner of choice for delivering this transformative initiative.

Final thoughts

Our research indicates the growing value of integrating generative AI solutions for process optimization. But as is the case with all new technologies, there is no one blueprint for implementation. Each use case demands a tailored strategy, and process optimization is no exception. At Capgemini Invent, we are dedicated to crafting solutions that meet the unique needs of our clients. Contact us to explore how Gen AI can unlock the potential of process optimization for your organization.

References: Oost, M., et al. (2023) Harnessing the Value of Generative AI. Capgemini Research Institute. [online][Accessed on the 30^th of August 2024] 

The high volume world

In its Annual World Airport Traffic Report for 2024, Airports Council International (ACI) projected a 10% growth for passenger traffic in 2024, to reach 9.5 billion. According to research from the supply chain platform Upply, the top 20 ports in the world generated 387.5 million twenty-foot equivalent units (TEUs) of traffic in 2023, a 1.24% increase from the previous year. 

Against this backdrop of increased volume, the modernization of airport and seaport operations is crucial for the efficient and secure transportation of people and goods. Integrating 5G and edge computing based applications can revolutionize these operations, by enabling real-time monitoring, automation, and data-driven decision making.

With 5G’s high-speed and low-latency connectivity, airports and seaports can leverage applications like smart surveillance, predictive maintenance, and autonomous vehicles, ensuring enhanced safety and reduced congestion.

Edge computing enables data processing and analysis closer to the source, reducing latency and improving response times. This modernization will streamline operations, increase cargo throughput, and enhance the overall passenger experience, making transportation more efficient, sustainable, and resilient. By embracing these technologies, airports and seaports can stay ahead of the curve and meet the growing demands of global trade and travel.

5G and edge computing are mainly technology platforms – they allow enterprises to ‘connect’ and ‘host’ services. But they also offer unparalleled levels of flexibility because they enable customers to tune both network and server resources to meet the specific requirements of each airport or seaport service. For example, a highly reliable, bandwidth-prioritized network for airport security, but a ‘best effort’ network for airport entertainment services.

This flexibility to provide different quality of service (QoS) for both data and applications sets 5G apart. Similarly, edge computing uses virtualization and cloud-native architecture to provide scalable compute for different applications. For example, in a seaport, the usage of container management and fleet control applications can increase when a ship berths in or leaves port.  

According to a report prepared by Avinor AS and Heathrow Airport Ltd., another factor driving the trend towards 5G and edge computing in airports has been the move towards ‘open architecture’ for airports. This calls for airport systems to communicate with each other over secure, but known interfaces and APIs. It helps the airport to absorb the most competitive applications from multiple vendors. These open architectures also need a platform for compute, communications and edge – which 5G does well.

The key benefits of 5G and edge based ‘smart services’ are discussed below.

Enhanced operational efficiency

One of the primary benefits of 5G and edge computing in airports and seaports is the significant improvement in operational efficiency. For instance, in airports, 5G networks facilitate real-time data exchange between ground handlers, aircraft teams, and operations centers. According to a blog by Ericsson, this seamless communication enables more efficient baggage handling, quicker turnaround times, and optimized flight schedules. Similarly, another Ericsson report argues that seaports benefit from the real-time tracking of cargo and automated logistics, reducing delays and improving the overall flow of goods.

Improved safety measures

Safety is paramount in both airports and seaports, and 5G combined with edge computing offers robust solutions. According to an article by Airport Technology, in airports, enhanced security measures, like 4K video surveillance, biometric services for boarding, and predictive maintenance of critical infrastructure are now possible. These technologies ensure that potential threats are identified and mitigated promptly, enhancing passenger safety. In seaports, Ericsson reports that smart sensors and IoT devices can monitor environmental conditions and structural integrity, providing early warnings of potential hazards. This proactive approach significantly reduces accident risk and ensures a safer environment for port staff.

Increased profitability

The integration of 5G and edge computing also drives profitability, by enabling new revenue streams and reducing operational costs. Airports, for example, can leverage these technologies to enhance retail operations within terminals. Additionally, predictive maintenance powered by real-time data analytics minimizes downtime and extends the lifespan of critical assets, reducing maintenance costs. In seaports, automated logistics and real-time tracking improve cargo handling efficiency, reducing turnaround times and increasing throughput.

Future proofing infrastructure

Investing in 5G and edge computing not only addresses current challenges, but also future-proofs airport and seaport infrastructure. These technologies provide a scalable, flexible foundation that can adapt to evolving demands and integrate with emerging technologies like augmented reality (AR) and AI. This adaptability ensures that airports and seaports remain competitive and capable of meeting future operational and security requirements. Digital twins for operations is another area of interest for both airports and seaports. These digital twins can provide valuable real time insights to drive better decision making; for example, on traffic flow, energy use, hardware status or local climate conditions.

The challenges of implementing 5G and edge

Seaports and airports are large areas of real estate with massive inventories of devices. Additionally, much of this inventory is mobile. This inventory requires efficient tracking, surveillance and operational management. As described above, digital twins of products and processes can bring operational efficiency, but to build such digital twins, data must be collected from the vast amount of mobile devices over a large area. 5G/edge is perfectly suited to be the digital fabric that enables the realization of these digital twins.

Smart services: a path to monetization for CSPs

CSPs and telcos worldwide see smart services as a way to offer complete packages to enterprise customers. This helps drive adoption for their 5G private network and SD-WAN communication platforms, as enterprises don’t just get a data pipe in their premises – they also get all the necessary application suites and devices. It also positions the CSP as a ‘tech-co’ instead of a ‘tel-co’, with offers in specific markets like manufacturing, mining, warehousing, etc. To that end, CSPs often partner with system integrators (like Capgemini) to tailor these smart service offerings to enterprise – right from early strategy to eventual roll-out.

Our smart service solutions are powered by Capgemini’s Intelligent Edge Application Platform (IEAP), which is designed to accelerate the development and deployment of edge applications, particularly for industries leveraging the Internet of Things (IoT) and real-time data processing. The platform enables efficient integration of edge computing with cloud infrastructure, facilitating faster data analysis and decision-making at the network edge. It supports scalable, low-latency solutions for industries like telecommunications, manufacturing, and automotive, enhancing operational efficiency, automation, and intelligent decision-making. IEAP simplifies the management of distributed systems and enhances the overall agility of edge-based digital services.

A smart services example: Capgemini’s collaboration with AWS

Capgemini is collaborating with AWS to build a solution, based on IEAP, that provides the advantages of a 5G network connected to edge computing running on AWS Hybrid cloud. This platform serves as a delivery vehicle for smart services for airports, seaports and other enterprises that want to run various applications locally, but with access to the AWS ecosystem of enterprise applications. It allows for localized and AI-powered services, providing security, traffic control, track-and-trace and end services to airports and seaports.

Conclusion: the transformation is happening now

The transformation of airports and seaports through 5G and edge computing, which is happening now, is a testament to the power of smart services. By enhancing operational efficiency, improving safety measures, increasing profitability, and future-proofing infrastructure, these technologies are setting new standards for the transportation industry. As 5G and edge computing continue to evolve, we can expect even more innovative solutions that will further revolutionize how airports and seaports operate, ensuring they remain vital hubs in the global economy.

Most companies that rely heavily on document stores for knowledge sharing and team collaboration often end up with many pages created by users.

The rapid growth and constant evolution of these knowledge bases pose significant challenges in finding relevant content. Despite diligent documentation, navigating to the pertinent information remains difficult – one either knows where the document is or the exact keywords to find it.

Real customer scenario

At one of Capgemini’s clients, a team operating and building a new data platform was entangled in customer support, reducing its ability to create new functionalities.

Allow me to briefly explain what a typical support request entailed:

1. The customer raises a question to the platform team.
2. A dedicated person from the platform team browses available Wiki documentation and searches for relevant information.
3. Several minutes or even hours later, the person passes the information (a link) to the requestor.
4. The answer may not be clear, prompting the need for another question to be asked.

The weekly effort spent on customer support is increasing, and it is projected to reach 2.5 “FTE” permanently occupied with customer support activity by the end of 2024, as the number of platform users grows. Moreover, the response time for support requests is too long, leading to a poor customer experience.

Talk to your data with Gen AI

The client uses several cloud technologies, including Snowflake, as the core database and data warehouse solution. Capgemini experts were quick to consider Snowflake Cortex AI technology as the key to creating a cutting-edge solution for tomorrow, addressing the client’s issues with operational costs.

Why not ramp down on operational costs and ramp up customer interactions to a new level like this?

1. Go straight to the chatbot and ask the question.
2. Still have a question? Ask another question.
3. Is the chatbot not able to answer your question? Contact a relevant person from the platform team.

With this vision in mind, Capgemini set out to implement a Gen AI-based chatbot that could answer customer questions efficiently. The chatbot, powered by the company’s extensive knowledge repositories, ensured that the provided answers were accurate and relevant. Additionally, the chatbot referenced the source Wiki documentation link as part of its responses, making it easier for users to find the information they needed.

The solution worked 24/7, ensuring that customers could get help at any time of the day or night. This innovative approach aimed to reduce the burden on the customer support team and enhance the overall customer experience. By leveraging the power of Cortex AI and Retrieval-augmented generation “RAG”-based Gen AI, Capgemini was poised to revolutionize how customer support was handled, paving the way for a more efficient future.

High-level architecture

The RAG architecture Capgemini proposed for the Cortex AI chatbot consisted of three service types:

• Cortex AI Functions for large language model (LLM) support: EMBED_TEXT_768, VECTOR_L2_DISTANCE, and COMPLETE)
• Snowpark Container Services for retrieval front-end.
• Snowflake tables as a vector store (native support of vectors as data types in Snowflake).

Let me explain some basic terms:

• RAG is an architectural approach that enhances the capabilities of large language models by incorporating an information retrieval system. This system retrieves relevant data or documents and provides them as context for the LLM, improving the accuracy and relevance of the generated responses.
• Snowflake Cortex AI is an intelligent, fully managed service within Snowflake that allows businesses to leverage the power of artificial intelligence (AI) that enables users to quickly analyze data and build AI applications without the need for extensive technical expertise.
• Snowflake Cortex AI Functions are a set of pre-built LLM functions that allow users to perform advanced data analysis and AI tasks directly within the Snowflake platform. These functions include capabilities such as text completion, sentiment analysis, and text summarization.
• Snowflake Container Services is a fully managed container offering that allows users to deploy, manage, and scale containerized applications within the Snowflake data platform.

By implementing a Gen AI chatbot based on Snowflake Cortex AI technology, evaluated by Capgemini, the client can streamline the customer support processes, reduce operational costs, and enhance customer interactions. This innovative solution leverages the power of AI to provide accurate and timely answers, ensuring that users can easily navigate through vast knowledge bases and find the information they need.

Cortex Search

I described the way Capgemini built a search tool for the client’s use case. The latest introduction of Cortex Search replaces the need for standalone vector tables and a self-managed embedding process with a fully managed RAG engine. This advancement not only streamlines development but also elevates the quality of outcomes with sophisticated retrieval and ranking techniques that merge semantic and lexical search. This effective approach is undoubtedly a game changer in building Gen AI RAG-based solutions.

Capgemini and Snowflake

The collaboration between Capgemini and Snowflake leverages Snowflake’s AI data cloud to enable businesses to unify and connect to a single copy of all data with ease. This partnership allows for the creation of collaborative data ecosystems, where businesses can effortlessly share and consume shared data and data services.

Capgemini and Snowflake are collaborating to develop generative AI solutions that leverage Snowflake’s advanced AI Data Cloud technology to drive innovation and enhance business outcomes across various industries.

This strategic relationship has led to Snowflake naming Capgemini the 2023 EMEA Global SI Partner of the Year.

ServiceNow can help create an environment that fosters employee engagement and growth  

People are a company’s greatest asset. However, maintaining enhanced employee and customer experiences has remained a challenge for organizations, especially those struggling to implement technological changes. With advancements in artificial intelligence to optimize and automate organizational workflows, companies at any stage of their digital transformation journeys can focus on an often-overlooked investment – employees – to gain a competitive edge. 

Promoting a people-first strategy through technology

Happy employees are more productive, and that leads to a wide range of quantifiable benefits across an organization. 

• 59 percent reduction in turnover 
• 41 percent lower absenteeism 
• 21 percent improvement in profitability 
• 20 percent increase in sales 
• 17 percent increase in productivity 
• 10 percent improvement in customer ratings 

Enabling the right technology is a foundation that can be built on to transform a company’s performance culture, allowing it to aim for these types of improvements. However, for many organizations, the employee journey from recruitment to retirement is a disconnected process rather than one that flows without friction. Instead of being proactive in promoting change and making strategic decisions based on data, company leaders often find themselves bogged down by trouble-shooting specific issues.  

A more holistic approach can create an environment that fosters employee engagement and growth. This involves adopting a combination of strategy and culture, technology, and operations that work together to deliver impactful moments and seamless employee experiences. Organizations that already rely on ServiceNow to deliver key business solutions can also use its integrated platform to advance how they manage people and processes quickly and easily.  

Implementing these changes might sound complex; however, Capgemini has a new pre-packaged ServiceNow HRSD product. It’s designed to help companies address everyday challenges, such as absenteeism and productivity, and enable data-driven decision-making to produce positive outcomes and, in turn, boost profitability. We can work collaboratively to identify your company’s points of friction, find opportunities to enhance existing process, and tailor solutions to your specific human resources needs. 

Producing impacts across the employee experience

Integrating technology is an essential element in creating a seamless people experience, from onboarding to offboarding. ServiceNow’s platform supports a number of third-party systems typically used by human resources and IT teams, which can be accessed through a user-friendly employee experience portal.  

Even that seemingly small entry point for employees can make a big impact. Having a single self-service interface immediately promotes autonomy and reduces friction; instead of needing to log in to a number of portals just to access tools for productivity and collaboration, everything is all in one place. Additionally, rather than working in siloed programs, managers can connect jobs and roles to outcomes, providing employees with clear and quantifiable objectives related to their day-to-day work, as well as their career growth potential. 

Consider your organization’s intranet as one type of employee portal. It’s probably populated with all types of information individuals might want to access, from financial results to company events. Having access to a wealth of information, however, doesn’t guarantee an inclusive employee experience. Data intelligence derived from analyzing patterns on intranet portals reveals that a tiny percentage of the information is ever looked at, much less used. Rather than being a resource, these internal pages are difficult to navigate when employees are seeking information and resources to do their jobs. 

Instead, envision how portals and collaborative spaces can be personalized for each employee. Only information relevant to their role’s objectives, the tools and technology they need for their work, and access to key data so they can track individual KPIs and other metrics are surfaced. ServiceNow can facilitate this type of targeted communication and assemble touchstones to drive business outcomes across the employee journey.  

Preparing for the future of work 

Using analytics and insights to continuously improve all aspects of the employee journey is already transforming the future of work. As generative AI technology continues to mature, manual processes can be improved through automation, freeing up staff to work more strategically, while maintaining the “human” in a people-first strategy. It’s accelerating the transformation of the employee experience, allowing people to be much more efficient in the ways they interact within the organization. 

Soon, employees will be able to access a personalized chat bot to find specific policies or learning opportunities. Consider how technology built upon large language models will soon deliver features such as scanning an employee’s list of skills, experience, and even language abilities to recommend new job opportunities or refine their career path within the organization. This also aids in succession planning and adds value to a company’s long-term investment in human capital. 

Such possibilities are already in the works, with generative AI bringing together all this complexity and delivering an end experience in a very simple and accessible way.  This type of environment fosters employee engagement and growth, a valuable resource that brings myriad benefits to an organization, ultimately giving it a competitive edge. 

ServiceNow helps eliminate friction in human resources by seamlessly integrating people and processes 

Companies are fundamentally composed of people. While this may seem obvious, the complexity of recruitment, onboarding, training, and retaining employees reveals just how intricate managing this essential resource can be.  

As technology continuously evolves, transforming the nature of work and the workplace, it is crucial that the workflows supporting the employee journey evolve as well. However, outdated technology, inefficient processes, and siloed teams often hinder companies from delivering a seamless employee experience. This is where ServiceNow comes in. By providing an integrated platform and employee portals, ServiceNow enables companies to revolutionize their investment in human capital. 

Envision a transformative employee culture

Human resources encompasses a broad spectrum where people and processes intersect at various touchpoints. However, amidst the analysis of terabytes of data and assessment of key performance indicators (KPIs) is “human.” To foster a positive and transformative company culture, the employee experience must be human-centric, composed of individual moments that matter, and seamlessly connected. 

For instance, companies often rely on various distinct systems from third parties, such as Microsoft, Oracle, SAP, and others, to manage key steps in the employee journey. These systems handle aspects like day-to-day objectives and KPIs, compensation and benefits, learning opportunities, and overall career roadmaps. However, navigating through these multiple systems can be time-consuming and inefficient, leading to frustration, especially among new hires, and wasting valuable company resources. 

ServiceNow is an integrated platform that provides human-centric advantages, effectively breaking down siloes and creating cohesive workflows in the back end, where third-party applications and data are housed. Simultaneously, ServiceNow’s employee experience portal offers user-friendly access to key information that can be personalized for an individual, based upon their role, responsibilities, interests, and even geographic region. 

Identify points of friction

The actual employee experience often differs significantly from what many company leaders in IT and HR might expect. According to Capgemini’s 2022 research of 2,250 employees across 750 organizations, less than one-third (30 percent) of employees are happy at work, yet 92 percent of managers mistakenly believe they are content. Even more worrying is that 68 per cent of employees did not know what they are expected to do in their role.  

For instance, employees reported they did not have access to data to make informed decisions. They lack clear, quantifiable objectives and do not understand how those objectives are aligned to the company’s overall targets.

Not surprisingly, these types of barriers can skew an employee’s perceived performance, leading to dissatisfaction and increased attrition. Our survey found that a staggering 20 percent of employees left the company within 45 days of being hired.  

Automate and optimize the employee experience

Enabling the right technology can help solve these issues, ensuring employees have the best experience throughout their career at the company, starting from the recruiting stages and continuing immediately after hiring. 

Consider the actual onboarding process, for example. It requires collaboration across departments and involves numerous stakeholders, including managers, facilities, security, and finance. Less than half of these activities or touchpoints are related to HR, which can lead to a fragmented employee journey. ServiceNow’s platform brings it all together to clearly integrate these crucial steps. This helps companies provide a smooth end-to-end experience for new employees, right from when they first join an organization.  

Capgemini also has a pre-packaged ServiceNow HRSD product that optimizes and automates manual processes and workflows. It provides managers (and new hires) with a detailed list of every onboarding activity required, from sending a welcome email and assigning a desk to ensuring the appropriate forms are filled out and training tasks are set up. This way new hires can get up to speed and become productive more quickly.  

ServiceNow’s capabilities continue across the employee journey, extending from “hire to retire,” culminating in a unified, consistent people experience. Capitalizing on such advancements in technology can streamline workflows, automate processes, and enable data-driven decision-making for companies. This helps managers make the most of “human” resources, while promoting a productive and positive workplace where people truly come first. 

CMS and Capgemini Invent: Joint consulting on digital regulation and transformation

By combining the expertise of international law firm, CMS, and leading strategy and technology consultancy, Capgemini Invent, we provide comprehensive and seamless advice on all aspects of digital transformation. Together, we present the foundational elements of AI governance, AI governance frameworks and platforms, and the importance of AI regulatory compliance.

We would like to thank the authors Björn Herbers, Philipp Heinzke, David Rappenglück and Sara Kapur (all CMS) and Philipp Wagner, Oliver Stuke, Lars Bennek and Catharina Schröder (all Capgemini Invent).

Legal assessment and implications of the AI Act

The “Regulation on Harmonized Rules for Artificial Intelligence” (AI Act), adopted by the European Parliament and the Council of the European Union, came into force on August 1st 2024. This concludes a long path of tough negotiations that began in 2021 with the European Commission’s proposal for EU-wide regulation of AI. Due to its direct applicability in all 27 member states, the AI Act will have far-reaching impacts on providers, operators, and users of AI.

• Prohibited practices under the AI Act (Art. 5) are those AI systems deemed incompatible with the fundamental rights of the EU.
• High-risk AI systems (Art. 6) are divided into those that are products or safety components of certain products and subject to third-party conformity assessment and such used in specific areas. Providers of such AI systems face high compliance requirements throughout the system’s lifecycle.
• Certain AI systems, such as those interacting with humans (e.g., chatbots), are subject to specific transparency obligations (Art. 50).
• General Purpose AI (GPAI) models (Art. 51 ff.) are versatile AI models that can perform various tasks and be integrated into systems. Compliance obligations vary based on classification as “normal” GPAI models or those with systemic risk.

From the date the AI Act came into effect, provisions on prohibited practices will apply for six months, 12 months for GPAI models, and between 24 and 36 months for high-risk AI systems.

Violations of the provisions can result in fines up to EUR 35 million or up to 7% of the previous year’s worldwide total revenue. In other cases, the penalty may amount to EUR 7.5 million or up to 1% of the previous year’s worldwide total revenue.

Strategic and operational implementation through AI governance

Implementing the requirements of the AI Act requires an overarching approach. With our comprehensive AI Governance Framework, we help organizations use AI responsibly and efficiently while minimizing risks. Processes and responsibilities must be defined and adhered to throughout the AI lifecycle, covering data, models, systems, and use cases, aligning with technical, procedural, and regulatory requirements.

Formulating a long-term vision for AI governance and developing ethical guidelines within the organization lays the foundation for any AI strategy. This strategy must then be effectively conveyed through a comprehensive communication plan. Subsequently, roles and responsibilities related to AI projects can be identified and defined, and processes for the development, implementation, and monitoring of AI projects can be adjusted.

Creating a handbook with security standards, best practices, and guidelines for implementing AI is recommended. Given the AI Act impacts such areas as data protection, copyright, and IT security law, it is advisable to continuously analyze regulatory requirements and translate them into technically measurable KPIs.

For providers of high-risk AI, setting up a risk management system is mandatory (specific components to be explored in a subsequent blog post). Here, an AI governance framework is essential. To effectively scale AI deployment and optimize operational processes, it is crucial to take an inventory of all AI systems and subsequently automate processes across development, deployment, monitoring, and documentation stages.

Establishing and monitoring metrics for quality, fairness, and robustness is a cornerstone of effective strategy. To foster knowledge among employees and mitigate biases against AI, continuous training and awareness initiatives should be integrated throughout the AI lifecycle in an iterative process, complemented by change management to ensure a seamless transition.

In our application example, the complexity and multifaceted nature of the topics are evident. Only by first clarifying the legal questions surrounding AI actors and the type of AI system can the resulting obligations be identified and effectively integrated into AI governance.

Strategic approaches to AI governance and risk management

The use of AI offers enormous potential for value creation but also carries functional and legal risks. Ongoing legislation, such as the AI Act, leads to a comprehensive regulatory framework but requires detailed implementation in organizations, considering technical, procedural, and human dimensions. Various projects have shown that AI governance only adds value if it keeps pace with the constant evolution of AI technology. Clear regulatory frameworks can catalyze increased AI applications in boundary-pushing areas following initial uncertainty. This not only fosters technological solutions for known issues but also unveils new use cases made possible by emerging capabilities. To remain competitive, implementation guidelines should strike a balance: offering necessary support while maintaining flexibility from the outset. Keep an eye out for our forthcoming deeper exploration of the risk management requirements outlined in Article 9 of the AI Act.