These big decisions will set corporate direction for years. They must properly analyse the full life cycle impact of any choice, whilst also considering systems outside of their control, from land, to energy infrastructure, to competition from other industries.

To take a top-level example, what is the most sustainable vehicle propulsion method – Electric, Hydrogen and E-fuels? We need to understand the full life cycle – by performing an integrative Life Cycle Assessment – in order to reliably make the comparison.

So we would need to look at the original fuel (eg energy mix of grid, power source for an electrolyser, or biomass) and its emissions profile. Then we’d need to look at the energy efficiency of each step between the energy inputs and the vehicle’s propulsion. Then you can compare how much of each you need to produce the same amount of propulsion.

We must also look at the inputs of creating the propulsion system itself – such as battery or engine components and materials.

We can then combine these to work out the most sustainable option. Maximum sustainability will need to address the fuel, the vehicle design and the energy systems that power it. The results will of course vary in different scenarios.

Making good decisions needs highly sophisticated system-of-systems modeling, combining your own engineering and supply chain models with climate, energy, demographic and macroeconomic models.

In our new whitepaper offer an introduction to planning strategic decisions for a sustainable transition, and provide top level worked examples of propulsion and battery choices, alongside some initial answers.

At the beginning of March, Capgemini’s Quantum Lab participated in the BIG HPC-AI-QC Hackathon organized by QuantX in collaboration with PRACE, GENCI, BCG and under the high patronage of Neil Abroug (Head of the French National Quantum Strategy) in Paris, France. Leading players of international Quantum Computing, HPC and AI ecosystems (e.g., industrial companies, quantum hardware and software providers, HPC centers, VC/PEs and consulting groups, representatives of academia and government) gathered to accelerate the transfer of competencies and advance hybrid HPC-AI-QC solutions and their practical application. The hackathon consisted of two parts: the technical phase and the business phase.

The solution co-crafted by our Capgemini team on a technical use case provided by BMW Group was crowned winner of the technical phase! The team consisted of Capgemini employees (Camille de Valk, Pierre-Olivier Vanheeckhoet and Kirill Shiianov) and students from Ecole Polytechnique de Paris (Bosco Picot de Moras d’Aligny), and Technical University Munich (TUM) (Fiona Fröhler). They were assisted by technical mentors Elvira Shishenina (BMW Group), Jean-Michel Torres and Elie Bermot (IBM), and Konrad Wojciechowski (PSNC).

The solution they proposed is the first step to improve the BMW cars acoustics. The international jury of experts was enthusiastic about the team’s technical solution, as well as their excellent presentation. The French minister, Jean-Noël Barrot, and Nobel prize winner, Alain Aspect, joined the awards ceremony to hand out the prizes to the winners.

Camille de Valk, one of Capgemini’s Quantum Lab Specialists, on the Technical Phase:

“BMW Group provided us with a challenging use-case for the technical phase of the hackathon. It’s all about optimizing the design of cars to have less irritating sound in the cabin. This involves complicated physics and mathematics, but luckily our team had both physicists and computer scientists. The teamwork was one of the best parts of the hackathon for me.

We created a toy-demonstration of a differential equation solver using variational quantum circuits and we explored its scaling in an artificial intelligence (AI), high performance computer (HPC) and quantum computing (QC) workflow. This was the first step to experiment with the efficiency of complex simulations around sound propagation, to improve the cabin’s acoustics by optimizing the design of the car. Working in this hackathon with such a talented team and great mentors was a great experience for me!”

Kirill Shiianov, Consultant at Capgemini Engineering, about the Business Phase:

“In the business phase of the BIG Hackathon, Capgemini’s Quantum Lab team took on a challenge to build a business case around one of the solutions from the participants from the technical phase. The team showed its best at developing the case of the business phase: uniting people from different backgrounds and business units. The team existed of different areas of expertise, which helped to understand different aspects of the problem and come up with creative solutions.

The use-case intended to augment Natural Language Processing (NLP) models with a quantum approach. The use-case provider was Merck Group, and the real-world application of the technology was incented to investigate promises of Symbolic AI and as concrete example detect differences between Adverse Event reports (AE, event and drug exposure) and causal Adverse Drug Reactions (ADR, event due to drug exposure) mentioned in textual sources, like medical reports or social networks.

During two intensive days, we fully immerged in the technology, built a complete business case, and presented it to a jury, consisting of technology VPs of high-tech companies, such as Quantinuum.

Interaction with the use-case provider (Thomas Ehmer from Merck Group), and technical people from Quantinuum helped us getting unique insights in the respective domains. It was a unique experience, and I am already looking forward to participating in the next editions of the hackathon!”

Why 5G security?

5G is the fifth generation of cellular technology, offering faster speeds and lower latency compared to 4G. It makes the connected era and Internet of Things (IoT) possible, and whether it’s smart cities, steelmaking, or healthcare, few industries will be untouched by its capabilities.

There are two types of 5G networks: public and private –

• Public 5G networks are primarily used by retail customers for smartphones and other day-to-day devices connected to the internet. Owned and operated by mobile carriers, public networks are available to anyone who subscribes to their service. As a network established by telco providers, the security rests with them for the most part.
• Private 5G networks are not accessible to the public. They are owned and operated by a single entity, such as a company or government agency, and are used to connect devices within a specific location or facility. For example, a factory might set up a private 5G network to connect its machines and other equipment to streamline operations and improve efficiency.

Most companies using 5G for manufacturing and operations will need to build a private network or employ a hybrid model of public and private, fitted to the requirements. Whichever model a company uses must be underpinned by robust security frameworks.

5G security is complex because, unlike 4G, it operates outside the perimeter of dedicated equipment, servers, and protocols. Instead, a highly vulnerable software ecosystem of virtualized RAN and cloud-forward services constitutes its core network. The concept of 5G security is new and evolving, which is why it’s essential to be alert to the challenges and develop and deploy new security measures in response.

5G security challenges

The introduction of new use cases, new business models, and new deployment architectures makes securing 5G networks more challenging. But without a cohesive approach to mitigating the security risks, it can be difficult to ensure that all potential vulnerabilities are identified and addressed.

These are the key security challenges for 5G as we see them:

• Increased attack surface: Millions of new connected devices are entering the digital ecosystem, which increases the attack surface exponentially. Many IoT devices are vulnerable and unprotected and typically operate with lower processing power, making them easy targets for attackers. This makes implementing zero-trust frameworks with true end-to-end coverage critical for protection against threats.

• New paradigms for telco: With 5G, the telco ecosystem is essentially inheriting IT challenges requiring a software security mindset. Whether public or private, 5G’s virtualized network architecture creates a new supply chain for software, hardware, and services, and this “virtualization” of traditional single-vendor hardware is a major security challenge. It’s time for professionals to acquaint themselves with network function virtualization (NFV), virtualized network functions (VNFs), service-based architectures (SBAs), software-defined networks (SDNs), network slicing, and edge computing.

• Operational challenges: The requirements or the capabilities needed to monitor a 5G network are different to IT and OT. This means that the tools used for monitoring the IT and the OT networks cannot be retrofitted or scaled for the cellular world, so 5G requires new tools and new capabilities. This involves training new people to understand the protocols and use cases.

• The complexity of implementation: There is no one way to build 5G architecture. It depends on the requirement of the organization and, as a result, the specification range can be extensive. Trying to bring these models together and manage them is one part of the challenge; the other is finding skilled professionals who know how to do it. Consequently, the margin for human error is another factor to bear in mind.

• Increased number of stakeholders: Finally, the industry recognizes that the success of building 5G networks is dependent on the entire ecosystem of hardware and software vendors spanning multiple suppliers, from chip vendors to cloud providers. Coordinating new stakeholders and their security efforts while ensuring that all potential vulnerabilities are covered is likely to be challenging. Note that different stakeholders may have different levels of knowledge and expertise when it comes to security.

Introducing 5G risk assessment

5G security is extensive and there are multiple parts to be cognizant of to understand where the risks and vulnerabilities are when running a network. You’ll see this mapped out into horizontal and vertical layers in the diagram. To conduct a comprehensive risk assessment of 5G, both axes need to be secured. Knowing where to start involves understanding what constitutes each layer:

• 5G horizontal security is the sum of five parts: user equipment, radio access, edge/multi-access edge computing, core network, and the cloud. Due diligence is necessary in every area to ensure assets are protected from confidentiality, integrity, and availability attacks.
• 5G vertical security is the sum of four layers: the product, the network, the applications, and the security operation layer on top. This is generally referred to “chip to cloud” security, particularly in the context of IoT devices.

A risk assessment, therefore, has to be holistic in nature, covering every aspect of the horizontal and vertical layers with due consideration of the threats, vulnerabilities, and assets that touch each of the specific components in the architecture. Such a risk assessment must also address any regional and industrial compliance requirements, and we will discuss this later in the series.

At Capgemini, we know that building and securing a 5G network is complex. We also know that everything must be protected end-to-end and in unison for it to work effectively. With deep technology, business, and engineering expertise, Capgemini has the unique capability to guide you on the 5G security journey end-to-end.

Security today adds value to a business tomorrow, and realizing the possibilities of a new, truly Intelligent era relies on it. Our experts can help you maximize the benefits.

The next blog in the series will consider how to conduct a robust risk assessment and monitoring in more detail. 

Great basketball teams and great personalization teams have a lot in common.

Imagine a shopping experience that’s completely generic. Worse than generic, it goes out of its way to recommend things you don’t want. It recommends actions that are the opposite of what you’re looking to do. It’s perfectly frustrating. How long will a business based on that sort of experience last?

Now imagine a personalization experience that knows you so well it’s constantly providing you with serendipitously delightful experiences. You’re discovering things you never knew you wanted. But you’re never allowed to use it because the experience never sees the light of day. The MVP never becomes an available product.

Both scenarios are terrible. Unfortunately, a variation of the second is more common. 77% of AI and analytics projects struggle to gain adoption. Fewer than 10 percent of analytics and AI projects make an impact financially because 87 percent of these fail to make it into production. What if we could flip the odds? What if rather than most recommendation projects failing, most of them succeeded? Cross-functional, product-centric, teams can do just that. It’s how innovators like Amazon and Netflix were able to succeed so quickly and so often in their personalization programs. It’s also been critical for the dozens of successful personalization programs we’ve delivered at Capgemini.

Recommendation experiences

Everything is a recommendation. That insight came from Netflix: “the Starbucks secret is a smile when you get your latte, ours is that the website adapts to the individual’s taste,” said Reed Hastings, co-founder of Netflix. Recommendations weren’t features or algorithms. They were the experience; the means to delight, surprise, frustrate, or anger customers. At Amazon, Jeff Bezos’ original goal was a store for every customer. This wasn’t AI for the sake of AI. Both companies made personalization central to their experiences, and personalization enabled Amazon and Netflix’s visions for more innovative, delightful, and serendipitous experiences. Recommendation experiences (RX) were critical to customer experiences (CX). Experiences were the product. Building products is hard. Josh Peterson co-founded the P13N (personalization) team at Amazon. He described the early days of Amazon as challenging because the company was siloed. Design, editorial, and software engineering were fragmented. “It was really hard to ever get anything all the way out to the site without begging and borrowing people from silos. The one time it was always different was when we did a product launch… So, if there was a big enough effort like launching music or auctions then you had permission to borrow everyone to put together your team.” In the early days of Amazon, there were many engineering efforts around personalization. Even though these efforts were led by brilliant engineers, they saw limited success. It wasn’t until after the launch of Amazon Auctions that personalization made a real impact.

After Amazon Auctions, Peterson and Greg Linden looked to make Bezos’ vision for a personalized store for every customer a reality. The goal was a team that could “own its whole space,” to break silos to create a cross-functional team to rapidly experiment and deliver. This was the first team, outside of the design organization, to have designers in their team embedded with web developers and technical project managers. This enabled a higher number of launches compared to other teams. The impact of their model was so successful that it became the basis of Amazon’s famous “Two Pizza Team” approach – essentially a team small enough that they could be fed with two pizzas. Small teams that were decentralized, autonomous, and were “owners” of the business could move faster and launch more experiments. More experiments would enable them to have more successful innovations.

Experimentation

Successful personalization teams foster a culture of experimentation. Creating a culture of experimentation requires diverse, multi-disciplinary teams. Below we show the various skillsets and domains that are required for modern personalization teams. The circles don’t represent people, they represent skills. Great basketball teams and great personalization teams have a lot in common. In basketball, you need defense. You need offense, both close to the rim and from afar. You need diversity in skillsets. You could get lucky and find a unicorn but fielding multiple teams of unicorns is not practical. Creating a team of all-stars sounds good on paper, but there are plenty of examples where those super teams fail to live up to expectations. A team without a diverse set of skills is unlikely to be very successful, and almost certainly not great.

“Experimentation requires blending creativity and data. Practically, this becomes a blend of statistics, behavioral economics, psychology, marketing, and expertise in experience design.”

Small teams with most of the skills above are more likely to do end-to-end personalization well. No one person will have all the skills needed, but together they’ll bring more experiments to the table. Early Amazon teams were engineering and data-science heavy. It wasn’t until the addition of design, business expertise, and a product-centric approach that they were able to execute end-to-end and achieve Bezos’ vision.

Velocity is a leading indicator. Successful personalization teams test many ideas. They break experiments into small chunks so no one failure is large enough to disrupt the business. They test and learn quickly. Testing a dozen ideas and refining them will be more efficient than trying to make one idea “perfect.” Our intuition on what is going to work is often wrong. Testing many ideas allows the data and results to guide us, rather than intuition. This requires personalization teams to develop many ideas end-to-end quickly.

What does the future hold? Cross-functional, product-centric teams are the beginning, not the end. Experimentation requires blending creativity and data. Practically, this becomes a blend of statistics, behavioral economics, psychology, marketing, and expertise in experience design.

These teams need to track which features drive results to understand what is working and what is not. The goal is to achieve consistent and reliable serendipity from personalization efforts. The obvious is not serendipitous. Experimentation is needed to discover that which is not obvious and that which drives business outcomes. Without that, we can’t scale serendipity.

INNOVATION TAKEAWAYS

DIVERSITY LEADS TO SPEED

Speed leads to innovation. Diversity leads to innovation. End-to-end cross-functional teams with dedicated resources are more likely to successfully implement personalization programs and innovate faster than their peers

A CULTURE OF EXPERIMENTATION IS CRITICAL

Velocity, variety, and volume of experiments are leading indicators of innovation. “Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day.” – Jeff Bezos

SPEED IS A COMPETITIVE ADVANTAGE

Testing and learning iteratively as well as being able to deploy quickly contribute to faster speed to market. “Companies rarely die from moving too fast, they frequently die from moving too slowly.” – Reed Hastings

Interesting read?

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

However, nothing compares to what might be about to happen – the convergence of artificial intelligence (AI), blockchain, and 6G/satellite connectivity combined with concepts like the metaverse will change the way we design, develop, and deploy software. For the purpose of this short blog, I will focus on the metaverse and the effect it might have on software developers. 

What is the metaverse? 

The metaverse is a virtual reality that allows us to interact with a fully virtual (and immersive) environment just as we do in real life, doing the same things we would in real life. According to Wikipedia, the metaverse “is a hypothesized iteration of the internet, supporting persistent online 3-D virtual environments through conventional personal computing, as well as virtual and augmented reality headsets.” A Capgemini publication focusing on metaverse in healthcare defines it as “a container of 2D or 3D virtual spaces, a persistent place parallel to the physical world, aiming to combine online digital and real-time interactions with the sense of presence.” 

For years, games like Roblox and Fortnite, but also older games like World of Warcraft, Minecraft, or Second Life, have developed a parallel virtual world where players can engage and connect with others in a mostly fantasy-like landscape. To illustrate the concept, one could also draw parallels with the film The Matrix; in the film, the main character “moves” between two reality-like parallel worlds.  

Many consider the metaverse as the internet V3, with V1 back in the 1990s, and the emergence of social media at the start of the 2000s as V2. Several use cases for the metaverse exist: for example, in the smart city space and in healthcare. However, there are also some who are more skeptical, who believe that the metaverse is already part of the past. The truth might lie somewhere in between. What seems clear, however, is that either the metaverse or part of the various metaverse concepts will impact the way we develop software: 

1. Moving away from mouse and keyboard 

When apple unveiled their iPhone in 2007, it heralded the start of the end of mobile phone keyboards. With the emergence of the metaverse, we might see the same happening to our PCs. The mouse, invented by IBM back in 1964, still the de facto PC input device next to the keyboard, might be slowly replaced by gesture, speech, and movement for end users (some state that using mind control devices might also become more mainstream). Of course, VR has been around since the mid-1990s after its invention in 1968, but due to various factors has not quite hit the mainstream. This might change now that developments in the metaverse have started, with more vendors announcing they are developing MR devices – Apple has started production in March 2023. 

For developers, the shift away from using text for coding is still a big unknown. If the shift occurs, then text input devices will slowly disappear. If it does not, then developers will have to deal with both traditional physical and new virtual ways of working. In any case, designing and developing software that supports different data input devices will require different skills, techniques, and tools compared to relying on mouse and keyboard only. It seems most likely that we will see a convergence in which developers use a mixture of traditional physical and new virtual ways of working.  

2. The move from 2D monitor screen interactions to full 3D with the use of VR, AR, MR, and XR 

It is not just our traditional user input devices that might change. We might also see our traditional user interaction devices change. Over the past 30 years, the PC monitor only changed in terms of resolution and size, but not really in concept: a screen that displays data in visual and text form, projected on a two-dimensional screen. The touch screen tried to allow for a better experience but failed to really take off. Driven by the metaverse, we might see a shift from today’s PC-based fixed and two-dimensional monitors to the use of mixed physical and virtual reality devices. Using virtual reality (VR) headsets or mixed reality (MR) glasses for user interaction combined further with either smartphones, gesture, or even mind reading might fundamentally change the way we design and develop applications. It is very likely that the shift will be a gradual process. The emergence of MR for both end users interacting with applications and for developers designing, developing, and delivering code might still be a way off. However, software developers must master the new (and currently various) software development kits (SDKs) to ensure that they can establish fully seamless and fluid interactions.    

3. New development platforms  

With the advent of the metaverse, organizations and communities are also starting to develop new programming languages. For instance, in December 2022, Epic Games launched the Metaverse programming language Verse. Verse is focused on making it possible to create social interactions in a shared three-dimensional (3D) world in real time. The web3 programming language family now includes Verse along with others like Clarity, Solidity, Curry, Mercury, and Rust. Verse also aims to support interoperable content by utilizing operational standards from several game engines, such as Unity, and live upgrades of active code. Another example is solidity. Created by Ethereum, solidity is a statically typed programming language designed for developing smart contracts that run on Ethereum. Solidity is used on the Ethereum blockchain, an object-oriented programming language, for building and developing smart contracts on blockchain systems. The question with all new programming languages is whether they will become mode dominant or widespread. Clearly only time will tell.  

4. Testing  

The quality of applications will be as important as in today’s applications. However, with MR as well as digital twin type environments, testing the use of both physical as well as virtual devices will be different as new testing facilities are needed to avoid manual interventions that might read “put the headset on, run the app, and see if it works.” The integration of MR and/or different VR devices as well as the use of different platforms might require different testing regimes.  

5. Being more aware of non-functional aspects like latency, security, and safety  

Walking around with Google Glass or any other VR or MR devices could pose various risk profiles, and developers must consider this when designing and developing metaverse-based solutions. In addition, latency – the time it takes for a service to respond (also sometimes referred to as “lag”) – is another aspect developers will have to consider more than in our current “traditional” 2D environment. User experience will be a key critical aspect in the metaverse, and a fully immersive experience can only be achieved if the rendering is fully fluid and seamless. With the end user being mobile or stationary with various data transfer opportunities (currently 5G, but soon 6G or even via low orbit satellites) it is important to ensure the developed metaverse solution fully considers that. With these requirements, more “traditional” aspects, like writing efficient netcode (referring to synchronization issues between clients and servers) and 3D engines, will become even more important.  

6. The move from two dominant mobile platforms (Android and Apple) to multiple platforms 

The metaverse will require massive 3D content to engage users, and 3D is expensive to make, to understand, to store, and to transport. Developing a metaverse application involves creating a virtual experience for platforms such as HTC Hive, Oculus Quest, and other VR or MR systems. Popular developer tools for metaverse focused on 3D creation include Epic’s Unreal Engine, Unity, Amazon Sumerian, Autodesk’s Maya, and Blender. And then there are the various (at the time of writing) development platforms that cover metaverse-related tools and accelerators like Webaverse, Ethereal Engine, JanusWeb, WebXR, Open Metaverse, Nvidia’s Omniverse, Hadea’s metaverse infrastructure, and the Microsoft metaverse stack.   

7. Increased importance of application programming interfaces (APIs)  

Interoperability (getting systems to talk to each other) will be one of the main challenges for developers writing metaverse applications. As with the advent of the internet in the mid-1990s, where multiple vendors as well as open communities developed and released new standards, the metaverse is also triggering numerous, and sometimes conflicting, standards. How it will all pan out is still open. However, what is clear is that software developers must have an excellent appreciation of data integration, particularly as data is being exchanged in real time between different platforms.   

8. Greater emphasis on real-time collaboration 

As applications in the metaverse will be used in an interactive and real-time manner, applications written for the metaverse will have to be able to respond to unpredictable events in a real-time manner, providing a seamless user experience. This means that software developers will have to use statistical techniques like deep learning on provided data and real-time user interactions to predict a response or next step, without the software having been specifically programmed for that task. 

9. Security and trust will be critical elements  

The success of the metaverse will also depend on users trusting the virtual counterparts; this means active and passive security will be a critical element. As the metaverse will evolve around the real-time exchange of virtual assets, new ways of securing and controlling virtual assets and interactions in real time will be needed. This will include authentication and access control, data privacy, securing interactions and transactions, and protecting virtual assets. In addition, passive security-related aspects, like strong network security protecting from cyberattacks, hacking, and other security threats, will be needed.  

10. The further use of tech like blockchain and NFTs 

One of the main use cases in the metaverse is the trading of goods and services. Therefore, it is likely that technologies like blockchain and non-fungible tokens (NFTs) will be supporting the exchange of virtual assets. And this means that software developers should have an understanding of how to manage NFTs as well as distributed ledgers like blockchain.  

11. AI will impact software development  

Another technology that will be part of the metaverse is AI. AI will be a key element in supporting the metaverse as it will help with end-user personalization, content creation to create more immersive and engaging virtual environments, as well as analysis of user behavior to help to identify trends and patterns, enabling developers to optimize the virtual world and provide a better user experience.  

Even without the emergence of the metaverse, AI will impact software development significantly. AI is positively impacting the way we design and develop software in these areas:  

1. Generating code: several AI tools can generate code, including DeepCoder developed by Microsoft, Kite, TabNine, GitHub Copilot, etc. 

2. Automation: AI can automate repetitive and time-consuming tasks in software development, such as testing, debugging, and code optimization. 

3. Quality: AI can improve the accuracy of software development by identifying potential bugs and vulnerabilities in code before it is deployed. 

4. Efficient resource utilization: AI can help software developers optimize resource utilization, such as server capacity and memory usage, to ensure that applications are running efficiently. 

5. Increasing immersion: by, for instance, making aspects more dynamic and immersive in the environment 

6. Creating virtual worlds: through, for instance, “text-to-environment” or “text-to-world.” Instead of placing assets using a mouse and keyboard, a developer could describe the environment instead. 

Today, many use cases exist where AI is aiding the entire software development process. The possible advent of the metaverse, or aspects of it, will further impact and change the way software developers work.  

Summary 

It is anyone’s guess as to whether the metaverse will indeed be the next incarnation of the internet. I remember an interview with David Bowie in 1999 in which he accurately predicted the impact the internet will have. He might have said the same about the metaverse today. In any case, technologies like VR, AR, MR, and AI will drive more and more user interactions into the virtual world, and software developers must deal with the shift in technology and the change in user experience. 

Special thanks to: Stuart Williams, Simon Spielmann and some support from ChatGPT 

Just as professional rally drivers rely on a navigator to get them from A to B, so the sales function depends on strong sales operations support.

It’s the role of the sales operations team to generate, track, and progress sales leads; to capture, validate, and track opportunities as part of sales forecasting; to move those offers forward to the offer stage with a configured and competitive quote; and when the sale is made, to convert the purchase order into a valid sales order for fulfilment.

These responsibilities are beset by all kinds of challenges. Sales operations teams frequently find they have insufficiently accurate, easy-to-access data and insight-driven forecasting; that their sales technology is outdated; and that they have inadequate resources and roles that are not clearly defined. At the same time, teams constantly need both to recruit and retain talent, and to adapt to changing business models.

All these challenges often mean that sales operations teams spend much of their time dealing with day-to-day tactical issues when they would rather be thinking and acting strategically – looking ahead, developing plans, testing them, and then putting them to work.

Design, build – and transform

What’s needed is a smart, seamless sales operations model (think of this as a sales operations-as-a-service concept) that can be tailored to the culture, practices, and needs of the individual organization – and that empowers the people who use it.

It’s the bespoke nature of the model that makes the design stage so important. If a service provider is involved, it’s our view that the best approach is for that provider to work closely with its client organization, designing and mapping processes based on lived experience within the sales operations function, and also on relevant personas.

What should emerge from this deep dive into future aspirations and current practices is a target operating and service model. The organization and its service partner work together to design and set up services including policies, process rules, a control framework, and new ways of supporting sales operations team members.

The final stage in the transition is to move from current processes to a more streamlined and coherent smart digital model. Technology collapses processes and creates a tremendous opportunity to eliminate drag in a process and improve how an internal or external user experiences it. Focusing on customer experience not only delivers hard gains (ROI, margins etc.), but also qualitative benefits such as CSAT/NPS that translates to stickiness, repurchase, loyalty, and “mind-share.”

What does success look like?

At Capgemini, our digital sales solutions take advantage of innovative technologies and sales systems to integrate, streamline, and optimize sales touchpoints and processes across the lead-to-order lifecycle – delivering accurate, easy-to-access data, enhanced sales support, and data-driven sales analytics.

The aim is to enrich our clients’ digital sales strategy with relevant insights and data that drive operational excellence and efficiency across the sales function. And we’ve seen some truly transformative business outcomes, including 15–25% reductions in turnaround time, 3–5% improvements in win-rate, 15–25% increases in time returned to sales, and 10–20% improvements in net promoter score.

Everybody wins

Intelligent, integrated sales operations of this kind not only address those organizational challenges I outlined earlier in this article – they also provide increased value for a company’s customers and business partners.

When sales processes are efficient and cost-effective, and when sales operations teams are well informed and in control, everyone is happy.

To learn how Capgemini’s Empowered Sales Operations solution delivers frictionless, digitally augmented, data-driven sales operations that drives competitive advantage across your business, contact: deepak.bhootra@capgemini.com

Deepak Bhootra is an established executive with two decades of global leadership experience. He delivers process excellence and sales growth for clients by optimizing processes and delivering seamless business transformation.

Based on the above, it’s perhaps easy to ask “Well, why can’t my ERP do all that?”, and to a certain extent it is possible for an ERP, or the combination of an ERP and eCom platform, to cover some of the functions described. But the challenge should not be “can my ERP do this?”; it should be “is my ERP the right system to do this both today and in the future?” If all your experience is with an ERP, then it’s tempting to see it as the solution to all types of problems; in the words of Abraham Maslow, “If the only tool you have is a hammer, you tend to see every problem as a nail.” But an OMS should be seen as another tool in your belt – perhaps not right for every task, but certainly an option to be considered in the right circumstances.

So, what could be some of the reasons for using an OMS rather than an ERP or a commerce platform?

• In many businesses, the ERP which manages back-end functions around supply chain or finance is not the same solution managing customer-facing or in-store functions. A customer offer doesn’t care about these boundaries (e.g. click and collect needs to know inventory, which may be in the ERP, but the store systems are critical in enabling the pick-up process), so strategy should be driven firstly by customer experience, customer-focused use cases and value drivers, and only then should existing organizational or systemic constraints be considered. A modern OMS connects one or many customer-facing front ends via APIs or built-in apps to back-end supply chain & finance processes, acting as a reliable bridge between channels, stores, warehouses, ERPs, and more.
• Customer service – like above, the ERP is usually not the system which enables customer services’ call center tools. An OMS can easily integrate through enterprise APIs to whichever systems the call centers are using to reduce complexity and connect the customer journey.
• Returns – returns are a huge cost driver for virtually all D2C businesses, and many resort to implementing a specific returns solution separate to their channels and/or ERP. Whilst this can enable more customer functionality, it is often at the expense of being able to tie the returns flow directly in with the outward fulfillment, often making the customer journey disjointed. An OMS enables retailers to automate and coordinate the return process to decrease cycle times and handling costs – all while simplifying the customer journey.
• ERP order fulfillment flows are typically more aligned to an order to cash process, driven by a limited number of customers ordering large quantities of products frequently via Electronic Data Interchange (EDI) or even through dedicated account managers, and making limited changes to those orders, rather than a consumer-focused fulfillment flow where large numbers of customers will order small baskets of products infrequently across a large number of self-service channels, and will often want real-time changes to those orders during or after that fulfilment – the differences in these two approaches are considerable, almost like speaking two different languages, and thus having an OMS in the middle can be the translator.
• As fulfillment networks grow and become more complex, with the options to fulfill both from owned warehouses, but also stores, 3^rd party logistics providers, retail partners, drop ship vendors, and other locations, it’s likely an ERP simply cannot master all inventory or location details (or perhaps would not want to, given that to do so may impact financial calculations), which then makes presentation of a consolidated supply view difficult, let alone accurate for a customer promise.
• ERPs are typically designed to mandate best practice flows to business processes, and deviation from those flows is often costly or not possible, so implementing new logic or processes to handle the ever-changing world of customer fulfillment can be disruptive and costly. By contrast, an OMS is set up to expect ongoing change in logic, new workflows, new offers, and new capabilities.
• Lastly, a modern OMS is highly modular and designed for agility – Fluent Commerce order management for example, is event-based, so inputs trigger outputs and operations are all in real time. A MACH (short for Microservice, API-drive, Composable & Headless) architected application like this means updates can be made often and changes are much easier to test, deploy, assess, and iterate, generally without continual needs to test end-to-end functionality.

Where could an ERP be suitable for enabling OMS capabilities?

All of the above is intended to point out some of the difficulties of trying to manage OMS functionality inside an ERP, but it isn’t the case that it’s always the wrong decision. Modern ERPs do allow more flexibility in their operations, and if you’re a D2C business which has grown up with the capabilities to deliver a customer-centric offer via digital channels, then many of the considerations above have probably already been considered and factored in. In such examples, the key consideration should perhaps not be whether an ERP is right for my business today, but whether there is enough agility in my operations that I could adapt ways of working to handle new channels, new offers, new products, etc. without impacting the wider ways of working each time.

So, in summary, what are some of the initial questions I need to consider when looking at my existing systems vs. a new OMS?

• What are the customer features/functions/offers which are going to add value to my business in the near term and long term?
• How feasible is it to adapt my current systems to handle these new customer offers/requirements? And how feasible is it given whatever else is already in the pipeline for these systems?
• If my requirements change, can my current system keep up with frequent changes? Is it future-proof? Scalable?
• Can I add additional value by decoupling my customer offer from my core processes through use of an OMS?
• How much would implementing an OMS cost vs. adapting my current ERP?

These aren’t easy questions and there will always be good arguments on both sides, so please reach out to the experts at Capgemini.