The current HR Cloud market is broad and offers a variety of unique features and functionalities that can often be difficult to keep track of. Regardless of the type of system (e.g., HR suite, HR enterprise platform, or experience solution), the right decisions should be taken with a focus on People’s Experience, Experience Excellence in HR and not forget the “Experience Excellence Value Case” focusing on the optimal balance between HR automation ambition, HR IT investments and the level of people experience and seamless workflow design desired. Careful consideration of all companies, business, people, HR and IT-related requirements is essential to ensure that the right solution is selected, the configuration is a success, and will fit with the company’s business case and experience culture.

HR Cloud selection and implementation

To take the right decisions, we recommend a structured vendor selection approach in the first step that guarantees the future HR Cloud system selected best fits the company’s budget, HR products & Service Delivery ambition, process requirements (e.g. in terms of ET&C, legal compliance) and the desired people experience & way of working culture. Afterward, a cloud readiness assessment stream ensures that the company can start the subsequent cloud implementation with all necessary preparations and requirements. To achieve all this, a detailed upfront consideration of the targeted HR IT strategy and vision is a must, defining the organization’s future HR IT Landscape ambition and related optimization and investment need decisions to be taken.

For a successful cloud implementation, it is crucial to also empower the HR team, employees, and managers as new processes are rolled out, political obstacles are carefully managed and moderated and a joint future cloud mindset is proactively promoted and growing. Our dedicated cloud advisory approach combined with agile program & change management capabilities is tailored for the suite, enterprise and experience solutions, and associated vendors. It is developed by agnostic HR Cloud Advisory & systems experts and enables customers to take the right decisions, from technical and functional perspectives. When handled in this way, organizations can generate major benefits towards seamless workflow and process design as well as HR IT architecture optimization and the defined Experience Excellence Value Case.

Rethink the onboarding experience

But what does this all mean in concrete terms? Let’s dig into one prominent example our clients frequently address as their major area of concern: large potential for experience-driven HR IT optimization is frequently identified e.g., in the Onboarding process. This process focuses on how organizations integrate new employees and prepare them for their new roles. When the process is successfully streamlined in our experience-driven way, we directly see project outcomes such as improved retention rates, employee engagement, and productivity as return on investment. But how to? As part of our HR IT Onboarding redesign of end-to-end processes, structures and responsibilities are reviewed and redefined in the experience-/ user journey-driven way considering all external and internal stakeholders involved, such as external workers, recruiters, hiring managers, IT employees, etc. This makes Onboarding processes for the HR IT landscape optimization certainly increasingly complex.

Almost all major HR management software platforms offer their own Onboarding modules. In this blog series, we take a closer look at the offerings from vendors of the suite solutions Oracle, SAP and Workday, as well as the enterprise solution, ServiceNow. Furthermore, we will offer an approach that leads to the optimal Onboarding experience, covering three Onboarding phases leveraged by HR Cloud technologies.

Our next blog #1 takes a closer look at creating the decisive factor for employees to stay by leveraging HR Cloud technologies.

Interested? Looking forward to reconnecting! 

Part of Capgemini’s HR CloudWatch Blog, hosted by Dr. Sandra Duesing, Vice President of Capgemini Invent Germany, and the Global Head of “Reinventing HR.”

If the global pandemic has taught us anything, it is the need to provide an irresistible and amazing people experience. Indeed, investment in the data, services, analytics, and tools to boost employee empowerment and engagement has had an extremely high return during this turbulent period.

According to a report of senior HR leaders, 79% see acceleration of digital transformation in their organizations due to the pandemic, while 96% of HR leaders see the role of HR shifting from being an administrative service provider to concentrating more on designing employee experiences and satisfaction, acting as change agents, and developing talent.

The HR function must keep up with this pace of digital transformation and disruptive business practices in order to deliver on growth, but is being impacted by a number of challenges, including labor shortages and the changing expectation of employees working from home.

Unique APAC challenges for HR

We only need to look at the APAC market, and its unique specificities, to understand the level of these challenges compared to the other regions. Up to 78% of the Asian workforce was working in person up until 2020 (pre-pandemic), much higher than in US and EMEA, making the move towards hybrid working more challenging.

Highly diverse work cultures, lack of strong tech infrastructure, and a high proportion of blue collar workforce in APAC has also contributed to this slow adoption of the hybrid work model in the region. In addition, APAC’s highly fragmented and diversified market led by a myriad of languages, local regulatory requirements, and varied ways of working is making it challenging to drive standardization.

Moreover, a recent survey by Korn Ferry states that APAC faces an imminent labor shortage of 47 million people and $4.238 trillion in unrealized annual revenue across the region by 2030. With the war for talent becoming increasingly competitive as employees prioritize experience over pay along with continuously changing skillsets, HR leaders have started to look at the gig economy to provide the greatest flexibility without hitting the bottom lines.

Another important report highlights the focus on HR tech modernization in APAC with 89% of respondents preferring to implement people analytics solutions, but only 38% believing their organizations are ready. The main reasons for this sluggish adoption are the diverse nature of the APAC region, differing levels of economic maturity, multiple HCM systems, complex data transmission, shortage of right talent, inadequate systems/processes, budget limitations, and difficulty in securing executive buy-in are.

People-centric, frictionless HR operations

These challenges and priorities are defining a new future state for HR shared services and outsourcing. For the first time, “focus on core business outcomes” is the most important driver for companies, with “cost” falling to second place. This shows that now, more than ever, companies view HR outsourcing and transformation as a strategic driver of business value creation through innovation and differentiation.

Given this shift, organizations are now looking for transformation partners who can proactively respond to regulatory changes and market shifts, while bringing cutting-edge HR and solutions to help with organizational and people challenges.

Today’s CHROs and CXOs need to now focus on standardizing and automating their employee processes to create consumer-grade people experiences. And all of this after designing and executing an efficient, end-to-end service delivery model driven by intelligent, data-driven, frictionless HR operations that seamlessly connects people, processes, and technology.

To learn how Capgemini’s Intelligent People Operations can drive a personalized and frictionless people experience across your organization, contact: ajay.chhabra@capgemini.com or rashmeet.kaur@capgemini.com

Among the most viable candidates and strongest contenders are quantum computing Monte Carlo (QCMC) simulations. Over the past few years, the pace of development has certainly accelerated, and we have seen breakthroughs, both in hardware and software, that bring a quantum advantage for finance ever closer.

Important computational and technical trends include the following:

• Roadmaps for hardware development have been defined and indicate that an estimated quantum advantage is within a 2–5-year reach. See for example IBM and IonQ, who both mention 2025 as a year where we can expect the first quantum advantage.
• End-to-end hardware requirements have been estimated for complex derivatives pricing at a T-depth of 50 million, and 8k qubits. Although this is beyond the reach of current devices, simple derivatives might be feasible with a gate depth of around 1k for one sample. These numbers indicate that initial applications could be around the corner and put a full-blown advantage on the roadmap. Do note, however, that these simple derivatives can also be efficiently priced by a classical computer.
• Advances in algorithmic development continue to reduce the required gate depth and number of qubits. Examples are variational data loaders, or iterative amplitude estimation (IAE), a simplified algorithm for amplitude estimation. For the “simple derivatives,” the IAE algorithm can run with around 10k gates as opposed to 100k gates for 100 samples with full amplitude estimation.
• There is an increasing focus on data orchestration, pipelines, and pre-processing, readying organizations for adoption. Also, financial institutions worldwide are setting up teams that work on QCMC implementation.

All these developments beg the question: what is the actual potential of quantum computing Monte Carlo? And should the financial services sector be looking into it sooner rather than later? Monte Carlo simulations are used extensively in the financial services sector to simulate the behavior of stochastic processes. For certain problems, analytical models (such as the Black-Scholes equation) are available that allow you to calculate the solution at any one moment in time. For many other problems, such an analytical model is just not available. Instead, the behavior of financial products can be simulated by starting with a portfolio and then simulating the market behavior.

Here are two important examples:

• Derivatives pricing: Derivatives – financial products that are derived from underlying assets – include options, futures contracts, and swaps. The underlying assets are expected to be stochastic variables as they behave according to some distribution function. To price derivatives, the behavior of underlying assets has to be modelled.
• Risk management: To evaluate the risk of a portfolio, for example interest rates or loans, simulations are performed that model the behaviour of the assets in order to discover the losses on the complete portfolio. Stress tests can be implemented to evaluate the performance of the portfolio under specified scenarios, or reverse stress tests can be carried out to discover scenarios that lead to a catastrophic portfolio performance.

Classical Monte Carlo simulations require in the order of (1/ε)^2 samples to be taken, where ‘ε’ is the confidence interval. For large cases, this easily becomes prohibitive. Suppose a confidence interval of 10^(-5), billions of samples are required. Even if workloads are parallelized on large clusters, this might not be feasible within an acceptable runtime or for cost reasons. Take for example the start of the Covid-19 crisis. Some risk models looking at the impact of Covid on worldwide economies almost certainly would have taken months to build and run, and it is likely that before completion, the stock market would have dropped 20%, making the modelling irrelevant.

Quantum computing Monte Carlo promises, in theory, a quadratic speedup over classical systems. Instead of (1/ε)^2  iterations on a classical system, (1/ε) iterations on a quantum computer would attain the same accuracy. This means that large risk models that take months to complete may become feasible within just hours.

Unfortunately, it’s never as easy as it seems! Although sampling on quantum computers is quadratically faster, a large overhead could completely diminish any quantum speedup. In practice, expressing a market model as quantum data seems extremely difficult. There are a few workarounds around this problem, such as the data loaders as announced by QCWare, or a variational procedure as published by IBM, but it is yet to be seen if these work well on real problems.

However, if quantum hardware and software continue to develop at their current pace, we can expect some very interesting and valuable uses for quantum Monte Carlo applications. A business case can easily be made, because if  QCMC improves risk management simulations, then the reserved capital required by compliance regulations could be reduced, freeing up capital that can be used in multiple other ways.

Furthermore, the derivatives market in Europe alone accounts for a notional €244 trillion. A slightly inaccurate evaluation of this market could lead to a large offset to its actual value, which in turn could lead to instability and risks. Given the huge potential for derivative pricing and risk management, the benefit of significant and deterministic speedups, and an industry that is fully geared up to benefit from quantum, QCMC seems to be one of the killer applications.

However, before QCMC works successfully in production, a lot of work remains to be done. Just like in any application, proper data pipelines needed to be implemented first. The time series required for risk management need to be processed on stationarity, frequency, or time period. If policy is adjusted to daily risk management, data streams also have to be up to date. If a quantum advantage needs to be benchmarked, then its classical counterpart must be benchmarked too. Additional necessary developments, such as building the required infrastructure (given the hybrid cloud nature of quantum applications), its relation to compliance regulations, and security considerations, are still in their early stages.

Given the huge potential of quantum computing Monte Carlo, a number of pioneering financial services companies have already picked it up; Wells Fargo, Goldman Sachs, JP Morgan Chase, and HSBC are well established in their research into using QCMC or subroutines. Certainly, these front runners, will not be late to the quantum party, and they will be expecting to see benefits from these exploratory POCs and early implementations, likely in the near future.

Deploying algorithms in productionized workflows is not easy, and it is even more difficult when a technology stack is fundamentally different. But, these challenges aside, if the sector as a whole wants to benefit from quantum technology, now is the time to get curious and start assessing this potential killer app.

First published January 2021; updated Nov 2022
Authors: Camille de Valk and Julian van Velzen

While we are impatiently waiting for the emergence of quantum technologies, which will bring major technological progress, the internet is preparing for the day when quantum computers will be able to decrypt our secure communications, called “Q-Day” by some.

But, while quantum hardware is not yet mature enough to decrypt the algorithms currently used, our data is already at risk from hackers, who accumulate encrypted data in order to decrypt it in the future. So, how do we prepare for this eventuality and how has this threat, as of today, changed the way we think about this relationship between cybersecurity and quantum computing?

Quantum computing is a scientific technological evolution. Conversely, digital security is a concept: in essence, it can be interpreted differently depending on its uses, which are sometimes competing (Read more on this subject from B. Buzan and D. Batistella). Exploring the relationship between quantum computing and digital security can therefore generate paradoxical discourse.

The paradox of quantum progress: threat or opportunity for security?  

The current investment in quantum technologies is teeming and patent publications have grown exponentially over the last 10 years. The emergence of numerous venture capital companies in the ecosystem also demonstrates the market’s interest in these technologies. This will lead to numerous use cases due to quantum computers providing exceptional computing power, but which will also offer tools to cybercriminals, to deconstruct the security systems already deployed.

For example, via the quantum implementation of Shor’s algorithms, it will be possible to break the current cryptographic protections, which are based mainly on the current difficulty in factoring large prime numbers in a reasonable amount of time. Therefore, the arrival of the quantum computer, and its use by networks of cybercriminals, will mean a significant risk for companies. Data requiring long-term storage (biometrics, strategic building plans, strategic IP, etc.) could then be easily accessible by malicious entities using quantum computing power.

These threats call into question the creation of a digital space known as “trusted.” The risk is therefore too great to stand still in the face of this quantum revolution, and the launch of initiatives is necessary, as soon as possible, to identify the risks associated with the various activities of the company, and to reassure on a large scale.

At the same time, new technologies promising quantum security are emerging.  First, new mathematically based cryptography is being developed that remains “unbreakable” even by quantum computers. According to the viewpoint published by ANSSI on January 4, 2022, the National Institute of Standards and Technology (NIST) has been analyzing “post-quantum cryptography” (PQC) algorithms, to increase the defense strengths of the virtual world tenfold since 2016. This standardization recommendation is expected to be published by 2024.

Second, physics-based cryptography, called quantum key distribution (QKD), is emerging as an alternative for ultra-secure communications. While post-quantum cryptography is based on the premise that no efficient (quantum) algorithm has been found to “break” it, QKD technology relies on physical principles to detect the interception of secure keys. In case of interception, these keys can be regenerated until the parties are certain that the communication is secure.

This standardization of post-quantum algorithms is focused on a competition organized by NIST in order to analyze different algorithms and keep the most efficient. In this competition, 69 candidate algorithms were submitted in 2017, of which only 7 reached the 3rd round of qualification in July 2020. The algorithms selected in this round are based on NP-Hard problems (whose solution is performed in exponential times, at best) and which will remain complicated to solve even for quantum computers. They are concentrated around “lattice” problems (based on the calculation of the smallest vector in a set of points) as well as “code-based” algorithms (based on the decoding of a linear code). The results of this first round were published on July 5, 2022.

Finally, by drastically accelerating the learning speed of artificial intelligence and machine learning algorithms, quantum computing will also strengthen all the security systems that rely more and more on these technologies. Security operations centers (SOCs), which deploy tools to detect weak signals of attacks, and which aim to quickly identify deviant behavior in networks and uses, will be all the more effective. In all sectors, from fraud detection in banking to industrial incidents, quantum computing will increase the effectiveness of SOCs. As a result, the need for security teams to ramp up on these already visible technologies will only increase in the coming years.

The paradox of quantum research: wait too long or act too fast?

In all public and private organizations, the development of security teams’ expertise in quantum issues is still in its infancy. In the best of cases, the first reflections are oriented around the business benefits of quantum technologies, leaving cybersecurity issues and the concrete analysis of risks and opportunities in the background. This observation is reinforced by a lack of awareness of the subject within companies, which means limited internal training initiatives for employees, and so leaves the subject to external expert partners.

How can we professionalize the approach to a technology that has not yet been democratized? At what point does research become real enough to trigger industrialized programs? Even as we enter the development phase of a new generation of more powerful quantum computers, the need for companies to experiment becomes essential, in order to avoid being threatened by cybercrime networks that are constantly one step ahead of the lines of defense, and more agile in absorbing new technologies and hijacking them.

The presence of Cloud leaders (Google, Amazon, Microsoft, IBM, etc.) in this market allows a democratization of, and a relatively easy access to, these technologies. The availability of quantum resources, via platforms already implemented by their customers, creates a very fertile ground for various experiments. But how to secure sensitive information from these projects when using external and shared devices, which is the very basis of the cloud model? So-called “blind quantum computing” ensures that even the owner of the quantum computer is unable to know what computations users are performing on them. However, while this can have great applications in terms of privacy protection, there is, in return, a risk of losing any insight into the user’s or users’ intentions.

Waiting too long or acting too fast? The answer is not simple and will come from a collective movement, driven by the construction of large training programs in quantum engineering (certification courses, allocation of research budgets, etc.), or the pairing between organizations and start-ups.

The geopolitical paradox: a universal issue or a question of sovereignty?

All scientific revolutions end up becoming universal, one way or another. They impact societal structures, means of production, and, de facto, citizens; the quantum revolution is no exception to the rule.

However, it is clear that it is the subject of a geopolitical confrontation. Like digital technology, quantum computing is a field of economic and political competition between states. Some states invest more than others, implying two phenomena: a competitive advantage in the context of the market economy around quantum computing that will be created in the coming years, and a use for national intelligence purposes.

China is well on its way to becoming the leader in quantum technologies, especially in the field of communications, with an estimated investment volume of 10 billion euros. It has the largest QKD quantum communication network, with satellites and a fiber optic network capable of communicating over 4600 km. This is a strategic project that aims to protect its commercial and military communications from intrusions.

France, for its part, aims to become a leader on a European scale, with a plan to invest 1.8 billion euros over five years, announced in January 2021. With a very dynamic ecosystem of start-ups, France is investing in ways to facilitate meetings between academic experts and industry. Examples include Pasqal, which is developing its offer (a quantum computer capable of computing at room temperature, providing the best ratio of computing capacity to energy consumed in the world), merging with Qu&Co to create a European leader, and Alice and Bob, which is raising €27 million – and academics around the Saclay plateau.

This dynamism has led to some initial achievements, such as the French Navy’s development of the Girafe system (interferometric gravimeters for research with embarkable cold atoms), an autonomous navigation system based on quantum detection technologies that can calculate its exact position without using the American GPS network, scheduled for 2026/2027.

On the cyber side, the inauguration of the Cyber Campus on February 15, 2022, is another example: it demonstrates a French willingness to organize cross-sectoral, public/private cooperation around major cybersecurity challenges and future innovations. It is typically the place where the challenges of the arrival of quantum computing can be discussed.

Quantum computing then becomes a question of digital sovereignty – the idea that in a polarized world order, it will be important for states to assert a quantum power and a capacity for self-determination over their digital space.

Quantum computing will bring intrinsic contradictions: on the one hand, universal scientific progress and the strengthening of our anti-cybercrime capacities, and on the other, the reinforcement of a geopolitical and economic conflict and a threat to digital trust.

First published in Les Echos, 15 September 2022 : Avec le quantique la sécurité numérique entre dans l’ère du paradoxe

Author: Clément Brauner, Quantum Computing Lead, Capgemini Invent.
Co-authors: Jeanne Heuré, Head of Digital Trust & Cyber, and Nicolas Gaudilliere, CTO – both of Capgemini Invent

According to recent Capgemini research, only 50% of organizations benefit from data-driven decision-making, while only 39% of organizations successfully turn data-driven insights into sustained competitive advantage.

In the world of finance and accounting (F&A), this really begs the question: how can your finance function implement an analytics platform that gives its users the insights they need to unlock potential and value for your organization?

No one size fits all

We live in a world full of information that comes at us from almost every aspect of our lives. We’re constantly bombarded with content, most of which has no relevance to us. And we appreciate the option to personalize how we receive and store this information through adding it to our feeds and favorites.

The F&A world works in exactly the same way. The three main personas that work with finance intelligence in your F&A function (CFO, transformation lead, and service delivery lead) need the right dashboards and metrics embedded into an easy-to-use platform that gives them the information they need to provide actionable insights at the touch of a button.

Let’s take accounts payable invoice processing as an example and reference the same input data. While a finance intelligence platform can give visibility on the status of all open or unpaid invoices to your service delivery leads (SDL), your transformation leads (TL) will be more typically interested in the healthiness of the overall invoice channels mix (paper, email, e-invoice, etc.), while the CFO will only want to look at the days payable outstanding (DPO) metric.

Furthermore, fraud risk alerts will be relevant for the CFO and your SDLs, while metrics such as on-time payment will only interest your TLs and SDLs.

The same metric – but at a different granularity level – can be relevant to different personas. For example, a single unpaid invoice vs. the systematic problem of late payment.

But how can you customize your finance intelligence platform for different personas and users?

Persona-led analytics platform design

Understanding the role and tasks of your users enables you to select the most relevant content for each persona and build customized dashboards that help them be more productive. Showing a limited number of meaningful and actionable KPIs also helps your users stay focused on the right areas.

The more seamless the adoption of a next-generation finance intelligence platform, the closer you are to establishing a data-driven culture. And while having the business insights alone will not make your challenges go away, having the right analytics and alerts in front of you will help you make the optimal decisions you need to succeed.

In turn, this enables you to give your customers what they want, while achieving the benefits of a truly Frictionless Enterprise.

To learn more about how Capgemini’s Finance Intelligence can help start your journey in smart analytics and real-time, frictionless decision-making, contact: daniel.jarzecki@capgemini.com

When it comes to data, the financial services industry has some of the greatest opportunities but also faces tremendous risks and pressures. As competition increases, along with customer expectations, financial companies are navigating regulatory, security, and privacy minefields along the road to delivering greater innovation.

To successfully make this data journey, finance has become a leading sector in terms of building data ecosystems. As such, it offers critical lessons for companies just starting to explore the possibilities that data sharing can unlock.

“It helps from the bottom-line perspective in terms of bringing greater efficiencies,” according to Ashvin Parmar, Vice President, Insights & Data Practice Leader at Capgemini. “But more importantly, it lays the foundation for innovation. You come up with new products and bring them to market faster.”

Parmar, who works closely with some of the biggest names in finance as well as emerging fintech startups, says the adoption of cloud computing and the shifting economics of data have enabled financial companies to become more aggressive and experimental with how they leverage their data. As the line blurs between traditional financial services and retail experiences, companies know they must rapidly adapt.

“The banks and insurers don’t have a choice but to start to collaborate,” Parmar says. “So, the desire is there to grow and reach new prospects and clients and to service them better. By procuring data from a variety of sources, they can enrich their own data and improve in areas like risk management. They also get a better view of the client and their preferences.”

Constructing data ecosystems has helped financial companies become nimbler and move faster.
For instance, developing robust know your customer (KYC) systems is vital for financial companies from both a security and regulatory perspective to fight fraud and money laundering. But if each company has to build its own KYC platform, it can be costly while offering only limited reach for the data it can access.

A Fintech startup is cutting across this problem, thanks to its neutral KYC platform. This startup has partnered with a wide range of financial players who are sharing their data in its system. Companies that engage them get access to a deeper pool of data without having to create their own algorithms and technology stacks.

“It reduces the cost and you can have better risk management because you’re benefiting from experiences and the data from your competitors,” Parmar says.

Capgemini is working with the fintech startup to create a credit analytics platform that brings in data from a wide range of sources while leveraging Capgemini’s domain expertise in this area, including its risk management model. Parmar believes this joint offering of credit analytics as a service will allow financial firms to remain focused on their core services rather than expending resources on making sure models are up to date and procuring the right data.

Companies can also accelerate their data transformation via 890 By Capgemini, a platform that offers access to ecosystems of industry-specific data that can be combined with exclusive internal data. This curated experience includes data, insights, and outcome exchanges that help companies quickly and securely benefit from the power of data sharing.

Getting started

When a company is ready to take the leap into data sharing, Parmar offers a few guidelines for getting started.

Naturally, a company should have a data-driven culture. But to compliment that, it’s also vital to have a privacy culture.

“Privacy is not just about the technology and the government’s regulations or the processes,” Parmar says. “A culture of privacy has to be there for all the other controls to work properly.”

Capgemini has a lightweight survey created in partnership with leading academics to allow a company to measure its internal privacy sensitivity. The survey delivers a heatmap so companies can see where their privacy blind spots are. That’s the first step toward addressing cultural issues.

From there, companies need to understand their data environment, including its maturity and readiness to undertake data sharing. This should also include a clear inventory of internal IT systems in terms of their capabilities (or lack thereof) for connecting to partners or any type of ecosystem.

Finally, companies need to set clear long-term goals and understand what drives them from a business perspective. Is it data monetization? Increasing the footprint? These answers will help identify the technology needs and business capabilities required to realize these plans.

“It’s a journey,” Parmar says. “Maybe you start with an internal data marketplace to build your first data exchange platform. And that evolves into an external data exchange. And that morphs into new data products.”

The good news is that once a company is ready to take those steps, cloud providers and hyperscalers now have tools that can help them move quickly. These companies are making massive investments in the technologies and services that are rapidly reducing costs even further and making them more accessible. While the focus was initially on just getting customers into the cloud and saving money, these cloud providers are now offering more sophisticated tools for specific verticals, such as finance.

“If you are a bank, you don’t have to start from scratch,” Parmar says. “There is a tremendous amount of R&D being done to facilitate these data exchanges. The barriers to entry into a marketplace are going away. It’s becoming easier and it’s becoming safer to innovate with data exchanges and data ecosystems.”

Think of the last innovative enterprise you interacted with. Can you think of one where some form of recommendation or personalization was not part of the experience? Recommendations are no longer product features, service attributes, gimmicks, or nice-to-haves. They are the central organizing design principle of modern experiences. Personalization wasn’t an add-on for Amazon, Uber, Netflix, or Airbnb. It was core to their experiences because personalization was core to their business model. We’re moving to a world where not doing personalization is a recipe for guaranteeing underperformance and obsolescence. So, how do we do this well?

Serendipitous influence

Architecting modern personalization systems isn’t just about data and algorithms. Recommendation architectures are those that persuade and influence our choices. The choices they present and how they are presented change not only how we discover products and experiences but how they promote internal self-reflection. Done well, recommendation and personalization are systems of “serendipitous influence.” The best recommendations are those that inspire you in a way you didn’t expect. They make you wish you’d gotten that recommendation earlier. Fostering serendipitous delight consistently becomes the hallmark for enterprises that want to provide delightful experiences. In our experiences in building such systems, we find there are four key dimensions that help firms elevate from personalization to serendipity:

Data

1. A consolidated view of “customers” and the enterprise is critical for consistent experiences. Data silos are likely to result in incongruent experiences as customers traverse channels and inconsistent recommendations between content, products, and services.
2. The proliferation of data across applications, devices, and brands makes it critical that data is connected, otherwise the customer is more likely to ask “Why don’t you know who I am?”
3. Resolution of unknown to known users is also critical in maximizing the total market of customers that you can personalize for. Amazon’s internal data lake (Project Andes) was the start of creating a producer consumer “data mesh” to democratize its insights on customers. This enables Amazon teams to use data across the entire ecosystem to recognize customers across channels and devices (e.g., from the web to mobile, to Prime Video, and to Alexa devices).

Intelligence

1. The best personalization systems are built from strong data-powered cultures. Literacy in data science, AI, and ML is necessary, but not sufficient. Teams need to be literate in utilizing insights to drive action.
2. Linking analytics to outcomes and a holistic set of KPIs is needed to monitor the health of recommendation systems and understand their impact (intentional or otherwise). YouTube found optimizing on clicks created click-bait recommendations that resulted in poor experiences and less user engagement versus optimizing for time spent watching videos.
3. Rules-based strategies help to “fake it till you make it” but they struggle at scale and often underperform due to biases in rules and an inability to iterate quickly. Personalization systems should allow employees to build campaigns to optimize based on business goals as opposed to rules (e.g., maximize revenue, minimize my carbon footprint through fewer shipments). This allows employees to be more strategic and creative while permitting underlying analytical models to make decisions on data volumes and patterns that would otherwise go unnoticed.

Amazon.com is a different store for every customer. Its Personalization Platform (P13N) allows business teams to set strategies and filters to leverage personalization algorithms optimized against business outcomes (e.g., improve conversion, drive engagement). The system also understands when signals like customer intent are changing. If I searched for shoes in the morning but now, I’m searching for toothpaste, my recommendations should understand my intent and needs have shifted, and consumer goods and staples are more relevant than athleisure products. These signals are optimized against business or experience KPIs which allows for dynamic recommendations to improve business outcomes while boosting the customer experience.

Design

1. Experimentation leads to better personalization. Design is key to better experimentation. The best recommendation cultures view experimentation of systems as an end-to-end exercise from data and analytics to UI/UX development to creative development and qualitative research.
2. Recommendation Experience Design (RXD) as a competency is integral to designing systems that create the proper nudges and in understanding the intended and unintended consequences of personalization. Done well, these teams are a blend of technologists, behavioral scientists, creatives, and experience designers.
3. Volume of experimentation can be a valuable KPI unto itself.
   “If you double the number of experiments you do per year, you’re going to double your inventiveness.” – Jeff Bezos.
4. Without trust in the system, it’s hard to get adoption. Without adoption the system will not survive. There’s a fine line between helping to make someone aware of a system and navigating their choices and manipulating them. Systems that manipulate are unlikely to survive.

Through user experience research, Stitch Fix found that people couldn’t judge what they would like from clothing images alone. Customers might say from an image they don’t like something, but when they interacted with it or put it on, they would find they loved it. This gave encouragement to be more aggressive in sending products that algorithms suggested a customer would like even if the customer stated they weren’t interested in those types of products.

Orchestration

1. Orchestrating intelligence and actions for interventions across the customer journey is essential to consistent experiences. Enterprises need a view of the critical journeys (if not all journeys), and this requires organizations to easily orchestrate data and analytics internally and externally to empower employees to improve customer experiences.
2. Employee experiences can be as critical as customer experiences. If employee experience is an afterthought (and poorly done) then adoption of the system will be low and the corresponding pace of experimentation will suffer (if it occurs at all).

Stitch Fix’s orchestration of customer and merchandising data enabled the development of Hybrid Designs, its internal AI-driven design group. The apparel designs are a true collaboration built on the orchestration of human intelligence and artificial intelligence that helped Stitch Fix generate 2021 revenue of $2.1 billion, with over four million active users

Recommendation systems that are thoughtfully architected across these four dimensions drive differentiated and innovative experiences through more experimentation and greater degrees of adoption within the enterprise and by customers. This results in systems that evolve from a goal that’s transactional – “Will I buy this?” – to those that make us wonder “How did I live without this?” What does the future hold? The expectation from consumers is some form of personalization. Moving forward, discovery should be like talking with a friend who knows you so well that they can anticipate your needs. This is a world where we’ve democratized access to serendipity, and firms should provide such experiences or be left behind. It is either serendipity at scale or obsolescence with haste.

INNOVATION TAKEAWAYS

Personalization is key
Personalization is no longer a nice to have, it’s critical for maintaining a competitive advantage.

Serendipity tops it
The best recommendations are those that inspire and delight you in a way you didn’t expect.

Four dimensions
Data, intelligence, design, and orchestration are the key dimensions for architecting innovative, serendipitous personalization systems.

Cover it all
End-to-end experimentation is critical in architecting and designing personalization systems that actually make an impact on consumer and employee experiences.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 4 features 18 such articles crafted by leading Capgemini and partner experts sharing inspiring examples of it – ranging from digital twins in the industrial metaverse, “humble” AI, serendipity in user experiences, all the way up to permacomputing and the battle against data waste.. In addition, several articles are in collaboration with key technology partners such as  Alation, Cognite, Toucan Toco, DataRobot, and The Open Group to reimagine what’s possible.  Find all previous Waves here.