Data is not an end in itself: it is a tool to help people. Put differently, it is the aggregation of data for specific business purpose that creates insight, for example, by providing the right information to the right people at the right time. Given their importance to our everyday lives, police, security and justice organizations need to excel at accessing, using, and managing data, and balancing civil liberties with the needs to prevent and detect crime. The benefits of making better use of data are clear – enabling police officers to be more effective when on patrol – reaching citizens in crisis faster, helping pedestrians where they need it, or catching criminals through better imaging and CCTV. 

But there are big challenges too: organizational barriers (legacy, siloed IT systems, the lack of funding, and not enough people with the necessary skills); structural challenges (the scale, complexity and interconnectivity of data, how it is held and shared, and the remit and jurisdiction of law enforcement agencies in the virtual world); and ethical concerns about what data is available, how it is being integrated and used, and how far we can and should go to stop criminals and terrorists. These limitations have kept the most revolutionary data technologies out of reach of most police, security and justice departments. In the meantime, private businesses – and cyber criminals – are racing ahead with their use of data. While the police are rightly constrained in lawful access, use and sharing data to avoid unnecessary surveillance and infringement on civil liberties, criminals are not so limited. We can’t afford for this gulf to grow any wider. Public understanding, trust and buy-in will be key to using data to achieve policing and justice aims, and to serve the needs of citizens, when they arise, day and night.  

2024 will be a game-changing year for police, security and justice organizations if the challenges can be addressed and opportunities realized. There needs to be a fundamental shift in the way police, security and justice organizations think about and use data. We have identified several key areas for debate:

Security and safety in the virtual world  

Virtual worlds are new territory for policing and justice organizations. Cyberspace is fundamentally open and can be anonymous: there is no guarantee over identity, personhood (given the widespread presence of automated bots), or intent. Whether or not a crime has been committed can be unclear, as are jurisdictions. The potential hazards of the emerging metaverse (interconnected, permanent virtual worlds) include harassment, abuse, grooming, identity theft, fraud, extortion, malware, and hacking. Serious organized crime groups and state-sponsored adversaries exploit the internet globally, hidden behind layers of anonymity, making attribution all but impossible. In this environment, how can we protect people from harm? One solution might be for international law enforcement agreements to share the burden of cyberspace security and agree thresholds for intervention.

Data lakes become the ocean 

One of the problems of data is that there is simply so much of it. Even the simplest criminal investigation is likely to have a digital element – and a single mobile phone can contain gigabytes of potentially relevant data (images, texts, messages, emails, internet searches), and access to more in cloud storage. At an aggregate scale, data will help the police to understand crime trends, predict future crime hotspots and provide strategies to prevent crime. But it is increasingly difficult to manage all this data effectively, link data sources together, and to find the relevant and useful information, at the right time – especially not at the pace of a criminal investigation. And it can be even more difficult to transfer digital information between organizations – for example from police to courts – in order to streamline the justice system. There are issues over disclosure, redaction, and expertise as well as technical barriers. The Capgemini Research Institute found that data ecosystems in the public sector improve public safety, but special concerns arise for data concerning police, security and justice. AI tools will also help with this, if they can be trusted to an evidential threshold. There are no easy answers.

Asymmetric access to data  

Data is constantly being generated by all of us nearly every moment we engage with digital media, use public transport, enter a public building, or walk down a street covered by CCTV. Most of the data which might be useful for public safety is not in the hands of police or security organizations but lies elsewhere. A lot of data is held by private companies, often based overseas, under T&Cs which few people ever read. In some ways people control their own data – such as what is on their mobile phones, and smart doorbells – and can choose to share this information locally with crime prevention neighborhood groups, in informal social networks, or through social media. ‘Open source’ information ‘in the wild’ can be a useful source of intelligence: a new commercial sector has even emerged to gather and analyze open-source information. While law enforcement agencies have no interest in blanket surveillance – which would be illegal and disproportionate – they do need to find ways to better access and use the available data for specific policing and justice purposes, perhaps through better partnering with the private sector.

Game-changing opportunities for police, security and justice organizations in 2024

There are many ways in which making better use of data will generate huge benefits for public safety and security. We have identified a few key areas that will transform policing in the coming year: 

Justice on the move

Full access to their police software and tools through mobile devices and intelligent vehicles will allow police officers to get away from their desks so they can be more visible and effective. Smart sensing capabilities seamlessly integrated into the IT ecosystem will contribute to predictive policing and crime mapping, helping police stay plugged in, even when they’re out, and directing them to where they are most needed. In future, networked sensors will allow the police to collect digital fingerprints on the scene (and perhaps facial recognition imaging) and instantly compare it to a database of suspects, identify individuals against previous convictions, and perhaps undertake immediate digital forensics on smartphones to help them make decisions in situ. Teaming police officers with mini drones feeding them live video in some operations will help them respond.

Automated data integration

Massive amounts of data are being generated by CCTV cameras (often not primarily for law enforcement investigations, but to deter shoplifters and antisocial behavior, or to enforce traffic restrictions), but obtaining, matching and analyzing the data relevant to a criminal investigation is time consuming. Finding ways to automate image analysis and pick out the relevant information would reduce this burden on police time. It also helps to organize the data, for example via the POLE method. Automated facial recognition systems could in future be used to monitor people breaching bail conditions or restraining orders, spot potential terrorists, or flag known troublemakers in crowded places, such as preventing violence at football stadiums. For upcoming events such as the Olympic Games in Paris in 2024 or the European Football Championship in Germany in 2026, these technologies will likely save lives.

One priority for police, security and justice organizations in the coming years will be to create data systems that facilitate the seamless flow of data across organizations. For example, the data gathered by stadium cameras could be made available not only to law enforcement, but to courts as well. Once again, the key is to make data accessible for the people who need it.

AI changes the world (or does it?)  

We have seen rapid developments in AI tools such as ChatGPT using large language models to provide easy-to-use interfaces: Gartner predicts that by 2026, over a third of emergency reports will be initiated by IoT devices or AI assistants. Used in the right way, AI tools will be transformative for policing and justice organizations. AI-powered tools can help the police navigate the data ocean by quickly analyzing large datasets such as criminal records, social media profiles, and surveillance footage. They will help the police to anticipate crime trends and make best use of resources, deploying officers to greatest effect.

AI can create automated tools to free up police time and help police officers make the right decisions by giving them the right information at the right time. But, there are real concerns over issues of bias, trust, and the potential for misuse: AI tools rely on datasets which may themselves be partial, skewed or false – some criminals may seek to ‘poison the data well’ with spoof datapoints, while privacy campaigners are seeking ways to exclude or modify data used by AI. Uncertainties over what can, or should, be done, may prevent policing and justice agencies from reaping the benefits of the technology unless there is clear agreement and guidance as well as ‘approved’ tools and standards. With the EU AI Act around the corner, it’s time to start defining parameters.

This is an exciting and scary time for police, security and justice organizations. We are at a tipping point from data being just one aspect, to being integral to how everything operates. And the data will only get bigger, more complex, and more difficult to use well. It is more critical than ever to build data systems strategically – aligning data strategy with the people who rely on it to serve the public. Getting all this right will need a lot of thinking, experimentation, and learning, as well as public trust and buy-in. Leadership, partnering, and collaboration will all be key, as will developing the right culture, ecosystem and approaches. There is a lot still to do.

This blog was co-authored by Lucy Mason, Anne Legrand, Nick James, Jayhon Zadeh and Vanshikha Bhat.

Further reading

For information about Capgemini’s public security services, visit here.

What is Invisible Autonomous Intelligence in MedTech?

This new engine of innovation is the result of three emergent technologies coming together:

1. Invisible Artificial Intelligence, refers to the autonomous use of artificial intelligence (AI) and machine learning (ML) technologies, with no direct human intervention required. In medicine, these AI-based systems work seamlessly in the background, making decisions and carrying out tasks without drawing much attention to themselves from clinicians or surgeons. Gen AI will enable these systems to evolve over time and adapt to new environments while ensuring the safe delivery of diagnosis and treatment.

2) The Power of Data, which lies at the heart of this transformation. Medical devices, electronic health records, wearable sensors, clinical trials, and countless other sources generate vast amounts of information. This data is now fuelling invisible autonomous intelligence, which is able to dissect and interpret at a speed and scale never before possible.

3) Medical Technology as a whole, encompassing robotics, sensor technology, software algorithms, devices, and other solutions that are leveraged to design medical devices in areas like SAMD, wearables and imaging technologies to extract meaningful data and improve results.

When we implement autonomous AI and ML applications – fuelled by medical data – into medical technologies, the result is Invisible Autonomous Intelligence. Here’s how it’s changing the world of MedTech.

Invisible Autonomous Intelligence in use

A rising STAR

One application of Invisible Autonomous Intelligence has arisen in surgery technology, specifically suturing and knot-tying. The smart tissue autonomous robot (STAR) from the Johns Hopkins University has demonstrated that it can outperform human surgeons in some surgical procedures such as bowel anastomosis in animal studies. This was possible due to the high level of repetition and precision required for such surgical procedures. STAR can adjust its surgical plan in real time, helping it to adapt to changing conditions during surgery. And as a self-learning AL, STAR’s abilities are likely to improve.

Enhanced enhancement

We’re all familiar with the scene from crime dramas: a detective spots a tiny reflection on a car mirror, or someone’s sunglasses. The technician enhances… enhances… and we watch as 4 pixels magically turn into a clear image. Impossible, right? But with Gen AI, that’s now coming fairly close to reality.

In medical imaging, generative AI can use probability and inference to enhance image quality even where information is missing. It can denoise scans, and even generate images of anatomical structures from different angles. This can aid in diagnosis, treatment planning, surgery and education.

Personalized assistants

AI-powered chatbots and virtual assistants are providing patients with personalized health information and treatment plans, answer questions, and offer guidance on managing their conditions, thus improving patient engagement and adherence to treatment plans.

Enhanced analysis

Leveraging Generative AI we can analyze patient data, including medical records, genetic information, and treatment outcomes, to generate personalized treatment plans with the ability for these plans to adapt over time based on new data inputs, thus optimizing patient care.

Drug discovery

Extending to drug discovery and development, there are now AI algorithms which can analyze vast amounts of genomic, proteomic, and chemical data to predict potential drug candidates. This could accelerate the drug discovery process and lead to more effective treatments for various diseases.

Design of new molecules

In the field of drug discovery, generative AI is transforming the way new molecules are designed. By understanding the intricate relationships between molecular structures and their effects on the human body, AI models can generate novel drug candidates that hold promise for treating diseases more effectively. This significantly accelerates the drug development process.

Virtual patients

Generative AI can even be used to create patient-specific models for simulations, treatment planning, and predicting disease progression. This means doctors can, in a sense, test an intervention in a virtual world first, before applying it to a living patient. Patients, too, gain access to an incredible tool, which demonstrates how a condition might progress, what they might expect from a treatment, and how their own behavior will likely impact their health.

Personalized medicine

The era of generalized treatments is gradually making way for precision medicine. Invisible autonomous intelligence fuels this shift, allowing medical professionals to tailor therapies to an individual’s unique genetic makeup, physiological responses, and lifestyle. AI algorithms today can assist is in designing custom prosthetics or implants tailored to an individual’s anatomy, minimizing interventions.

Challenges going forward

The word “disruption” is overused, but it is accurate. Existing norms and structures will be forced to change as the impact of Invisible Autonomous Intelligence reverberates across disciplines. This will include the need for large and diverse datasets, ethical considerations, and ensuring that the generated content is accurate and safe for clinical use. Collaborations between AI researchers, medical professionals, and regulatory bodies are essential to harness the benefits of such solutions while maintaining patient safety and regulatory compliance.

Some questions we’ll face for sure include:

• How much oversight will automated systems require?
• How can transparency be maximized, and who will have access to what data, when?
• As automated surgeries take on more and more decision-making, who will be liable in cases of harm?
• In an area growing faster than regulation, what guidelines should MedTech companies follow?

The life sciences industry is being rocked by so many waves, it’s hard to identify the tsunamis. Gen AI has the potential to change everything – most notably in the form of Invisible Autonomous Intelligence. The possibilities for patient care, medical analysis and research are hard to imagine.

The future is invisible.

Capgemini experts were part of this dynamic scene, leading conversations on how brands can navigate the changing landscape. Here we share our three interconnected guiding principles for harnessing the power of disruption to drive innovation and recap the major takeaways from the event’s speakers, exhibitors and attendees.

LEAD WITH PURPOSE

To succeed, retailers need to embrace a mentality that serves customers, communities, and the planet.

1. Athlete’s mindset: Developing leadership skills

On the morning of Martin Luther King Jr. Day, basketball legend Earvin “Magic” Johnson told a rapt audience about his journey from the basketball court to the boardroom.

Johnson, the Chairman and CEO of Magic Johnson Enterprises, talked about how the same athlete’s attitude that led his Los Angeles Lakers to five NBA championships contributes to his success in the business world.

“If I’ve done everything I’m supposed to do, I feel that I’m going to win. And I only hire people like that. The same type of mindset. The same type of dedication. The same type of discipline. The same type of focus,” Johnson said.

2. Self-confidence: Supporting women in business

Stephanie Linnartz, the first female CEO of Under Armour, and Diamond Miller, Forward for the WNBA’s Minnesota Lynx, spoke about their respective successes in business and sports, as well as the connection between the fields.

Linnartz emphasized the importance of supporting women’s and girls’ sports and always being “athlete first.”

“[We are] working closely with our athletes to meet their needs – look good, feel good, and perform well – and we’re going to be able to appeal to a broader consumer base,” Linnartz said.

3. Social consciousness: Staying relevant in changing times

Many businesses see a need to engage on the major issues of our time.

Michelle Gass, President of Levi Strauss & Co., said the brand is dedicated to being at the center of contemporary cultural conversations, “connecting it to everything that’s going on socially today.”

Anton Vincent, President of Mars Wrigley, said during inflationary periods manufacturers always assess what they can do to reduce costs and maintain quality. While there are many ways for a candy manufacturer to provide value, social concerns have become more important over time.

“Something we learned coming out of COVID was that consumers are much more introspective…. ‘Who’s running this company? How do I feel about it?’” Vincent explained. “People are really connecting their values with their transactions.”

4. Sustainable business: Reducing environmental impacts

Contributing to the global effort to reduce CO2 emissions to avert the worst consequences of climate change was also a big topic.

Steven Williams, CEO of PepsiCo, talked about how launching pep+, an end-to-end transformation with positive agriculture, positive value chain, and positive choices threaded throughout the company, is helping the company drive sustainable long-term value and competitive advantage.

“It’s about doing the right thing for people and the planet,” Williams said.

David Kat, Senior Vice President of Business Development at Wasteless, a Capgemini partner, said supermarkets often lose more money from food waste than they make through sales. To address this problem, Wasteless provides an AI-powered solution to dynamically reprice items with shorter expiration dates.

“We can cut more than half food waste, making retailers substantially more profitable, increasing revenues but also reducing their carbon footprints,” Kat said.

ADAPT TO COMPETE

To remain competitive, retailers need to navigate disruption and meet consumer’s changing expectations.

1. Dealing with disruption: Protecting supply chains

Raj Subramaniam, President and CEO of FedEx Corporation, wants to make supply chains smarter for everyone. He emphasized the importance of digital platforms to safeguard supply chains and foster trust and loyalty.

“Upheavals in global supply chains have reshaped retail and the tools that you need to be competitive, efficient, and resilient,” Subramaniam said. “Physical networks get us where we want to be, but digital supply chains make that journey faster, more precise, more reliable, and equally critical, more seamless and personal.”

2. Pricing rationale: Demonstrating quality in luxury

If luxury brands and retailers need to maintain or raise high prices, they must communicate what sets their products apart.

Anusha Couttigane, Head of Advisory at Vogue Business, discussed how the luxury fashion market has decelerated as consumers cut back. Despite this bleak outlook, she’s optimistic about growth potential in Brazil, the Middle East, and South Korea, where there’s great demand but low brand awareness.

“Think about the brand’s story and the credentials that you need to boost conversion,” Couttigane said. “Influence and advocacy don’t look the same in every market. Consider the reach of those cultural moments.”

3. Affordable choices: Keeping costs down for consumers

Drew Barrymore, Co-Founder of kitchenware company Beautiful, said she draws inspiration from niche products while prioritizing accessibility and affordability.

“It’s about figuring out that balance,” Barrymore said. “Ultimately the person who benefits the most at the end of the day is the consumer, but it has to be smart business because nobody’s here to do favors.”

Bob Eddy, Chairman and CEO of BJ’s Wholesale Club, emphasized that their entire business centers around providing members with tremendous value.

“Value always wins. So that’s my job, to make sure we have the right partnerships, the right products, and the right procedures to offer that value,” Eddy said.

4. The future of technology: Embracing AI in retail and life

During Capgemini’s NRF celebration at City Winery, Zack Kass, an AI Futurist and Former Head of GTM at OpenAI, delivered an insightful keynote on unleashing the potential of AI in retail.

He encouraged retailers to ask themselves, “What are the things that our vendors won’t do that if our competitors did would sink our ship?” Then, point everything at that goal.

“You don’t need to boil the ocean. One or two really cool applications will catapult your businesses forward,” Kass said.

He’s optimistic about how AI will shape our collective futures and warned against pessimism and cynicism stemming from dystopian science-fiction films.

“I think the future is exceptionally bright,” Kass said. “I think we are still in the very early days of the human experience. We won’t accomplish it unless we imagine it together.”

5. Repetitive-task automation: Increasing human capacity for better projects

A recurring theme throughout the conference was that we shouldn’t just replace human labor with AI, but use automation as a way to augment the human workforce.

Adrian Mitchell, Chief Operating Officer and Chief Financial Officer at Macy’s, said allowing AI to take over some business processes creates greater capacity to focus on strategy, execution, and innovation.

“What’s selling and what’s not? What’s profitable or not? A lot can be automated, which allows the team to spend more time on problem-solving and less time on reporting,” Mitchell said.

At the same time, the ultimate beneficiary of AI is the end customer.

Dave Kimbell, CEO of Ulta Beauty, explained that incorporating Gen AI and other emerging technologies into the products and services of beauty retailers starts with human connection.

“Despite all the technology that’s brought forward, there is a strong desire to discover and engage in beauty in human terms,” Kimbell said. “When we think of beauty innovation, it has to start with how we can complement, elevate, and highlight the human experience.”

UNLOCK CHANNEL GROWTH

To grow, retailers need to understand that channels must connect, not compete.

1. Intelligent retail: Integrating smart technologies into stores

A team from Capgemini demoed Casey, a conversational commerce digital assistant who can engage with customers in new and exciting ways: taking orders, providing directions, offering product suggestions, etc.

“Our initial vision for Casey was omnichannel growth, reaching customers with less infrastructure,” said Jennifer Marchand, Principal at Capgemini.

The team created a modular architecture for the custom user interface – combining visual reinforcements, conversational flow, and human empathy – to create conversations that supersede traditional chatbots.

“We can port and deliver Casey in a very quick fashion to across domains and enterprises,” says Mayank Chandra, Senior Director at Capgemini.

Grant Anderson, Developer at Capgemini, explained Casey doesn’t just help customers find products in stores but also provides tips on nutrition, mental wellness, immune issues, and other topics.

Dan Révah, Global Head of Presales at Shopic, a Capgemini partner, demoed a groundbreaking solution powered by AI called Smart Cart, “the world’s smartest shopping cart.” A sensor uses computer vision to detect each item added to the cart, present a live bill on a touch screen, and enable the customer to skip the checkout.

2. Brand focus: Mastering specific offerings

Too often, businesses spread themselves thin trying to be everything to everybody and lose sight of what made them special.

Martha Stewart, Founder of Martha Stewart Living Omnimedia, said it’s important to pay attention to your customer’s needs, but warned against overcomplicating one’s portfolio.

“I thought we were offering too many different styles of placemats in too many of the same colors. I wanted more colors and less variety,” Stewart said.

Shay Mitchell, Founder and Chief Brand Officer of Béis Travel talked about disrupting the retail industry with Kyle Leahy, CEO of DTC cosmetics brand Glossier.

“We have always seen ourselves as more of an on-the-go brand as opposed to a luggage brand,” Mitchell said. “We purposely launched with shoulder bags and accessories over rollers, so that we could solidify our space in the market as more than just luggage from the start.”

3. Empowered workers: Reimagining strategy for major chains

Many large retailers might not realize that “playing it safe” can be riskier than imagined.

James Daunt, CEO of Barnes & Noble, explained that the bookseller is mounting a comeback by decentralizing decision-making for individual stores. This empowers local teams but also gives the larger company the freedom to operate stores of different sizes.

“People like being in our stores,” Daunt said. “And if you run better ones with better service and make them more attractive, you will get more customers.”

BOLDLY STEP INTO THE FUTURE

Successfully navigating these changes takes a team.

Team player: Cultivating partnerships for success

During her session, Barrymore said, “We never do anything in this world, in this life alone. So, find that partner. Find those people.”

At Capgemini, we echo that sentiment, offering a range of services and solutions, as well as digital and industry expertise and ecosystem partnerships, to help our clients capitalize on the opportunities of the market and turn NRF inspiration into action.

I’d like to conclude this post with a short list of thought-starters from our most recent consumer trends research, What Matters to Today’s Consumer 2024. Now in its third installment, this consumer behavior tracker identifies the latest trends and shopper preferences across the consumer products and retail industries.

Adapt to compete

• Use generative AI tools to become more consumer-centric: A personalized experience has never been more important. Generative AI-enabled tools can help retailers and brands create timely and relevant interactions for shoppers, enabling a truly individualized experience.
• Leverage technology to reduce costs for consumers: In a tough economy, retailers need to take a transformational approach to cost reduction so that they can remain competitive for consumers. IoT technology, data, and AI need to play a central role in every aspect of the business—from remotely controlling lighting and HVAC systems in stores to optimizing shipping routes—all in the name of reducing costs for shoppers.

Unlock channel growth

• Leverage social as both a feeder channel and a sales channel in its own right: Our research indicates that social media is playing an increasingly important role in discovery across all demographics and shopper journeys. It also reveals that for many shoppers, especially younger ones, social sites are the preferred platform for purchase. Retailers need to recognize the expanded role that social media is playing for many consumers and invest accordingly.
• Tap into the power of influencers with specially designed programs for story-selling: People buy from people—and that makes influencers very important to brands and retailers. Now is the time to find authentic brand ambassadors and invite them to share their real-life stories on brand platforms to reach consumers on a deeper, more personal level.

Lead with purpose

• Educate consumers on the sustainability impact of their shopping: Our research showed that sustainability is an important issue for shoppers—but it is also a potentially confusing topic. Consumers are looking to brands and retailers to help them understand sustainability claims and make good decisions. Companies that help people navigate this issue will likely build loyalty in the process.
• Reduce food waste to help combat the cost-of-living crisis: Food waste is one of the biggest challenges our society is facing. Retailers have an important role to play in solving this problem at the root level by eliminating food waste in stores. From dynamic pricing to consumer education programs, retailers can lead the way to a less wasteful future.

Retailers certainly face a challenging year ahead. But in reviewing the highlights of NRF, it has become even more clear to me that with the right partners, digital capabilities and strategy, companies can effectively manage many of the issues on the horizon. If your retail organization would like to discuss your plans for the coming year, Capgemini is here to help. Please feel free to contact me to set up a consultation and download our recent research for more insights on the trends that are shaping the industry in real-time.

Here, at the start of a new year, we’re taking a step back to look at the big picture. In the space of a few years, tax authorities have transitioned from relying heavily on paper, to a primarily digital system. The speed and efficiency unleashed by this change have been incredible, and have opened the door to a world of possibilities. So what lies ahead? Which technologies will tax offices take advantage of in the coming year, and which will have to wait? Here’s what tax authorities and taxpayers can expect.

Tax process modernization for a user-friendly future

In a world of smartphones, same-day delivery and personalized e-commerce, citizens’ expectations are changing. Tax authorities around the globe are thus constantly looking for ways to keep pace with the demand of the new digital age.

One pioneer in tax process modernization has been the Swedish tax authority, Skatteverket. In addition to Skatti, a digital chatbot using AI, Skatteverket has also implemented several other innovative tax process modernization initiatives. For example, it has developed a 360° system that automatically matches taxpayer data from different sources, such as employers, banks, and investment firms. This system reduces the tax declaration burden on taxpayers, ensures higher tax compliance and makes taxation just easier for taxpayers by eliminating the need to manually enter the same information on multiple tax forms.

In some European countries different institutions are exploring the use of blockchain and Distributed Ledger Technology (DLT) for tax-related purposes. Businesses might adopt these technologies as a secure and transparent way to record and verify transactions, which can be particularly useful in areas like transfer pricing and supply chain management. Tax offices could then use this information to assess taxes, which saves time on both sides. Blockchain can also be used to store sensitive information and define who has access and in what circumstances. For example, a citizen’s income might be completely transparent to a tax authority, while a taxpayer’s bank might see a simplified version which helps determine credit worthiness.

Last, but definitely not least: GenAI has taken the world by storm, and it may soon play a role in tax offices as well. GenAI can be used to draw inferences, autofill forms based on past declarations and employer forms, and answer questions better than a simple algorithm-based chatbot.

Each of these changes has the potential to substantially modernize the experience of paying taxes. However, they all depend on a well-funded workforce – the next trend we’ll explore.

Workforce availability and tight budgets

Tax authorities are stretched. This is in part due to demographic shifts, coupled with a limited pool of available talent, made worse by competition from other sectors. In 2024, we can expect this problem to reach a tipping point. We’re going to see staffing shortages, increased workload for existing employees, and potentially even a risk to tax agencies’ ability to deliver services efficiently. These challenges are coming at the worst possible time, coinciding with a budget crunch.

As a result of limited funds and due to efficiency reasons, many tax agencies are strategically adopting package-based solutions, so called components-off-the-shelf (COTS). Their progression to the cloud unfolds with a deliberate approach to Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).

Other agencies have begun to re-prioritize some activities, which may reduce the scope or quality of services. Budget constraints may also impact the agency’s ability to implement new initiatives, invest in modernizing their systems, and enhance taxpayer support. How do you do more with less? This is where transformative solutions come in with new efficiencies. We expect a greater convergence of business and IT, bringing IT “up to the business,” in other words, ensuring that IT strategy actively contributes to the success and growth of the overall administration. At the same time, many efficiencies can be found in day-to-day operations through modern workplace tools, such as AI-copilots and videoconferencing.

Legacy systems modernization and taxpayer experience

Legacy system modernization is still an important part of modernizing the overall tax experience for taxpayers and the tax administration. By updating outdated systems, tax authorities can reduce the time, effort and costs for processing within the tax administration and provide taxpayers with a more streamlined, secure, and efficient filing experience. These improvements lead to a better overall experience, as well as improved compliance with tax regulations.

At the same time, the modernization of legacy systems presents some challenges. It can be cumbersome and costly, and requires a significant amount of time and effort to ensure that the new systems are properly integrated and secure. Additionally, some taxpayers may have difficulty adapting to the new systems, and may require additional support and training to understand how to use them.

Nonetheless, the benefits of modernization are overwhelming. In the last few years we’ve worked with the tax authorities of Norway and the UK to run full-stack modernizations to the cloud, and the savings are already accruing. One such benefit is the role of modern systems in risk management, as the know-how of legacy technology dilutes over the time. The strategies used to evade taxes are more complex than ever, and the right technology will be crucial.

Combatting fraud and tax gaps

In a challenging economic context, it becomes more critical than ever for tax authorities to increase yield by clamping down on the deliberate underpayment of taxes. International tax havens are an ever-present challenge and a source of deep public discontent. Losses in the form of Non-VAT Compliance will soon exceed 100 billion EUR per year, caused in part by legal exploitation of loopholes in the tax systems, and partly by evasion or organized large-scale tax fraud. Tackling tax evasion and the most aggressive forms of tax avoidance are a priority for tax authorities and core to their remit. Reeling in these losses will require data tools and strategies that are at least as sophisticated as those currently employed by tax evaders. As an additional benefit, stronger data tools will also help efficiently identify and correct innocent mistakes of ordinary taxpayers. 

Combatting tax gaps puts tax authorities in a difficult spot, as the solutions often lie outside the remit of tax authorities. This makes close coordination with other parts of government essential. When this coordination is lacking, tax authorities are often left with mandates that – without the necessary tools – which may be difficult or impossible to fully carry out. It’s thus crucial for policy makers to have some understanding of the types of the challenges tax authorities face, and the solutions they have access to. Alignment on this scale is not easy, but the potential savings make it a priority.

Taxation through e-invoicing

One more way for tax authorities to improve taxpayers’ experience is through e-invoicing. In the LATAM and Middle East, e-invoicing has been practiced quite some time. Now, e-invoicing is finally top-of-mind for the European tax authorities. The following predictions come easily, as we’ve already seen the benefits of e-invoicing play out:

Throughout 2024, e-invoicing will have a profoundly positive impact on tax authorities. It will reduce the amount of time and money spent on manual processing. It will provide a more efficient and accurate way to collect taxes from businesses, leading to increased revenue for governments. In addition, the EU currently has an EU VAT gap of approximately 61 billion euros. E-invoicing will help authorities better monitor business transactions and identify any potential irregularities, reducing fraud and increasing compliance with tax laws.

Finally, e-invoicing will make it easier for businesses to stay compliant with tax laws. By automating the process of tracking and reporting taxes, businesses will be able to save time and money by avoiding costly fines from non-compliance.

Cause and effect

The trends we’ve discussed above are closely related. In order for tax authorities to provide citizens with simpler, more modern experience, they need resources. But when workforce and budgets are stretched, where do those resources come from? First, tax authorities can create new efficiencies by upgrading legacy systems. Second, by finding ways to reduce tax fraud, they can dramatically increase the revenue coming in. The greatest gains will thus come from holistic transformation, all based around a clear, future-focused strategy.

2024 will be a challenging year. But with the right tools and strategies, tax authorities have the opportunity to make significant strides forward.

Further reading

For information about Capgemini’s tax and customs services, visit here.

We’re at a big turning point with tech.

 Mixing AI with good values is super important. But how do AI, doing the right thing, and trust all connect?
 
IMHO being ethical and trustworthy in AI is key to making cool, responsible stuff. The choices we make with AI now will affect our future a lot. It’s not just about smart machines, but about making sure they fit our values.
 
Naturally (?) Capgemini gets how big AI is for everyone. We’re really committed to making AI the right way – it’s a big part of our plan. We use AI to make things that are good for everyone, clear, and fair. That’s also the reason we have the Capgemini AI Lab.
 
We’re chasing ethical AI by following and contributing to top industry standards. We don’t see ethical issues as roadblocks, but as chances to be more creative. Adding these good values to AI means less risk and more trust from users and everyone involved.
 
With new AI tech coming up, it’s even more important to stick to our good values. We’re leading the conversation on AI. We’re pushing for AI that’s ethical, respects everyone’s differences, keeps things private, and is fair.
 
I’m calling all leaders and thinkers to talk about AI and doing the right thing. Let’s create a future where AI makes life better and stays true to our values. Together, we can make a world powered by AI that we’ll be proud to pass on.

Ask a public sector CIO in Europe why they have yet to adopt the cloud and the chances that data privacy, security, and compliance with GDPR will rank among their top concerns.

This stems from the fact that cloud infrastructure lives in third-party data centers – often in Asia or the US – that are shared with other users and operated by unknown parties. Which potentially means trusting a lot of people with your sensitive data. 

For years, encryption has been the de facto solution to this problem. This approach sees cloud providers convert plaintext data into ciphertext to protect data in storage and in transit. And because only the user has the key, not even your cloud provider can access your data.

Sovereign cloud solutions offer further peace of mind by giving users an appropriate level of control over their data, technology and operations. All in a dedicated environment within their nation, region or jurisdiction, and complying with its laws.

But what happens when you need to process or analyze that data?

Keeping your data encrypted at all times

To do this, you need to move your data to main computer memory and make it plaintext, so the CPU can process it. But that means anyone with access to that machine (wherever it is in the world) could make a copy of the memory or attack it – comprising your data on every level.

Confidential computing closes this remaining security gap. Put simply, it combines hardware (new CPU functions) with specially adapted software to protect data during use.

First, a secure physical environment within the CPU, called a Trusted Execution Environment (TEE), isolates data during processing, analysis, and runtime. You could think of it as a kind of black box.

Only defined interfaces are authorized to transfer encrypted data in and out of this black box. The box then converts that data into plaintext for processing and re-encrypts it before sending it back.

Second, encryption key management software guarantees that no one can access the data inside the black box. Effectively, this means that data is always encrypted: at rest, in transit and in use. Even if attackers have managed to gain physical access to a server in a data center. 

In fact, from a legal point of view, confidential computing is considered so safe that the data it protects could be classed as anonymized. And if data is anonymous, it doesn’t matter where it is, or whether it’s processed alongside other sensitive data.

For example, you could analyze finance and health data together to identify fraud or abuse – without either party ever viewing the other party’s data. You could even use artificial intelligence to crunch larger data sets inside the black box. (Though this would need to take within a GPU, rather than a CPU.)

Finally, confidential computing offers another crucial feature: “remote attestation”. This means the CPU issues a cryptographic certificate to prove the confidentiality, integrity, and authenticity of the black box. This certificate can, and should be, checked on the user side before sensitive data is transferred to the TEE.

So, why aren’t all public sector organizations using confidential computing?

If this sounds like a new concept, it isn’t. Confidential computing has been around for over a decade. And almost all current server hardware and cloud providers support it.

In theory, then, many public sector organizations should be moving sensitive workloads to the cloud, confident that their data is locked down at every stage. Yet only a few are taking advantage of this functionality. Why?

1. It’s complex, and needs configuring differently depending on the cloud provider you use. And it’s hard to build the specialist expertise needed to implement it yourself.
2. Confidential computing is mainly available for smaller, modern workloads, such as those running on “containers”. It wouldn’t be possible to lift and shift a large legacy workload to the cloud and apply confidential computing to it.

To benefit from confidential computing, then, you need to understand where it’s really needed and applicable – and make it simple to use.

Partnering to make security in the cloud easy for public sector organizations

At Capgemini, we recognize that this need will only grow as cloud technologies become more prevalent. So, we’ve partnered with the German start-up, Edgeless Systems, to give clients in Europe access to leading open-source software for confidential computing.

Edgeless Systems’ solutions support all confidential computing CPUs and numerous cloud providers, from US-based hyperscalers to smaller, European players. They also take data protection to the next level by offering end-to-end solutions. That means they shield your network, key management and storage from access by your cloud provider’s people or infrastructure – not only your containers.

In other words, our partnership with Edgeless will make security in the cloud easy for public sector organizations. Even when they deal with highly protected personal data.

For example, software from Edgeless Systems provides the confidential computing foundation for Germany’s electronic patient record (ePA) system. This system enables highly sensitive data to be shared between patients, doctors, hospitals and research institutions. All in line with GDPR, the German Federal Data Protection Act and requirements set by the National Agency for Digital Medicine (Gematik).

It’s a great example of how confidential computing can help to digitize and modernize critical areas of public administration.

If it can protect the most protected health data, what could it do for you?

Find out more about Edgeless Systems