Key drivers

So then – what’s the current status? A recent report by the Capgemini Research Institute (CRI) aimed to find out. It’s called “Digital Twins: Adding Intelligence to the Real World ,” and it examines where organizations are now, and what results they are seeing.

In a survey of 1,000 organizations worldwide, the CRI found that over 80% of them have an on-going digital twin program, and the rest are planning to start one. As many as 60% said they consider digital twins a strategic element of their overall digital transformation.

What’s prompting this enthusiasm? The key drivers relate as much to horizontal processes such as customer operations and finance as they do to vertical sectors such as manufacturing and life sciences. Around two-thirds of respondents said introducing new business models (67%) and customer-centricity (65%) were top-line drivers, and even more of them (73%) mentioned reducing time to market.

Bottom-line drivers are also pretty much universal, and are probably as predictable as those top-line factors. A sizeable 79% mentioned cost savings, while almost as many (71%) mentioned improvements in operational efficiency.

Value…

To what extent are these ambitions being achieved? The reported benefits of digital twins are impressive. On average, survey respondents said they were seeing a 17% increase in customer engagement and satisfaction, and the same increase in operational efficiency; a 16% increase in sales; a 15% decrease in turnaround time; and a 14% decrease in costs.

The report breaks down the benefits by digital twins by types in the physical world – in other words, in the areas of product development, of asset management, and of logistics performance.

But once again, there’s nothing exclusive about these performance gains. A service-based business, or a horizontal administrative function such as accounts payable or an employee helpdesk, can also employ digital twins and achieve similar outcomes in terms of, say, supplier and staff satisfaction, or of overall efficiency.

… and sustainability

Similarly, the sustainability benefits of digital twins aren’t restricted to physical environments. Yes, digital simulations in manufacturing, aviation, automotive industries and building management can help to identify and reduce product and energy waste – but gains are possible in other areas, and in other ways.

For instance, take accounts payable (AP) processes. AP is a key element of sustainability best-in-class practice. It means resource efficiency and the best use of scare materials to minimize impact on environment as well as reduce risk of finance supply chain disruption caused by climate change.

When digital twins are applied to AP processes, it helps to streamline operations, resulting in the elimination of paper invoices and a reduced need to send documents physically via post or courier, which creates a reduction in CO₂ emissions. The same approach can be applied to other sectors of F&A. Everybody wins.

In the cash conversion cycle, AP is the mechanism for sustainable business practice and financing of debt and ratios of solvency, etc. It provides adaptability to circumstances for example diversification in markets and ensuring financial certainty, as well as diversification to give competitive advantage and strong stakeholder support – cash flow is critical to sustainability of any business and attention to detail in AP yields positive sustainability benefits.

Bringing it all together

Digital twins bring all kinds of specific benefits and incremental gains in efficiency, productivity, and quality. But one of the most important deliverables is holistic by nature.

For instance, digital twins can identify the bottlenecks from the purchase request/purchase order side and the various types of spend (e.g., maverick spend), which has a negative impact on processing AP invoices. As the core of all processes, digital twins enable us to discover the inefficiencies in master data that help to further streamline the payment processing.

A digital twin can replicate and demonstrate this cause-and-effect while everything is still in the pilot phase. But – and this is an important but – it can only do this if everything is connected. The specific supply chain function can’t be twinned and trialed in isolation. To address the potential wider problem, it needs to be part of a fully integrated model.

It needs, in short, to be part of what we at Capgemini call the Frictionless Enterprise  – and in the second and final article in this short series, I’ll be looking at how it can be achieved, and at how the transformation it delivers can be accelerated.

To learn more about the benefits of digital twins and how they can help your organization to drive improved business operations and transition to the Frictionless Enterprise, contact: mariia.nalapko@capgemini.com

Mitigating carbon-hungry tech

With evidence of the climate emergency now irrefutable, public-sector organizations are upping their game in pursuit of net-zero carbon emissions. To this end, we hear about sustainable transport strategies, zero-emission fleet vehicles, smart cities, and investment in green public spaces. What’s not so frequently discussed is the impact of IT on the environment and what needs to be done to mitigate it.

Yet, enterprise IT has a substantial carbon footprint. A recent Capgemini Research Institute report, Sustainable IT — Why it’s time for a green revolution for your organization’s IT, points out that data centers across both the public and private sectors represent nearly 1% of the world’s energy demand, with the majority still largely powered by fossil fuels. The report cites a forecast stating that the growth rate of enterprise IT’s contribution to global CO2 emissions could grow from 8.4% in 2020 to 20.5% in 2025. That’s not all. There’s a distinct lack of recycling, with 89% of organizations recycling less than 10% of their IT hardware. Further, as more and more organizations seek to use artificial intelligence (AI) to inform efficiency gains and service excellence, it’s important to recognize that AI itself has a carbon footprint. One estimate suggests that training an AI language processing system produced 1,400 pounds of emissions, which is equivalent to the amount produced by one person taking a round-trip flight between New York and San Francisco.

Advancing the sustainability agenda

For the public sector with its vast IT estate and growing technology spend, there is thus potential for IT leaders to play a significant role in advancing the sustainability agenda through the implementation of sustainable IT strategies. This is where IT adopts an environmental, sustainability-focused approach to the design, usage, and disposal of IT, software applications, and accompanying business processes.

However, while the global findings show that the wider technology industry is responding to the climate urgency, respondents in public sector organizations exhibit relatively low levels of awareness of the environmental impact of their IT (36%) compared with other industries, such as banking (52%) and consumer products (51%).

More broadly, outside IT functions, the awareness and implementation of sustainable IT initiatives is very low. For example, awareness/implementation amongst HR respondents in the public sector is just 5%, although this is a little higher than the all-sector average for HR of 1%. Likewise, public sector finance functions have a low awareness/implementation score at 5%, just below the all-sector average of 7%.

Tapping into the capabilities of tech firms

Only 10% of public sector organizations (against an 18% global average) have a sustainable IT strategy with well-defined goals and target timelines. This compares to the broader sustainability strategy for the entire enterprise, which 52% of public sector organizations say they have. This low figure for sustainable IT in the public sector comes despite the existence of national regulations and frameworks for eco-responsible digital strategies. So, what’s stopping the adoption of these frameworks? Above all, there remains a major challenge in measuring the carbon impact of public IT and this hinders decision-makers in committing to reduction trajectories/targets.

Clearly, help is needed to accelerate the adoption of sustainable IT in this sector. And public sector leaders recognize this, with 62% saying technology firms can help them measure the environmental impact of IT. A further 53% believe that the tech firms should incorporate a sustainable IT dimension in their products and services, and 47% would be willing to pay a premium of up to 5% for them.

The barriers to sustainable IT

The biggest roadblock for sustainable IT implementation in the public sector is a lack of domain expertise, with 60% of organizations citing this as a challenge versus the all-sector average of 53%. Cost is another factor, with 55% of public sector organizations citing the high cost of setting up sustainable IT infrastructure. Again, this is above the all-industry average, which stands at 48%. When decarbonization is not an administration’s priority, it becomes difficult to justify both the recruitment effort and cost.

Of course, sustainability maturity varies from country to country, and this will have an impact on how these barriers are handled. For example, Germany has set in law its objective of carbon neutrality for public administrations (Federal Climate Act), which means public sector organizations are legally obliged to find ways to overcome whatever barriers to climate neutrality they face. In contrast the ‘burning platform’ for the public sector in other countries is lacking because there is no legally binding target for them to achieve.

As a provider of essential citizen and business services and national infrastructures, it is no surprise that the public sector also has a heightened concern (55% v 45%) about the impact on business continuity of shifting from legacy systems to more sustainable alternatives.

Nonetheless, removing these barriers will bring substantial benefit aside from the huge value of reducing the public sector IT carbon footprint. For example, organizations that have scaled up their sustainable IT use cases have achieved on average a 12% cost reduction. Those deemed to have highly mature sustainable IT practices score  higher in terms of improved customer/client satisfaction at 56% versus 43% for less mature organizations. Just one measure alone, that of implementing auto switch-off of hardware/features, can deliver up to 14% in operational cost savings from energy reduction, while also reducing carbon emissions.

Recommendations for sustainable IT implementation

As leaders in shaping policy to mitigate climate change and working towards net zero, government and public sector bodies must accelerate their own implementation of sustainable IT strategies and approaches.

The following recommendations form a roadmap for achieving this.

• Assess: Set the foundations for sustainable IT initiatives with a qualitative and quantitative diagnostic assessment and a sustainable IT strategy that aligns with the broader organizational sustainability strategy. This should include the definition of key performance indicators, targets, frameworks, and the setting of a carbon cost against IT operations so that different functions really understand the impact of their IT footprint.
• Target and trajectory: Equip the organization with the ability to determine the impact of the measures envisaged when setting a target and trajectory, whether these measures concern the evolution of existing IT or the development of new digital offers.
• Governance plan: Set up robust governance approaches to be implemented at both national and local level. The national plan will be key to establishing common rules, such as a sustainable procurement guide, for implementation across the public sector.  Empower a dedicated sustainable IT team with support from the top leadership team to ensure all stakeholders are committed to implementing sustainable IT initiatives. The governance plan should align services and citizen-engagement strategies with sustainable IT.
• Implement: Operationalize sustainable IT initiatives, with sustainability a key pillar of software architecture, a sustainability culture across teams, and by identifying the environmental impact of AI during design and training. Environmental impact should also be an IT vendor selection criterion, while selecting and scaling the right sustainable IT use cases is vital, such as moving to enterprise cloud applications.

Responding to growing digitalization

Public sector organizations are on a steep and rapid digital trajectory. As our Public Sector IT Trends 2021-22 research showed, the pandemic has demonstrated a requirement for contactless digital services, while in Europe the EU’s new goals stipulate that all public administration services should be available online by 2030. This will inevitably have an impact on C02 emissions and the volume of e-waste generated.

Public sector leaders can mitigate the environmental impact of this digitalization by implementing sustainable IT. At the same time, a sustainable IT strategy must consider both the positive and negative environmental impact of the changes proposed. For example, a digital citizen service will have its own IT carbon footprint, yet it could also make it possible to significantly reduce user travel and the ensuing carbon impact.

Moving forward, from the adoption of diagnostic tools and the definition of a strategy to the development of a roadmap for sustainable IT and internal behavioral change, the stage is set for a more resilient, sustainable public sector IT landscape.

Find out more

Read the full Capgemini Research Institute report, Sustainable IT — Why it’s time for a green revolution for your organization’s IT. For more insights on the public sector, follow us on LinkedIn.

Enabling Findability

Findability is about ensuring that data can be searched, identified, and retrieved by a range of users. Enabling Findability involves getting the right balance of technology, processes, expertise, and data management support. You need to consider the following: 

• Metadata development and capture: you need to ensure that all data is labelled with enough key attributes that your users can easily search for it. For instance a user might want all data collected within the last 12 months that relates to Breast Cancer. Therefore, you would need to introduce key attributes such as Date of Collection, and Disease Type.
• Storage / Centralisation of data: for data to be physically findable it needs to be ‘centralised’, so your users know where to look. You could achieve this through physical centralisation e.g. a data lake, or a hub and spoke style centralisation where data is kept in domain specific storage, and a catalogue us used to direct users to its location. What makes sense for your business will depend on your tech and process history, and future expectations.
• Searching for data: you need to enable effective querying of metadata. Solutions such as cataloguing software supports key-word searches and filtering of data, to support identification of data and location.

Enabling Accessibility

Accessibility is about making the right data available to the right people, at the right time, with the right supporting information. This is not a free-for-all, and data access should be restricted as much as needed (e.g. privacy, sensitivity etc.) and as open as allowed (e.g. licensing, regulations etc.). You will need to consider the following:

• Data Vs Metadata: FAIR makes a key distinction between metadata and data. You will need to decide what information about the data to expose to ensure staff can identify suitable access permission channels as needed.
• Legal and contractual requirements: what legal or contractual restrictions does your data have?  You need to ensure that users are only able to view and access data that they have adequate permissions for, which also means you need to consider how to match users to permissions.
• Technical considerations: What is your current technology stack? FAIR emphasizes making data accessible through a standardized communication protocol, for instance through a secure internal web app.

Enabling Interoperability

Interoperability is the ability of people, computer systems and/or software to exchange and use information. You then need to decide what levels of interoperability are both feasible and beneficial to your use cases over your expected timescales to ensure you get the most value from what you implement.

There are varying degrees of Interoperability, each incrementally improving the holistic interoperation of information: 

• Foundational Interoperability: this involves business information moving between different systems in a way that the purpose and meaning of the data is preserved e.g., a report in Word, or a PDF. In this instance it is the user that interprets the understanding; the systems only support the transfer of the file structure. 
• Structural Interoperability: this involves business information being exchanged between systems, with the systems able to absorb the data automatically using pre-defined context mapping of data columns. In this instance the systems don’t ‘understand’ the data, simply implement programmed mapping.
• Semantic interoperability: this involves systems exchanging information seamlessly through full understanding of the origin, context, and meaning of the data. It relies on structured Formats (e.g. JSON, SML, RDF, API, etc.), and Controlled Vocabularies, Ontologies and Synonym libraries to ensure that each system can seamlessly interpret the true meaning of each piece of information.

Enabling Reusability

Formulating the right data management approach to Findability, Accessibility, and Interoperability, are the foundations of Reusable data. In addition, to ensure Reusability you need to ensure you have the right:

• Data Linking / Provenance: for data to be truly reusable a user needs to understand the original context of the data, including processing methods or transformations that were performed to achieve the current data. This is particularly important in scientific data. The origins are the key to valid reuse.
• Technology: data that relies on specific and / or legacy software to interpret and understand needs supporting technology to ensure suitable re-use. This includes e.g. containerization, virtual machines, or legacy computer set-ups to support older data.
• Culture: for effective re-use, culture is king. Through establishing good sharing practices, suitable organizational support and structures, and creating incentives for FAIR data sharing, a company can rapidly improve the reusability of its data.

Conclusion

Strategically implementing the FAIR Guiding Principles enables you to establish a data-drive organization, impacting many aspects of your business value, including: enabling analytics capabilities; improving the speed and reliability of auditing; and ensuring long term value of laboratory data. If you are unsure where to start, Capgemini’s many years of experience with FAIR and data management will help you identify the best strategies for you, and enable you to truly embrace becoming a data-driven R&D organization

Where it differs from the tale is that many of the cries are not unfounded, and yet, they might not always be as fatal as they’re made out to be. With SAP security, however, the risks are real and the consequences of not acting on the dangers, seriously, can be serious.

Used by the vast majority of multinationals around the world, SAP (systems, applications, and products) security protects business processes and data of high value, such as sales, finance, and personnel information. Traditionally, businesses would lock critical information in a data center, protected by a proverbial lock and key, with peace of mind. SAP, however, is primarily focused on authorization management and segregation of duties.

While this approach was once enough, today it is not. The digitization and movement of assets in the cloud means that online threats are increasing in number, diversity, and impact, leaving organizations under a far greater threat of opportunistic attacks. In 2019, research found that nearly two-thirds of organizations reported an ERP (enterprise resource planning) system breach over a 24-month period from attackers after critical data. More often than not, this is a result of unsuitable SAP risk management solutions.

Shifting sands

In 2021, SAP and Onapsis issued an intelligence report warning organizations to take immediate action and review and monitor their SAP landscape. This was aimed at companies using old and vulnerable SAP versions, which are difficult to patch by today’s security standards.

Just the year before, the RECON vulnerability left tens of thousands of customers’ data exposed to attackers; if exploited, an unauthenticated user would have been able to create a new SAP profile with maximum privileges to circumnavigate access and authorization controls. It was patched soon after, but it exposed the risks associated with relying on a system without proper monitoring.

Today’s leap to the digital economy creates opportunities for companies to transform and scale, and SAP encourages customers to move to SAP S/4HANA – a cloud-based ERP system – to reap such benefits. This migration is a necessary modernization for many businesses, but it’s worth bearing in mind that it doesn’t guarantee out-of-the-box security. It still requires continuous monitoring to identify threats and vulnerabilities.

Getting ahead

Securing SAP platforms demands a proactive approach. With more and more critical infrastructure connected to the internet, possible entry points multiply every day. Research by Statista estimates that the number of connected devices is set to triple from 8.7 billion in 2020 to 24.4 billion in 2030, and this number will only continue to grow.

Hackers generally want to create the greatest impact possible, which is why we’re seeing increasing attacks on critical sectors such as life sciences and energy and utilities. Take the example of medicine: if an attacker can bypass security and enter through the back door, there’s a chance they could edit essential information related to the product make-up at the production stage. This goes beyond business disruption to affect consumers, sometimes with life threatening consequences.

Ideally, SAP applications provide businesses with a way to manage their departments effortlessly. But nothing should be taken for granted, especially as businesses move data and even applications into the cloud. Whether you have public or private clouds, you must have security measures so that you know who is taking care of what when it comes to security. To understand what they are or aren’t doing, it’s important to be proactive by examining, monitoring, and assessing.

Assessment and management

Locking down SAP security may seem like a complex task – and it is without the right practical processes in place.

This is why by implementing effective vulnerability assessment and vulnerability management, you’ll be able to identify new threats and weaknesses in security configurations and prioritize vulnerability remediations for mission-critical SAP systems.

To cover these bases, Capgemini has developed a unique holistic solution that shields your system from data breaches and potential losses. With quick and real-time insights, our Vulnerability Assessment reviews, evaluates, identifies, and reports on SAP weaknesses. But with the landscape always changing, it cannot end there, which is why we combine continuous management to monitor and identify new vulnerabilities in the environment.

For businesses to seize the advantages of cloud-based security, it is essential they understand the SAP landscape better. To navigate complexity, improve compliance, and seize the potential of cloud-based digitization they must be able to know which cry of the wolf to prioritize. This is no small feat, but through better collaboration, it is more than possible.

Contact Capgemini today to find out how our network of global SAP experts can help your organization embrace this new generation of cybersecurity.

Energy consumption has always been a significant consideration for service providers as it is one of the highest operating costs. But it is becoming even more important due to climate change and sustainability considerations.

As per the GSMA, energy costs account for 20-25% of a network’s total cost of ownership for 4G operators. Operators globally spend approximately USD 17 billion on energy every year. With the pending deployment of more NR base stations in 5G, such as small base stations with massive MIMO in high band and 5G networks, energy consumption is expected to increase by as much as 140% in some 5G deployment scenarios. It therefore calls for immediate action.

As operators aiming to decrease power consumption in 5G networks through different energy-saving techniques, the complexity of non-standardized energy-saving mechanisms are becoming a blocker to achieving the desired energy reduction targets.
However, AI/ML intelligence, combined with technology enablers like NWDAF/RIC in 5G, has unlocked various innovation paths for the telco industry to design and execute such energy efficiency solutions in an economical and standard way to overcome the challenges encountered in current methods.

Project Bose represents a great example of such innovative use cases. Its objective is to create a sustainable 5G network – and beyond – using a data-driven approach. To achieve this, it has introduced five energy-saving levers – directional UE paging, MICO mode, energy aware NF placement, smart UPF selection, and intelligent CPU tuning – that leverage analytics information from an underlying Capgemini NWDAF framework, and work in tandem to optimize energy consumption in the network and associated IoT devices.

This result in significant CO2 emission reductions and cost savings, with no negative impact on end users’ QoE. Project Bose also provides a platform to build new energy-saving levers in the future.
Thanks to a collaboration with Intel for its observability framework, Project Bose is able to capture a vast variety of network and infra metrics at different levels in the 5G ecosystem and provides a holistic energy-saving solution for operators.

In tests conducted with this solution in the lab, we have observed an average energy saving of 18%, resulting in around a 14% reduction in CO2 emissions. It is a very impressive first step. But it is just the beginning. Project Bose still has many exciting innovations to come.

Organizations today need to provide up-to-date travel information to consulting professionals travelling on business at speed – including accurate information on local hotel rates, food allowances, and travel entitlements.

This process is typically handled manually, with requests often sent to a third-party system that can lengthen response times significantly. In addition, requests are often sent to teams outside of office hours, which means questions frequently go unanswered until the next business day – much to the frustration of traveling professionals.

Enable instant and accurate travel information

What travel and expenses (T&E) teams needed is a tool that delivers simple, quick, and user-friendly support to its business people, without any specific training, implementation costs, or additional software installation.

On top of this, the tool would ideally leverage an extensive knowledge repository, simple plug-and-play architecture, an intuitive user interface, and intelligent automation to enable finance teams to respond accurately to a wide range of T&E, HR, IT, finance, and policy-related queries.

NLP and machine learning drives speed and accuracy

Enter, Capgemini’s Microsoft-based chatbot. With natural language processing (NLP) embedded at its core, the chatbot recalls all previous user conversations, while leveraging machine learning to predict future queries and inputs. This ensures that the accuracy and amount of travel information it provides continuously improves over time.

We’re extremely proud that our Microsoft-based chatbot recently won the “Chatbot Innovation” category at the 2022 AI Breakthrough Awards – for the fourth year running. Not only that, the tool is also being currently being implemented across many of our internal engagements, which is helping our people to travel safely in a post-pandemic world.

To learn how Capgemini can help you provide up-to-date, accurate travel information to your people through leveraging Intelligent Process Automation, contact: bartosz.grochowski@capgemini.com

The current rise of AI was made possible by four enabling conditions:

• With the advent of the internet and the development of connected objects, tremendous quantities of data are now available. In 2020, 1.7 MB of data was created each second by every person. In the past two years alone, an astonishing 90% of the world’s data has been created. Every day, 95 million photos and videos are shared on Instagram, 306.4 billion emails are sent, and 5 million Tweets are made¹. IDC predicted that the global datasphere will reach 175 zb by 2025.
• New technologies such as cloud computing have emerged, and we are witnessing an exponential increase in storage capacity and computing power.
• Society assist to the growing progress in available algorithms developed by researchers. Libraries like TensorFlow (Google) or scikit-learn (Inria: National Institute for Research in Digital Science and Technology), which contain major AI algorithms, are available with no fees. Many communities, like Stack Overflow, are helping AI developers solve problems.
• The support from industries is growing. Many business sectors have come to understand the importance of AI and are investing massively in this exponential technology.

The importance of data in AI

Data is the new oil, and some big companies like the GAFA (Google, Amazon, Facebook, and Apple) are monetizing it. Today, one of the main challenges faced by business leaders is how to improve productivity and increase profits by using their data assets efficiently.

Then comes the question of what data policy to implement. An efficient data strategy must ensure that good quality data sets are collected and can be used, shared, and moved easily from one system to another. The objective being to make information usable at the right time, in the right place, and by the right person to bring added value to the organization.

AI business applications

Many AI applications have already been deployed in diverse sectors of activities, with great impact on our daily lives as users, consumers, customers, and more. In the following paragraphs, we propose categories of AI usage and propose with concrete examples based on our experience in the AI development area.

Customer first

Customer segmentation

It is a targeted advertising approach. Customer data is used to suggest homogeneous groups for marketing. This classification approach is based on common characteristics, such as demographics (age, geography, urbanization, income, family, job type, etc.) or behaviors (basket size, share of wallet, long-term loyalty). Customer segmentation is popular because it helps you market and sell more effectively. This is because you can develop a better understanding of your customers’ needs and desires. Clustering algorithms are key techniques in building a personalized customer experience. In a Capgemini Research Institute report about customer experience, we found that “66% of consumers want to be made aware when they interact with an AI system.” Being able to implement AI in such processes while  saving the human intelligence part of it is essential.

Weekly churn prediction

Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries. It’s one of the most well-known AI applications in the customer relationship management (CRM) and marketing areas. A company that predicts churn can take proactive action to retain valuable customers and get ahead of the competition. Consumer characteristics and history are used to give a churn score to marketing leaders every week using the cloud.

Real-time chatbot

We’ve all had to deal with a voice server at least once. Behind this technology, you may have a real-time chatbot. It’s a conversational robot that communicates with users in natural language. It is a permanent point of contact for customers, users, or employees. It acts as a virtual assistant and sends them the right information in real time. For the most performant virtual assistant, the benefits are reducing human interaction costs and increasing user satisfaction with immediate and 24/7 responses. Natural language understanding (NLU) algorithms and cloud infrastructure are used here. Not all instant messaging and virtual assistants are based on AI techniques utilizing NLP and NLU. Some of them are mainly rules based.

Intelligent industry

Prescriptive maintenance

With the emergence of the industrial internet of things (IIoT), the field of maintenance is connecting tools, software, and sensors to collect, store, and analyze multiple data sources in one place. Those tools are already unlocking predictive maintenance, where sensors and software predict future failures. However, many maintenance leaders are looking towards a near future based on prescriptive maintenance, where AI machines not only predict failures but also identify solutions. Prescriptive maintenance uses AI with IIoT to make specific recommendations for equipment maintenance. It combines technologies that analyze histories, make assumptions, and test and retest data freely. Complex AI algorithms enable software to automatically identify and learn from trends, recognize data patterns, and apply the best maintenance plan. This AI application, which uses reinforcement learning, helps to reduce maintenance costs.

Real-time anomaly detection

There are three commonly accepted types of anomalies in statistics and data science:

• Global outliers represent rare events that have likely never happened before.
• Contextual outliers represent events that fall within a normal range in a global sense but are abnormal in the context of seasonal patterns.
• Collective outliers represent events that on their own do not fall outside of the standard expected behavior, but when combined represent an anomaly.

Anomalies within a company’s data set can represent opportunities and threats to the business. Real-time detection of anomalies empowers enterprises to make the right decisions to seize revenue opportunities and avoid potential losses. Data from production is used to detect anomalies in a plant in real time thanks to unsupervised learning and a SCADA (Supervisory Control And Data Acquisition) system.

Forecast methods

Business forecasting is the process of using time series data to estimate and predict future developments in areas such as sales, revenue, and demand for resources and inventory. Business forecasting can be divided into two main categories:

• Demand forecasting: Anticipate demand for inventory, products, service calls, and much more.
• Growth forecasting: Anticipate revenue growth, expenses, cash flow, and other KPIs.

Time-series algorithms are designed for these categories. These methods are widely used to estimate the evolution of the Covid-19 pandemic.

Many other AI applications developing computer vision and deep learning algorithms are discovering drugs, identifying cancer cells, and used for sorting devices in factories.

Enterprise management

Monthly KPI dashboard

Financial data is used to display important KPIs for top managers every month in a slideshow. An automation system is set to guarantee the quality of the data and the results. The technologies used are ETL (extract, transform, load), Analytics, and Dataviz. In the context of enterprise management, connecting siloed data across the sales, finance, supply chain, and services domains and embedding AI is keyfor better and smarter decisions. Such achievements help large organizations reduce costs, optimize operating performance, and harness the power of data.

Career management

Digitalization entered HR departments several years ago, and AI has logically become part of this evolution. For many entities, it’s today a part of all career management processes.

• In recruitment, AI significantly reduces delays thanks to intelligent CV sorting. Some HR departments go so far as to carry out a first interview with chatbots.
• For training, AI allows employees to benefit from an ideal training plan for their skills development.
• When it comes to internal mobility, today there are solutions for finding the best profile corresponding to a position that needs to be filled within a company.

Among plenty of successful applications in the HR field, AI is bringing productivity gains, procedure reliability, HR processes improvement, and responsiveness in career management.

Conclusion

Artificial intelligence has so far delivered many benefits and is a huge economic growth accelerator. It embraces many sectors of activities and is already impacting our daily lives. It also raises questions about embedding humans in the process, sharing the benefits, being fair, employment, data confidentiality, privacy, violation of ethical values, ​​and trust in results. These concerns need to be addressed through global regulations, certifications of AI models, and more. In a coming blog article, we will address these necessary fundaments around trusted AI.