As the digital landscape continues to evolve, process mining enables organizations to unlock hidden potential from data-driven insights – streamlining operations, enhancing customer satisfaction, and driving a competitive advantage.

“There’s gold in them thar hills.” Those were the words that prompted the gold rush in the US almost 200 years ago.

Today’s enterprises face a similar challenge, and a similar opportunity. This time, lying hidden in them thar hills are insights – information about operations and market trends and business outcomes and more, all buried under vast amounts of data.

Process mining and analytics can take advantage of event logs and process data to find the nuggets of knowledge on which informed decision-making and smart new ventures are based.

Understanding process mining

Process assessment interviews and workshops are costly and time-consuming, and the results can be skewed by internal politics and the status quo. That’s why today we use process mining and analytics instead.

This is a technique that extracts the as-is process from event logs and data recorded during operational processes. It enables organizations to visualize, analyze, and improve their processes based on real data rather than assumptions.

By analyzing the sequence of events, timestamps, and interdependencies between activities, process mining reveals inefficiencies, bottlenecks, and variations that may otherwise have gone unnoticed.

Figure 1. The traditional vs. process mining approach to improving processes

Unlocking hidden opportunities

Process mining doesn’t just highlight operational problems. It also identifies opportunities for improvement – for example, by streamlining current processes.

If the solution is set up properly, all relevant digital systems are connected to it and made available to the designated stakeholder across the organization.

With a few clicks, intelligent algorithms can pinpoint opportunities, from efficiency gains, cost cutting, and over top-line optimization to sustainability increases and emissions reductions.

These opportunities include the detection of patterns and correlations between process activities and outcomes, enabling organizations to reduce costs, improve customer satisfaction, and optimize revenue. By earmarking areas with the highest potential for improvement, organizations can prioritize their efforts and allocate resources effectively.

Figure 2. An organization can be understood as a city with a public transportation system in which customers, suppliers, and employees travel via subway trains

Many enterprises analyze and optimize within their organizational structures, such as by team, function, or region. However, the digital “subway map,” a visualization of the connection and interplay of processes and teams (see Figure 2), enables the analysis of respective cause and effect relationships, providing a systemic optimization instead of a fragmented one.

Enhancing data-driven decision-making

Let’s look in a little more detail at the ways in which process mining can deliver value.

A smart and comprehensive approach can include:

• A large portfolio of KPI value trees with up to five levels to fan out, for the automatic detection of root-causes:
  • Key sustainable indicators (KSIs)
  • Process performance indicators (PPIs)
  • Key risk indicator (KRIs)
• Key information throughout the hierarchy to focus on most relevant opportunities:
  • Strategic for the C-suite and other senior executives
  • Tactical for process and country owners
  • Operative for real-time process execution
• Benchmarks on KPIs, trends and targets, as well as activity duration:
  • Internal benchmarks for dimension comparison
  • External benchmarks for target setting
• An approach to streamlining and re-engineering processes to accelerate and prioritize findings.

Extending the reach of analysis

Not all processes are fully integrated into production systems, and some of them aren’t even digitalized. This compromises the value of analysis, which can only be at its optimum when it’s comprehensive.

With an integrating tool stack, all process steps can be uncovered, added to the analysis, and even digitalized. Task and desktop mining can be used to analyze software program usage, and for example the amount of manual copy paste activities that might ideally be automated.

Also, the analysis of manual and physical process steps such as customer interaction can be supported by motion mining or the use of artificial intelligence for image recognition, where digital footsteps and information are anonymously derived from gadgets such as bracelets, handheld devices, or cameras. Paper-based process steps can be quickly digitalized and brought into a structured format with intelligent object character recognition (ICR and OCR).

All these methods can be integrated into the process analysis tool, enabling near-real-time process analysis as well as process execution.

Taking process mining enterprise-wide

To take full advantage of process mining, organizations need to establish a framework for its adoption (see Figure 3). This means gathering and integrating process data from various sources, such as enterprise systems, databases, and application logs. It also means investing in specialized process mining tools and building a team with the skills needed to analyze and interpret the data.

Organizational culture has a major role to play too. Encouraging a data-driven mindset and fostering collaboration between process owners, analysts, and IT departments is crucial. This ensures that insights derived from process mining are translated into tangible actions and continuous process improvement.

Figure 3. The five dimensions of the target operating model are the foundation of a scalable process mining initiative

Taking stock

Process mining provides a significant opportunity for organizations to unlock the potential hidden within their operational processes. By taking full advantage of data-driven insights, organizations can streamline their operations, improve customer satisfaction, and gain a competitive edge.

As the digital landscape continues to evolve, process mining will increasingly be an essential strategic tool, helping organizations to identify and capitalize on new opportunities – and to thrive in a data-driven business environment.

There’s gold in them thar process mines.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

Smart technology and innovation in AI shouldn’t be seen as a pipedream. Connecting AI innovation can help organizations to operationalize delivery and drive substantial benefits and business outcomes.

There is frequent discussion these days about the broad potential of artificial intelligence (AI), and we’ve recently seen much debate about the ramifications it may have for the way we live.

But if you’re like me, you’ll feel there’s no substitute for real-world examples. Rather than consider AI in the abstract, it’s always good to see tangible instances of how the technology’s innovations can be applied to business operations in order to achieve positive business outcomes.

Enhancing T&E processing

Capgemini has developed a smart chatbot for a leading global provider of pulp and paper products. The chatbot holds information about the organization’s travel and expenses (T&E) processes, and helps with issues related to expense reporting, approval flow and payment, giving instructions on how to resolve or correctly address an issue.

Employees aren’t always familiar with processes or terminologies at work, and so the chatbot provides a logical path, leading them through possible scenarios of their issue – so even when they are not sure how to put the exact question, they can select the most appropriate area of interest.

If people have a query the system can’t handle, the chatbot advises them who they need to contact and how, and advises them about what information they’ll need to provide to get the issue resolved.

By answering employees’ queries itself as well as giving them shortcuts to in-person issues resolution, the chatbot is expected to eliminate up to 25% of all transactions received each month by the organization’s travel and expenses team.

Easy access to medical records

We’ve seen that some employees may find modern technology challenging, but for healthcare patients – especially for the elderly – it can be especially daunting.

Capgemini has developed a Digital Avatar solution that combines robotic process automation (RPA), conversational AI, multi-lingual natural language voice processing, digital twin technology, and enterprise platforms with next-generation human avatar digitalization technology to translate incoming calls in multiple source languages into the patient’s desired language.

The avatar gives patients quick and easy access to all their medical records, ensuring they get the critical information they need, when they need it – without having to explain their medical history every time they connect with their healthcare providers. It responds to situations and requests just as a regular human adviser would, passing on critical information in simple and straightforward ways. It’s easy and natural for patients to use – and it lowers operational costs, too.

Improving data extraction accuracy

A major organization employed a team to painstakingly pick out relevant data manually from multiple documents and create summaries, but the process was slow and was also prone to errors and rework.

Capgemini introduced a smart solution that automatically scans documents using optical character recognition (OCR) technology, indexes and classifies them, retrieves the necessary data, validates it with reference to a defined taxonomy, and finally exports it for further use.

After initial tuning, the accuracy achieved for all document types is 95%.

Automatic invoice processing

A US media services organization wanted to reduce manual efforts and speed up the turn-around time for processing invoices through its accounts payable function. With a daily throughput of over 200 supplier invoices, the workload was substantial and its operational significance was high.

Capgemini’s smart solution collects unprocessed invoices from the shared mailbox, scans them, and extracts necessary data. Once validated, dedicated routines submit the invoice to the system. Any exceptions are identified automatically and flagged to the business. This automated approach lowers costs, streamlines processes, and reduces the need for manual intervention.

These are just a few brief examples of smart technology in action. They demonstrate that AI needn’t be regarded as a futuristic abstract: instead, it’s here, it’s now, and it’s providing substantial and welcome benefits to everyday operations.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

When data becomes intelligent, accessible, and tailored to individual needs, it evolves into game-changing, actionable insights that drive a more connected, intelligent organization.

It is sometimes assumed that data, in and of itself, constitutes a benefit to any organization.

If that were true, data would be synonymous with insight, and insight, in turn, would always deliver value. But this is not the case. All too often, data and analytics aren’t delivering the improvements businesses need.

There are several reasons for this:

• Failure to provide the broad view – dashboards of near real-time data feeds may be flashy and dynamic, but they don’t elicit any strategic foresight from which transformative action can be taken.
• Failure to connect – if systems across the enterprise are disparate, the data points they deliver will also be diverse, and actionable correlations will be hidden.
• Failure to deliver when needed – if data is not available and meaningful, it won’t be actionable.
• Failure to tailor – people in different roles have different information needs. One size doesn’t fit all.
• Failure to hit the mark – tables, charts, and spreadsheets may aggregate data – but it still needs to be presented in a way that enables people to identify patterns and act.

Key trends in data and analytics

That’s why we’re seeing the emergence of new trends that address these challenges:

• Data delivery is moving from generic dashboards to personalized stories, enabling different people in different roles to make better decisions faster. Data is, in short, becoming democratized.
• AI enables people to have conversations with their data. It is like having your own personal assistant.
• There is a growing sense that data is more a means than an end. When people, processes, and technology come together, data can become actionable information – information that could transform programs and prospects.
• Advanced analytics are enabling headlamps to shine further along the road. New smart techniques are facilitating automated compliance, predictions, and simulations, so organizations can look further into the future – and be ready for it.
• Cohesion across processes is increasing. When the data ecosystem is connected enterprise-wide, unified insights are both possible – and actionable.

Generating value from intelligent analytics

Many organizations nowadays, often in partnership with knowledgeable and experienced service providers, are working to connect their data and to make their analytics intelligent, accessible, and tailored to individual requirements. When this happens, data evolves into something much more useful: it becomes actionable information.
The steps organizations could typically take are as follows:

• Identify high-value data – designing services and processes to foster insights and automate processes effectively.
• Connect and collect data – building robust and scalable ecosystems for automation collection.
• Design guiding principles – creating a solid foundation by establishing ground rules for data access, user experience, usage, security, sustainability, and ethical issues.
• Synchronize processes and systems – increasing system and data interconnectivity to facilitate source sharing and to ensure alignment with process designs, to boost performance and compliance.
• Connect real and digital workers – developing an analytics approach that brings disparate business processes together, including not just people but ERP systems and RPA-based routines.
• Active data with CXO templates – embedding data and insights into core processes that are aligned with CXO personas, their roles, and their information needs.
• Unlock actionable insights – achieving data value with self-service analytics, generative comments, interactive visuals, and built-in process automation triggers.
• Foster a data and improvement culture – deploying data-powered practices with automated compliance checks to guide a change in organizational culture and to ensure the impact of actioned insights are successful over the long term.

A comprehensive approach to driving actionable insights

It’s possible to develop a comprehensive approach that brings together people, processes, and technology, and that addresses the challenges with which this article began.

The graphic below shows typical constituent elements, the facilities they provide, and the C-suite roles the entire model serves.

The concentric circles demonstrate the logic of it. The connectivity of data gives it context, and this in turn means it evolves into intelligence. Connected and contextualized intelligence is no longer data – it’s information, and with the right tools, and when tailored to meet the strategic needs of the right people, it’s actionable.

When everything is connected in this way, advanced and automated insights mean users will have the flexibility to predict and simulate before committing to new courses of action. If they want to, they will be able to replace hundreds of dashboards and reports with a single, unified monitoring and governance platform – but the insight emphasis can nonetheless be weighted in individual cases to suit the information needs of different users.

What’s more, this web of connectivity not only increases synchronicity and facilitates insight – it also reduces data replication across the enterprise.

Delivering outcomes that meet persona needs

How might this intelligent, connected approach to analytics be adapted to meet the expectations of different senior players in the organization?

A CFO needs comprehensive and integrated insights into revenue growth; risk and reputation; margin improvement; and working capital optimizations. An intelligent and unified approach can reduce costs, increase revenue, improve compliance, and release cash.

A CSCO or a CPO needs a 360 view of inventory, procurement, logistics and transportation, and order management. A smart, cohesive approach can reduce costs, improve service levels, and improve cash flow.

A chief marketing officer needs in-depth, immediate, and comprehensive knowledge of consumer insights, marketing insights, and contact center analytics. An intelligent, unified solution can improve revenue, reduce costs, reduce time-to-market, and deliver an improved customer experience.

And how might this approach to intelligent business insights deliver a tangible difference?

Business outcomes include improvement in cash allocation through optimizing payment terms and accounts receivables balance, and a reduction in the cost of goods sold by optimizing aged purchase orders and direct spend reduction. Some real-world examples that we’ve helped deliver at Capgemini include:

• Optimizing working capital by 5% and achieving savings of $38 million across the procure-to-pay process, through implementing AI-enabled data processing and tailored CXO insights for a major industrial chemical products company
• Reducing reporting turnaround time by 80% and accelerating the speed to improvement by 95%, through modernizing the analytics estate and introducing a new insights governance model for a leading pharmaceutical services business
• Reducing the marketing services costs by 35% and optimizing and integrating more than 1,000 reports, through introducing customer voice analytics and setting up a managed insights service for a global consumer products organization.

Delivering game-changing, actionable value

What these examples demonstrate is that when everything is brought together in an intelligent solution that assesses significance relative to the needs of the organization, or to the needs of specific roles within the organization – that’s when data stops being data, and becomes not just information, but actionable information.

Game-changing information, in fact. Information that delivers genuine value, and significant business outcomes.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

Innovation Nation talks to Capgemini’s Ewelina Kałucka, Vijaya Yellu, and Anuradha Raghuraman about how women bring a unique perspective to IT and automation roles and what Capgemini is doing to attract more female talent to expand its IT and automation workforce.

Innovation Nation: Hello Ewa, Vijaya, and Anu – thanks for joining me today. Could you start by telling us about your role within Capgemini’s Intelligent Process Automation (IPA) practice and what first interested you in a career in IT and automation?

Ewelina Kałucka: Hello, I’m Ewelina and I work as a team leader for our IPA practice. I’m responsible for providing our customers with tailored software solutions that meet their needs quickly and effectively.

My interest in IT started with video games. I remember creating a video game for a final year project when I was 16. This was my initial interaction with IT, and I really liked it, so I enrolled in a computer science course two years later.

Vijaya Yellu: I’m Vijaya and I work as a technical automation architect for Capgemini’s Intelligent Command Center, ensuring our automated solutions are optimized to work as they should.

My journey began when I was waiting for my undergraduate studies to begin. I Joined a C programming training course and quickly realized coding suited my logical personality, as it enabled me to see the results of my efforts and provided transparency around where I could improve. This is where my love for IT began.

Anuradha Raghuraman: And I’m Anu. I’m a techno functional delivery lead for our IPA practice. I help businesses automate their process with the help of various RPA and AI tools and chatbots.

I’ve always been a problem solver, but I didn’t fully realize this until I was in at university. One of my assignments helped me improve some of my academic administration work and saw the benefits of automation. This was where my initial interest in IT came from.

How do women bring a unique perspective to IT and automation teams?

Anuradha: That’s a really important question. Women are empathetic and can understand the needs of others more easily. Women are also influencers, which helps them achieve goals, make people feel more confident in their abilities, and produce better products and services for clients.

Vijaya: Women tend to show a great deal of empathy and emotional intelligence – and we’re also great multi-taskers. We do this every morning while getting ready for work and making sure our kids are up and ready for school. This is something that women bring to any team.

What is Capgemini doing to ensure more women join its IT workforce?

Ewelina: I think Capgemini is already succeeding here. For example, Capgemini recently partnered with the IT Girls Revolution Academy (ITGrA) initiative in Poland, which helps young female high-school students take their first steps into the IT world. I took part in it myself this year, as an instructor, and can honestly say Capgemini is doing a great job getting young women interested in IT, based on my own experiences with this program.

Anuradha: Capgemini offers many engaging initiatives and training programs designed to help women grow their skills. Initiatives such as our Avancer program, which ensures women are given the skills to advance their careers further and climb the corporate ladder.

Vijaya: Capgemini is good at getting the work-life balance right. We have good management that supports women – even if they’ve been with the team for a short space of time – and this is crucial, especially when it comes to balancing team needs with family needs.

What advice would you give to women starting out in IT and automation roles?

Vijaya: I would tell them you need to be comfortable being flexible – even if you have a family. There are times when you have to work with teams in other time zones or work to extremely tight deadlines. So it’s important you train and work with companies that respect your time.

Anuradha: I would also add that you need to keep up with new technologies – even if you work in a managerial role at an IT company. You also need to find ways to upskill, regardless of the size of the company you’re working for. Upskilling is how you truly excel in IT. Secondly, women need to identify the skill gaps in their team or practice, and try to pick up those skills and technologies.

Ewelina: These are all great points, but I would also add that it’s important to follow your passion. If you do this, you’ll find success in any field.

Finally, why would you recommend Capgemini as an employer for IA-focused talent?

Anuradha: At Capgemini, we work with multiple, cutting-edge technologies and explore them to deliver the best possible product to our clients. For anyone who wants to explore the value that IA can bring to businesses across the globe, this makes Capgemini a great place to work.

Ewelina: The initiatives Capgemini offers and the cutting-edge projects we work on is one reason why I would encourage anyone interested in working in IT to join Capgemini – regardless of gender. But the other reason is that, at Capgemini, everyone will find a project that will both interest and challenge them on a daily basis, while giving them plenty of opportunities to hone their IT skills from day one.

Vijaya: I would add more, but Anu and Ewa have taken the words right out of my mouth – if you work in automation and want a challenging experience then Capgemini is the place to be.

Ewa, Vijaya, Anu – thank you for taking the time to talk to us today.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

Innovation Nation talks to Capgemini’s Lalitha Kompella about how AI, smart analytics, and Generative AI are underpinning intelligent, data-driven, and connected process automation to drive enterprise-wide transformation and enhanced outcomes for our clients.

Innovation Nation: Hello Lalitha, thank you for joining us today. I’d like to start by asking you about how you became involved with Capgemini and how you’re driving process transformation for our clients?

Lalitha Kompella: Thank you, it’s great to be here. I’ve been working in the field of digital, consulting, and transformation for over three decades, and in the last 10 years have held a number of leadership roles in intelligent automation.

I joined Capgemini in 2023, where I was presented with a new challenge – transforming Capgemini’s Intelligent Automation practice to drive enterprise process transformation and deliver better outcomes and more value for our clients.

One way to realize successful transformation is by implementing excellence across functions such as finance and accounting, supply chain, HR, customer service, marketing, sales risk and compliance, and sustainability that are critical for the business operations of an enterprise.

Recent advancements in artificial intelligence (AI), such as large language models have also made it much easier to drive process transformation and enterprise excellence. And I’ve used these technologies to increase awareness of the benefits of enterprise transformation across the entire Capgemini Group.

And how are you implementing these technologies to drive process transformation?

Firstly, I look at this from a strategic standpoint. For example, what is the maximum efficiency we can achieve from enterprise processes? And I look for different avenues for building the best, most efficient processes possible before any technology is used. Processes might be retained, outsourced, or even eliminated because of new or future technologies. But against this constantly changing technology, how do you continue to deliver value?

But efficiency is just half of the equation, I also look at how I can make the processes more effective. While, traditionally, people may not consider themselves a part of intelligent process automation, armed with augmented capabilities to see through processes, transactions, and operations – using data to derive insights, create intelligence, and drive decision-making – people can drive the effectiveness of processes.

Achieving this meant rethinking and developing on our capabilities with four main building blocks:

• Robotic process automation (RPA) – leveraging RPA and hyper-automation enabled by AI
• Artificial intelligence – the traditional, machine learning kind of AI that people already know
• Smart analytics – AI-powered intelligent, actionable insights to help make better decisions
• Generative AI – that is already transforming process automation.

By working together, these four building blocks enable our clients to put AI at the heart of their business, ensuring their processes are automated in a truly intelligent way.

Could you explain how you’re delivering enhanced values and outcomes to clients?

We drive value and outcomes for our clients in a number of different ways. Firstly, we use AI to drive several kinds of insights. From discovering, analyzing, and understanding a process, we draw insights from the transactional, operational, and process data and leverage “data-as-an-asset” to create solutions for our clients. We build outcomes around these assets using our analytics capabilities to engineer value for our clients.

Second, we use our ESOAR methodology to identify what processes can be systematically eliminated, standardized, optimized, automated, and robotized. Coupled with the solution assets of our practice, this enables us to deliver greater business value in an accelerated manner.

Thirdly, we look at the effectiveness of the processes we’ve identified for optimization with the help of our analytics tools. This makes it easier for us to drive efficiency improvements in, for example, collections and accounts receivables. This is crucial as finance teams often struggle to detect simple anomalies such as incorrect tax codes, which are responsible for around 30% of the backlog in finance collections.

However, it’s important to note that automation isn’t an endless value ladder. There comes a point where you can’t generate any more efficiency. If you automate too much, it becomes counterintuitive to improving customer experience. One of my goals was to work out the right amount of automation to guarantee effectiveness and efficiency without organizations having to over invest.

And I’m happy to say I have now found the automation sweet spot – enabling us to provide cost effective automation solutions that deliver maximum efficiency to our clients and their customers alike.

What role does intelligent automation play within Capgemini’s Connected Enterprise approach?

Capgemini’s Connected Enterprise approach means looking at a process end-to-end – and considering processes as platforms of value.

When I say “platform,” I’m referring to one built on our collective, end-to-end process-level knowledge and solutions. This approach, when combined with our intelligent and acceleration assets – which are purpose-built to bridge gaps – creates a new operating model based on the unique connection between humans and machines.

This focus on seamlessly connecting people and machines enables us to deliver frictionless processes quickly and easily, leveraging various cloud and platform providers, third-party products, curated AI products, and automation and analytics capabilities, at our disposal.

This ensures we play our part in Capgemini’s Connected Enterprise strategy by enhancing value and outcomes for our clients.

Finally, how do you see these technologies evolving in the future?

These are the two trends I’m seeing in the growth of technology in right now.

In the next three to five years, task automation may become obsolete as newer working models come into play, driven by advanced predictive analytics and AI models.

And as AI-driven operations and machine learning grow, transactional processing work will gradually cease. Indeed, when conversational processes are completely taken over by digital human avatars with more human-like capabilities than they have now, customer experience scores will increase further than we’ve ever seen before.

It may take more than three to five years, but I believe it’s inevitable.

Lalitha, thank you for taking the time to talk to us today.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

When the entire enterprise is connected and processes are intelligently automated, organizations can innovate and to create lasting, sustainable value and tangible business outcomes.

In Arthur Conan Doyle’s first short story about fictional detective Sherlock Holmes, a mysterious letter is delivered to Baker Street.

Dr Watson asks him what it means – and Holmes replies, “I have no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

He was right, of course. It’s unwise to shape new ideas without any data. But we’d add, it’s not just about the presence or absence of data, but about the quality, specificity, and the insight it reveals.

The more of it you have, and the timelier it is, and the more comprehensive it is in scope, and the more relevant it is to your purpose, the better you’ll be able to innovate and to put momentum behind your ability to create value.

Data + intelligence = information

What’s needed first is an approach to digitizing and integrating business processes. Data drawn from individual areas of the organization is just that: it’s data. It’s partial. It’s like being given directions for ten miles of a 100-mile trip.

What’s also needed is the introduction of smart technologies that help to organize, streamline, and prioritize all that data, so that it starts to make sense and becomes a basis on which judgements can be formed and decisions made. That’s when data stops being data, and becomes information.

At Capgemini, our Intelligent Process Automation (IPA) offer is a case in point. Its aims are to integrate and automate business processes, and reduce and eliminate any sources of friction (see Figure 1).

By introducing robotic process automation (RPA), artificial intelligence (AI), and smart analytics, businesses can make data actionable. The workforce is given the information and the tools to gauge the status of things, and to identify and respond to challenges and opportunities, so a climate of creativity can emerge and evolve.

Figure 1. An approach to end-to-end advisory services for Intelligent Process Automation

Transformational differences

When you can see everything, and when smart tools help you spot key facts and trends, you can think in new ways, and then develop and implement your innovations in ways that radically transform entire processes, rather than merely tinkering around the edges.

Let’s take a look at how an approach such as Intelligent Process Automation can make a difference in foundational areas of the organization.

Human resources

The ultimate aim of HR includes the ability to attract candidates and to support and retain employees.

A smart and frictionless HR operation can, for example, extract information from a CV, pre-create contracts, schedule interviews, validate documents, and source and recommend candidates.

We’ve found that a smart, integrated solution such as IPA can typically deliver a 40% reduction in cost of service and productivity savings of 50%.

Finance and accounting

Similarly, an intelligent and integrated approach to finance and accounting can enable information to flow seamlessly in areas including accounts receivable, cash applications (the matching of information), invoice processing, and employee expense management.

For example, a smart, automated approach to the SAP operations of a Swedish multinational power company rapidly and accurately processes millions of invoices. Benefits include the automatic processing of 550,000 transactions a year, the automatic dispatch of 82% of tickets, and the automation of a full 100% of daily scheduled runs.

Supply chain

Supply chains probably provide the most tangible demonstrations of the case that can be made for integration and intelligence, since they are by their nature physically disparate.

A smart, cohesive, and comprehensive approach can increase accuracy and efficiency in the creation, editing, and transmission of purchase orders; in shipment document creation; in order status tracking; in updates to workers’ timesheets and inventories; in smart transport planning; in supplier risk assessment; and in demand forecasting.

For instance, a European multinational food packaging and processing company integrated and automated functions across its operations. The benefits in supply chain management alone include the use of AI to reduce the time taken to process inbound warehousing documentation from eight hours to just two hours.

Customer operations

A smart and seamless model can be a major factor in a business’s ability to attract and retain customers. Relevant customer operations areas include query classification, creating and updating tickets, generating call transcripts, automating audits, providing near-live translations, sentiment analysis, and contextual pop-ups to assist customer agents.

For example, a multinational healthcare organization uses AI-powered virtual assistants for sensitive personal conversations with patients. The aims include the ability to reduce their pre-and post-surgery burden, provide treatment guidance in a discrete and interactive conversational format, offer a supportive and caring approach facilitating peace of mind, and to answer pre-defined scientific and fact-based FAQs.

As a result, productivity has increased 225%, and there have been over 100 instances of minor continuous improvement of the items implemented.

The Connected Enterprise

The examples above have been drawn from individual business areas within organizations – but as we made plain earlier, benefits accrue to an even greater degree when the entire enterprise is integrated, and when intelligent processes are brought to bear. When everything is connected, and when everything can be seen and examined, organizations find it much easier to innovate and to create lasting value.

It’s what we at Capgemini call the Connected Enterprise – an approach we use to seamlessly orchestrate an integrated, intelligent ecosystem of people, processes, and technology that drives enhanced, sustainable business value and outcomes across your organization.

In short, a Connected Enterprise can innovate based on knowledge and insight. It can twist theories to suit facts, rather than vice versa.

We’d like to think Sherlock Holmes would approve.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

Brandon Deer, Chief Strategy Officer at UiPath, talks to Capgemini’s Marek Sowa about how UiPath’s automation and AI solutions are driving people to achieve more and how its partnership with Capgemini continues to deliver business value for our clients.

Marek Sowa: Brandon – thanks for joining me today. I’d like to start by asking you about how UiPath’s strategy has changed from being RPA-first to being a market-leading enterprise automation platform?

Brandon Deer: In the last several years, UiPath has transitioned from being a relatively small startup based out of Bucharest with a single product set, to a global organization of over 4,000 people and a platform of over 20 different products that are connected into a seamless and fully integrated service.

All of UiPath’s tools are designed to lower the entry barrier for companies who want to get involved in automation, while our own curiosity helps us find new avenues for improving our clients’ ways of working. However, we noticed one, small problem with our strategy recently – we realized that not everyone is an automation or AI expert.

To use an analogy, we’ve gone from providing a blank canvas, the best paints, and the finest brushes, to understanding that our clients might actually be daunted by an empty canvas and might not know how, or where, to start their automation journey. What they need is some kind of structure to follow – a little nudge in the right direction in terms of the business processes or areas in their organization that could be automated and what their ROI might be.

And in some cases – for example our communications mining tool – we provide a fully painted canvas. You simply point out a specific set of tasks and the tool does the work.

How have automation and AI become the differentiator for human achievement?

That’s a really interesting question. In many ways, there are a lot of parallels to be drawn between the automation and AI revolution and the industrial revolution. The combination of AI and enterprise automation provides a leapfrog capability to humans who have historically just been able to move one foot in front of the other.

UiPath has always been focused on a set of highly manual, repetitive tasks that were largely back-office oriented. This was our bread and butter. But the emergence of AI has helped us remove much of the grunt work in the front office.

For employees, it means having more time to work on higher level, more strategic work. While for enterprises, automation and AI are the most scalable component of their human workforce – which makes it more flexible, productive, and profitable as a result. If you’re freeing people up to do strategic tasks that up leveling is unbelievably critical in producing more productivity and providing more margins.

In practice, automation and AI are uniquely situated between the applications and systems used by many global corporations – putting them in the ideal position for gathering and interpreting data quickly and easily. For example, think of a JIRA ticket. Automation- or AI-based models can interpret what customers are asking for, and instantly provide a recommendation on how to resolve their issue.

Once a decision is made, the technology reaches out to the customer or sends the conversation to a human manager for escalation – without breaking any governance or security protocols. This is what makes UiPath’s technology special, it provides a huge technological advancement to all its users.

What skills do individuals need to excel in today’s increasingly automated world?

Anytime you have a paradigm shift like the one taking place with AI right now, there will inevitably be skill changes in your workforce. For instance, many of us would benefit from being proficient in AI. Knowing how to work with this technology is going to be crucial for us to get the best out of our careers in the very near future.

I also think that understanding bias detection and handling is important. For example, if you’re going to be working with large language models you need to have good judgment skills. Both in terms of translating the transcripts they provide into plain English, and understanding what information will work best if you want to improve their future outputs.

These two skills will be crucial to excelling in today’s increasingly automated world.

How will advancements in automation change enterprise-level decision-making?

If you put openness, flexibility, and infrastructure front-and-center I don’t think you need a new set of security and change management protocols. Additionally, a simple governance layer – something that UiPath provides out of the box – will help you provide data-masking capabilities as well as certain parameters around the data types that can be uploaded into your new AI model. All of which ensures it will run off the best data possible.

Adding layers of auditability, data masking governance, and security to your business through automation- and AI-based models is possible, without making huge changes to your business. This is key for any organization that wants to leverage and democratize automation without causing major disruptions to their business.

How is automation impacting innovation?

Automation enables companies to do more with less, which enables them to invest more time into business-critical tasks. This is the core of what UiPath does.

In fact, just as cloud technology enabled agility, speed, and cost savings in the early 2000s, so automation and AI are the next step function shift for enterprises to continue on that same path forward.

And finally, how is UiPath’s partnership with Capgemini driving a compelling IA value proposition for its clients?

Building on a robust foundation of technical collaboration that started in 2015, Capgemini and UiPath have worked hard to define and deliver an AI and automation platform that meets the needs of today’s enterprises.

Together, we deliver business value that goes beyond simply automating repetitive tasks. Through standardizing and optimizing processes, our integrated, AI-enabled solutions help enterprises boost productivity, customer experience, and employee satisfaction while improving efficiency and reducing operational costs.

But I believe there is much more for us to achieve together as we head into the cognitive era – an era that is not only redefining business, but what businesses need from a joint Capgemini and UiPath intelligent automation platform.

As enterprises worldwide shift gears to drive greater profitability and competitive edge in today’s highly volatile and customer-centric market, our partnership and joint, synergistic solutions will help them effectively navigate the new paradigm.

Brandon, thank you for talking to me today. What you’ve said really highlights the strength of the Capgemini-UiPath partnership and the potential automation has, not only for organizations, but for society as a whole.

Brandon Deer is the Chief Strategy Officer at UiPath. Brandon was an early employee of UiPath and has helped grow the business from $30 million in revenue and a couple hundred employees to over $1 billion in revenue and 4,000 employees. In addition to Brandon’s commitments at UiPath, he is the Founder & General Partner of Crew Capital, a global, early-stage VC fund that focuses on innovative and industry-disrupting software companies.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

The competitive advantage promised by applied AI is very much here. And it’s delivering competitive advantage for organizations in the transformation business operations.

As my colleague Arul Pradeep writes elsewhere in this edition of Innovation Nation, practical examples are generally better than theory, and show is generally better than tell.

Which is why I thought it would be useful for me to provide examples of applied artificial intelligence (AI). The two implementations I’ve summarized below are very much in the real world.

Language dependency reduction

Whether they are for customer services, supplier assistance, or HR purposes, the support functions of major enterprises have a major hurdle to overcome – and that’s language. Global enterprises must serve the information needs of everyone who gets in touch, no matter where those people are and no matter what language they speak.

Multilingual helpdesk staff can help, but that only gets you so far: they can’t cover every translation permutation. Nor can they give assistance at the scale a multinational organization would need. This is partly because they can only help at a conversational level and can’t translate documents over the phone, for instance.

Capgemini’s language dependency reduction (LDR) solution was developed to meet this challenge. It automates the translation of text in documents while maintaining their format and document structure. This helps reduce dependency on language resources and perform operations globally.

What’s more, it infuses custom translation instructions with enterprise glossaries that extend beyond standard language to improve translation quality. These include generic business terms and acronyms such as “P2P” (procure-to-pay), but also includes context- and abbreviation-aware text enrichment specific to domains and organizations, such as abbreviations of product names and context-based translation.

The solution can handle emails as well as documents in xls, pdf, jpg, doc, html, and ppt formats. It can translate over 150 languages, it holds its data securely, and it easily integrates into applications APIs. It’s this specificity of the solution to the language, terms, and customs of individual enterprises that sets it apart from other online translation offerings.

Predictive analytics

A US-based multinational office supply company was constantly improvising its collection strategy. Before payments had reached a point at which they were deemed to be ageing, it was difficult to go through call logs manually to check whether settlement had been promised or refused. With over 60,000 call logs each month, it was proving to be a challenge to manage the process manually where actions are pending to expedite cash collection.

Capgemini proposed a scalable solution driven by AI and natural language processing (NLP) that can read through the user comments from the call history and automatically derive insights from the call log. It can also classify the call logs into the required groups to derive business insights and provide courses of action for the collection strategy.

Each call is automatically collected from Webcollect and the conversation transcript fed into NLP-based model to provide predictions in real time on the required onward action. The call log is classified and assigned to one of 12 different categories by the solution’s machine learning algorithm. The system also integrates these predictive analytics with an Intelligent Control Center dashboard that provides overall visibility over finance operations.

So far, our client has found the solution’s predictions are 90% accurate. Cash collection has been expedited and efficiency has improved. What’s more, trials have shown not only that the approach is scalable, but that model accuracy can be heightened with the introduction of more data.

Tangible results

At the time of writing, these two projects are close to deployment, and the competitive advantage they promise to deliver explains why I’ve been unable to name the clients involved. It’s safe to say, though, that both organizations are excited by what they’ve seen so far.

Artificial intelligence is very much here, very much now – and it’s very much delivering.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine

Creating a transformative people experience that caters to the needs of both the organization and its workforce requires embarking on a journey comprising 12 crucial steps.

In this article, we will outline the People Experience approach, developed by Capgemini, and summarize the 12 necessary steps to make it a reality.

Pillars of an impactful people experience

To ensure success, organizations must build their approach on four foundational pillars:

• People first – establish a mindset that prioritizes employees at the core, just as customers are valued externally. Develop a value promise tailored to individual employees, recognizing their unique needs and aspirations. Utilize real-time profiling to personalize the employee experience and deliver value in the most effective way
• Technology – enable employees with the right technology and ensure a satisfying user experience. This includes personal and office systems, platforms, and enterprise-wide enablement through connected devices. The aim is to leverage technology to support employees’ individual workstyles and enhance their productivity
• Processes and operations – streamline processes to deliver value for both employees and the organization. Simplify operations, establish an HR target operating model focused on service excellence, knowledge management, engagement, and empowerment
• Mindset and culture – foster a culture that embraces inclusivity, wellbeing, and sustainability. Define the organization’s values and goals, aligning them with management practices and corporate responsibility. Clear communication of actual proof-points of the culture to employees is crucial.

12 steps to an impactful people experience

To build an integrated and impactful people experience, organizations should consider and follow these 12 steps, addressing the four pillars mentioned above:

People first:

1. Develop a people promise that connects jobs and roles to outcomes and communicates career growth potential


2. Embrace continuous listening, learning, and feedback mechanisms to understand employee pain points and foster growth


3. Embed autonomy, self-service, and empowerment to provide employees with a sense of freedom in their work.

Technology:

4. Understand employee personas to provide the right technology tools, including productivity and collaboration tools, employee portals, and digital core applications


5. Implement collaboration spaces and immersive experiences, driving adoption and progression through data insights.

Processes and operations:

6. Define an HR operating model and services excellence to support the desired ways of working


7. Design frictionless processes and operations that align with the employee promise


8. Create physical environments that meet employee experience needs and support their work effectively


9. Utilize analytics and insights to continuously improve all aspects of the employee journey.

Mindset and culture:

10. Establish a global/local people strategy for implementing a People Experience across all employee profiles


11. Align managerial practices with the organization’s ambitions and purpose to foster a consistent, inclusive, and wellbeing-driven experience


12. Ensure consistency in all moments that matter to deliver an exceptional employee experience.

In the final article of this series, we will summarize a to-do list for those ready to embark on this journey and highlight real-world success stories.

To discover how Capgemini’s People Experience can help your organization create and sustain an environment where your employees thrive and become a true competitive advantage, contact jan.krogel@capgemini.com or jon.harriman@capgemini.com.

To drive business value, it is important to leverage all the data from within your organization as well as from partners outside of it. Such a collaborative data ecosystem is an alignment of business goals, data, and technology, among one or more participants, to collectively create value that is greater than each can create individually. It is both combining and collaborating on that data.

With a little help from your friends

John Lennon and Paul McCartney met by chance in 1957 when Lennon’s band The Quarrymen was performing in Liverpool. McCartney then joined The Quarrymen and, after the band had already changed its name to The Beatles, they were by chance discovered by Brian Epstein, at that time a local record store manager who became the band’s manager in 1962.

The way we see data ecosystems is similar: it is sometimes about a chance encounter and then bringing various elements together. We could refer to the well-known Beatles song from 1969 Come Together as the unifying theme of this article but instead let’s choose another one, namely With a Little Help from My Friends, which was released in 1967. In the context of this story, a little help comes in the form of a little data. Bringing together data from your friends (customers, suppliers, partners, vendors, whoever) is what we would call “organized serendipity.”

Imagine you’re a retailer operating in a competitive market needing to stay on top of trends, having to make sure your shelves (whether physical or virtual) are filled and are appealing to your customers. As an example, out-of-stocks remain the single largest problem in retail. The challenge with keeping products stocked involves a complex value chain that must anticipate and respond to dynamic market forces. Extreme weather, local events, and even activity from social influencers can quickly alter the demand for a product. In an optimal world, suppliers, distributors, retailers, and other partners would have visibility to changing dynamics and consumption in real-time, enabling them to optimize their operational decisions on-the-fly. And yet supply chains across retail and consumer goods still operate much as they have for decades, making decisions on data that is days or weeks old. It is this delay between changes in demand and our ability to respond that lead to out-of-stocks.

The main sources for retail data are operations by the retailer, data from their ecosystem, competitive data from syndicated sources, and external environmental data from governments and commercial sources.

• Retail operational data comes as a result of business operations, and includes everything from customer-facing retail sales data, advertising, e-commerce, customer support, reviews, and loyalty to back-of-house data from inventory, distribution, planning, and other management systems.

• Retailers operate in a complex value chain, with data coming upstream from suppliers, wholesalers, and distributors, and integrating downstream with advertising and delivery partners.

• Competitive data sources help retailers understand how their key competitors are operating in similar areas. Competitive distribution, assortment, pricing, promotions and advertising, sales, and other sources help retailers index their performance.

• Environmental data helps retailers understand the context in which consumers are making decisions. This includes environmental data such as weather, local economic forces, census information, local events and foot traffic data, legal and regulatory changes, social data, keyword searches, and more.

Finding a cost-effective technology

No two organizations leverage the same data in the same way. The differences in their strategies, operations, competitors, geography, and the systems that support them are designed to help the company succeed. But this means that no two businesses have the same data ecosystem. Companies may exchange data in key areas but increasingly the differences in data between companies is perceived as a competitive advantage. Legacy data-sharing technologies were designed to support the lowest common denominator of collaboration but have struggled to meet the needs of real-time data sharing, quality, and governance and decisioning. Companies want the flexibility to communicate in real-time with a variety of information and across platforms.

The key to achieving this is to select a cost-effective technology that enables the broadest range of sharing options without proprietary technology or vendor lock-in, facilitates real-time data sharing and collaboration, ensures the control of quality and governance of data, and enables companies to focus on immediately leveraging all types of data to drive better decisions.

A retail lakehouse simplifies collaboration

A data lakehouse is a modern data-management architecture that combines the features of both data lakes and data warehouses. It is a unified platform for storing, processing, analyzing, and sharing large volumes of data, both structured and unstructured, in its native format, with support for batch and real-time data processing.

Databricks’ Lakehouse is built on open-standards and open-source, which avoids proprietary lock-in. This importantly extends to data sharing and collaboration. Databricks introduced Delta Sharing, which is an open-source project started by Databricks that allows companies to share large-scale, real-time data between organizations in a secure and efficient manner.

A Lakehouse is the optimal method for data collaboration as it addresses the critical needs in retail.

• Real-time collaboration. Not only can companies share data that is being continuously updated, but Delta Sharing also enables sharing without movement of data.

• Collaborate on all of your data. Unlike legacy systems, Delta Sharing enables companies to share images, video, data-science models, structured data, and all other types of data.

• Centralized data storage. The Lakehouse architecture makes it easier for different users or groups to access and share data from a single source of truth, eliminating data silos and enabling seamless data sharing across various stakeholders.

• It supports quality and compliance. A Lakehouse architecture helps ensure data integrity, traceability, and compliance with regulatory requirements, which are important considerations when sharing data with external users or organizations.

• It simplifies data management and discovery. The Lakehouse architecture includes a robust data catalog and metadata management system that helps in documenting and organizing data assets.

“Collaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry.”

With Delta Sharing, companies can securely share data with other organizations without having to copy or move data across different systems. Delta Sharing uses a federated model, which means that data remains in the original location and is accessed remotely by the recipient organization. This approach allows organizations to maintain control over their data while still sharing it with others.

Collaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry. By leveraging these ecosystems, retailers can optimize their supply chain, gain valuable customer insights, make informed decisions, foster collaboration, and ensure data security and compliance. As more organizations recognize the value of such ecosystems, we can expect the retail industry to become even more connected, efficient, and customer-centric.

INNOVATION TAKEAWAYS

EMPOWERING COLLABORATION

By leveraging data from within and outside their organization, businesses can create collective value that surpasses individual capabilities, fostering collaboration and innovation.

BRIDGING THE GAP

Outdated supply chains hinder retailers from effectively responding to dynamic market forces, making real-time data sharing imperative for optimizing operational decisions and reducing out-of-stock issues.

LAKEHOUSE ARCHITECTURE

A modern data-management approach, the Lakehouse architecture combines data lakes and data warehouses, enabling real-time collaboration, centralized storage, and simplified data management for improved decision-making.

DELTA SHARING

Delta Sharing, an open-source project, empowers companies to securely share large-scale, real-time data without data movement, unlocking the potential for seamless collaboration, compliance, and valuable insights in the retail industry.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 6 features 19 such fascinating articles, crafted by leading experts from Capgemini, and key technology partners like Google,  Starburst,  Microsoft, Snowflake and Databricks. Learn about generative AI, collaborative data ecosystems, and an exploration of how data an AI can enable the biodiversity of urban forests. Find all previous waves here.