Landing and Starting a Job in Kilpatrick… virtually!

Digital Technology as an enabler

By Tatiana Martinez Coto | Divisional Manager 

Starting a new job is always challenging, now with the “new normal” it has become even more so. 

We are used to having days or even weeks of onboarding where we get to know about the company, our team and our boss, facilitating our understanding and development within the company. The days from 9:00 to 6:00 go quickly because we go to meetings, we participate in calls and we talk with our colleagues, sometimes they give us specific tasks but in general the initial days keeps you busy.

But What about the “new normal”? I recently had the opportunity to join the Kilpatrick family in Mexico. Living in the middle of a pandemic, all my interviews were digital as well as my first and the following days. I had the opportunity to meet my boss once before accepting the proposal and in the three months since I started this new challenge, I have not had the opportunity to meet anyone else from the company in person.

This opportunity has definitely represented in addition to the challenge of entering a new company, doing it remotely has been a bit more difficult. As human beings we need direct contact with people to establish coexistence relationships more easily.

My first day started at 10:00 am with a digital meeting with the Mexico team. One of my colleagues helped me by scheduling different meetings with the rest of the team around the world.

During the week I had a meeting with the team where they explained to me how the tools we use every day at Kilpatrick work. In the first few weeks I had two to three one-hour meetings a day. But what about the rest of the time? If your boss doesn’t give you specific tasks, what are you supposed to do? Your team is working and by not being physically with them it is not possible for you to “stick” to them to learn and develop good practices.

In my case it was easier because I was hired to develop a new solution for digital validation of candidates. A completely new service for the company where my knowledge and expertise were the reason for my hiring.

When I didn’t have a meeting, I spent my time researching the market, the competition, making a work plan, critical routes, etc.

Due to the nature of my job, I had the opportunity to keep busy for the first few weeks, but as the project progressed, the more I needed to be in contact with the IT team, MKT, etc. I was used to working in the office, so if I had a question or needed something, it was very easy to approach a colleague and ask for help, but now I had to do it by means of a message or an email, which made the job slower and more frustrating.

The learning processes, including integration, have become more complicated, despite holding virtual meetings week by week, being a new person on a team who knows each other physically and who have had the opportunity to interact and talk about issues not related to the work allows you to generate a link that cannot be developed so quickly in a virtual way. The opportunity to go for a coffee or sit down to eat with your colleagues to talk about personal matters no longer exists, if you have a meeting it is to see work topics and all these personal connections are lost.

The schedules and limits are no longer established, we were used to having a very marked schedule and limit, when we left the office “the day was over”, our lunch hour was a moment for us. Now, being all the time in the same place working and living, schedules and limits become more and more blurred, affecting our family relationships and emotional balance.

I can say that I was lucky, it is true that our virtual meetings week after week are for work, but my team are very human people who are in line with the culture of the company. What does this mean to me? That I can feel part of the team despite never having met them in person. We spend time talking about ourselves and how we are doing.

Our director always starts meetings with “how are you?” If it is on Monday with a “how was your weekend and what did you do?” 

These are situations that we previously took for granted, hallway talks that we can easily forget to integrate into our day to day now that everything is virtual, but now more than ever they are of the utmost importance. We are living in a time full of anguish and frustration where we should not lose human relations.

As I mentioned at the beginning, it is definitely a much greater challenge than the one we faced before the pandemic, but it is also an opportunity from which we can take advantage and learn a lot, especially if you are lucky enough to do it in a company with a culture as open and inclusive as Kilpatrick.

In short, the “new normal” presents us with the possibility of learning to be “more human” and develop skills that we previously took for granted.

These skills will be what make the difference to achieve a pleasant work environment that encourages improvement.

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Facing ‘New Normal’ Challenges: SENTIMENT ANALYSIS

By team Kilpatrick Digital

Gabriele Volpi | Head of Digital Technology

Supatchara Schuller | Big Data and Analytics Consultant 

It is well known that the health emergency has accelerated the arrival of a re-shaped model of organization with most part of the employees working from home and then partially even after the medical emergency restrictions are loosen up. Plus, the high levels of uncertainty and fear, the sudden changes in routines and lifestyles that this unprecedented crisis has brought, is causing employees to feel helpless, desperate, unmotivated and unproductive. In this context, it’s increasingly difficult to monitor and improve employees’ engagement and well-being.

Fortunately, new technological tools like Sentiment Analysis together with the most advanced Artificial Intelligence techniques of Natural Languages Processing (NLP) come in handy.

Sentiment analysis is the type of emotional artificial intelligence used for processing natural language, computational linguistics, biometrics, and text analysis to quantify, identify, and extract systematically the sentiment beyond what is written. Sentiment Analysisregards indeed the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques.

Therefore, sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback. It also helps to understand how employees feel about the company and their working conditions (even when they are working remotely) enabling companies to develop strategies to improve retention and employees’ satisfaction, thus greatly improving productivity and business results.

The Training and Prediction Processes

In the first part of every Natural Language Processing project, the text to be analysed is tokenized or in single words, parsed, removing non useful parts (points, special characters, articles…) and then embedded into numerical vectors, each representing its tokenized words, which represents their features and the similarities between words (in the sense of topic domain, such as love-affect…).

As a supervised Classification Problem of Machine Learning, each phases of the sequence of action which compose the Sentiment Analysis is divided into a training and a prediction process.

In the training process, our model learns to associate a particular text to the corresponding tag based on the test samples used for training. Pairs of feature vectors and tags (e.g. positivenegative, or neutral) are fed into the machine learning algorithm to generate a model.

In the prediction process, the feature extractor is used to transform unseen text inputs into feature vectors. These feature vectors are then fed into the trained model, which uses a statistical model to predict the most appropriate tag (positivenegative, or neutral).

Before the development of Deep Learning Neural Network, several classical Machine Learning approaches with statistical model Naïve Bayes Classifier, Logistic Regression and Support Vector Machines were used.

In recent years, Neural Networks outperformed classical models in every step, allowing to take into account a large number of hidden features without applying a lot of Feature Engineering work. In particular, Convolutional Neural Networks (CNN) allow to take into account quite all local features words and sentences have, while more recently the application of Recurrent Neural Network (RNN), in particular of Long-Short Term Memory network (LSTM), allowed to obtain even better results taking into account long-distance dependency features, that reflect syntactic and semantic information.

Data Mining: Active and Passive Sentiment Analysis

Sentiment Analysis can be applied to every type of written text, so there could be a lot of possible sources of information to take data from.

Normally, for an Employee Sentiment Analysis, two different sets of data acquisition types are considered: an active one, which basically consists of an open question survey, and a passive one, which through the use of API collects and analyses internal text messages of employees such as emails or Intranet tickets. Both modalities have their advantages and disadvantages, in terms of privacy concerns, reality and continuity of data.

Benefits of Sentiment Analysis 

Sentiment Analysis is well known to be used for Customer Retention strategies, analyzing feedbacks and providing a real Data Driven Strategy, but in recent years its application for internal use is becoming more and more interesting.

In fact, the use of Deep Learning technology for Sentiment Analysis can be very useful for managers and HR sector, which can understand and extract deep insights from employees’ feelings and concerns without processing manually a lot of surveys’ answers. Following these insights, target-specific actions can be driven optimally to the best effective actions. Moreover, knowing employees’ feeling is also helpful to better communicate new decisions, and if done together with an ONA (Organizational Network Analysis) it could help manage organizational ineffectiveness. 

Success Cases: implementation of Sentiment Analysis 

Orange is one of the world’s leading telecommunications operators. The group serves 264 million customers, including 204 million mobile customers and 20 million fixed broadband customers. 

In early 2019 Orange began to look for a new social listening tool in order to continue their transformation into a truly data-driven organization. They wanted to have a
tool that could be used at all levels and empower employees to make data-driven decisions. They wanted a tool that was both accessible and easy to use but was also robust enough to provide comprehensive consumer insights into the global markets where Orange operates. 

Orange started to use an external Sentiment Analysis tool to monitor reviews on different App Store platforms. In this way Orange can easily understand what people are saying about their applications, understand their feedback, and get instant insight into the sentiment of customers. They also can have instant alerts whenever there is a negative review so the right manager can deal with issue as soon as possible in order to protect the brand. Orange managed to train and deploy more than 800 employees to follow the customers’ sentiments, in order to drive a real Data Driven market strategy.

Tesco is a British multinational groceries and general merchandise retailer, which with an underlying profit of 3.4 billions pounds is the most profitable online grocery retailer in the world.

Some of the most vital Tesco’s objectives were to make the company a better place to shop and work via a more engaged workforce, and to gain a deeper understanding of what really mattered in all areas of the business (stores, distribution, Tesco.com, Tesco Bank). 

For this reason, Tesco started an Employees’ Sentiment Analysis project called Listen and Fix. With the aim to also facilitate two-way communication between company and them, Tesco invited its employees to send in their thoughts by text or email, which meant a low barrier to participation. Feedback was then aggregated and categorized through functions, departments, topics. All these insights were very useful for Tesco’s Data Scientists team to identify key issues and then take actions accordingly.

Looking ahead

At Kilpatrick, we have realized that Sentiment Analysis is a unique instrument, useful to have meaningful insights for company’s Data Driven strategies, both for Customer and Employees purpose. As a resilient company, and due to our expertise in People and HR Tech we can assure that investing in digitalization processes with frameworks like this could be essential for companies to survive to present and future challenges. 

Bibliography

Ramesh Wadawadagi, Veerappa Pagi, “Sentiment analysis with deep neural networks: comparative study and performance assessment”, Springer Nature B.V. 2020 

Li Deng, Yang Liu, “Deep Learning in Natural Language Processing”, Springer

Kilpatrick Digital Know How

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Predicting Employee Turnover using Machine Learning

by Gabriele Volpi – Head of Digital Technology, Kilpatrick Digital

Predicting employee turnover is a key priority of a HR Manager. Turnover rate represents a major challenge for today’s businesses, particularly when the labor market is competitive and specific abilities are in high demand. 

It concerns the percentage of employees who leave a company and are replaced by new workers, which implies the loss of talent in the workforce over time. This includes any employee departure including resignations, dismissals, terminations, retirements or transfers of residence.

If and when an employee leaves, the cost isn’t negligible: according to some studies, such as the Society for Human Resource Management (SHRM), every time a company replaces a salaried employee, it costs 6 to 9 months’ salary on average. Moreover, finding substitutes can require months of time and effort on the part of human resources directors and recruiting team. An organization must spend a lot of time and money to search for the best replacements through advertising, recruiting companies, screening, interviewing and hiring. When the right candidate is found, it may require weeks to months for the new staff member to be entirely onboarded and working at full capacity. Consequently, measuring employee turnover can be useful to recruiters that want to explore the reasons for turnover or estimate the cost-to-hire for budget purposes.

Turnover prediction has been a research topic from the beginning of the 20thcentury, with different studies approaching the problem with different methodologies, as you can read in the article “One hundred years of employee turnover theory and research”. But with the developing of Machine Learning techniques, thanks to the advance on computational power, this kind of assignment can be carried out effectively, as a Supervised Regression.

Like every Data Science project, the analysis and prediction of employee turnover is divided into multiple steps, each one fundamental for the entire lifecycle of the project. In particular, it is possible to identify 6 phases:

  • Business Understanding and Data Understanding, the first step in this phase is understanding your client’s needs and goals, since those goals will become the objectives of the project. Within this phase, we collect information through the means of interviews and technical literature. The data-understanding phase includes also four tasks: gathering data, describing data, exploring data, verifying data quality;
  • Data Acquisition, the delicate phase where data is acquired from all the data sources acquirable;

  • Data Preparation, the stage where data is normalized in order to create a unique dataset and to correct missing values and errors;
  • Modeling, the step where the best modelis selected by testing the performance on the dataset;
  • Evaluation and Visualization, where the results obtained by the best model are evaluated and visualized with the best method;
  • Deployment, where thedeveloped framework is installedon IT systems.

BUSINESS UNDERSTANDING AND DATA UNDERSTANDING

The first phase could be seen as the most important one, because it allows to understand what is realistically obtainable from the entire data analysis process.

In fact, even the most sophisticated and evolved model cannot predict anything if there is not the right amount of the right type of data. 

Indeed, often, real life company has not well prepared and accessible data sources, so it is hard to generalize the results obtainable from a predictive model, as these depend on data quality.

In addition, HR data is often noisy, inconsistent and contains missing information, a problem that is exacerbated by the small proportion of employee turnover that typically exists within a given set of HR data. 

Several remedials operation can be adopted to deal with these problems, such as data anonymization and data regularization (see Data Preparation paragraph), but it is important to understand well from the beginning what can be the real achievement of the project.

Obviously, the more data one can have access to, the better it is, so it is normal that best results can be achieved with big companies with lots of employees, a good Data Quality framework and – particularly important – a sufficiently long turnover time series.

DATA ACQUISITION

Once the data situation is clear, the second relevant phase is data acquisition. In an ideal joyful world, all data could be imported from one source, all ordered and normalized and organized with unique primary keys in a single database. Unfortunately, facing with an average company’s data sources is like discovering that Santa Claus doesn’t exist…

In order to deal with this chaotic situation, a cost-benefit analysis of every data source is needed: will the advantages obtained from this analysis be sufficiently useful to justify the efforts used to reach the source? It is important to remember that the usefulness of the features varies depending on the models used for the analysis.

Moreover, some data could be useful for a big company with big redundancy of instances, but in small companies the number of employees could be not sufficient for that feature to be significant.

DATA PREPARATION

After all data has been acquired, the next big task is data preprocessing. This phase concerns every action needed to have a clean and standardized dataset ready to be used for training models, such as facing missing value computation, data type converting and feature scaling.

In the database of a real company, missing data (or irremediable typos) are extremely common. To handle them, they are generally replaced with default values based on data type: as regards numerical data types, the missing entries are typically replaced by the median value, while for the categorical data types, the missing entries can be replaced by the mode, and so on.

For some models, converting categorical features to numerical ones could be essential.

Furthermore, in the case of a big number of features, it could be appropriate to make a selection (with method like PCAor similar).

Last but not least, big differences of scaling in features dimension are generally not favored within the optimization stage of these algorithms. For example, the age of employees could range between 24 and 65, consequently the salary range could vary from 20k to 1M euros. It is recommended to normalize all features – in the range 0-1 – and to standardize them.

MODELING

In the modeling phase, the best algorithm for predicting employee turnover is found.

As every supervised machine learning task, in order to train the predictive model, dataset is splitted into a Training Dataset, where the model is trained and where its parameters are fine-tuned to best fit the target variable, and into a Test Dataset, where the performance of the trained model is tested.

Usually there are a lot of algorithms that can be tried (such as Decision Tree, Random Forest, XGBoosted tree, KNNs, SVM, Neural Networks…), and the one which will perform better depends often on the conditions (company’s dimension, number of historical data, number of features, …) and it’s not so obvious that it will be the most sophisticated one!

Even the performance evaluation of a model must be done carefully: typically, people use as a metric the Accuracy, which is calculated as the number of correctly predicted cases divided per the total number of predictions. But this metric alone could be biased! If for example the positive (employee gives up) and negative (employee doesn’t give up) classes are highly disproportioned, like 5% positive and 95% negative, a dummy classifier which always tag the negative class would have 95% accuracy even if doesn’t predict anything! In order to have a correct predictivity evaluation other metrics have to be taken care of (such as Precision, Recall, ROC curve and so on).

EVALUATION AND VISUALIZATION

Once the best model has been chosen and trained and predictions have been made, it is time to draw conclusion. The best way is through graphical visualization, which is an essential part in order to also interact with the customer. The principal languages used for modeling (like R, Python,…) have a lot of packages which allow you to use a big variety of different useful and beautiful graphs, or depending on the architecture used there are a lot of framework and different apps that work very well too.

DEPLOYMENT

The last important phase of the project is the interface with customers and with their IT system. Project team should keep in mind, from the very beginning, that all the work and results produced must be easily developed in the customer system, if they don’t want to live in a deep nightmare.

Works cited:
http://dx.doi.org/10.1037/apl0000103
https://dx.doi.org/10.1007/978-3-030-01057-7_56

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The future of customer loyalty is digital

Certainly, at a time when competitors are multiplying and offers are increasingly diversified, companies must work harder and harder to turn casual buyers into regular customers.

After all, with just a few taps of their smartphone or laptop keyboard, customers can buy a tablet of a different brand or change their energy and gas suppliers.

In this scenario, how digital transformation can boost customer loyalty?

To answer this question, it is necessary to explain well what Customer Loyalty really stand for.

Customer loyalty can be defined as a measure of customer’s likeliness to do repeat business with a firm or brand. It is the result of customer satisfaction, positive customer experienceand the overall valueof the products or services a customer receives from a company.

Customer loyalty is indeed a very important boost for economic performance of a company, guaranteeing regular customers who will provide repeated purchases and who will recommend a company’s product to their family and friends. All of these consequences are obviously connected to an increase of profits.

Customer loyalty is the result of a bigger framework of brand’s actions and interventions called customer retention, which refers to all activities and actions that companies and organizations take to retain their customers and to establish a lasting relationship between the company and its customers.

For example, think about every new Apple product: Apple is a brand with one of the highest levels of customer loyalty and this has launched them to the dominant position they enjoy today.

This happened for various reasons such as their continuous innovation in consumer tech, the guaranteed high quality of the entire product range and how they satisfy their consumers ambitions.

It should be noted that in some departments the main objective is to increase the company revenue, which commonly implies acquiring new clients, without considering the importance of existing customers. But, according to Marketing Metrics, the success rate of selling to a new client is very low – about 5-20% – much lower than the success rate of 60-70% when it comes to selling to regular customers. It is well known that regular customers provide as much revenue as new client acquisitions and therefore it is important to maintain excellent customer relationships. 

Nowadays, customers are “always-connected” and in order to keep up with the new generation of tech clients, companies must embrace technology to create an unforgettable customer experience.

Digital transformation is indeed imposing organizations to change their business models and adapt to the new digital market. That’s why digital transformationcan be defined as the process of using digital technologies to implement new – or change existing – business processes, culture and customer experiences to meet changing business and market needs.

Digital transformation it’s about transforming the way a business interfaces with its clients and how they provide their clients with an immersive experience whenever and wherever they need it.

Companies that are integrating digital transformation are creating high involved customers, which are more inclined to test a new product or service from their favorite brand and more inclined to recommend their preferred brand to their family, friends and colleagues.

It is really curious to explore how two giant well-established companies are applying the digital transformation tools to their strategies to increase customer loyalty: Disney and KFC China.

  • Disney

Disney’s technology strategy was based on four objectives: changing the customer experience, driving operational efficiency, personalization using connected products, and enhanced interactivity across channels.

Using analytics to personalize customer experience: Disney uses data mining to comprehend past behavior and preferences of individual visitors. Forecasting models are used to understand the type of holiday packages preferred by visitors and to help the company provide targeted hotel offers to its clients. Using this method, Disney’s call center workers were able to offer families low-priced hotels available on its list, which has led to improved repeat business. Disney implements relevant real-time analytics in its daily operations in order to improve customer experience at its theme parks. Through analytics it is possible for example to predict waiting times at popular rides.  

Using a Data-Driven Attitude to Operational Efficiency:Disney has a very large cast and therefore it becomes difficult to program thousands of shifts. The solution was the implementation of a rule-based, on-demand technology that allowed Disney to improve labor resources by 20%. Disney uses also analytics to simplify back-house operations, for example to handle its garment inventory and laundry.

Customizing through Connected Products:their strategy includes MyMagic+ initiative which is a mixture of a website, a mobile application and a wristband that permit guests to customize their experience at a Disney park. The project has required the training of many employees on new technology as well as the introduction of radio frequency readers and the installation of scanners at its parks, hotels and stores. With MyMagic+ Disney was able to attract 3000 additional daily guests during the Christmas holiday season.

Adopting Digital Transformation to Create New Interactive Consumer Experiences: the intention of ensuring a memorable customer experience concerns not only theme parks, the digital transformation also involved different parts of the Disney family as demonstrated by the high-tech makeover of the traditional bricks-and-mortar stores. The store renewals have contributed boost profit margins by 20%.

  • KFC China

KFC China, the major restaurant chain in the biggest country in the world, is a company that has entirely reinvented itself in recent years. Such extraordinary transformation was driven by the need to connect with China’s growing millennial audience and to meet their needs.
Below are a few examples of KFC China projects that show their digital transformation. 

Loyalty App Benefits: with the KFC China app the company not only allows to make online orders and payments, but they also send exclusive offers, coupons, rewards and packages to loyal customers.

Gamification: in collaboration with the country’s award-winning gaming giant Onmyoji, KFC China has produced a personalized game that, through augmented reality and location-based services, invites users to meet in store to play together.

eGifts: to expand their digital distribution channels, the company has also opened a digital store on a Chinese eCommerce marketplace, an innovative way to sell gift cards and coupons and to drive store visits.

Store Innovations: technological innovation has also involved their stores, now equipped with charging stations, a facial recognition payment system and DUMI, the first AI service robot in the world, capable of recognizing local dialects, profiles and even moods.

The above-mentioned examples are just a couple of the many ways that show how digital transformation is able to fortify company’s Brand Loyalty and Customer Loyalty, as well as to improve Customer Acquisition. 
It is a necessary condition in order to stay competitive on the market. 

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McDonald’s, Uber and Johnson & Johnson no longer have chief marketing officers — here’s what that means

By Lucy Handley – cnbc.com

This week, McDonald’s announced that its Chief Marketing Officer (CMO) Silvia Lagnado is set to leave the company in October, and her role will not be directly replaced.

According to industry publication AdAge, a new global CMO at McDonald’s will not be named. However, two executives have been promoted: Bob Rupczynski has become senior vice-president, marketing technology, and Colin Mitchell takes on the title of senior vice-president, global marketing.

McDonald’s is the latest of a few global companies to reshuffle marketing. Uber’s CMO Rebecca Messina is stepping down, the company announced in June, and marketing duties will instead be handled by Jill Hazelbaker, its senior vice president of communications and public policy before the reorganization. Uber has also made the chief operating officer job, held by Barney Harford, redundant.

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How to Reskill for the Workplace of the Future

From digitalhrtech.com

It’s no secret that today’s jobs are quickly evolving and that in the near future jobs will be created that never existed before.

According to research by the World Economic Forum (WEF), the arrival of Globalization 4.0 means that 75 million jobs are expected to be displaced by 2022 in 20 major economies.

In this new reality, people will have to reskill, as not only will it be challenging to hire new talent fast enough to fill the gaps, but HR doesn’t yet know how to recruit for these new roles.

This means that upskilling or reskilling the current workforce is actually a cost-effective way of dealing with these changes, all while ensuring people don’t have to be made redundant.

In this context, what role has HR – and more specifically HR tech – got to play?

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Businesses Predict Digital Transformation to Be Biggest Risk Factor in 2019

By Mengqi Sun – The Wall Street Journal

Board members and executives are particularly focused on potential operational risks.
 

Directors and executives sense the business environment will become more perilous in the coming year and are increasingly concerned about the operational challenges surrounding digital transformation, according to a new report published Wednesday.

Existing operations and legacy technology infrastructure pose a risk to companies that can’t transform quickly enough to compete against companies that were “born digital,” according to research conducted by North Carolina State University’s Enterprise Risk Management Initiative and management consulting firm Protiviti Inc. This risk factor surged to the top spot for 2019, up from 10th place in the 2018 report.

The rise is an acknowledgement of the growing threat of a constellation of risks facing companies, including the viability and resilience of business models and shifting customer preferences. Workplace dynamics, such as resistance to change, and the ability to hire in a tight labor market are also factors, the report says.

“Organizations need to gear up and align the culture, people, processes and intelligence gathering to embrace this rapidly changing environment,” said Protiviti Managing Director Jim DeLoach.

Large companies that have long histories and extensive operations could find it particularly difficult to adapt quickly to competition posed by younger companies that digitize products and services or use technology to operate more efficiently, said Mark Beasley, a professor at N.C. State’s Poole College of Management and director of the Enterprise Risk Management Initiative.

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Davos 2019: A Workforce Transformed—Not Displaced

By Renée McGowan– Mercer Chief Executive Officer, Asia

Business and government leaders gather next week in Davos-Klosters, Switzerland, for the World Economic Forum’s Annual Meeting, and each year the stakes—for the future of work, global growth and well-being—get higher.

How do we master a world of automation and artificial intelligence? Of longer life spans and multigenerational workforces? How do we deliver on the reskilling demanded by tomorrow’s jobs?

WEF 2019will focus on a vision of tomorrow’s industry, referred to as Industry 4.0, reflecting the tech-driven momentum of a Fourth Industrial Revolution, powered by generational changes in skill and requiring a new vision of employment.

It’s a challenging future. But it’s not a dark one by any means. For the corporate sector, leadership will need to balance the promise of automation and advanced data with the financial health of the workforce—a special focus of Mercer, which will host a group of corporate and government global leaders for discussion on January 23 in Davos.

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Europe plays catch-up in digital innovation

by John Gapper and Maija Palmer – The Financial Times 

The continent needs to shout louder about its strengths in research and skilled staff.

Europe faces an innovation gap. Despite being home to some of the world’s leading engineering and pharmaceuticals groups, it lags the US and China in applying digital technology to a fresh generation of products and services. Companies such as Alphabet, Facebook, Alibaba and Tencent — global leaders in consumer technology — were all founded outside Europe. 

But there are signs of change across the continent. Europe has a strong record of research and development, accounting for 20 per cent of global R&D spending. Venture capital has poured into start-ups and growth companies, with clusters forming in cities from London to Paris and Berlin. Incumbents, facing disruption in their industries, are adopting new approaches. 

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The digital skills gap is widening fast. Here’s how to bridge it

Access to skilled workers is already a key factor that sets successful companies apart from failing ones. In an increasingly data-driven future – the European Commission believes there could be as many as 756,000 unfilled jobs in the European ICT sector by 2020 – this difference will become even more acute.

Skills gaps across all industries are poised to grow in the Fourth Industrial Revolution. Rapid advances in artificial intelligence (AI), robotics and other emerging technologies are happening in ever shorter cycles, changing the very nature of the jobs that need to be done – and the skills needed to do them – faster than ever before.

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