Articles and Podcasts for C-suite

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Articles and Podcasts for C-suite

KEY TAKEAWAYS

  • Effective professional development of employees will result in better financial performance. Defining metrics to understand how people move through and grow within the organization, and also who is growing and who is being left behind, is extremely important nowadays.
  • Diversity, equity, inclusion (DEI), and well-being are inextricably linked. To address the intersection, leaders may implement some strategies. For example, bringing in subject matter experts, upskilling the managers, embedding mindful DEI practices, showcasing employee stories, and creating a well-being-centered employee resource group.
  • There are many myths related to the Great Resignation. Three of them are the following. 1) Only Gen Z and Millennial employees are resigning, and they’re opting out of the workforce entirely. 2) Candidates can be won over with competitive compensation and flexibility. 3) HR leaders are responsible for managing turnover during the Great Resignation.
  • Early chatbots were programmed with a predetermined set of questions and answers, but innovation is allowing to improve the customer experience and nurture brand loyalty, with sophisticated tools that provide solutions to an impressive number of questions and requests. With the help of technology, agents will focus on solving more complex situations.
  • To implement AI-driven forecasting in data-light environments choose the right AI model, leverage data-smoothing and augmentation techniques, and prepare for uncertainty.

INTRODUCTION

This research provides summaries of five articles and one podcast for C-suite. They are related to metrics of human capital, diversity, equity, and inclusion (DEI), the Great Resignation, customer reviews, intelligence chatbots, and AI-driven forecasting.

ARTICLES AND PODCASTS FOR C-SUITE

Does Your Company Offer Fruitful Careers — Or Dead-End Jobs?

  • Quantification and measuring the organization’s “human capital” is difficult, and many times, managers struggle to define what human capital really means. Moreover, few have defined and implemented measures to determine the effectiveness of their employment strategies.
  • It is to document the quality of the jobs that companies create. This signifies identifying and separating dead-end jobs from those that lead to a fruitful career.
  • Effective professional development of employees will result in better financial performance and, in this sense, creating clear paths to high-quality jobs has important societal implications.
  • Under the current labor environment, employers are having difficulty filling entry-level roles. That is why managers need to think of these workers as investments more than just resources.
  • HBR suggests a set of questions to easily create metrics that track whether these investments are effective at improving job quality. These questions are about job quality, job mobility, and job equity.
  • Job quality.
    • What percent of workers earn the local living wage?
    • How many workers have health care?
    • How many new jobs are created each year where pay is above the local living wage?
  • Job mobility
    • What percentage of workers that started below the living wage moved to jobs that paid above the living wage each year?
    • What percentage of the lowest-paid workers left the company before their one-year anniversary? How many left before the two-year mark?
  • Job equity
    • What is the demographic composition in the company’s high-wage occupations? How does it compare to that of the local pool of potential labor?
    • What is the difference in mobility rates in each of the company’s wage quintiles for different demographic groups?
  • The metrics can help managers create a roadmap for developing their workforce. For example, a very large food services company examined how training and job opportunities were created, and, in the beginning, it seemed that training expenditures were highest among low-wage workers at the company. But when those low-wage workers changed jobs within the company, many soon left. Only 17% of low-wage workers enjoyed an important pay increase.
  • Most other training expenditures were directed at higher-wage workers and these workers were more likely to take advantage of training benefits offered by the company.
  • This analysis helps explain the disconnection between training and retention at this company. Additionally, it is also possible to conclude that offering training without offering the time to take advantage of that training may be negative.
  • The metrics are designed to help managers make internal decisions. The primary goal is to develop measures on worker well-being that reflect society’s challenges and workers’ aspirations.
  • Managers will quickly discover that improvements to their workers’ well-being will fall into two types: those that prove material to the firm and those that don’t.

Supporting the Well-Being of Your Underrepresented Employees

  • Article published by Harvard Business Review. March 04, 2022.
  • Many business leaders know that company success is inextricably tied to employee presence, engagement, and productivity, and they have made great efforts toward supporting their employees’ well-being.
  • However, there are generally failures when trying to consider the link between well-being and diversity, equity, and inclusion (DEI). Research has proved that organizations with diverse workforce enjoy several benefits such as more innovation and lower attrition, for example. The reality shows that the efforts to address DEI in the workplace tend to be disconnected from those aimed at supporting employee health and wellness. Failing to address the intersectionality of DEI and well-being does a substantial disservice to employees.
  • For example, research shows police killings contribute to 1.7 additional poor mental health days for Black Americans. Compared to their white counterparts, American Indian or Alaska Native (AIAN) people are more than twice as likely to be uninsured, leaving many without access to health care. Consequently, AIAN people have a higher prevalence of many chronic health conditions than those from any other racial or ethnic group.
  • The pandemic has exacerbated many existing disparities in the U.S. For example, Black, Hispanic, and Asian people record substantially higher rates of Covid-19 infection, hospitalization, and death when compared with white people.
  • There is not a single solution for holistically addressing employee well-being and DEI but several actions can be taken, such as the following.
  • Bring in — and compensate — subject matter experts.
    • For example, a month-long “Gathering Space” at Virgin Pulse facilitated by a licensed psychologist and DEI expert provided a healing experience regarding the daily impact of racial trauma and injustice.
  • Upskill the managers
    • Given that 45% of employee experiences of inclusion are explained by their manager’s inclusive behaviors, it is critical to help them develop the necessary skills and behaviors.
  • Embed mindful DEI practices
    • Small changes can help encourage employees to take a more active role in their well-being. Find ways to weave DEI and well-being into your broader talent strategy.
  • Showcase employee stories
    • Sharing stories about people’s personal journeys create excellent opportunities to learn about different perspectives and be more inclusive colleagues.
  • Create a well-being-centered ERG.
    • Employee resource groups (ERGs) are useful tools that can help foster a diverse, inclusive workplace. Alignment with the organization’s mission, values, and goals is always needed.

Three Myths Of The Great Resignation And What HR Leaders Can Learn

  • A record 4.5 million people quit their jobs in November 2021, according to the Bureau of Labor Statistics. This trend is not going back since standard hiring and retention practices are failing to attract the appropriate workforce. In this regard, the following three myths are present these days.
Myth No. 1: Only Gen Z and Millennial employees are resigning, and they’re opting out of the workforce entirely.
  • Resignation occurs among employees across all generational groups. It is not a trend exclusive to the younger employees. Most of them want jobs aligned with their values.
Myth No. 2: Candidates can be won over with competitive compensation and flexibility.
  • After the pandemic period, all employees expect a competitive salary and flexible schedule. Because of that, those benefits are not enough to attract top talent. It is required to establish a value proposition around culture.
  • Since applicants are more informed than ever HR leaders are urged to share results, outputs, and actions that show what the company culture looks like.
Myth No. 3: HR leaders are responsible for managing turnover during the Great Resignation.
  • Certainly, HR leaders can influence strategy and take the leading role when recruiting and retaining talent. But all from the C-suite must communicate the values and culture of their organization both internally and externally. Leaders must not only be willing to join conversations that are important to their workforce, but also commit to acting in accordance with the organization’s values.
Some strategic ideas for HR leaders
  • Exit interviews and retention interviews to obtain valuable feedback.
  • Use the information to influence decisions and drive the people strategy for hiring and retention.
  • Establish a value proposition and communicate it with authenticity.

Ending the Chatbot’s ‘Spiral of Misery’

  • Bad experiences with chatbots are so common that customer service experts now have a name for it: “the spiral of misery.”
  • Thanks to technological advances, customer service chatbots are becoming less robotic. They are becoming more intelligent, more conversational, more humanlike, and, most important, more helpful.
  • “Even now, there are times you sort of can’t tell it’s not a human,” said Bern Elliot, an analyst at Gartner. “It’s not as good as you’d like, but it is moving in that direction. And innovation is occurring at a rapid pace.”
  • For most companies, customer information resides inside corporate data centers and they generally have less data than the internet giants. Worst of that, all data is stored in different formats and in different places.
  • VP for A.I. technologies at IBM Research Aya Soffer says that the starting point for improvement is a deeper understanding of what happens in the interactions with customers.
  • Early chatbots were programmed with a predetermined set of questions and answers, but much of the recent innovation lies in “teaching the system to understand and tease out a person’s intent.”
  • The main purpose of the chatbot technology, Mr. Beatty from GM Financial said, is to improve the customer experience and nurture brand loyalty for its parent company, General Motors. But the average call-center inquiry lasts six minutes and costs $16, according to industry estimates. At G.M. Financial, many customer questions are now answered by the chatbot. In January, Mr. Beatty estimated, the company saved a total of $935,000. Thanks to the technological tool, agents now spend more time on difficult problems — for example, speaking to a customer who has lost a job and needs to extend a car lease or loan.
  • Anthem is a major health insurer covering more than 45 million people. Its current technology, including its mobile app, is called Sydney and is 90 percent accurate in answering questions about co-payments and medications (“Does my prescription have any drug-drug interactions?”), according to the company. But the long-term goal is to use A.I. to sift through all its claims and clinical data to deliver personalized health advice. Sydney can even upload fitness tracker information. A.I., Mr. Ronanki from Anthem said, can “help us move from reactive sick care to proactive, predictive and personalized health care.”

AI-Driven Operations Forecasting In Data-Light Environments

  • Article published by McKinsey. February 15, 2022.
  • Too many companies still rely on manual forecasting because they think AI requires better-quality data than they have available. But demand forecasting is an essential analytical process. More companies have come to rely on AI algorithms, which have become increasingly sophisticated in learning from historical patterns.
  • Applying AI-driven forecasting to supply chain management, for example, can reduce errors by between 20 and 50 percent—and translate into a reduction in lost sales and product unavailability of up to 65 percent. Continuing the virtuous circle, warehousing costs can fall by 5 to 10 percent, and administration costs by 25 to 40 percent. Companies in the telecommunications, electric power, natural gas, and healthcare industries have found that AI forecasting engines can automate up to 50 percent of workforce-management tasks, leading to cost reductions of 10 to 15 percent while gradually improving hiring decisions—and operational resilience.
  • Automated AI-driven forecasting promotes these benefits by consuming real-time data and continuously identifying new patterns. The model anticipates demand changes rather than just responding to them.
  • As of 2021, a solid majority—56 percent—of surveyed organizations reported that they had adopted AI in at least one function. That’s progress but, for many organizations, limited data availability—or limited usefulness of the data that are available—is still a problem.
  • The experiences of companies with widely disparate levels of data quality show that most organizations have enough data to derive value from AI-driven forecasting. It’s a matter of building specific and actionable strategies to apply these models even in data-light environments.
Strategies for data-light environments
  • Choosing the right AI model
    • The first step is to identify the most appropriate AI algorithm, based on the amount and quality of available data. In many instances, machine-learning (ML) models can test and validate multiple models to find the optimal choice, with minimal human involvement.
    • For example, the complexity of forecasting at a call center can be daunting because of the variety of customer interactions call centers tend to handle. Seasonality and variations over time make it even more difficult to manage (see Figure below). One utility company’s call center identified 15 types of calls, categorized by the underlying reason—such as for technical support or payment processing. Within each call type, managers tracked three metrics: total volume of calls, average handle time (AHT) for calls directed to internal staff, and a separate “external” AHT metric for calls addressed by vendors. The company aggregated the forecasts at three levels: monthly, daily, and hourly. The combination of call types, metrics, and aggregation levels required 135 independent forecasts.
    • The call center applied an ensemble of forecasting models with different strengths to all call types and metrics, in an automated way. The algorithm chose simpler models when the data yielded only a small sample size and more complex ones when larger sample sizes were available. Simpler models have smaller parameters to tune and therefore would require a smaller sample size to train. By contrast, complex models generally perform better when large amounts of data are available because the numerous parameters in these models require many iterations to train.
    • Testing a range of models with different complexity levels for every data set improved forecast accuracy by almost 10 percent for volume, and about half that for average holding time. Overall, this forecasting approach reduced costs by about 10 to 15 percent, while improving service levels by 5 to 10 percent—particularly by enabling faster transaction time.
  • Leveraging data-smoothing and augmentation techniques
    • This technique works when a period within a time series is not representative of the rest of the data. For example, sales data during the COVID-19 pandemic has usually shown anomalous trends and seasonality. Often, time-series data are influenced by anomalous periods that disrupt overall trend patterns and make it extremely difficult for any AI model to learn and forecast properly. Smoothing is a technique to reduce the significant variation between time steps. It removes noise and creates a more representative data set for models to learn from.
    • The impact of smoothing becomes more evident when the time-series data are affected by a particular event in the past that is not expected to recur regularly in the future. In the example shown in Exhibit 3, the company’s goal was to forecast sales in its retail stores. Although the drop in sales volume during April and May seemed to have been a one-time event, it significantly affected the machine-learning process. The anomalous period has completely different patterns of seasonality and trend compared with the rest of the time series. But the machine-learning models will not automatically treat this period as anomalous. Instead, they will try to learn from it alongside the rest of the time series as they generalize the overall patterns. In this example, the anomalous period confused the model, and it was unable to learn the intrinsic seasonality patterns as expected.
  • Preparing for prediction uncertainties
    • Relying on statistical forecasts alone may not achieve the required business insight. This is especially true for long-term forecasting because unexpected events that affect trends and seasonality make it harder to learn from historical patterns.
    • Given the intrinsic uncertainty of forecasting analysis in such cases, it is valuable to use what-if scenarios. They are particularly important when data samples are too small.
    • The utility used this methodology both for long-term workforce planning and for specifying required head count over the next year. This forecasting model seeks to capture the year-over-year trends and seasonality. However, the model does not enable planning for unexpected events in the future. To address the uncertainties, specialists designed the user interface (UI), which enables users to change specific parameters and create scenarios.
    • In this regard, it is important to define the critical parameters that could potentially affect the target variables and to design an interactive UI. Although some base scenarios can be designed in advance, it is critical the creation of interactive tools for users.
    • What-if scenario tools are particularly valuable when demand and supply patterns are volatile and multiple new business initiatives arise in close succession. In such cases, AI-driven forecasting models that are heavily dependent on historic data fall short. But what-if scenario tools have shown to improve the delivery rate of capital projects by 10 to 15% while applying a consistent and transparent scenario planning process across different business units and teams have increased workforce flexibility by about 20 percent.
  • Externally sourced data can cover a variety of sources and content, including social media activity, web-scraping content, financial transactions, weather forecasts, mobile-device location data, and satellite images. Incorporating these data sets can significantly improve forecast accuracy, especially in data-light environments. These sources provide an excellent option for the inputs required for AI-driven models and create reasonable outputs. The market for external data is expected to have a CAGR of 58 percent, reflecting the increasing popularity of these data sources and the significant expansion in the types of external data available.

Why Business Must Heed Customer Reviews

  • Podcast published by McKinsey. February 22, 2022.
  • The total number of global reviews approximately doubled in the year after COVID-19 started. Going solely into the store and observing what’s on the shelf or any traditional marketing lever—such as the power of the brand, the availability in the store, or some sort of in-store promotion—that’s the old world.
  • In the world before reviews, It was needed the marketing to tell consumers what the product was like. What it is seeing as a result now is new brands disrupting spaces that had been held by longstanding brands—as this new kind of dynamic plays very well to the disruptor in that sense.
  • There’s no point in marketing a product that is not good. It is necessary to think about what categories are more susceptible to these disruptions is by thinking about the category along a couple of dimensions. The first dimension is what is the fundamental benefit or equity that this product is delivering to the consumer? Is it essentially just a functional attribute?
  • Maybe 15 years ago, if a company wanted to build a new product and sell it in the US market, one of your biggest challenges would be how do I get inside large brick-and-mortar retailers like Walmart and Target or Home Depot and Lowe’s. That type of barrier is nil today.
  • Now you pair that with a post-COVID-19 purchasing population that is more willing than ever to try new things, and then you find yourself with a bunch of product categories that are highly susceptible to being disrupted in this new environment.
  • The natural-language processing takes these reviews—which, in the industry, are called unstructured text—and it does two things. It clusters the reviews into themes, and those themes are usually very product-attribute related because of the nature of the reviews. And then, it can take that large data set of unstructured text and cluster it into product attributes, and it can assign a positive, negative, or neutral valence to a given review.
  • The dynamic in the marketplace is such that the length of time that you have to do those iterations is smaller than it’s ever been and will only continue to decrease because of the ease of entry for competitive products.
  • And the output of that is an attribute taxonomy that is essentially from the mouths of the users and a performance metric.
  • Recommendations to market the product. Be clear about your product and what it is—and in some cases what it isn’t. Be aware not only of the product and its performance but also of what the experience is going to be like.

RESEARCH STRATEGY

For this research on articles and podcasts for C-suite, we leveraged the most reputable sources available in the public domain such as Harvard Business Review, McKinsey, Forbes, and the New York Times. We reviewed diverse articles and podcasts published over the last month and selected six of them, which address interesting and challenging subjects related to the topics requested by the client.

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