Technology has transformed the way people work. Leaders can resolidify their teams by developing a robust Workforce Augmented Strategy to adjust their leadership behaviour, embrace digital workforce platforms and deepen their engagement with digitally enabled workers.
Malaysian Insurance Institute (MII) together with The Center of Applied Data Science (CADS) Founder and CEO Sharala Axryd will run a webinar for leaders to create a center of excellence for data literacy that addresses business needs and talent potential identification.
In doing so, leaders will be able to:
- improve employee engagement and talent retention
- improve data literacy and close competency gap
- digitize operations and automate process
Bridge The Human-Digital Divide
Technology has transformed the way people work. Leaders can resolidify their teams by developing a robust Workforce Augmented Strategy to adjust their leadership behaviour, embrace digital workforce platforms and deepen their engagement with digitally enabled workers.
Malaysian Insurance Institute (MII) together with The Center of Applied Data Science (CADS) Founder and CEO Sharala Axryd will run a webinar for leaders to create a center of excellence for data literacy that addresses business needs and talent potential identification.
In doing so, leaders will be able to:
• improve employee engagement and talent retention
• improve data literacy and close competency gap
• digitize operations and automate process
With over 15 years of experience in the telecommunications field, award-winning entrepreneur Ms Sharala Axryd established The Center of Applied Data Science (CADS) as a platform for data, analytics and artificial intelligence in workforce transformation solutions. She also brought The Data Incubator and Harvard Business School programmes to Malaysia and launched ASEAN’s first data science accelerator program.
Date: 20 April 2020
Time: 2:00pm
There’s a funny image circulating right now of a survey that asks, “Who led the digital transformation of your company? A) CEO B) CTO C) COVID-19.” COVID-19 is circled.
COVID-19 is accelerating digital transformation at many companies, knocking through long-standing resistance and silos. As ITWC CIO Jim Love noted, “Sometimes it takes a crisis to turn people’s mindsets around.”
The crisis is also setting the agenda and the priorities of where to focus digital transformation efforts, such as facilitating the needs of a mass, remote workforce.
But there’s still not a shared understanding of what digital transformation actually means. It stands for much more than suddenly working from home over Zoom. It asks businesses to rethink how they deliver value to their customers in a digital world.
As Sandy Shen, senior director at Gartner, put it recently:
“The value of digital channels, products and operations is immediately obvious to companies everywhere right now. This is a wake-up call for organizations that have placed too much focus on daily operational needs at the expense of investing in digital business and long-term resilience. Businesses that can shift technology capacity and investments to digital platforms will mitigate the impact of the outbreak and keep their companies running smoothly now, and over the long term.”
Transforming into a digital business is the number one priority of most organizations today .
As a digital business, Data and Analytics become the two important elements in the organization.
As new technologies and competency are quired to support Data and Analytics capabilities, CADS helps close competency gap and improve Data Literacy among workforce and help organization to be Data-Driven.
Breaking down the what and how of AI, and why insurance carriers need to map out their game plan ASAP.
A recent Accenture Technology Vision for Insurance survey shows that the majority of insurers know DARQ technologies—specifically AI—will be transformational or will bring extensive change to their businesses (DARQ includes distributed ledgers, AI, extended reality and quantum computing). The insurance industry is among those most susceptible to future disruption, but it has been hesitant to fully adopt AI and take the reins. Only 50% of carriers are in the evaluation or pilot stage and only 29% have adopted a form of AI into the fold.
In an industry based on assessing risk, insurers may perhaps feel a bit mystified or overwhelmed by AI. Technology continues to evolve at a pace that the industry is not accustomed to, but firms that haven’t adopted AI need to dive in and keep up with competitors who are maximizing the benefits of the technologies.
McKinsey estimates a potential annual value of up to $1.1 trillion if AI tech is fully applied to the Insurance industry.
Skills gap are widened by adoption of new technologies.
“by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, acknowledging their extreme deficiency. Data and analytics leaders should evaluate and close competency gaps today to secure the data-driven enterprise of tomorrow,” - Gartner
How are Insurers Adopting AI?
In fact, insurance is an industry that venture capitalists consider so ripe for disruption that the founders of Lemonade, a New York-based insurtech company, raised one of the largest seed rounds in history simply by talking. It’s not just the venture crowd. Warren Buffett has gone on the record saying that the coming of autonomous vehicles will hurt premiums for Berkshire-owned Geico.
Buffett may have been referring to a 2015 KPMG report which predicts that “radically safer” vehicles, including driverless technology, will shrink the auto insurance industry by a whopping 60% over the next 25 years. Readers should note that auto insurance is more than 40% of the insurance industry as a whole.
Examples:
Health Insurance
In a world where the cost and complexity of health insurance is increasing, Accolade Inc’s Maya Intelligence platform uses machine learning to help patients and employers select the most relevant and cost effective health insurance coverage. Accolade reportedly serves over 1.1 million clients.
British firm Kirontech claims that its software KironMed uses machine learning to analyze medical claims and detect patterns that may signify health insurance fraud or waste (underutilized services). The company has reportedly raised $3.5 million in Series A funding.
In 2018, SwissRe and Max Bupa Health entered into a partnership with Indian fitness tech startup GOQii Health. GOQii uses data from wearable devices and their own AI-driven ‘wellness engine’ to track health vitals and provide healthy living advice and risk reports to individual users. When partnering with or acquiring these AI and tech-driven startups, insurers are betting that it will lead to fewer claim payouts and more attractive premiums for health insurance customers down the line.
Auto Insurance
As far back as 2017, US insurer Liberty Mutual unveiled a new developer portal through its innovation incubator Solaria Labs. This open API portal combines public data with proprietary insurance data to enable the creation of better insurance products for customers. One such product was reportedly a mobile app that allows drivers involved in accidents to assess damage to their car in real-time using their smartphone camera. The app would also provide repair cost estimates. The AI powering the app will be trained using thousands of images of car accidents.
Ant Financial, the Chinese fintech firm part of the Chinese giant Alibaba Group, released software called Ding Sun Bao to analyze car accident damage and process claims. Ding Sun Bao uses machine vision, enabling drivers to take pictures of their damaged car using their smartphone camera. The app then compares the photo with its image database to determine the severity of damage, estimate repair costs, and analyze the accident’s impact on the driver’s future insurance premiums. Crucially, Ant Financial claims that the app assesses damages and handles claims in six seconds, whereas human claims adjusters reportedly take around six minutes and 48 seconds.
Operational Efficiency
US insurer Allstate partnered with the Earley Information Science (EIS) agency to develop a virtual assistant called ABIe (pronounced ‘Abbie’). ABIe was designed to answer common queries of Allstate’s insurance agents who had switched from selling one insurance product to another. ABIe uses natural language processing to process 25,000 inquiries per month.
When switching between product lines, many sales agents faced a steep learning curve and Allstate found that its call centers were inundated with questions from their own sales agents on new products. This led to long wait times at call centers for actual customers, which resulted in potentially lost business opportunities. Solutions like ABIe can potentially make a huge difference.
The skills that were relevant just a few months ago are a little less relevant now. And there are a number of skills that are predominant and very important — not only now, but looking into the future. That makes it really important for us to be on the front end of acknowledging where there are opportunities.
How to change? Start by transforming the workforce
To attain this level of collaboration, insurers need to transform their workforce. To do this, we believe three steps are essential:
Reimagine work to better understand how machines and people can collaborate.
Pivot the workforce to areas that create new forms of value.
Scale up ‘new skilling’ to enable people to work with intelligent machines.
As we have seen, the majority of insurance executives believe human-machine collaboration is important if they are to achieve their strategic objectives. Since the workforce is a critical enabler of any AI strategy, insurers need to develop a workforce that is equipped and willing to work at a higher level in collaboration with intelligent machines. Insurers, in our view, cannot afford to wait until the future is clear and predictable, but need to start now.
There is a clear choice insurers need to make: Which company do they want to be? The one who has strategically leveraged intelligent technology, data and upskilled its people, or the one who has not?
Transforming the workforce to collaborate effectively with AI won’t be easy or quick, but it is essential if the potential of artificial intelligence is to be realized. The time to start is now.
The half-life of skills is shrinking—and it’s on everyone to pick up the slack
Career life spans are lengthening: We now expect people to have careers that last six or seven decades. Paradoxically, the half lives of the skills people need to stay useful in those careers are shrinking—down to five years and continuing to fall.
Workers used to enter the workforce with skills that could carry them through most of their careers already in place. Now, on-the-job learning and development is necessary to keep people relevant in a world of constantly shifting roles, responsibilities, tools, and technologies. This applies to software engineers as well as to those in marketing, finance, law, and other industries.
What does it mean to have a career today? More specifically, what does it mean in a world where careers span 60 years, even as the half-life of learned skills continues to fall to only about five years? In the past, employees learned to gain skills for a career; now, the career itself is a journey of learning.
As companies build the organization of the future, continuous learning is critical for business success. For today’s digital organizations, the new rules call for a learning and development organization that can deliver learning that is always on and always available over a range of mobile platforms.
In many instances, employees themselves are pushing for continuous skill development and dynamic careers. Glassdoor data reveal that among Millennials, the “ability to learn and progress” is now the principal driver of a company’s employment brand.2 Yet only one-third of Millennials believe their organizations are using their skills well, and 42 percent say they are likely to leave because they are not learning fast enough.3
Leading organizations are paying attention. Companies with dynamic career models outperform their peers by providing continuous learning opportunities and a deeply embedded culture of development.4 As the authors of The 100-Year Life point out, employees facing careers spanning 60 to 70 years expect employers to help them continually reinvent themselves, move from role to role, and find their calling over time.5
Companies worldwide are scrambling to catch up with employees’ desires. Fully 83 percent of the respondents we surveyed this year say their organizations are shifting to flexible, open career models that offer enriching assignments, projects, and experiences rather than a static career progression. And 42 percent of surveyed respondents now believe their organization’s employees will have careers that span five years or less.
https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017/learning-in-the-digital-age.html
WEF ‘Future of Jobs’ report, published on the 17th September 2018, predicts massive workforce changes in both workforce composition and skills demand as businesses expand the use of automation and artificial intelligence in their daily operations.
It suggests that by 2022 133 million new jobs will emerge as businesses develop a new division of labour between people and machines whilst 75 million jobs may be lost as companies shift to more automation.
In less than a decade, most workplace tasks will be done by machines rather than humans, according to the World Economic Forum’s latest AI job forecast.
The report argues that these forecasts shouldn’t be seen through the usual gloomy lense. As the report argues, automation brings a number of job opportunities, as well as job losses. This is more a story of change rather than downturn:
While the Fourth Industrial Revolution’s wave of technological advancement will reduce the number of workers required to perform certain work tasks, responses by the employers surveyed for this report indicate that it will create increased demand for the performance of others, leading to new job creation.
But the World Economic Forum report confirms that a key challenge for grappling with the future of work will be equipping staff with new skills and fostering workplace flexibility. If businesses are to thrive in this new technology driven environment, they will need to buy-in of a motivated and agile workforce, equipped with futureproof skills to take advantage of new opportunities through continuous retraining and upskilling.
The majority of employers surveyed for the report expect that the skills required to a particular job will have shifted significantly. The proportion of core skills required to perform a job that will remain the same—is expected to be about 58%, meaning an average shift of 42% in required workforce skills over the 2018–2022 period.
Even though AI can reduce costs, improve efficiency, empower consumers and employees alike, failing to optimize AI in the right way can lead to premature organizational changes, loss of employee engagement and productivity, as well as consumer alienation.
At their core, AI systems are designed with the intention of augmenting, not completely replacing, human contributions. So how can insurance executives ensure that their organization is capitalizing on this disruption and using AI to increase productivity and efficiency through their people and not despite their people?
According to recent research on AI's psychological impact on employees in the workplace: AI can have a positive effect on motivation among employees and managers who rely on it to carry out their duties at work. More specifically, the results indicate that AI can have a substantial positive influence on individuals' perceptions of autonomy, competence, connectedness and outlook for the future.
People leaders in insurance organizations that use AI applications can direct AI to perform the most time-consuming aspects of their employees' jobs and provide employees with more flexibility to decide where to focus their efforts. Employees in certain functions -- such as claims processing, underwriting, fraud detection and customer service -- can be redirected by their managers to offer more holistic, intuitive and context-specific expertise in those functions and use AI to enhance their capabilities to be even more effective in their roles.
These next-generation insurance professionals will be highly sought after and must have a unique mix of talent, skill and technological adeptness that will enable them to be successful in this semiautomated work environment that is continually evolving. Generating value from an AI-augmented workforce requires a conscious culture shift for most carriers today. Doing so demands an aggressive strategy to attract, develop, engage and retain this AI-augmented workforce in order to thrive in the era of AI.
At the same time, organizations require an integrated view of all data related to their workforce to drive internal mobility and skills development activities to improve employee retention. In large organizations, fragmentation of employee data across disjoined point or country-specific solutions is the main reason for reduced internal mobility and career progression of the best performers.
There are complex feedback loops between new technology, jobs and skills. New technologies can drive business growth, job creation and demand for specialist skills but they can also displace entire roles when certain tasks become obsolete or automated. Skills gaps—both among workers and among the leadership of organizations—can speed up the trends towards automation in some cases but can also pose barriers to the adoption of new technologies and therefore impede business growth.
Yet in order to harness the transformative potential of the Fourth Industrial Revolution, business leaders across all industries and regions will increasingly be called upon to formulate a comprehensive workforce strategy ready to meet the challenges of this new era of accelerating change and innovation.
1. Workforce Planning and Modelling
Business Impact:
Simply displaying an accurate organization structure enables the HR department to help create transparency and organizational efficiency, and to improve communications. Providing line managers with an easy-to-understand visualization of their teams will improve manager self-service adoption — and better visualization improves usability. Operational workforce planning and resource planning tools can significantly increase forecast accuracy and enable business, HR and finance leaders to optimize workforce size to meet business demand at the right cost. Strategic workforce planning is a significant differentiator for organizations because HR brings a strategic view of workforce capacity and market availability of resources. This helps them evaluate what workforce-related investments would be necessary to meet short- and long-term strategic business goals, whether those are related to digital business transformation, growth, or changes through merger and acquisition or divestiture activities.
Invest in learning productivity (experience) platform (LPP/LEP) to improve learner experience and engagement by providing learners a more open, interactive and effective way to continually learn. These tools allow organizations to deal with the accelerating pace of the digital workplace. Organizations looking to improve their learning culture through improved personalization, collaboration and knowledge retention have made investments in LPPs. Healthy learning cultures with a wide range of development opportunities often correlate with higher employee engagement, which translates to stronger business performance.
(leaderhip)
Business Impact: Application leaders responsible for transforming HCM should provide business executives and HR leaders with the technology to employ strategic workforce analysis tools to better manage talent and support their desired business outcomes. These tools should help line managers and HR understand talent mobility, gaps and risks. This information can be used to inform appropriate talent management strategies (for example, hiring versus development to fill the gaps). Leading companies combine people-related data with other data (for example, finance or sales data, or outside data such as census data). Workforce analytics investments, when targeted and specific to a given business problem, provide highly valuable input in the decision-making process, either in terms of workforce-related cost avoidance or future productivity optimization.
2. Internal Marketplace
Business Impact:
The gig economy and the need for business agility have opened up new ways of working and challenged established notions of employment. It has led to changing worker expectations and preferences on how individual workers manage their careers and build their portfolio of work experiences. Marketplace-based platforms make it much easier to connect customers directly to suppliers. Internal talent marketplaces take advantage of the increased flexibility of the gig economy and such marketplace-based platforms, without requiring changes to employment categories. Large enterprises looking to push innovation to the edges may be encumbered by heavy management and control structures. Internal talent marketplaces have the potential to change that. They establish trust through feedback mechanisms. They allow for workerled innovation and contribute to workers fully taking control of their own careers. They will enable tomorrow's more lean, agile and adaptive organizations.
Business Impact: Assessments that address multiple job roles, leverage emerging neuroscience discoveries, effectively apply AI and deliver more-immersive user experiences can help reduce administrative costs. They can also improve the effectiveness of workers by more accurately identifying best-fit candidates for critical roles. Finally, they can reduce costs and improve productivity, since best-fit candidates generally have a longer tenure in jobs and within the enterprise. This can in turn reduce direct offboarding, hiring and onboarding costs, decrease the cost of training new hires and speed their time to competency.
3. Talent Acquisition
Business Impact: Candidate relationship management and recruitment marketing software significantly improves recruiting processes in terms of quality, speed and cost. Jobs are filled faster when leveraging the higher usage rates and quicker response times of social sites, as opposed to using traditional job boards. Data mining and social matching can help uncover hidden talent that traditional candidate sourcing strategies miss (an example of this functionality is the use of AI to identify "applicant/prospect eagerness"). Candidate relationship management tools and ATSs have established the capability for talent pools and recruiter folders for some time; the evolution in this space is coming from platforms that populate those talent pools through AI-driven searches and automation.
These tools are positioning the recruiting team to spend time engaging the highlighted matches within the talent pool, rather than generating the pool itself. Recruitment marketing via social media increases employer brand and visibility, and messaging via social channels is often perceived as more personalized and engaging than it is in traditional email campaigns. With tight labor markets, increased candidate expectations and a new generation of well-researched tech-savvy job seekers, the talent technology landscape has expanded outward to attract, engage and nurture both passive and active candidates. Much of this functionality and capability sits in the candidate engagement and marketing space.
Business Impact: AI solutions can improve recruiter and hiring manager productivity, as well as
allow organizations to better utilize the resources involved in the sourcing, screening, interviewing, and the assessment process. Investments in AI can map to common recruiting KPIs such as:
■ Candidate experience (chatbots, campaign tools, AI-driven personalization).
■ Diversity and inclusion (pipeline sourcing, advanced analytics, branding, job descriptions).
■ Quality of hire (candidate ranking, planning, competency/skill identification and mapping).
■ Cost per hire.
■ Time to hire.
■ Process efficiency (market optimization, scheduling automation, recruiter/candidate/manager assistants).
Overall Impact
Business Impact: Employees are the biggest cost category for most organizations, and can be a source of competitive advantage. Organizations that understand their people better, and use their insight wisely, will outperform those that do not. They hire better, and have a more engaged workforce, less discrimination and better managers. Machine learning in HCM has the potential to transform how organizations look at the workforce as well as the efficiency and productivity of certain processes. Advanced analytical models that show the links between workforce practices and business results can help business and HR leaders invest in the right talent and the right HR programs to support that talent. Machine learning techniques, which reveal the factors important for team and organizational success, can help business leaders act on tough situations earlier, or make more timely decisions to avoid pitfalls.
2nd last slide. Final slide will be the same as the 1st slide.