Emerging HR Technologies & Disruptive Trends

Emerging HR Technologies & Disruptive Trends

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Sustainable Growth and Trends
  • Human Resources
  • Digital Transformation

Publication | Update: Sep 2020

Due to the sheer volume of start-ups and how fragmented the HR Tech space is, it can be difficult for HR leaders to keep abreast of new and emerging technologies and prioritise which technologies are worth exploring.

According to CB Insights research, the HR tech space is exploding, with over 135 start-ups innovating across the value chain. The HR Tech space is fragmented and it can be difficult for HR leaders to keep abreast of new and emerging technologies and prioritise which technologies are worth exploring.

With that in mind, below we explore a discussion on emerging HR technology trends hosted by Lisa Gerhardt, Partner and Global Lead for Savannah Group’s Global HR Practice.



Many large organisations have always had some kind of screening technology, but the next generation of screening technology, automated screening, is getting very sophisticated. This new type of technology is being paired with chat bots, and ultimately will be able to set up interviews and, through facial expression analysis and video interviewing, will likely start running early parts of the interview process. Some of the bigger organisations are trialling this successfully and are seeing it reap significant benefits in terms of both time and cost savings.

It does however rely on having a very good data set and the implementation and learning phase can be quite slow. There are also some potential hurdles to overcome in terms of data privacy and ensuring that there is enough accountability and explainability of the process so that if challenged, an organisation can explain why a candidate was rejected.


Chatbots have become increasingly popular, and there are hundreds of start-ups in the space. Following their success and popularity in customer service and marketing, chatbots are increasingly being deployed within HR. For years, cost and sophistication has been holding organisations back, but as sophistication increases and costs decrease, they are becoming increasingly more attractive to organisations. The chatbot space is very crowded, with over 250 start-ups in the US alone.

 Artificial Intelligence and Machine Learning

A very popular topic in the press due to the exciting (and sometimes terrifying!) potential use cases, AI and specifically machine learning are paving the way for an incredibly advanced type of technology deployment within businesses. Largely the reserve of enterprise sized technology companies, systems like IBM’s Watson, Salesforce’s Einstein and Google’s Deep Mind labs are developing technologies that can process vast quantities of data to find relationships and patterns that were previously unthinkable to human analysts. When paired with larger data sets as a result of more sophisticated recruitment, wellness and L&D programs, from an HR function’s perspective, machine learning could be used to identify patterns among the highest performing, happiest, and most engaged individuals to further assist recruitment and L&D.


Not just limited to currency type applications like Bitcoin, blockchain can help HR to provide a better audit trail on sensitive issues. Sexual harassment cases for example, where a rogue individual or organisation may try to erase evidence of a complaint being lodged could instead be securely stored on the blockchain to avoid tampering. Very recently, the Ethereum blockchain was used for just that, after a Chinese student activist had an open letter censored after requesting information about a decade-old sexual misconduct case at the prestigious Peking University where she was a student. Referencing could be uploaded to a blockchain as well, allowing universities to upload verified qualifications so that organisations can check and verify the accuracy of an individual’s education.

Virtual Reality

VR is typically thought of as an immersive media experience for gaming or exploring a yet to be built physical space, but it’s also finding a place in HR. From a diversity and inclusion perspective, virtual reality (VR) can be used to emotionally immerse an individual and create a consequential shift in terms of their behaviour. For instance, one type of technology allows a user to wear a VR headset and inhabit a virtual body of the opposite gender. Studies have shown that ‘living’ inside a body of the opposite sex for just 20 minutes can create a powerful change to an individual’s outlook. This type of emotional immersion can also be used to help understand mental health issues.

Facial Recognition

From a retail point of view, facial recognition is increasingly being used to see what areas of a shop customers gravitate towards and how they react to products, offers or display items. From an HR point of view, this could either be used in a team training environment or in a larger space, for instance a canteen, to get an understanding of morale or motivation when the organisation is going through a big change for instance a merger or acquisition.



One of the areas where we are seeing the most disruption is recruitment and specifically how to enhance the employer brand while making the process more efficient. There are more than 250 recruitment tech start-ups alone, with volume recruitment a popular area of focus for many of these start-ups. Culture and team fit is also a significant focus for these start-ups, and some are hoping to incorporate advanced psychometrics to give a more accurate fit.

Learning and development

L&D used to largely consist of a content depositary that relied on the individual to find what they needed. There have been videos and interactivity in the past, but it was often expensive and not hugely sophisticated. Learning and telling isn’t the favoured style of approach any more, and there is a big shift to focus on the individual rather than L&D being an organisational initiative. The thinking is that if learning is a life-long process, let’s have something that individuals can carry with themselves as they move from organisation to organisation. There is also a trend to create sophisticated curated content which is much more engaging and dynamic than previous versions.

Employee engagement and feedback

Marketing is a good function to look at for innovation – whatever becomes popular from a consumer branding point of view normally then gets applied to employer branding. Surveying tools have become much better and predictive analytics have made the process of issuing, populating and analysing surveys faster, allowing organisations to gather feedback more regularly and more quickly.


There are a number of start-ups that are shifting their focus from the crowded consumer wellness space to corporate wellness. This can be physical wellness but also financial wellness such as clearing and restructuring debt, discounts etc. While these types of systems have been around for a while, there is now a move to make them more mobile, more accessible, and to be proactive in prompting and supporting an individual.

The Gig Economy

Historically certain industries have had high numbers of contractors such as oil and gas, but a more flexible approach to work is getting more popular across other industries. The gig economy is much more than just a difference in payroll. Businesses are seeing the advantages of flexibly outsourcing specific parts of their processes, and individuals like the empowerment and freedom to choose their next assignment.

Performance Management

Interestingly, performance management is a space which is quite underdeveloped from a start-up perspective. There are solutions that look at feedback but nothing that covers the whole life cycle from strategy and interaction through to performance management and how to have difficult conversations.

HR Operations Automation

Probably one of the biggest areas for disruption is how HR runs itself, and the next phase of automation for HR functions is underway. Interestingly, the start-ups active in this space don’t typically see HR as the customers and are instead approaching line managers directly. These start-ups are creating solutions for a problem that they think HR are creating. Similarly, this is happening in Finance, and is part of a bigger back office automation theme.

What is appealing for businesses now is that these systems and applications are much easier to deploy rather than the big systems that have traditionally been bought for huge expense and took time and effort to deploy. There is an argument however that deploying numerous ‘mini’ systems to automate a specific part of the HR function may lead to a fragmented, inefficient and disjointed function.

Challenges for HR Leaders

From HR’s point of view, the fact that many tech start-ups are approaching line managers directly could be seen as a challenge to the control they have over an organisation. In reality, it’s very hard for any HR function to have absolute control over every HR process in every location, particularly for international businesses. This does however present some organisational challenges particularly in terms of continuity of service or leveraging economies of scale when you have a number of different locations using niche software to solve specific problems. Whereas the trend several years ago was around integrating technologies, in a lot of ways, this latest trend is a move towards the opposite and HR leaders will increasingly need to get comfortable with managing ambiguity.

As the technology develops, HR leaders will be expected to create an ever-more engaging and personalised employee experience while improving productivity and keeping costs under control. Similarly, technology will impact organisational development, workforce planning, reskilling and ultimately create a cultural change. HR leaders also have a role to play from an ethical perspective, as data privacy and data ownership issues increasingly effect organisations.

As a result, it’s important for HR leaders to stay up to date and to experiment and disrupt current processes.

Lisa Gerhardt, Savannah Group Partner, Human Resources Practice, has nearly 20 years executive search experience and leads the global HR practice. This breadth of knowledge enables Lisa to assist clients with complex global talent and resourcing challenges across all functions. With her depth of expertise in international HR appointments she brings significant added value to organisations as they plan their overall talent and resourcing strategies.

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology


We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

  • EMIS: EMIS Insight and EMIS Dealwatch

  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

  • The Wall Street Journal

  • The Wall Street Transcript

  • Bloomberg

  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

  • Hoover’s

  • MGI

  • LSE

  • MIT

  • ERA

  • BBVA

  • IDC

  • IdExec

  • Moody’s

  • Factiva

  • Forrester Research

  • Computer Economics

  • Voice and Data

  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

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  • Oliver Wyman

  • Faulkner Information Services

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  • Berg Insight

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  • Gartner Group

  • Juniper Research

  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

  • DDAR

  • ISS Corporate Governance

  • BoardEx

  • Board Analyst

  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.