HR is Dead — Long Live HR
Dr Reg Butterfield 2023 ©
Long comprehensive newsletter. Reading time aprox. 1 hour 20 minutes.
Organisations cannot survive without some form of support from People oriented specialists. What that means is changing rapidly. We posit that the old HR approaches are now dead or dying and that a new world of populating and supporting people in organisations is being born. The advent of Generative Artificial Intelligence (G-AI) will enable a new world that encourages the strategic focus of people in a collaborative world based on mutuality. HR’s part in this new world is one of strategically connecting and optimising people through collaboration to achieve the purpose and objectives of the organisation.
In our last three HR-AI related newsletters we discussed the roles of HR over time, the associated activities, and how traditionally these were undertaken. We extrapolated the history in simple terms to demonstrate that the functions of HR were moving into a more relationship world of connecting and optimising across organisations and their external relationships with customers, suppliers, and more; figure 1 is a reminder. The social and network collaboration, shown in red, is a new perspective for most HR operations.
Figure 1: AI working with people.
We also discussed the need to move from linear activities within HR expert fields to a more collaborative approach with not just HR colleagues, but also the wider organisation. In doing so identified a need for a common technology platform. Basically, the whole organisation connected through one platform that is accessible across the business on a needs-basis.
In our earlier discussions we refrained from offering individual or collective solutions for how organisations will use the varying apps and other technology to meet the emerging new era world of HR, organisations, and society.
Background to this newsletter
In this newsletter we discuss the urgent need for HR to consider the implications of generative artificial intelligence (G-AI) on not just HR’s operations but also across the whole organisation, as this in turn will impact on the future of HR. We also describe how the future can play out if G-AI continues to progress in its current rate of increased complexity and applications.
Most organisations were slow to understand and introduce automation and digitalisation, which had a slower process of technical innovation and introduction than G-AI promises to have. It was possible to catch up without too many detrimental effects on business, yet this may not be the case with G-AI because it affects everything from how work is undertaken through to the outputs of work, as well as the people or automaton.
This means that G-AI will impact not only the hours that people work, how they work, where they work, the tasks that they do, and the responsibilities that they have, but also on their knowledge and experience through education and training systems that will not be the same as today.
HR has approached technology in a piecemeal fashion over decades, which has resulted in a fragmented set of linear partial automation of administrative tasks in most cases, particularly small to medium enterprises (SME). Some organisations have used a mix of emerging technologies such as avatars and chat bots in small, distinct processes with limited success, which we mentioned in our last HR-AI newsletter.
Today, we offer what we believe is just one real-world approach that uses “emerging technology” that may appear challenging and perhaps a little scary for some people currently employed in HR operations. It is also an opportunity to consider the skills and motivation people may need to meet the challenges of the working arrangements and relationships between people and technology that everybody will experience at work, irrespective of the nature of their work activities.
The story of HR and its possible future
The story of HR is a complex one and we use the following roadmap to bring out the key elements of our story.
· The current state of the app and technology landscape of HR in operation.
· Immediate strategic discussion and action by the C-suite and HR.
· The current known issues of G-AI and potential impact on management credibility.
· G-AI impacts all people.
· Understanding the various types of G-AI.
· Organisational design and management will change.
· Current model of HR Operations
· The growth of HR operations
· Recruitment and selection as a discussion example
· Moving into the world of G-AI and its relationship with HR
· Breathing New Life into the People Side of Organisations
· Futocracy as the New Era organisational model for illustrative and discussion purposes
· The current state of G-AI
· Decision Time
· G-AI and the Future of HR
· G-AI and the Recruiting Process as an example for extrapolation of concepts
· Summary of the recruiting process and G-AI
· HR and the Connecting and Collaborating Activities in Organisations
· The Network Weaver role extended by/integrated with the activities of the HR Business Partner
· Connecting and Collaborating at the Strategic Level
· G-AI improves the strategy development.
· Performance management
· Workforce development
A new approach and model of HR.
The current state of the HR technology industry
The HR tech industry has over forty thousand apps spread across a range of different categories. The market value of this vast industry was estimated to be between US $17B and US $23B in 2021 and expected to reach more than US $76B by 2031; these estimates were made before the recent rapid advances made by G-AI. Figure 2 is a small sample of this large number of vendor apps split into the different categories of activity and some are operating in more than one category (Source: Albert Loyola — LinkedIn 26/9/2023).
Figure 2: HR Technology Ecosystem 2023.
It can be no surprise that consultancy companies and other major providers of IT services are spending large sums of money to get their slice of this revenue pie; Amazon alone is investing US $4B. As all the vendors rally to refine their individual products, the fight to remain relevant and in business is on. For example, Canva is now challenging the Adobe world of image development through new uses of G-AI; is this a potential Kodak moment or will Adobe Firefly save the day?
The impact of this “vendor battle for the future” on the HR operations is unknown. The discussion in this newsletter is based on a combination of current knowledge, past experiences with technology in HR, and taking an effectuation approach to how things can be if HR takes control of its future now. [For readers who are not acquainted with the differences between an effectuation approach to business and work compared to a causation approach, which tends to be the most common approach, see the short discussion in the indented section below].
As we suggested in our previous newsletters, the vendors have been in control to date and HR have accepted the minor degrees of ‘refinement’ of the offers as a way forward, particularly SMEs who do not have the budget and/or knowledge that is available to the large organisations. The outcome has been the fragmented linearity and mixture of apps that we mentioned above.
Causation and Effectuation approach.
The causation approach is more traditional and follows a linear, planned, and predictive model, while the effectuation approach is more experimental, adaptive, and involves leveraging existing resources and partnerships to create opportunities. The choice between these approaches often depends on the context and the level of uncertainty in a given business environment. Some businesses may even use a combination of both approaches, applying causation for certain aspects and effectuation for others.
Current state calls for immediate HR and C-Suite action.
With this in mind, one of the first activities for the chief human resources principle (CHRO) is to engage with the C-suite to decide on how they will approach this significant challenge of gaining control of their technology and not let the vendors drive its use and in doing impact on HR’s strategy. This is crucial if HR is to provide the people-side of operations in a strategic manner that not only supports the current activities, but also proactively identifies and supplies the skills required to support future business success.
It follows that it is vital that the C-suite discussions ensure that G-AI (we use G-AI as a term that also includes other forms of AI in this discussion unless shown otherwise) is integrated in their business strategy from the outset if they are to achieve real impact. This will mean ensuring cross-functional collaboration and a clear outcome-focused G-AI strategy and solution — hence the observation in our last HR-AI newsletter that a common central platform for all parts of the organisation to connect to is desirable.
Mutuality and collaboration* need to underscore the way that the organisation works and is supported by G-AI.
An important part of the strategy discussion is a strategic way forward that will include which applications they will build themselves and which ones to adapt from those offered by vendors. There are significant implications caused by both routes in respect of timing, cost, training, maintenance, and skills required; the scarcity of the latter being a major impediment.
*A detailed discussion of the difference between collaboration and mutuality is in our newsletter dated 23 July 2023, “Is the Future of HR — AI?”.
Current issues regarding G-AI credibility
When developing their strategy, the C-suite must consider the current issues surrounding G-AI if they are to retain credibility with the workforce during any implementation and new operating processes. This is particularly important given the outcome of a McKinsey & Co., report just two years ago that stated it is time for HR to re-focus on people again (“Back to Human, why HR leaders want to focus on people again”, 2021, June). The risk of organisations staying on the cost-efficiency treadmill is now higher than ever with the introduction of G-AI. People must not be sacrificed to achieve this limiting shareholder value perspective, albeit investment in G-AI will require some form of return. Where G-AI is used appropriately, one of the returns will be the reduction or removal of “non-value added activities” (NVA), which lead to improved performance and outcomes, and reduced costs.
Risks
Much has been written about the risks associated with using G-AI and it is freely available on the internet, we will not discuss these in detail here, suffice to mention the main areas obtained from various sources to consider in this discussion.
· Lack of transparency — what logic is used and how decisions are made.
· Bias and discrimination — inadvertent amplification of societal and political bias.
· Privacy concerns — unknown sources of personal and private data used.
· Ethical dilemmas — ensuring moral and ethical values in AI systems, especially in decision-making contexts with significant consequences.
· Security risks — as AI technologies become increasingly sophisticated, the security risks associated with their use and the potential for misuse also increase.
· Concentration of power — risk of AI development being dominated by a small number of large corporations and governments could exacerbate inequality and limit diversity in AI applications.
· Dependence on AI — overreliance on AI systems may lead to a loss of creativity, critical thinking skills, and human intuition.
· Job displacement — as AI technologies continue to develop and become more efficient, the workforce must adapt and acquire new skills to remain relevant in the changing landscape.
· Economic inequality — policies and initiatives that promote economic equity — like reskilling programs, social safety nets, and inclusive AI development that ensures a more balanced distribution of opportunities — can help combat economic inequality.
· Legal and Regulatory Challenges — currently, legal systems not evolving to keep pace with technological advancements and protect the rights of everyone.
· Loss of Human Connection — reliance on AI-driven communication and interactions could lead to diminished empathy, social skills, and human connections.
· Misinformation and Manipulation — AI-generated content, such as deepfakes, contributes to the spread of false information and the manipulation of public opinion.
· Unintended Consequences — AI systems, due to their complexity and lack of human oversight, might exhibit unexpected behaviours or make decisions with unforeseen consequences (hallucinations).
· Liability for actions — lack of clarity about the legal aspects of systems that become increasingly smart. What is the situation in terms of liability when the AI system makes an error?, for example.
“AI today is unbelievably intelligent and then shockingly stupid.”
Computer scientist Yejin Choi sums up the current situation, “AI today is unbelievably intelligent and then shockingly stupid.” (http://t.ted.com/n5dgEHm). It is important for us to bear this in mind when identifying the future of HR and its relationship with AI in bringing about that future.
Whilst there are many risks in using G-AI it can be a powerful tool for employee empowerment. Those who currently consider it a threat will need to be helped in understanding how it can assist them in ways that they would normally never consider, whilst others may see the opportunities of new types of work being added to the employment pot.
G-AI impacts all people.
Not so long ago most IT pundits and commentators were suggesting that AI would impact blue-collar workers the most. However, this has now changed. Since the emergence of G-AI studies by McKinsey (2023) and other commentators now indicate that it will have an especially profound effect on professions traditionally requiring higher levels of education, such as educators and lawyers. This means that the current focus on recruiting “knowledge workers” may need to be reviewed and/or changed. What constitutes as “knowledge workers” is evolving in the light of the application of G-AI.
This technological impact across all forms of work has a silver lining as well as a potential threat for organisational design. It raises the question “what will the new G-AI supported work enable organisations to do differently and how will this be supported in its design and management system?”
The various types of G-AI
Before discussing how HR can transform itself through using G-AI, it is important to know and understand the types of G-AI that are currently available at the time of writing this newsletter. They tend to be grouped into two different categories, currently: text models and multimodal models.
Types of text models: (Source: BCG, 2023)
· GPT-3, or Generative Pretrained Transformer 3, is an autoregressive model pre-trained on a large corpus of text to generate high-quality natural language text. GPT-3 is designed to be flexible and can be fine-tuned for a variety of language tasks, such as language translation, summarisation, and question answering.
· LaMDA, or Language Model for Dialogue Applications, is a pre-trained transformer language model to generate high-quality natural language text, similar to GPT. However, LaMDA was trained on dialogue with the goal of picking up nuances of open-ended conversation.
· LlaMA is a smaller natural language processing model compared to GPT-4 and LaMDA, with the goal of being as performant. While also being an autoregressive language model based on transformers, LLaMA is trained on more tokens to improve performance with lower numbers of parameters.
Types of multimodal models:
· GPT-4 is the latest release of GPT class of models, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. GPT-4 is a transformer-based model pretrained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior.
· DALL-E is a type of multimodal algorithm that can operate across different data modalities and create novel images or artwork from natural language text input.
· Stable Diffusion is a text-to-image model similar to DALL-E, but uses a process called “diffusion” to gradually reduce noise in the image until it matches the text description.
· Progen is a multimodal model trained on 280 million protein samples to generate proteins based on desired properties specificised using natural language text input.
If HR is to gain best use of this technology, it is important to understand the style of organisational design and management used by the relevant organisation. This is because the ability to use the technology effectively may differ if used by traditional hierarchical organisations or the emerging New Era organisational design and management systems.
Organisational design and management.
One of the challenges of the industrial age was to move away from the hierarchical designs and associated management systems that were less able to keep up with societal and technological changes of the last few decades.
The introduction of G-AI across the whole organisational work practices presents an opportunity to re-think how the work is organised and managed. This is because the behaviour of the workforce will be based on a mixture of technology and people like never before. It is also anticipated that the trend to automate work will be achieved a decade earlier than most estimates of the recent past (McKinsey 2023). Add to this the support provided by G-AI to the workforce in areas that are not automated, then behavioural change will be significant because it changes how the work is undertaken and probably the skills needed to undertake that work.
Most current HR activities are based on meeting the needs of the industrial age organisational designs and management systems. These consist of variations of jobs designed and based within specialist or business functions managed by scalar management systems operating top-down control. Recent demands for skills-based approaches within flatter organisations and/or New Era organisations of networks have already started to create new HR thinking and support, which we suggest will need to continue if the best use of G-AI is to be achieved.
Irrespective of whether hierarchical or not, the impact of G-AI will mean HR operations will change significantly.
Current Model for HR Operations
Before discussing the future of HR, it is important to make some sense of the current state of HR. We do this in two main ways: current examples of trends and statistics of HR; and a general view of how HR works.
There are many titles and associated roles and activities within HR. For readers who are not familiar with these, the following indented section provides brief descriptors of the nine main roles.
HR Assistant — primary job is administrative to assist HR generalists, managers, and directors accomplish HR-related tasks.
HR Generalist — responsibilities cover a wide span of tasks including benefit plans, compensation, talent acquisition, onboarding, performance management, and employee relations and/or labour relations.
HR Specialist — is someone who specialises in a particular aspect of the HR function e.g. compensation, benefits, recruitment, payroll, onboarding, people analytics, mergers and acquisition, organisational development, DE & I (Diversity, Equality, and Inclusion), human resources information systems (HRIS), and communications.
HR Manager — has many roles and is often highly competent in a few of the specialist areas above, particularly recruitment and development. They also support the relationship between management and employees.
HR Consultant — focuses on effective use of personnel to achieve the goals of a company. Develops strategies to gain the most effective use of personnel within the company.
HR Business Partner (HRBP) — is a strategic liaison between HR and the business. These senior HR professionals have a deep understanding of the business and ensure that HR helps the business make an impact.
HR Director — responsible for creating, implementing, and executing human resource HR department strategies. In developing HR strategies and delegating tasks to HR employees, HR directors work to equip a company’s workforce with all the tools it needs to succeed.
Vice President of Human Resources (VPHR) — a senior executive responsible for leading and overseeing an organisation’s HR function. They play a strategic role in developing and implementing HR plans, policies, and initiatives that align with the company’s vision and mission.
Chief Human Resources Officer (CHRO) — a corporate officer who oversees the organisation’s HR management and labour relations policies, practices, and operations.
It is important to note that whilst this list consists of nine titles, which are also linked to the role and hierarchical level of the holder, the actual number used in HR varies from three to six layers between the CHRO and the lowest level of HR; it depends on the size and nature of the organisation. For example, on average companies with fewer than 25,000 employees tend to have 4.5 layers and companies with more than 25,000 employees have 5.6 layers (TSG, 2023).
As an indication of the nature of the work of HR and the involvement of the above roles, figure 3 shows the fifteen major functions and the roles typically involved in those functions.
The individual involvement varies from direct day-to-day activity, monitoring, to managing the function. Figure 4 is an illustrative indication of the complexity and extent of the overlap of the roles, albeit all the roles are shown, which may not be the case in the majority of organisations, particularly small to medium enterprises (SME). The solid lines indicate direct involvement, the dashed lines occasional involvement, and the dotted lines administrative involvement. Individual organisations may see these relationships differently as this is purely an example.
Figure 3: Examples of Functions and Roles in HR.
Figure 4: An illustrative example of how the roles allocated to different functions overlap.
Whilst the detail of the activities associated with each of the fifteen functions and nine roles are not visible, the image does provide non-HR readers with some perspective of the complexity and inter-relationships between the roles and the work, as well as resulting in the risk of non-value-added activities. The item with no connections is the role of internal consultant, which tends to be used by larger organisations for a variety of different types of activity.
The Growth of HR
It is not surprising that the changes to organisations post-Covid, such as hybrid, four-day working, and the rapid introduction of G-AI, mean HR is busy and is growing in size overall. If there was any doubt about the importance of HR operations in the past, the manner in which HR together with IT, helped keep organisations working and surviving during Covid has removed it.
The following information is from a report by The Talent Strategy Group entitled, “HR Operating Model Report 2023”. Whilst this data is based on a representative survey across 200 companies worldwide, it cannot be considered as a ‘benchmark’.
We use it as an illustration of the growth of HR operations in all areas of large companies led by Diversity, Equality, & Inclusion (net + 43%), and People Analytics (net + 52%). However, the smaller companies (less than 5K employees) were more balanced with the greatest gains occurring in Talent Management (net +28%), Learning and Development (net +25%) and Talent Acquisition (net +22%). The large companies (25K+ employees) made a slight reduction in Learning and Development (L&D), and Assessment. Figure 5 illustrates the changes over the last 3–4 years in both large and small companies.
The ratio of HR employees and HRBPs to overall employees are two important metrics used to identify the overall leanness of an HR organisation. Historically, the classic ratio of HR employees to organisational employees has centred around 1:100, which has remained steady in 2023 even though the number of employees overall has increased. Making sense of this metric is important to gain some insights about HR operations. For example, is HR adding more value today through the inclusion of services such as talent management, DE&I, people analytics, etc.? If so, it may suggest that the HR function has swapped some lower value work for higher value work. On the other hand, it could also mean that HR has not found efficiencies to lower the ratio through a failure to empower managers, use technology effectively, or to design simpler solutions to the challenges it faces.
Figure 5: Growth in HR — large and small organisations.
Source: “HR Operating Model Report 2023”, The Talent Strategy Group, pp 9 and 10
Whatever the cause of this HR ratio inflation in real terms, the impact of G-AI is an opportunity to rethink the role and operations of HR.
Current time spent on HR activities.
When considering doing things differently in an organisation, it is helpful to understand the percentage of time that HR operations spend on their main activities. Whilst each industry and company size will create different work activity timings, it is sufficient for our needs here to use some averages based on data across the Internet as a guide.
Interestingly, there is little variation when comparing small organisations (less than 5,000 employees), medium organisations (5,000 to 25,000 employees), and large organisations (more than 25,000 employees). Figure 6 provides a comparison of the time spent as a percentage on the activities associated with six HR operational functions.
Figure 6: Time spent on HR activities as a percentage across six functions.
Indications are that as companies grow, they tend to use more technology for the administration process, and this may account for the difference here. Whilst the same applies to recruitment regarding use of technology, savings in time may be offset by increased recruitment in larger organisations. Figure 7 illustrates a traditional example of the recruitment process, and we will use this example later when we explore the use of G-AI.
Figure 7: Example of a recruiting process.
This recruiting process is a good example of a “Fordian” linear approach to solving what is often a high-volume complex process [even small organisations can have high volumes of applications for vacancies]. It tends to be set out so that the costlier elements of the process such as ‘selection process’ and ‘final interview stage’, are at the end, prior to agreeing contracts. This makes sense from a time and cost perspective and yet final checks and references are almost the last point of activity.
Social media checks (considering legal implications of decisions based on such checks) are now highlighting concerns about the chosen candidate and increasingly lead to not offering the position to that candidate. [A consistent statistic over the last five years is that 57% of employers use social media checks to reject candidates, whilst over 70% regularly do such checks]. To reject a candidate at such a late stage does not make sense. If such checks are an important indicator of suitability for a position, why not make these earlier? The answer is that each check can take a person around 30 minutes to complete, which is not sustainable for volume checking when undertaken manually.
Using a combination of timings found on recruiting platforms and reports, the following is an example of the time taken for major activities in the recruiting process. Some are already automated to a certain extent, particularly CV scanning. It is important to note that many of these will take longer for a senior or ‘knowledge worker’ position.
· Posting vacancy on various job boards: 20–30 minutes per vacancy
· Screening resumes (scanning for key words): 30–90 seconds per resume
· Conducting social media background check: 20–30 minutes per applicant
· Doing reference checks: 20–30 minutes per applicant
· Contacting applicants prior to interview: 5–15 minutes per applicant
· Interviewing applicant: 20–40 minutes per applicant per interview
· Discussing candidate with team: 20–45 minutes per applicant
· Creating job offer: 15–30 minutes per applicant
· Contacting applicant after the interview: 5–15 minutes per applicant
· Conducting criminal or other background check: 30–45 minutes per applicant
· Administering credit or other check: 20–30 minutes per applicant
· Coordinating and following up with a third-party drug test (where this is allowed): 15–20 minutes
When associated administration tasks are included it basically sums up to one HR employee day per candidate process. It is little wonder that HR are busy as the staff turnover rates are increasing post-Covid.
The impact of HR being very busy is that candidate complaints about the service of recruiting are rising. In today’s candidate-driven market, the candidate experience is more important than ever before. They are looking for a seamless and positive experience from start to finish, and any problems along the way can not only give them a negative opinion of the company, but also deter good candidates from accepting a position. Any new approach using G-AI must take this into account.
The main complaints that need to be considered by any new approach to recruiting are:
· Vague job descriptions
o Any new approach must ensure sufficient and clear detail in a concise manner.
o Vague descriptions can lead to high turnover.
· Lack of communication from recruiters and/or hiring managers
o Probably the biggest complaint of all.
o Gives a feeling of not being valued and time means nothing to the company.
· Lengthy and unnecessary application process
o Often too long, overly complicated, and requires too much information.
o Dissuades candidates who have the necessary skills and experience. They move on to another company who value them more.
· Feeling just like a number
o Personalise the experience or lose the candidate.
· Feeling unheard
o Concerns not being addressed or not receiving feedback.
· Feeling unimportant
o Phone calls or emails not responded to, taking too long to make a decision or not keeping the candidate up to date with the status of their application…
None of these complaints are new and early attempts at introducing technology did not improve the situation. In many cases it made things worse. The challenge for HR is to ensure that the emerging G-AI, together with taking more control of the formulation of the apps used, is fit for purpose before its introduction.
Summary of Current HR Practices
This recruiting example is a good illustration of how the current predominantly manually operated process is not able to cope with the demands. It is not a reflection on the ability of the dedicated people wrestling with the challenges of recruiting today’s less compliant workforce. It is an excellent example of how the industrial age way of working is in urgent need of change; G-AI presents that opportunity. We will use this process to illustrate our case for a new G-AI relationship approach to the recruiting challenge and in doing so make references to other examples of the current work of HR.
It is not possible in this newsletter to go into the minutiae of all HR activities and functions and we suggest that our purpose for this newsletter will be served without doing so. Future voyages into the detail will continue by you and by others who work with us at Futocracy.org.
Breathing New Life into the People Side of Organisations
Introduction
In this section we will discuss how the relationship between people and G-AI can not only work for the benefit of the organisation, but also provide a more supportive and collaborative environment for the people who deliver the purpose and objectives of their organisation through their work.
In doing so, we will focus on the New Era approach to organisations as opposed to the more traditional hierarchical design and management systems. Even though G-AI can enhance the performance of both types of organisations, we see no sense in re-inventing a scalar management system and structure that we believe research indicates has outlived its ability to cope with the New Era of society, individual, and business expectations.
By way of example, the New Era approach that we use is that of Futocracy. Futocracy is an organisational approach that is flexible and based on a set of building blocks of five foundational principles:
· Organise around the Work
· Autonomy — (independence) through Distributed Authority & Decision-making
· (Strategic) Entrepreneurial Mind-set
· Purpose Alignment
· Transparency
The basic model has no scalar hierarchy and is a flat network of networks. It has three domains that undertake specific activities: Strategic, Purpose alignment, and Operations. There is no line management in the traditional sense of the role whilst network weavers function as coaches, advisors, and are member(s) of the Purpose Alignment Team.
The domains are described to some extent during the discussions in this newsletter, whilst figure 8 provides a diagrammatic overview of a Futocracy network (of networks).
Figure 8: Futocracy Network
The Current State of G-AI
To help make sense of the following applications and their use we briefly bring readers up to date with the rapid emergence of G-AI as it stands today, whilst writing this newsletter.
Although AI was recognised as strategically important before generative AI became prominent, the 2022 survey by MIT found CIOs’ (chief information officers) ambitions limited: while 94% of organisations were using AI in some way, only 14% were aiming to achieve “enterprise-wide” AI by 2025. By contrast, the power of generative AI tools to democratise AI — to spread it through every function of the enterprise, to support every employee, and to engage every customer — heralds an inflection point where AI can grow from a technology employed for particular use cases to one that truly defines the modern enterprise (MIT Technology Review, 2023). Figure 9 illustrates a few of the business functions compellingly addressed by G-AI.
Figure 9: Enterprise applications for G-AI
Just a mere glance at the subject areas and associated activities can leave no doubt in people’s minds that it is now imperative for us to explore its use and application in HR operations. We will refer to some of the actual G-AI methodologies as we progress.
Whilst some readers may feel a level of automation anxiety, which should not be ignored, we believe that the general workforce will be liberated from time-consuming work to focus on higher value areas of insight, strategy, and business value. This aligns with our earlier observation about HR personnel moving from mundane repetitive and administrative tasks to more satisfying work of collaboratively connecting and optimising the work of people. In doing so, ensure the people are achieving the purpose of the organisation, its strategy, and its objectives are realised. If organisations get this right, they can finally cast aside the outdated performance management techniques used to ensure compliance through management control mechanisms. The opportunities for real personal motivation and engagement make the transition worthwhile.
Decision Time
Before starting the process, we refer back to one of the earliest comments in this discussion, “the strategic approach to using G-AI”. One key aspect of the C-suite decision is that whatever approach is taken in respect of HR and technology, organisational integration will be the decider between success and failure of the HR operation (Orion, 2014; Xingze Wu et al, 2020). Whilst there are many models that can be applied by HR, we discuss just two approaches in this newsletter to provide worked examples: the Ulrich Model and Network Dispersion model.
Ulrich model
The first is the adoptation of the Prof. Dave Ulrich’s approach, which is commonly known as the “three legged stool” model where the HR work is divided into three types. The first type is Human Resource Sharing Service Center (SSC) -which is responsible for providing various daily affairs services. The second type is human resources business partners (HRBP) which provides HRM consulting service to business departments. HRBP is key to ensure that HRM service can meet business needs. The third type is Center Of Expertise (COE) ,which is responsible for formulating corporate human resources strategic plans, putting together various work policies, systems, processes, and developing human resource products. The ultimate goal of the three pillars is to break the past function-centric management model and return to business, thereby creating greater value.
However, researchers and commentators all agree that experience of using this approach shows that it is more suitable for larger organisations and even then, not without its problems.
Network dispersion model
The second approach is to disperse the HR operations amongst the working teams, external providers, or a combination of both. The strategic element would be maintained within the organisation. How this is achieved will depend on the design of the organisation. This approach is used by many New Era organisations, particularly those based on networked micro-organisations; Haier and Buurtzorg are such examples.
In many ways this dispersal approach is more suitable for G-AI enabled HR set-ups because it allows for a lot of freedom to break away from old HR habits and processes. It also encourages more direct involvement of the whole workforce in key aspects of HR such as skill definitions, moving skills (people) around to meet emerging challenges and shortages, and more.
A New Era solution
In the case of Futocracy, both options can be accommodated as follows:
The SSC can be either one team in the working network providing HR services to the rest of the network or dispersal of the HR services amongst the teams and/or externally, with each team having a degree of HR expertise that they provide to other teams on a “need” basis; this is the Buurtzorg approach. See figure 10 as an example of the HR team approach, which can also be based on a commercial internal contract to provide defined services and replaced in the event of not meeting their responsibilities, e.g., the Haier approach.
The COE is typically undertaken by the Purpose Alignment Team (PaT), where a senior HR expert is a member who works with the operational, finance, and other necessary expert team members.
Whilst he HRBP has been criticised for a lack of clarity in its operational role and overlap with the HR Director activities, there is arguably some similarity to the role and set of activities undertaken by the Network Weaver, although the Network Weaver has tasks beyond that of the HBPR standard remit. The Network Weaver as a member of the team works in concert with the PaT and in doing so overcomes some of the downsides of the HRBP role identified by many researchers, particularly the lack of integration and separation of activities.
Figure 10: The Ulrich HR model “mapped” on Futocracy.
G-AI and the Future of HR
Earlier we described and discussed the recruitment process, which consists of a mixture of transactional and administrative work. Transactional and administrative work is a consistent theme throughout HR and as such, using the recruitment process to demonstrate the use and type of G-AI makes sense and technology examples discussed here can be extrapolated across the various tasks undertaken by HR.
When deciding on how G-AI can assist in the processes it is important to remember that G-AI is about data and people are about emotions. Currently, G-AI cannot bring the two together very well, albeit great strides are being made in this direction.
For example, MIT researchers have developed a computational model capable of predicting human emotions, encapsulating social intelligence attributes typically associated with the human ‘theory of mind’. The model forecasts emotions such as joy, regret, and embarrassment, based on scenarios from the prisoner’s dilemma game theory. The system uses factors including a person’s desires, their expectations, and whether their actions are being observed to predict emotional responses. According to the researchers, the model has outperformed previous ones in predicting emotions, signaling significant progress in emotional artificial intelligence.
It is also important to remember that G-AI does not have any bias unless humans build it into the system. The ability to remove bias, particularly in decision-making is crucial for HR where ensuring an inclusive workplace and processes such as personal selection methods is non-negotiable. It is hard for people to understand their own unconscious bias, and this is also where G-AI steps in.
Language is important for humans. Where we see language, we feel the need to associate it with some sort of intelligence and emotion. When we are having a discussion with G-AI all we are talking with is a system that can be haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot. Whilst the outcomes of this process are improving exponentially, it is still important to remember that the “espoused truth is not always the real truth”.
Context is a crucial aspect of how humans make decisions. Gen-AI has advanced significantly in this arena and in the words of Charles Phiri, PhD, CITP (“The Mechanics of Context-Aware Decision-Making Using AI”, LinkedIn, Sept. 21, 2023), “…the intricate interplay between AI, which includes Machine Learning (ML) components, and Information Structures is pivotal in the progress of Artificial Intelligence. Through a steadfast dedication to Information Structures, we have reached the forefront of enriched context-aware decision-making.” However, there is still much to be done here and care is still the order of the day.
Finally, emotional intelligence and G-AI do not currently go together. Whilst G-AI is improving in how it “reads” the emotions of humans and reacts with facial expressions reflecting emotional states, enabling empathy within AI is hard and some way in the future, if ever. A major challenge is how can a machine “feel” like a human?
G-AI and the Recruiting Process
As we discuss the various recruiting stages and processes, we do so with one important warning. In our earlier HR-AI newsletters, which we repeated here earlier in this discussion, we argued that there must be a single platform that brings together the different systems used across the whole organisation. If G-AI is to reach its potential in organisations, such a set-up is essential, we argue. This boundary condition is probably one of the first stages of introducing G-AI into organisations and it is no surprise that Microsoft and other providers are bringing their apps together on one platform as we discussed in our last HR-AI newsletter.
The recruiting process:
For ease of reference figure 11 repeats the recruiting image overview.
Figure 11: Recruiting process overview.
Identify Vacancy
Traditionally vacancies are identified by HR when they are aware of circumstances such as replacing a person who is leaving the organisation or part of a change strategy. The other main source is from the various managers across the organisation who make bids for more resources or those with a different skills profile as the activities change and so on. The process is predominantly focused on individual positions and specific skills.
When G-AI is working across the organisation, the situation is augmented to include a more strategic approach right from the beginning. For example, the system sends automatic reminders or nudges to all parties who need to know about the current situation regarding the use of people across the organisation.
These reminders and nudges are based on the actual work activities, the ratio of people to achieve the work, the skills and/or competencies required for each set of activities, the identification of the relevant skills already dispersed around the organisation [ Note: research identified that about 20% of vacancies can be filled by existing holders of skills not known to exist in the organisation because the people are not currently using them in their job and they are not shown in their records; this has led to a wide range of apps to reduce this phenomena], project information identifies potential skill shortages upstream in advance based on work patterns — current and past, and so on.
It means that even the “simple” process of identifying vacancies can be managed more strategically because the organisation as a whole is considered and not just the immediate needs of a particular role or function.
All management are aware of the situation across the whole organisation, and this provides an opportunity for a collaborative approach in using those skills. All organisations have areas of work that have varying levels of demand on their people and yet research around strategy in action shows clearly that the managers seldom, if ever, agree to share their resources across or even within the silo functions of traditional organisations. G-AI and the New Era organisations can break down this mindset and enable real strategic distribution of people skills, which may even mean that there is no need to recruit for a new person to provide that skill at that time.
We are not encouraging a more Taylorist approach to maximising the workload of individuals, we are using a combination of G-AI and the emotional intelligence of those who make decisions; they have an improved understanding of the workload and needs of the people on which to base their decisions.
Job description / Person specification
The main approach to obtain the information needed to design the marketing and selection documents is for HR experts (internal or external) to discuss with the relevant manager the nature and type of vacancy to be filled. Once this is ascertained, the HR expert uses either a skills matrix or other set of information that they have from prior positions to prepare the documents. If it is a new role or it has changed significantly, they have a variety of instruments to design a new set of documents, many are now semi-automated using apps.
Using G-AI most of this process can be undertaken extremely quickly in a variety of ways, automatically. The system already has access to all current skills, competencies, and roles; either from purpose-built databases such as a skills matrix, or directly from personnel records, for example.
It also has data regarding the actual work being undertaken in that role (where it exists currently) and can identify some types of issues that may need to be considered that are not in the typical HR databases. For example, if a process is continually being held up at the decision point, this can indicate an issue around people making decisions. Whilst the system will have notified the relevant manager about this issue in its daily reports, it also remembers to bring this up when asked about the skills or competencies required for this role.
For new positions, G-AI can search externally for similar positions to use as a starting point for the HR expert to build upon. Increasingly G-AI can use a photo of a person at work, such as a machine operator, and from this develop a skills profile for operating that type of machinery!
With such assistance from G-AI, it raises the question as to whether an HR expert is required in this recruiting process at this stage, or whether the relevant manager or a team member can undertake this task, particularly for standard positions. After all, even in the current process it is the relevant manager who says yes or no to the description and/or specification.
Advertise vacancy
Before going into the more traditional approach to identifying where and how to “fish for talent”, it is important to mention an emerging approach by organisations, which is to use the internal pool of existing talent and skills. This is commonly known as the Internal Marketplace. It is part of a wider use of technology to support both the HR activities as well as assisting in the career and development of the workforce.
Schneider Electric was among the first of the pioneering organisations that launched such a platform, and their VP of Digital Talent Transformation, Jean Pelletier, is quoted as saying, “It’s a complete rewrite of HR. You need to think differently about speed and how to go deep and broad in an organisation using AI.”
Deloitte (2021) research shows that these marketplaces provide substantial opportunities for access, enablement, and advancement of people by changing workforce processes with a systems-based approach. “An internal talent marketplace is an AI-enabled platform that allows organisations to connect talent to opportunities across full-time and part-time roles, short-term assignments, projects or gigs, volunteering, and mentoring, among others. Skills are the data point that connects talent to opportunities. Because skills are objective, they help remove the barriers to opportunity and provide the transparency that enables equitable outcomes. They empower employees to own their development and growth in the organisation.” (Deloitte (2021) “How internal talent marketplaces can help overcome seven common DEI strategy pitfalls: Providing equitable access to experiences can aid in creating a diverse and inclusive workforce”, 7 December.)
Whilst there are different types of Marketplaces, some now allow people to use the platform to identify their own choice of work or advancement and apply for that position without being asked. They can also apply even if there is currently no open vacancy and are also encouraged to undertake “job swaps” with colleagues. The openness and transparency of such an approach supports a wider source of applicants and reduces some of the sourcing and recruiting issues often encountered around diversity, equality, and inclusion (DE&I).
However, for most organisations at this point, typically the job description and/or person specification is translated into a less detailed form to use for advertising the position. This is done in a variety of ways that vary from an HR expert, an internal marketing expert, or a specialist external company writing them, depending on the role and seniority.
[It is important to note here that when using job descriptions and person specification they will also need to be prepared for use in a variety of media, including social media as well as complying with various legislative demands re diversity, equality, and inclusion (DE&I). In some cases, this can be a lengthy process, particularly for new or senior positions.]
G-AI is pretty good at this task already and improves almost daily; it is even developing and setting up websites using this aspect of the technology. G-AI can take onboard a written description of the role or person, or a photo of handwritten notes, or spoken into the system, and even any photo to be included in the advertisement. It then produces the advertisement. Once agreed by the relevant person, it will even publish it on the designated site, depending on the configuration of the marketing site or platform used.
The intervention of a human in this process is becoming less with each iteration of G-AI development. The information required for regional adverts and associated legal requirements are considered during the design and can result in different suggestions for different platforms and regions. [Note: with G-AI this process can be undertaken simultaneously with the original role and person specification process earlier.]
Once again, at what stage of this development will the need for managers to use external people or local HR to support them in advertising vacancies disappear?
Manage the responses
This is a part of the recruiting that probably has the longest history of using technology as an integral part of the process, particularly for volume management. Typically, it is the first stage of the process where some form of scanning takes place using source documents completed online by candidates, or documents sent in by other forms such as email and standard post. Whatever route, G-AI will scan the content to reduce the volume to a set level first; the level being set by the HR expert or the HR assistant. The next stage is to use set criterion to place the applications into groups such as “possible for this role” or “maybe suitable for another current vacancy” or “place in applicant pool for future vacancies”.
A major complaint from candidates mentioned earlier is the lack of transparency and communication in the recruiting process. This is an opportunity to start the process of rectifying the situation.
G-AI can send a personalised response to each individual candidate instead of the standard response currently used by most organisations. It can even identify some elements of the empathic style to be used where the application contains free text as opposed to set text choices that are often used in online forms. In some European countries, candidates send in a letter of motivation when making an application and this is ideal for G-AI to identify appropriate empathic styles to respond in.
G-AI also reduces the content of applications into non-specific identity formats. This means that gender, race, and other potential sources of bias are removed in preparation for the process of reduction to the number of candidates for short listing and subsequent selection process.
The use of G-AI in this process significantly reduces the time involved, which also helps to resolve another candidate complaint about lengthy recruitment processes.
Short listing
Where organisations use social media searches as part of their process of gathering information about candidates, taking into account the potential legal minefields of using this data during the selection process, it makes sense for G-AI to undertake these prior to the shortlisting process. It is currently undertaken as one of the last parts of the process before the contractual stage, which means that in some cases a lot of unnecessary short listing work has resulted in wasting time for both the candidates and organisation.
This part of recruiting usually involves the relevant manager and increasingly team members who will work with the candidate selected. The decision that they make is based on a combination of technical and/or other qualification, prior experience, identified competencies, employment history, and a contextual summation based on the experience and gut feeling of the manager and any other person involved, including HR expertise.
G-AI is able to prepare the candidate information in a format that makes this important process easier for the decision-makers. It can compare them in specific critical areas and any other aspect that helps the process. It cannot make that gut-based decision, it can only support it by assembling the data in the preferred way.
This part of the process is often a bottleneck because the decision-makers are also undertaking their normal work and bringing the people together is not always easy. G-AI can help here by issuing reminders to the decision-makers, identifying vacant time slots across the relevant calendars, and, when necessary, update candidates in line with agreed timetables. This assists in removing the complaint regarding a lack of transparency of the process.
Once the short list is produced and agreed with the relevant person, G-AI can send a personalised update to the candidates short listed and to those who are no longer part of the process, once again in a personal and empathic style.
Selection process
The selection process is a variable feast of different processes and methodologies. It is also where G-AI can have a significant impact on both the candidate experience and the support provided to assist the employer in gaining a deeper understanding of the candidate.
Depending on the vacancy type, the process can be simply just one or more interviews by individuals and/or a panel [interviews are the most used process in smaller companies and/or for simple task driven work], work-related tasks, group exercises, through to full-blown assessment centres.
In the case of interviews, the trend is to use a more structured approach with questions relating to the vacancy. The questions are based on a combination of checks on the application content and the perceived ability to succeed in the job itself. G-AI can assist in creating a series of questions based on a combination of the applicant’s information, job description, person specification, and identified problems or issues that the successful candidate will face [this latter information is gathered from the operational issues and information gathered during normal working across the organisation]. G-AI can also interview the candidate in the absence of a human interviewer! The following is an illustration of how this can be achieved successfully. It is also an illustration of how the normally expensive and time-consuming assessment centre process can be undertaken in a more affordable way using G-AI.
We can learn from the current and emerging educational uses of G-AI in identifying alternative approaches to candidate selection; particularly in cognitive ability, making sense of complexity, creativity, and more. For example, give a candidate a document to read and then G-AI will use voice or text instructions to ask questions of the person regarding comprehension of the text. Based on the answers given by the candidate, G-AI will ask further questions to test understanding or to widen the context of the question, and so forth. Another example is to ask the candidate to write about something that you want to test them on, such as a previous experience or how they can solve a job-related situation using their creativity and/or experience. Once again G-AI will use the response as a source of questions that really test the candidate’s comprehension of the task, get them to expand on their solutions, and so on.
If it is important to test their numerical dexterity the same process can be used using data and or Excel spreadsheets. G-AI will ask questions to test assumptions, accuracy, and more. Such approaches can use graphics, images, and any other medium, including music, as the basis of the interactions and analysis. This is already being used in education at an even higher level of interaction where G-AI acts as a tutor in amazing ways (example can be found at: https://www.ted.com/talks/sal_khan_how_ai_could_save_not_destroy_education?language=en)
Arranging and conducting final interviews
Once the relevant manager is ready to set up and conduct the final interviews, G-AI can undertake the scheduling and sending out the invitations to the candidates. This final interview type varies according to the size of the organisation, the nature of the vacancy, and any local legal formalities that need to be understood on both sides.
G-AI can assist in the preparation of any information required that is different to the original interview and include any points from the previous interview that were not covered or need further clarification to remove any doubts or inconsistencies.
In some organisations, the decision is made quickly in the less complex vacancies and the successful candidate is asked to speak to the relevant HR person to discuss contracts and next steps. In the more complex or senior positions, the contracting stage follows a number of checks.
Checks and references
Where the candidate has not been informed of the decision or a contract agreed, there tends to be a series of checks made before making the final decision. These checks will vary according to the type of vacancy and the legal requirements of that vacancy or geographical region. For example, in cases of education and other work with vulnerable people, criminal checks need to be made in some countries. Whilst unusual in Europe, the US often uses drug testing as part of the decision-making process. Character references are still quite popular albeit the reference people are increasingly concerned about personal liability issues in making such references; hence the impact on the decision-making is limited.
G-AI can carry out the administrative elements of these checks and references and inform the decision-makers of the results and how they relate to the laws and regulations appertaining to the vacancy.
Contracting
This is typically undertaken by HR and/or the legal department, depending on the size and nature of the organisation. G-AI can draw up the contract draft for checking by the relevant parties and in many cases a standard format is used for most employees up to the senior levels, which tend to be more individualised.
As more organisations move away from specific job roles and/or job descriptions [subject to local legal requirements and trade union agreements] to a skills-based form of employment, hybrid working, and short-term/project-based contract hiring, the role of the next stage has become even more important than the past.
Onboarding
Onboarding is another historical challenge because many organisations did not have a comprehensive onboarding process, particularly smaller organisations. As the workforce is no longer a predominantly resident workforce in the organisation’s buildings, it is necessary to provide more detailed and flexible approaches to onboarding. G-AI is an excellent supplier of such methods. The following is an indented list of examples followed by another set of approaches for onboarding based on the virtual world.
Setting up an onboarding process for employees with various working approaches can be complex, but G-AI can certainly assist in making it more efficient and effective. The following are some ways that G-AI can be integrated into an onboarding process and where it’s best to use it. They are not in any particular order or preference as they will be organisation contextual:
G-AI and onboarding
Personalised Training Plans:
G-AI-Powered Learning Platforms: Use to analyse the skills and knowledge gaps of each new employee. Based on the analysis, it recommends personalised training modules and resources to help them get up to speed quickly.
Communication and Engagement:
Chatbots and Virtual Assistants: chatbots or virtual assistants can provide 24/7 support for answering common questions and guiding new employees through the onboarding process. This can be especially helpful for remote and hybrid workers.
Email Automation:
Use G-AI-driven email tools to automate personalised welcome emails, reminders, and follow-ups to keep employees engaged and informed.
Documentation and Compliance:
G-AI can help categorise and organise documents related to compliance, policies, and procedures. It can also assist in version control and auditing, ensuring that employees are accessing the latest information.
Performance Tracking:
Use G-AI to track employee progress and performance during the onboarding process. This can help identify areas where additional support or training may be needed.
Onboarding Portals:
Develop onboarding portals that utilise G-AI to recommend relevant resources, connect new employees with mentors or colleagues, and provide a seamless user experience for all working approaches.
Cultural Integration:
Use G-AI to analyse communication within the organisation to gauge the sentiment and identify areas where new employees may be struggling to integrate. This can help HR and management proactively address cultural integration challenges.
Feedback and Improvement:
Implement G-AI powered surveys and sentiment analysis to gather feedback from new employees about the onboarding process. Use this data to continuously improve and adapt the onboarding experience.
Accessibility:
Ensure that onboarding materials and resources are accessible to all employees, including those with disabilities. G-AI can assist in making content more accessible by providing alt text for images, generating captions for videos, and more.
Adaptation to Work Schedules:
Use G-AI to schedule onboarding activities, training sessions, and meetings in a way that accommodates different working approaches. This includes considering different time zones for remote workers and part-timers.
Security and Compliance:
Incorporate G-AI for security and compliance checks during the onboarding process, especially for external employees. AI can help identify potential security risks and ensure that all necessary compliance requirements are met.
Resource Recommendations:
Provide G-AI-driven content recommendations based on each employee’s role, department, and progress in the onboarding process. This ensures that they have the most relevant resources at their disposal.
Incorporating G-AI in the onboarding process can enhance the experience for employees with different working approaches and help an organisation streamline and optimise the onboarding process. However, it’s important to strike a balance between automation and the human touch to create a welcoming and supportive onboarding experience.
Virtual worlds and onboarding
In addition to the more traditional uses of G-AI during the onboarding process, using virtual worlds and associated approaches can significantly enhance the onboarding process. They do so by creating immersive, engaging, and interactive experiences for new employees. They are becoming cheaper and more accessible for all organisations with some vendors providing apps to assist in their development.
Here’s how you can leverage virtual worlds and related technologies:
Virtual Reality (VR) and Augmented Reality (AR):
· Immersive Training: Create VR or AR simulations that replicate real-life work scenarios, allowing employees to practice their tasks in a safe and controlled environment.
· Equipment Familiarisation: Use AR to overlay information on physical equipment, helping employees learn about machinery, tools, or processes.
· Onboarding Tours: Provide virtual tours of physical office spaces, remote work hubs, or company facilities, helping new employees get acclimated. This also enables remote workers to be familiar with the office environment of their colleagues.
Gamification:
Gamify the onboarding process by incorporating game elements, such as badges, leader boards, and rewards, to make learning and training more engaging and fun.
Virtual Worlds:
· Simulated Work Environments: Build virtual worlds that mimic the company’s digital workspaces, allowing new employees to navigate and interact with the company’s systems, tools, and processes.
· Collaborative Spaces: Create virtual team meeting spaces for remote and hybrid teams to collaborate, discuss, and hold virtual meetings.
· Networking Events: Host virtual networking events and social gatherings where employees can meet and connect in a virtual environment.
360-Degree Videos:
· Virtual Office Tours: Use 360-degree videos to offer new employees a virtual tour of your physical office spaces, helping them get a feel for the workplace even if they work remotely.
· Training Demonstrations: Create 360-degree videos that showcase specific job tasks, allowing new employees to see and understand processes from all angles.
Mixed Reality (MR):
MR combines elements of VR and AR to provide holographic training experiences. Employees can interact with holographic models of products, machinery, or systems, gaining a deeper understanding.
Social VR Platforms:
· Company Social Spaces: Utilise social VR platforms to create company-specific virtual spaces where employees can interact, socialise, and collaborate with colleagues, irrespective of their working approach.
· Virtual Team Building: Host team-building activities and events in the virtual world, fostering a sense of camaraderie among employees.
Webinars and Online Workshops:
· Virtual Speaker Series: Invite guest speakers and subject matter experts for virtual webinars to impart industry knowledge and insights to new employees.
· Remote Workshops: Conduct online workshops on topics like time management, remote work best practices, and stress management for remote and hybrid employees.
Virtual Mentorship and Coaching:
Match new employees with mentors and coaches who can guide them through their onboarding journey via video conferencing or virtual meetings.
Data Analytics:
Use G-AI and data analytics to track employee engagement and performance within virtual environments, providing insights to enhance the onboarding process continually.
Remote Team Building and Icebreakers:
Host virtual icebreaker activities and team-building games to help remote and hybrid teams build connections and feel more integrated.
Leveraging virtual worlds and associated technologies can make onboarding more engaging, accessible, and inclusive for all employees, regardless of their working approach. It can also help foster a sense of belonging and alignment with the company culture, even in a remote or hybrid work setting.
Summary of the recruiting process and G-AI
The recruiting process is a time-demanding and highly administrative process, that accounts for a major commitment of the HR resources. The actual transactional element requiring HR expertise has also been demanding, particularly when large numbers of vacancies and/or candidates need to be processed; HR tends to be represented at most interview processes as well as involved in the preparation of job designs and person specifications.
We have demonstrated that using G-AI will have a significant impact on how people are engaged in the recruiting process with much of the work being undertaken by G-AI and local managers.
The same can be said for the training function in much the same way, albeit the training itself will be delivered by a mixture of people and G-AI methodologies. This is already happening in many organisations and external training suppliers; online training is a growth industry in both traditional and business education.
Whilst travelling through the various stages of the recruitment process, the impact of G-AI may have appeared to be relatively small and predominantly administrative. Yet, when added together and making sense of the transactional aspects, the impact is actually very significant in size and potential to improve the whole process. Importantly, it also provides the candidates with a quicker and improved experience and service.
HR and the Connecting and Collaborating Activities in Organisations
In this and all our previous HR-AI newsletters we have said that the future of HR is more about connecting and collaborating roles. These important activities have hitherto been neglected or not undertaken effectively because of competing HR demands and/or lack of role clarity.
Critics of Prof. Ulrich’s “three-legged stool” HR model highlighted this lack of clarity in one of the key roles, the HRBP, which we mentioned earlier. The HRBP role was designed to connect HR and the business activities and in doing so, bring about collaboration and strategic alliance of the people and business objectives. The researchers and critics also identified that it was difficult to find people for such a role as they were either HR people with little or no real understanding of business, or businesspeople who were not sufficiently aware of how to deal with the less predictable people side of events and situations.
In the New Era Futocratic organisational model mentioned earlier, the connecting of the people in business operations is undertaken in three ways. The first is the actual design of the work setting, which is a team-based network that brings people together to deliver one or more outcomes based on objectives, collaboratively. The second is the way that the objectives are set by the purpose alignment team (PaT). Their design and allocation encourage collaboration with other teams in those cases where the work is undertaken by more than one team. Where only a single team is involved, the behaviour of mutuality throughout the network reinforces the importance of approaching situations collaboratively, even though not involved in the specific project or work. For example, mutuality reinforces sharing resources and people as necessary. The third way is the activity of the Network Weaver (an extended version of the HRBP) whose work involves a range of skills and competencies of both an HR and a businessperson.
To overcome the challenges identified by the critics of the HRBP we call upon G-AI. G-AI is already able to work as a tutor and coach from a data perspective in many ways, such as helping make sense of complex data sets, translating drawings into basic processes, developing code, translating statistics into information, and more. This is helpful from the business aspect of the Network Weaver/HRBP perspective. The soft skills, empathic relationship, personal and team development activities are more the emotional person side of this joint role. Arguably, such a person can be found more easily amongst those trained for some aspects of HR work as opposed to the businesspeople, who tend to focus more on facts and meeting business objectives through more rigid processes and associated behaviours.
Bringing an HR type person, together with G-AI into this important role is a major part of the future of HR. This will require a significant change in competencies for those in HR who not only focus more on the administrative tasks currently, but also those whose transactional activities are more akin to the policing of regulations or acting as a supplier of services such as recruitment.
Connecting and Collaborating at the Strategic Level
A good business strategy is informed by its people. Most organisations today recognise that people are fundamental to sustainable value creation, which is why they are often referred to as the ‘most important asset’ of a business. It follows that employees’ knowledge, skills and abilities are assets which the organisation should invest in and use to create sustainable value for the organisation and its various stakeholders.
Individual HR strategies may be shaped by the business strategy, but it’s too simplistic to suggest that strategic HR simply follows on from business strategy; the two must inform one another. The way in which people are managed, motivated and deployed, and the availability of skills and knowledge, should all shape the business strategy.
This relationship emphasises the importance of individual elements of HR strategies fitting together and operating within a strategic framework that incorporates both people and business issues. Research by Bath University (2003) found that individual HR practices alone do not drive better business performance. For example, highly skilled individuals with valuable talent can only generate value if they also have positive relationships with their managers and colleagues in a supportive, value driven environment. When all these factors are brought together, they will promote ‘discretionary behaviour’, i.e. individual’s willingness to perform above the minimum standard or give extra effort. Discretionary behaviour in this context may be considered as motivation. (Bath University, 2003, “Understanding the People and Performance Link”, McGraw-Hill Education, 12 May).
Earlier we discussed how the Futocracy design principles enable the three-legged stool approach to operate effectively in organisations, these being: 1. Human Resource Sharing Service Center (SSC) — which is responsible for providing various daily affairs services, 2. human resources business partners (HRBP) who provide an HR consulting service to business departments, and 3. Center Of Expertise (COE) ,which is responsible for formulating corporate human resources strategic plans, putting together various work policies, systems, processes, and developing human resource products.
Whilst the SSC and HRBP/Network Weaver are the means to ensure the connection of operational needs and collaboration of the work itself, the strategic element and its development is undertaken by a close relationship and collaboration of the HR expert and other member colleagues in the purpose alignment team (PaT). In doing so it becomes the equivalent of the centre of expertise (COE). This centre of excellence collaborates with the Strategy Development Team (SDT) and in doing so brings into life the first of the three pillars of the Ulrich model, Strategic Planning, see figure 12. [The other two pillars are Workforce Development, and Performance Management].
Figure 12: Futocracy model supporting the Ulrich model of Strategic HR
G-AI improves the strategy development.
Using G-AI in developing a business strategy is increasingly the way forward as it enables organisations to become more flexible in both the development stage and fine tuning of objectives and activities in the light of current and predicted business and environmental changes.
G-AI technologies can provide valuable insights, enhance decision-making, and optimise various aspects of strategy development. Whilst this is not a lesson in strategy development, it is important to understand some of the critical ways that the process can be enhanced for the parties involved.
For the business side G-AI can analyse vast amounts of data from diverse sources to provide real-time insights through Customer Segmentation and Personalisation, Supply Chain Optimisation, Risk Management, Product Development, Process Automation, Data-Driven Decision-Making, Competitive Intelligence, and Scenario Planning.
This business data can then be integrated with the people information of knowing where the people are, what they do, the skills that they have, projected retirements and other staff turnover, absence from operations for training, sickness, and so on.
This integration by using a single platform approach improves scenario planning based on real-time information as well as predicted people issues with far more accurate data and information than those without G-AI.
In the context of our example organisation Futocracy, the strategy development team, together with the COE are now able to create a more sustainable strategy than in the past because it is built on a more informed and sustainable base. It is important to remember that G-AI should complement human decision-making, not replace it entirely, and be regularly updated to adapt to changing business conditions.
Strategy is not just an annual process as may have been in the past for many organisations. The dynamic nature of the business environment, social changes, and technological advances dictate a continuing appraisal of both the business and people sides. This leads us to the other two Ulrich pillars, performance management and workforce development.
Performance management
It is widely assumed that employees who are highly motivated will not only be happier, healthier and more fulfilled, but also more likely to deliver better performance, services, and innovation. This assumption lies at the heart of what is often referred to as ‘employee engagement’, a concept that has become mainstream in management thinking over the last decade.
HR is at the centre of the whole performance management story and as such it is important to make sense of it here.
There is a whole industry around tools, techniques, apps, books, and more about the motivation of people to undertake the work that needs to be done for business success. At the same time, strategic human resource management (sHRM) is probably the most widely used HR model approach to bring the business objectives and people activity together.
All of the performance management approaches are predicated on different motivation theories the majority of which do not seem to translate well in workplace application, hence the continual focus on the subject.
An investigation into motivation at work was undertaken by the Center for Evidence-Based Management (CEBMa) at the request of the UK Chartered Institute of Personnel and Development (CIPD). They reviewed the research literature published between 2000 and 2020 to learn more about the evidence on work motivation. Their report, “Work Motivation — an evidence review” was published in January 2021.
“Because the general construct ‘motivation’ is rather abstract, in the context of the workplace it is often tied to specific work-related behaviour (for example, the motivation to work from home or the motivation to participate in organisational change) or a specific outcome (for example, task performance or innovation). In the context of this REA [restricted early action], the focus of motivation is an employee’s day-to-day job. Thus, ‘work motivation’ refers to the need or reason(s) why employees make an effort to perform their day-to-day job to the best of their ability.”
Whilst their findings will make for a great newsletter later, there are some major points to raise here if we are to use G-AI to assist us in performance management. This is important because some theories referred to in the popular (HR) management literature lack a solid evidence base and are therefore considered obsolete by academics.
The major contemporary evidence-based theories that are seen as relevant to motivation are: Social exchange theory, Social-identity theory, Self-determination theory, and Self-regulation theory.
Theories that are outdated or integrated in other motivational theories include: Reinforcement theory, Drive theory, Cognitive dissonance theory, Job characteristic theory, Expectancy theory, Social-comparison theory, and Equity theory.
Discredited theories include Maslow’s hierarchy of needs theory, and Herzberg’s motivation — hygiene theory.
Human motivation has been a topic in the research literature for more than 80 years. During this time many scales and questionnaires have been developed to measure an individual’s motivational state. One of the most widely used scales is the Multidimensional Work Motivation Scale (MWMS), which is translated and validated in several languages (Gagné et al 2015). The MWMS is based on the framework of self-determination theory and not only measures an employee’s motivational state, but also assesses the source of an employee’s work motivation. It consists of six question categories: Amotivation, Extrinsic regulation — social, Extrinsic regulation — material, Introjected regulation, Identified regulation, and Intrinsic motivation. We only mention it here because it is a popular instrument, and it is an example of how G-AI can be used easily and effectively.
G-AI can analyse such instruments very quickly on demand. However, G-AI can take us into a real-time world that is beyond such instruments. It enables people to just speak or write to an app on their mobile phone to express how they are feeling at any time in any circumstances. In doing so, it translates that input into a range of different measures of both activity and feelings within the ongoing situation. This is arguably a much wider extension of the MWMS approach because it allows immediate responses by relevant management or other remedial support such as counselling and training based on the fuller set of circumatnces.
This approach fits well within the motivation theories that are seen to be relevant today. We will refrain from going down the road of using sensors and similar devices that many organisations are using to monitor performance today using G-AI. We see these as methods of ensuring compliance and not as motivators. Any search on the Internet will take readers to such instruments.
Workforce development
Workforce development is the third of the three pillars of Ulrich HR model. It focuses on enhancing the capabilities and skills of employees to meet the organisation’s current and future needs.
Incorporating G-AI into workforce development can help organisations enhance their talent management strategies, improve the skills of their workforce, and create a more engaged and productive workforce, which, in turn, can contribute to the achievement of strategic business objectives. Insofar as Futocracy is concerned, this is undertaken in a range of ways depending on the type of HR operations. Earlier we showed the SSC as a team within the network of networks, which can take on some aspects of this work and traditionally this would be the case. However, we suggest that it is a major part of the COE (within the PaT) and HRBP (as part of the Networker weaver activity) roles.
Currently, many organisations attempt to do this through a range of different apps, most independently located on the organisation’s IT system. We have proposed throughout this newsletter that success with G-AI is increased if implemented within a corporate platform that enables the integration and/or retrieval of relevant data and information across all aspects of the business. The “Talent Marketplace” discussed earlier is an example of how doing so can create real additional benefit for the individual members of the workforce and management alike.
The following are examples of G-AI in action currently. Each of these applications are improving significantly as G-AI advances in capability and ‘personalise learning’ is an example that we will mention at the end of the list:
Skill Gap Analysis
identify skill gaps within the workforce by analysing performance data and competency assessments. This information can guide targeted training and development initiatives.
Predictive Analytics
can help predict which employees are most likely to excel in leadership roles or other key positions, aiding in succession planning and talent management.
Performance Feedback
performance management tools can provide continuous feedback, helping employees understand their strengths and areas for improvement. They can also assist managers in providing more objective feedback.
Learning Management Systems
can enhance learning management systems by recommending courses, modules, or resources based on an employee’s role, performance, and career goals.
Data-Driven Decisions
can analyse large datasets to identify patterns and trends in workforce development. This data can guide decision-making for training investments and development strategies.
Automation of Routine Tasks
By automating repetitive HR tasks, HR can focus on more strategic aspects of workforce development.
Employee Engagement Analytics
can monitor employee sentiment and engagement through sentiment analysis of surveys, social media, or other feedback mechanisms. This helps HR identify areas for improvement and take action to boost engagement.
Dynamic Career Pathing
can provide employees with dynamic career path options, taking into account their skills, interests, and the organisation’s needs, thus aiding in career development.
Adaptive Onboarding
can customise the onboarding process for new employees, ensuring that they receive the necessary training and resources tailored to their specific roles.
Incorporating G-AI into workforce development can help organisations enhance their talent management strategies, improve the skills of their workforce, and create a more engaged and productive workforce, which, in turn, can contribute to the achievement of strategic business objectives.
Personalised Learning
The most common method is to use learning platforms that can analyse individual employee performance and learning preferences to provide personalised training and development recommendations. The “Talent Marketplace” is a recent addition to this G-AI supported approach. This ensures that employees receive the right content at the right time, however the process of learning has advanced significantly with G-AI. Every person in the organisation can have their own G-AI consultant/trainer. For an example of this in action we again recommend you view the TED video at: (https://www.ted.com/talks/sal_khan_how_ai_could_save_not_destroy_education?language=en)
Conclusion
Throughout this discussion about HR and G-AI we have referred to the work of Prof. Dave Ulrich and his colleagues around the model of the “three-legged stool” and the three pillars of HR, Strategic Planning, Workforce Development, and Performance Management. It seems appropriate the quote his words when he was summing up three decades of the work and progress of HR in his 2017 LinkedIn article.
“Maybe it is time for the HR profession to recognize and appreciate progress that has been made. While individual experiences may differ, our data clearly shows that HR professionals have become more competent over the last 30 years. Instead of bemoaning what HR professionals lack, maybe it is time to relish the progress that has been made. Do these results imply that HR “has arrived?” No, there is always more to do, but the base for moving forward is strong and getting stronger.”
Since that article six years ago, much has happened in the world of business, society, and technology, with each change challenging the role and work of HR. HR has sought to rise to those challenges, with much success. We suggest that the biggest challenge in the history of HR and work itself is now in progress; it is known as Generative AI.
In this newsletter we have sought to identify some of the ways that G-AI will and can impact on the role and work of HR. We believe that the evidence points to a totally new form of HR operations emerging as a result of the technology, much of which we have yet to see and experience.
People will continue to be an important part of how work is undertaken. We suggest that HR’s main role will be ensuring that they, the people, are connecting and collaborating in how they work together with one another and the different forms of technology, their new working partners.
