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Early on, either week one or two, a topic centered on why use AI and why not - what considerations should be used, decisions frameworks, etc. There may be some ethical component to this but mainly this should then help drive all the other modules and should help students understand the decisions and thought process behind when to use AI and when not to. - Angela H

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May want to consider an additional aspect of AI it the TA space which is how candidates are using AI to write resumes, prep for interviews, etc. - Angela H

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AI can be helpful in goal setting and this will have less emotional implications than using AI in the actual evaluation component of performance management. - Angela H

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This is a hot topic right now and may need to be integrated somewhere as a discussion or broader topic HR Startup Lattice Drops Plans to Treat AI Like Real Employees | Lifehacker - Angela H

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Update (July 15, 2024): A Lattice spokesperson reached out to me, sharing the news that the company will not be following through on its plans to onboard AI employees. The company's CEO, Sarah Franklin, says, "This innovation sparked a lot of conversation and questions that have no clear answers yet. We look forward to continuing to work with our customers on the responsible use of AI but will not further pursue digital workers in the product."

The original article appears below:

The rapid rise of generative AI over the past couple years has inspired concern in the workforce: Will companies simply replace human employees with AI tools? The revolution hasn't quite come yet: Companies are dipping their toes into using AI to do work normally done by humans, but most are stopping short of explicitly replacing people with machines. However, one organization in particular is embracing the AI future with gusto, onboarding AI bots as official employees.

Lattice is "hiring" AI bots

The company in question, Lattice, made the announcement on Tuesday, referring to these bots as both "digital workers" and "AI employees." The company's CEO, Sarah Franklin, believes the AI workplace revolution is here, and, as such, companies like Lattice need to adapt. For Lattice, that means treating an AI tool they'll integrate into their workspace as if it were a human employee. That vision includes onboarding the bot, setting goals for the AI, and offering the tool feedback. Lattice will give these "digital workers" employee records, add them to their human resource management system, and offer them the same trainings a typical employee would receive. "AI employees" will also have managers, who, I assume, will be human. (For now.)

Franklin also shared the news on LinkedIn, in a post that has done the rounds on social media sites from Reddit to X. In this post, Franklin acknowledges that "this process will raise a lot of questions and we don't yet have all the answers," but that they're looking to find them by "breaking ground" and "bending minds." (This post has 314 comments, but they are currently disabled.) In a separate post on Lattice's site, Franklin shares some of those potential questions, including: "What does it mean to hire a digital worker? How are they onboarded? How are they measured? What does this mean for my job? For the future jobs of our children? Will they share our values, or is that anthropomorphism of AI?"

You can see in this blog post how Lattice envisions AI employees in their workplace suite: In one screenshot, an org chart shows "Piper AI," a sales development representative, as part of a three "person" team all reporting to a manager. Lattice gives Piper AI a full employee record, including legal name (Piper AI), preferred full name (Piper AI), work email ([email protected]), and a bio, which reads, "I'm Piper, an AI tool used to generate leads, take notes, draft emails, and schedule your next call." (So where does "Esther" come from?)

This is not the company's first foray into AI: Lattice offers companies AI-powered HR software. To Franklin, and Lattice as a whole, this announcement likely fits an AI-plan they've developed. To outsiders, however, it's entirely bizarre.

"AI employees" are bogus

Without much further context, I find all this deeply weird. It's one thing to integrate an AI bot into your platform, as many companies have done and continue to do. I mean, Piper AI would make sense as an assistant that hangs out in your work suite: If you want to use it to schedule a meeting or draft an email, great. If not, ignore it. Instead, Lattice wants to "hire" an AI bot and treat it the same way it treats you, albeit without the pay and the benefits. Does Piper AI also get unlimited PTO, or will it be forced to work 24/7, 365 days a year?

To me, "digital workers" and "AI employees" are buzzwords, and "onboarding" AI tools to employee resources is all about appearances: Lattice can say it's embracing AI in a "real way," and important people who care about cutting-edge tech but don't fully understand how it works will be impressed. But "AI" isn't actually intelligent. There's no "worker" to hire. Generative AI is based on a model, and responds to prompts based on that model's training set. For a text-based large language mode, it isn't actually "thinking"; rather, it's predicting what words should come next, based on the millions, billions, or trillions of words it has seen before.

If the tool is designed to take notes during a meeting, it's going to take notes whether you assign it a manager or keep it as a floating window in your management system. Sure, you can train the bot to respond in ways that are more useful to your organization and workflow, if you know what you're doing, but that does not require you to onboard the bot to your staff.

In fact, giving "AI employees" too much credit could backfire when the bots inevitably return incorrect information in their queries. AI has a habit of hallucinating, in which the bot makes things up and insisting it's true. Even with huge amounts of training data, companies have not solved this problem, and now slap warnings on their bots so you know, "Hey, don't just trust everything this thing says." Sure, humans make mistakes all the time, but some people might be more inclined to believe what their AI coworker tells them, especially if you're pushing the tech as "the next big thing."

I'm struggling to imagine how an employee (human, mind you) would feel when their boss tells them they have to start managing a glorified chatbot as if they were any typical new hire. ("Hey Mike: You're going to be managing Piper AI from now on. Make sure to meet weekly, give feedback, and monitor the growth of this AI bot that isn't actually real. We totally won't replace you with a digital worker, too, so don't worry about that.")

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HR Startup Lattice Drops Plans to Treat AI Like Real Employees

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Lattice, an HR software company, announced this week it is onboarding AI employees, and will treat them largely the same as human workers. Following backlash, the company will no longer follow through with its plans.

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In L&D I am seeing AI as being able to drive more on-demand learning, learning libraries in LMS's, and microlearning. May want to add those as topics in L&D module. - Angela H

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Maybe this is a woven across topic or maybe a stand-alone, but we need to address how (and why) to be human centric in a world that is deeply embracing AI. The balance between the two and how things like "soft" skills are going to become more important as AI replaces the others. - Angela H

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Early on in the course there's a need to discuss how to approach AI as a capability; aligning with the business strategy to determine where and how AI is used across all business functions, identifying organiztional structure and design impacts, and creating a plan to upskill/reskill the current workforce. To me this was missing in the overall curriculum. It's a "T" model with horizontal focusing on bsuiness strategy and the HR implications, and the vertical focused on different HR functions and how AI will impact these. Without the horizontal, it will be quite difficult to operate as a strategic HR business partner. This could also be covered in HR 4140. I've attached an interesting academic article in this light

--Sarah McGowan

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Abstract

Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner's standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM.

Introduction

The availability of big data and emergence of Internet of Things in the past decade has made Artificial Intelligence (AI) enabled technologies top priority for business organisations. AI has become the key source of business model innovation, process transformation, disruption and achieving competitive advantage in organisations embracing data-centric and digital culture (Ransbotham et al., 2020). In this context, existing literature has reported that the adoption of AI has increased by 70% in the last five years (Ghosh, Daugherty, Wilson, & Burden, 2019). International Data Corporation has predicted that the global spending in AI will increase from $85.3 billion in 2021 to more than $204 billion in 2025, making the compound annual growth rate 2021-2025 to be 24.5%. According to the predictions made by World Economic Forum adoption of AI will make 75 million jobs redundant and create 133 million new ones worldwide by 2022 (WEF, 2018).

The impact of AI in transforming both businesses and societies is comparable to that of the internet and world wide web, and latter led to the emergence of ecommerce, consume-centric practices, sharing economy and gig economy (Malik, Budhwar, & Srikanth, 2020). The emergence of AI-based systems in the business organisations will significantly transform work force demographics, nature and meaningfulness of jobs, employer-employee relationship, relationship between people and technology, customer experience, and competitive advantage within dynamic market environment (Connelly, Fieseler, Černe, Giessner, & Wong, 2021; Wilson, Daugherty, & Bianzino, 2017). A study conducted with 8,370 employees, managers and HR leaders across 10 countries that has been reported in Oracle and Future Workplace (2019) found that: (1) 50% of the human workforce are using some form of AI in their workplace in 2019, compared to 32% in 2018; (2) 76 percent of workers (and 81 percent of HR leaders) find it challenging to keep up with the pace of technological changes in the workplace; (3) 64% of people will trust a robot more than their manager. Workers want a simplified experience with AI at work, asking for a better user interface (34 percent), best practice training (30 percent) and an experience that is personalized to their behaviour (30 percent).

Even though AI has been a focal topic for several decades, there is currently no single universally accepted definition throughout the literature, which leads to a fundamental problem of coherent understanding of AI (Mikalef & Gupta, 2021). We have identified and selected eight definitions of AI from multiple disciplines to enable a more comprehensive understanding of AI in the HRM context (see Table 1). These definitions capture the overlap between AI, business analytics, mimicking human like behaviour, i.e., replicating human cognitive processes and emulating human learning mechanisms. Considering the AI conceptualisations presented in Table 1, we define, AI as the ability of a manmade system comprising of algorithms and software programs, to identify, interpret, generate insights, and learn from the data sources to achieve specific predetermined goals and tasks. In line with this definition, our understanding of an AI application (in the HRM context) is that of any form of manmade system comprising of algorithms (derived from computing and mathematics literature), which are translated into software programs. While calling AI as manmade may be debatable (Steels & Brooks, 2018), we are yet to come across an AI system which is developed by AI itself in HRM. The software program has analytical capabilities and computational power to efficiently process big data, generate insights and simultaneously learn from it (Malik et al., 2020). Finally, the tasks and goals are predetermined, i.e., AI algorithms employed have a specific purpose and context to achieve desired outcomes (Kaplan & Haenlein, 2020). For e.g., the algorithms used for analysing sentiments from textual data differ from the ones used for analysing emotions in a static photograph and when compared to emotion detection in live videos. Therefore, the definition primarily covers the two core aspects of this emerging technology: (1) it is manufactured (artificial); (2) it has some form of intelligence (i.e., ability to learn from the data just like human beings learn from their experiences in life).

The existing literature has claimed and outlined several benefits of AI adoption which includes, enhancing business productivity by optimising business operations and resources (Faulds & Raju, 2021), business model transformation/re-engineering (Duan, Edwards, & Dwivedi, 2019), decision-making through predictive intelligence (Paschen, Wilson, & Ferreira, 2020), reducing employee costs and enhancing employee experience, job satisfaction and customer service (Bughin et al., 2018). This has led to increasing uptake of AI-enabled solutions in HRM sub-functional domains such as talent acquisition, video interviews, employee training and development (Maity, 2019), performance evaluation, talent prediction (Upadhyay & Khandelwal, 2018) and employee engagement (Bankins & Formosa, 2020). In this context, recent reviews have outlined the role of AI to facilitate HR analytics (Margherita, 2021), and its potential impact on HRM processes and practices (Vrontis et al., 2021).

Despite the above interest and claims regarding the applications, benefits, and impact of AI in HRM, the existing literature has found that many companies have failed to experience anticipated benefits (Fountaine, McCarthy, & Saleh, 2019). A survey conducted by Boston Consulting Group and MIT found that the seven out of ten AI projects generated limited impact (business value), and therefore AI implementation plans had dropped from 20% in 2019 to 4% in 2020 (The Economist, 2020; Deloitte, 2017). Research has also found that organisations often find it difficult to integrate AI within their business processes and systems, which inhibits AI adoption (Davenport & Ronanki, 2018; Mikalef, Krogstie, Pappas, & Pavlou, 2020). On one hand the reports from early adopters have indicated that investment in AI is failing to incur business value, while existing literature has outlined the potential of AI to generate business (Ransbotham, Kiron, Gerbert, & Reeves, 2017). While the recent academic reviews in the HRM literature (Margherita, 2021; Vrontis et al., 2021), have focussed on the impact of intelligent automation on firm performance, and facilitating human resource analytics, our study builds on and further extends them by answering the following research question.

The primary objective of this article is to systematize the academic inputs through a comprehensive and systematic review of literature drawn from multiple disciplines, which includes HRM, international Business (IB), operations management (OM), information management (IM), general management (GM), addressing the recent calls and reviews on AI in HRM (Budhwar & Malik, 2020a; Budhwar & Malik, 2020b; Vrontis et al., 2021). Therefore, we are able to go beyond the boundary of HRM to synthesize and consolidate the current state of knowledge in the context of AI applications and adoption in HRM. In doing so, we theoretically contribute to AI scholarship in HRM, by developing the AI capability framework, consolidating all the technical and non-technical organisational resources that will help to capture the potential value from AI implementation (Mikalef & Gupta, 2021). This framework provides a holistic understanding of resources and strategies necessary to adopt AI within HRM. Second, the framework helps to unearth the importance of complementary organisational resources and not just technical infrastructure to adopt AI and achieve organisationally valued outcomes (e.g., business and employee productivity). Third, our review sheds light on the importance of developing collective intelligence within the organisations, i.e., a collaborative working environment where AI and human intelligence (HI) can co-exist, therefore introducing the new research stream, AI-employee integration, and the role of HRM in this context. Finally, the framework consolidates the multi-disciplinary literature to create a research agenda shaping the direction of future AI research in HRM.

In terms of implications for HRM managers, the proposed capability framework will offer an objective tool to self-assess the organisational resources, which will help to determine the organisational readiness to adopt and implement AI-enabled solutions. The self-assessment will help HRM managers and senior leadership to develop AI strategies clearly focussing on the purpose of using AI, the fit between the desired purpose and AI adoption, anticipated outcomes, and key performance indicators to measure the benefits of AI implementation. This will facilitate developing concrete business cases of AI implementation aligned to solving HRM problems, enhancing HR processes, and therefore serve as a blueprint for other players in the industry. Our review also provides useful insights that will help managers to strategize AI-HI integration within the organisation, to develop collective intelligence capabilities. While this is an unknown territory for HRM practitioners, our initial recommendations will help organisations to enhance understanding, trust, confidence, and satisfaction of employees with regards to AI adoption for developing symbiotic partnership between AI and human workforce,

The rest of this paper is organised as follows: Section 2 will present the review methodology. Section 3 will discuss the findings from the systematic review of literature, which is classified into five different themes. Section 4 will present the AI capability framework summarising the findings of the review and developing linkages between the five discussed in Section 3. The research propositions stemming from the review are presented in Section 5. Finally, we conclude with the research implications and limitations of the review in Section 6.

Section snippets

Review methodology

We have conducted a systematic review of literature following the protocol suggested in the existing literature (Hopp, Antons, Kaminski, & Salge, 2018; Tranfield, Denyer, & Smart, 2003) to ensure that the review process is transparent, easily reproducible, and systematises research themes critically.

Findings

We have used topic modelling algorithms and coding conducted by the research team members to find common topics between the articles, and then clustered similar topics together to form research themes, which will: (1) enable us to answer the research question; (2) identify the trending research areas; (3) find the knowledge gaps and inconsistencies; (4) propose research priorities. The key research themes stemming from our review analysis and topic clustering are: (1) Applications of AI in HRM;

Framework development

The aim of this review is to coalesce both the popular and under-researched research themes exploring the links between AI and HRM. The framework put forward (Figure:2) seeks to illustrate the multiple resources required to build capability to integrate AI into HR processes and practices within the context of a firm. We demonstrate the impact of these transformations on the business organisations, derived from the literature in this area. The framework draws upon and integrates the theories of

Discussion and research propositions

Considering the systematic review of literature and the AI capability framework, we present five key research priorities and associated pathways that will help develop conducive organisational context and effective strategies to adopt AI in HRM. In doing so, we also identify emerging research areas considering the most recent developments and advances in the adoption and implementation of AI-enabled applications for human resource management.

Conclusion, implications and limitations

The aim of this paper was to conduct a systematic review of literature concerning the state of AI research in HRM. We reviewed potentially relevant studies in top-tier HRM, GM, IB and IM journals, and based on the inclusion criteria as well the research question. The review has identified following key themes in the literature: AI applications in HRM; collective intelligence; AI employment and skills; AI drivers and barriers to adoption. We found that the current literature has primarily

Author statement

The authors of the paper titled - Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework, hereby declare no competing interests.

Funding

The research reported in this manuscript is funded by College of Busines and Social and Social Science (Aston University) Seed Grants 2020-21 for the project, 'Developing Artificial Intelligence Capacity, Capability and Strategy'.

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Unlocking the value of artificial intelligence in human resource management through AI capability framework

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Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employ...

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I might consider combining the ethics and DEI topics (focused on bias reduction) and move that module to the beginning of the course, so that students can evaluate potential ethical and DEI concerns within each module thereafter.

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Colorado has the first in the US law governing the use of AI. Colorado Governor Polis issued a signing statement, which is an interesting read: https://www.dwt.com/-/media/files/blogs/artificial-intelligence-law-advisor/2024/05/sb24205-signing-statement.pdf?rev=a902184eafe046cfb615bb047484e11c&hash=213F4C6CDFF52A876011290C24406E7F

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On and after February 1, 2026, the act requires a developer of a high-risk artificial intelligence system (high-risk system) to use reasonable care to protect consumers from any known or reasonably foreseeable risks of algorithmic discrimination in the high-risk system. There is a rebuttable presumption that a developer used reasonable care if the developer complied with specified provisions in the act, including:

Making available to a deployer of the high-risk system a statement disclosing specified information about the high-risk system;

Making available to a deployer of the high-risk system information and documentation necessary to complete an impact assessment of the high-risk system;

Making a publicly available statement summarizing the types of high-risk systems that the developer has developed or intentionally and substantially modified and currently makes available to a deployer or other developer and how the developer manages any known or reasonably foreseeable risks of algorithmic discrimination that may arise from the development or intentional and substantial modification of each of these high-risk systems; and

Disclosing to the attorney general and known deployers or other developers of the high-risk system any known or reasonably foreseeable risks of algorithmic discrimination, within 90 days after the discovery or receipt of a credible report from the deployer, that the high-risk system has caused or is reasonably likely to have caused.

The act also, on and after February 1, 2026, requires a deployer of a high-risk system to use reasonable care to protect consumers from any known or reasonably foreseeable risks of algorithmic discrimination in the high-risk system. There is a rebuttable presumption that a deployer used reasonable care if the deployer complied with specified provisions in the act, including:

Implementing a risk management policy and program for the high-risk system;

Completing an impact assessment of the high-risk system;

Annually reviewing the deployment of each high-risk system deployed by the deployer to ensure that the high-risk system is not causing algorithmic discrimination;

Notifying a consumer of specified items if the high-risk system makes, or will be a substantial factor in making, a consequential decision concerning the consumer;

Providing a consumer with an opportunity to correct any incorrect personal data that a high-risk system processed in making a consequential decision;

Providing a consumer with an opportunity to appeal, via human review if technically feasible, an adverse consequential decision concerning the consumer arising from the deployment of a high-risk system;

Making a publicly available statement summarizing the types of high-risk systems that the deployer currently deploys, how the deployer manages any known or reasonably foreseeable risks of algorithmic discrimination that may arise from deployment of each of these high-risk systems, and the nature, source, and extent of the information collected and used by the deployer; and

Disclosing to the attorney general the discovery of algorithmic discrimination, within 90 days after the discovery, that the high-risk system has caused.

A person doing business in this state, including a deployer or other developer, that deploys or makes available an artificial intelligence system that is intended to interact with consumers must ensure disclosure to each consumer who interacts with the artificial intelligence system that the consumer is interacting with an artificial intelligence system.

The act does not restrict a developer's, deployer's, or other person's ability to engage in specified activities, including:

Complying with federal, state, or municipal laws, ordinances, or regulations;

Cooperating with and conducting specified investigations;

Taking immediate steps to protect an interest that is essential for the life or physical safety of a consumer;

Conducting and engaging in specified research activities; and

Effectuating a product recall or repairing technical errors that impair product functionality.

The act provides an affirmative defense for a developer, deployer, or other person if:

The developer, deployer, or other person involved in a potential violation is in compliance with a nationally or internationally recognized risk management framework for artificial intelligence systems that the act or the attorney general designates; and

The developer, deployer, or other person takes specified measures to discover and correct violations of the act.

An insurer, a fraternal benefit society, or a developer of an artificial intelligence system used by an insurer is in full compliance with the act if the entity is subject to specified laws governing insurers' use of external consumer data and information sources, algorithms, and predictive models and rules adopted by the commissioner of insurance.

A bank, out-of-state bank, credit union chartered by the state of Colorado, federal credit union, out-of-state credit union, or any affiliate or subsidiary thereof, is in full compliance with the act if the entity is subject to examination by a state or federal prudential regulator under any published guidance or regulations that apply to the use of high-risk systems and the guidance or regulations meet criteria specified in the act.

The act grants the attorney general rule-making authority to implement, and exclusive authority to enforce, the requirements of the act. A person who violates the act engages in a deceptive trade practice pursuant to the "Colorado Consumer Protection Act".

APPROVED by Governor May 17, 2024

EFFECTIVE May 17, 2024
(Note: This summary applies to this bill as enacted.)

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Consumer Protections for Artificial Intelligence

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On and after February 1, 2026, the act requires a developer of a high-risk artificial intelligence system (high-risk system) to use reasonable care to protect consumers from any known or reasonably foreseeable risks of algorithmic discrimination in the high-risk system.