- ESG Analyst, Investment Research
Denmark, Institutional
ChangeThe views expressed are those of the author at the time of writing. Other teams may hold different views and make different investment decisions. The value of your investment may become worth more or less than at the time of original investment. While any third-party data used is considered reliable, its accuracy is not guaranteed. For professional, institutional or accredited investors only.
Key points
- Amid excitement about the potential for artificial intelligence (AI) to drive productivity, innovation, and profits, corporate culture and human capital management (HCM) will become more important considerations, not less.
- Research shows that strong workforce initiatives and talent development are critical for success when adopting any new technology. We expect these practices to likewise distinguish companies that reap AI’s benefits from those that struggle with its adoption.
- Here, we outline human capital topics for investors to research and discuss with management teams as companies pursue AI benefits.
Introduction
As AI adoption accelerates worldwide, we believe companies that effectively integrate AI may experience substantially better financial outcomes compared to those that struggle with adoption. Maximizing the benefits of AI will depend on a company’s scale, willingness to invest in technology, and disciplined processes and data governance. Similarly, the strength of a company’s human capital management (HCM) practices, including corporate culture and a focus on employee engagement, plays a key role. Strong HCM practices are already good predictors of a company’s ability to leverage new technologies to boost productivity, innovation, and profits.
This is even more apparent today given the rapid pace of AI adoption, decision-making capabilities, and its wider range of applications than previous tech waves.
To assist investors in their analysis, we have been working to better understand which companies already demonstrate robust HCM practices and how they leverage these strengths to integrate AI across their organizations. In this guide, we provide insights from our investment research and dialogue with companies. We also suggest HCM-specific engagement questions for investors to consider when discussing companies’ AI journeys.
HCM is often a predictor of successful technology adoption
When enthusiasm about potential productivity from new technologies is high, companies and investors alike can sometimes deprioritize the implications for human capital. However, strong HCM is essential to delivering productivity gains from new technology alongside innovation benefits, both of which affect the bottom line. Multiple studies have linked robust HCM initiatives with better results in both areas. For instance, a Gallup survey of 3.3 million workers found that companies in the top quartile for employee engagement enjoyed a 15% – 20% productivity advantage over bottom-quartile peers.1 Researchers have also found that each additional percentage gain in engagement delivers a 150% increase in innovative behavior.2 These benefits are reflected in financial results: Gallup’s survey connects employee engagement to higher earnings per share, citing faster growth acceleration after negative macro events. Similarly, a Microsoft Worklab study identifies a connection to better stock performance.3
Strong HCM may be even more important in the current wave of AI adoption
Although many aspects of AI adoption are akin to earlier technology waves, there are important distinctions — several of which are described below. Poorly managed decisions on where and how to apply AI may lead to staff disruption and disengagement. Well-managed decisions can encourage employees to actively participate in identifying AI applications with more significant bottom-line impacts.
Speed of AI adoption
AI adoption is already faster than previous technology waves and should accelerate over the next 18 months. Corporate roles and responsibilities will likely change, and companies may restructure their workforces as a result. Proactive employee engagement and careful management of these transitions are to ensure that workers deliver productivity and innovation gains amid these rapid changes.
Impact of AI on key decisions
Companies may allocate more decision making to new AI tools, given their potential to free up employees to focus efforts on higher-value work. Many decisions still require human judgment and institutional expertise, and misapplication of AI can harm productivity or lead to worse innovation decisions. Organizations that start with a strong understanding of job functions, consistently track productivity and innovation metrics, have clear skill set maps and development plans, and objectively measure performance may be better prepared to identify where and how AI can be additive.
Use of AI in central business functions
Current AI capabilities are strongly aligned with activities concentrated in central business functions. As a result, interest in using AI is high across finance, legal, sales and marketing, research and development, engineering and coding, and strategy and operations. Because decisions made in these areas can also disproportionately drive performance, staff acceptance and adoption are especially important. Organizations that manage staff engagement in these functions are more likely to introduce AI tools thoughtfully and engage teams as co-designers of relevant solutions.
Priority HCM attributes for investors
Tech companies have been natural early adopters of this newest wave of AI, and their experience so far is a good illustration of different rates of success. Certain companies have moved too fast and had to pull back on AI-driven job-replacement plans. A few have seen employee engagement suffer due to mishandled layoffs, with negative implications for team dynamics and future hiring. Others have established clear plans to assess job functions, invest in training, engage staff on AI solutions, prioritize AI applications, and monitor productivity and innovation results.
Based on our engagements in the sector to date, we have prioritized the following topics for investors to discuss with boards and management teams as they navigate AI adoption at any company:
Organizational structure
While no single organizational structure is right for every business, “flatter” companies with appropriate levels of distributed decision-making and open workforce access to technology resources tend to be innovative, flexible, and adept at integrating new technologies. Companies with hierarchical structures can also benefit from AI but probing how they distribute access to AI tools and make human capital decisions centrally in anticipation of AI-driven efficiency is particularly vital.
Questions to ask:
- Would you describe your company’s organizational structure as more flat or more vertical?
- Within this structure, how has technology been diffused throughout the organization in the past, and has anything changed since you started to adopt AI?
- What has your experience adopting AI been like so far, including any feedback mechanisms or forums for employees to participate in this process?
Job function and visibility on talent
Companies’ understanding of their talent bases can vary to a surprising degree. While most companies focus on the C-suite, this attention does not always extend to the broader workforce. Companies that start with clearly defined job roles and responsibilities, establish structures for assessing skill sets, use consistent metrics and mechanisms to monitor employee performance, and invest in talent development should be better positioned to accurately identify where AI can enhance or supplant tasks and where it cannot.
Questions to ask:
- What level of visibility do you have into your talent base, and how deeply does this extend into the organization?
- How are you assessing and prioritizing the tasks and job functions for AI to enhance or replace?
- What metrics are you using to monitor both positive and negative effects on productivity, innovation, and employee engagement as AI is rolled out?
Investment in skills development
From a macro perspective, questions remain about the degree to which AI reskilling or upskilling will balance the impact on the labor force. At the individual company level, investment in AI training is still needed to ensure that employees are prepared for change, engaged in decision-making, and motivated to leverage their expertise to build AI solutions that deliver real innovation and productivity gains.
Questions to ask:
- What is your current assessment of the AI skill set across your talent base? Are you looking at hiring AI specialists, and where are these roles sitting within your organization?
- Have you assessed what AI skills might be required in non-technical roles going forward, and are these reflected in your job postings?
- Do you have an internal AI training program? What is the technical level of this training, and who can participate across organizational functions?
Culture of feedback and employee engagement
The effectiveness of technology adoption throughout an organization depends on a culture of disciplined employee risk-taking. This cultural behavior is stronger when staff are encouraged to speak up and feel supported by intentional practices, including “open-door” feedback, non-retaliation policies, and employee forums. Given the focus of new AI tools on deeper decision-making in central functions, employee participation is even more important to finding, iterating on, and troubleshooting new AI applications that are relevant to the business. Some tech companies have been particularly innovative in putting employees at the center of their AI-deployment strategies, including creating employee AI task forces and directly involving staff in prioritizing and testing new AI solutions.
Questions to ask:
- What mechanisms are in place for general employee feedback, and how do you encourage disciplined risk-taking? Do these practices differ across business functions?
- Who, specifically, is responsible for deciding where to roll out AI tools in different areas of the organization?
- What mechanisms are in place to capture feedback during AI adoption?
Management of past workforce transitions
As current AI tools promise substantial productivity gains, workforce restructuring — including layoffs and job allocations — should be expected at some companies. In addition to applying AI to relevant job tasks and involving employees in the process, how a company approaches any large-scale workforce transition can affect the commitment to AI adoption from remaining staff. Evaluating how companies have managed past transitions may indicate the potential for success with future transitions and delivery of real productivity and innovation gains.
Questions to ask:
- During past transitions, what steps did you take to monitor and sustain employee engagement and your corporate culture?
- What is your level of visibility on how AI adoption may affect your workforce in the near term (including differences across functions), and how do you plan to manage any resulting workforce transitions?
AI governance as an indirect indicator
Companies that have a clear approach to AI governance are also more likely to manage workforce implications effectively. In the technology sector, companies that proactively establish AI governance and oversight mechanisms tend to also take a more robust approach to human capital implications.
Questions to ask:
- Has your company adopted or created a formal AI governance structure and oversight responsibilities?
- What areas have emerged as potential risks, and what actions have you taken to mitigate these?
- In parallel to employee engagement in the development of AI, what mechanisms are in place for the workforce to raise concerns about potential risks?
Case studies
Several technology companies have stood out in our engagements on AI and human capital practices over the last few years:
Media company
- Started with an existing culture of innovation and employee autonomy that was supported by open-door practices and a structured, outcome-based performance-management framework
- Leveraged these elements to facilitate experimentation and rapid deployment of new AI tools into workstreams
- Sustained a focus on productivity and innovation metrics to ensure financial results
Software/cloud computing business
- Built upon existing employee learning and development programs and communication across business functions to maximize participation in designing AI solutions
- Established working groups and feedback loops to iterate AI solutions and identify both productivity and growth opportunities
- Extended participation to external partners, resulting in additional growth opportunities
Flexible labor platform
- Leveraged an existing employee-driven technology co-design program to include newer AI tools
- Participating workers systematically develop, test, and provide feedback on features from pilot to platform integration
Semiconductor manufacturer
- Proactively set up a machine-learning training track to involve employees in delivering on productivity targets
- Established a citizen data-scientist program for functional experts to train in AI and champion relevant use cases in each area of the business
- Focused on sustaining engagement scores in parallel to rolling out multiple AI tools
Cybersecurity company
- Included representatives from finance, human resources, product management, and research and development in formation of AI governance and oversight team
- Prioritized AI training and career paths for employees to stay ahead of concerns and maximize productivity
- Established a parallel strategic process to envision future workforce and skills requirements in the context of becoming an AI-forward company
1“The Q12: The world’s leading employee engagement survey,” Gallup, Gallup.com. | 2Bilgees Ghani, et al., “Does employee engagement promote innovation? The facilitators of innovative workplace behavior via mediation and moderation,” Heliyon, Volume 9, Issue 11, November 2023. | 3“The new performance equation in the age of AI,” Microsoft Worklab, 20 April 2023.
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