AI is transforming the workforce faster than ever, compelling corporate boards to balance AI-driven efficiencies with broader human capital planning to drive sustainable growth and competitive advantage.
This article dives into industry-specific examples and critical questions to help board members evaluate key workforce costs, like selling, general and administrative expenses (SG&A); stock-based compensation (SBCE), and AI investment, ensuring alignment with the company’s unique growth and profitability story.
Recognizing and Anticipating Workforce Disruptions
Broadly understood, the adoption of AI seems to presage lower labor costs. However, while AI will affect workforces across the globe, the timing and nature of its impact depends on sector-specific dynamics and unique product and talent strategies. While head count may be replaced in some areas, many companies will also need to recruit additional talent or make significant up-front investments to make the most of this powerful technology, as seen in the following examples:
- Data-centric companies. Businesses with a heavy focus on analytics, automation or data-driven insights may be able to leverage AI tools to optimize operations and improve workforce efficiency sooner than most, demonstrating measurable returns earlier in the investment cycle. However, this shift may require developing or retaining employees with AI experience to best make sense of algorithm-driven insights, and programs may require up-front investments in servers and computing power.
- Health care companies. Health sciences are investing in AI at one of the highest rates of any industry, as the technology is projected to continue cutting administrative costs and burdens on doctors, while assisting in data analysis, drug development and illness prediction. While administrative head count may shrink, health care decisions are literally life-and-death matters, and AI training or expertise may be required to help properly anticipate and catch errors.
- Customer forward/service companies. One of the earliest uses of AI has been replacing customer service professionals with AI chatbots. While the initial investment to bring these technologies to scale is likely significant (training a model, for example, to respond to an organization’s unique consumer needs), it may lead to significantly lower labor costs once customer service centers are closed.
- Enterprise software companies. Unlike the previous examples, many software firms are not just using AI to optimize their workforce but selling AI-driven products. These firms often require substantial investments in human talent during the initial stages of adoption, as developers and engineers are essential to building and delivering unique AI-enabled products. While these investments may initially increase SG&A and SBCE, they lay the foundation for long-term growth as the more nimble, adaptable products hit the market.
Anticipating AI-Driven Workforce Costs and Investment Planning
AI adoption brings undeniable efficiency gains but also requires significant up-front and ongoing investments. Boards must consider how to balance these costs with long-term workforce planning and investor expectations.
Technology & infrastructure
Companies will face both capital expenditures (e.g., computing power and AI development) and ongoing operational costs (e.g., third-party AI software and cloud processing) as they transition toward more AI-centric workflows.
Workforce transformation
While AI may reduce head count in some areas, companies will still need skilled talent to develop, train and maintain AI models. This often means retaining or expanding high-cost tech talent to ensure AI investments deliver expected efficiencies
Regulatory & compliance risks
With increasing AI-related legislation, companies must allocate resources to monitor legal changes and ensure compliance.
Navigating workplace culture
Managing the organizational culture around AI will be a major factor driving the success or failure of AI investments in the workforce. In a survey of employees across multiple industries, McKinsey found that nearly two-thirds of telecom, media, real estate and energy-sector employees are hopeful about how generative AI will shape their professions, while less than a third of aerospace and public-sector workers feel similarly optimistic.
Managing AI Efficiencies and Investor Expectations
Boards are under increasing pressure from investors to articulate a compelling strategy that balances growth, profitability and SBCE. Workforce planning must evolve to reflect AI-driven efficiencies while ensuring the right level of human capital investment to support financial targets. As AI becomes a larger part of human capital management strategies, boards will want to balance the opportunities for growth with pragmatic assessment of the risks. To make informed decisions, they might consider these areas:
Talent investment vs. AI productivity gains
Many companies are aggressively hiring AI talent with the expectation that advanced models will drive future profitability. Boards must assess whether these investments align with long-term workforce planning and business goals. Ask forward-thinking questions like:
- Which roles are still critical for long-term success?
- Where can AI replace or augment human talent?
- Where can additional talent help leverage AI investments?
AI cost accounting
Companies should distinguish between AI-driven cost savings in cost of goods sold (COGS) versus SG&A to track where efficiencies emerge. Efforts should be made to predict additional costs that AI may require, such as increased computing power, technical support or an increase of licensing fees.
SBCE-to-revenue ratios
AI efficiencies may shift compensation structures, requiring boards to redefine acceptable levels of overall compensation spend, including stock-based compensation. Consider questions like, “How do historical SG&A and SBCE costs for human employees inform future budgeting alongside AI investments?”
Proactive risk management
Investors will need to be assured that proper due diligence has been given when choosing AI partners. Does the board have the technical experience necessary to stay ahead of potential technological, ethical or regulatory risks?
Investor messaging
Boards would be wise to develop coherent messaging to investors about AI-related workforce changes. Questions might include:
- What is the company’s narrative to investors about balancing short-term cost increases with long-term growth and profitability?
- How are current steps ensuring future sustainability?
- When will AI investments translate into measurable operational efficiencies and financial returns?
Navigating Workforce Strategy in the AI Era
AI is fundamentally changing the rules of workforce planning, creating a complex but essential challenge for boards and compensation committees. Ultimately, long-term success depends on balancing AI-driven efficiencies with human capital strategy. Companies that can clearly communicate how AI investments will impact workforce structure, financial performance and shareholder value will be best positioned to gain investor confidence. By asking the right questions, focusing on financial metrics and aligning workforce decisions with growth and profitability goals, boards can position their companies for long-term success.
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