You Might Be Looking for AI Value in the Wrong Place: Unlocking True Potential of AI in Recruiting and Beyond

Artificial Intelligence (AI) is transforming industries at an unprecedented pace, and its role in recruiting is no exception. However, the true value of AI in recruiting—and in any business function—often lies beyond the immediate, tangible cost savings or efficiency gains. To fully harness AI’s potential, organizations must adopt a strategic mindset about value generation that goes deeper than simple metrics. This article explores how companies can create measurable, meaningful value with AI, drawing on insights from industry experts who have studied the nuances of AI investment and value realization.

Understanding Value Beyond the Obvious

The most straightforward benefits from AI investments, such as reducing headcount or automating repetitive tasks, are often the easiest to quantify. For example, in recruiting, AI-powered tools can automate resume screening or candidate outreach, leading to direct cost reductions. But this represents just the tip of the iceberg.

Many of the most impactful benefits are indirect or intangible. These benefits might unfold over multiple stages or “hops” away from the initial AI intervention. Consider productivity improvements: AI can save recruiters or hiring managers time, but unless that saved time is strategically reinvested in activities that drive financial outcomes—like engaging top talent or improving candidate experience—the theoretical productivity gain may never translate into real value.

Take coding assistance tools as a parallel example. Software engineers might gain an hour of time back weekly through AI assistance. But if that hour is spent on non-work activities or less impactful tasks, the organization experiences what experts call “productivity leak.” The key to maximizing value lies in proactively managing how AI-generated time savings are used and incentivized.

Why Managing Productivity Leak Matters in AI-Driven Recruiting

In recruiting, productivity leak can manifest when AI tools free up recruiters’ time, but that time isn’t redirected to high-value activities such as strategic talent sourcing or candidate relationship building. Organizations need to design frameworks that encourage the use of AI-generated capacity in ways that align with strategic priorities.

For example, if AI helps reduce the time spent on administrative tasks, recruiters can focus more on personalized candidate engagement, improving hiring outcomes and employer branding. But this requires deliberate change management and incentive structures.

Setting the Stage: Aligning AI Ambition with Business Priorities

Before investing in AI, organizations must clarify their ambition with the technology. Are they looking to defend their current competitive position, extend existing processes, or upend and transform their industry? This ambition shapes the types of AI use cases to pursue and the expected value horizon.

• Defend: Use AI to maintain current market standing through incremental improvements and cost reductions.
• Extend: Differentiate by enhancing existing capabilities or processes with AI-driven innovations.
• Upend: Pursue transformative AI projects that radically change products, services, or business models.

Most companies today focus on defending and extending strategies—leveraging AI for productivity tools, coding assistance, or automated workflows that are easier to implement and justify. Yet, these often yield benefits that are difficult to quantify precisely.

Lessons from History: The Word Processor Analogy

To understand the challenge of measuring AI’s value, consider the example of the word processor. When first introduced, the immediate measurable benefit was reducing secretarial staff. But the true impact—enabling iterative thinking, collaboration, and creativity—was far more profound and difficult to quantify at the time.

Similarly, AI tools in recruiting may initially show modest cost savings but can unlock greater strategic benefits by transforming how talent acquisition teams work, collaborate, and innovate. Unfortunately, CFOs under pressure for quarterly savings may find it hard to justify investments based on these intangible benefits alone.

Balancing Risk and Reward: The Courage to Upend

Upending the industry with AI is inherently riskier and requires imagination, courage, and a willingness to accept uncertainty. It often involves rethinking fundamental aspects of the business model, such as how value is created, who the customers are, and how products or services are delivered.

For example, an insurance company might develop AI models to microprice risk with unprecedented granularity, dramatically reducing losses and gaining a competitive advantage. Or a company might create a new AI-native product that reshapes the industry landscape.

These initiatives typically require custom AI models, unique data sets, and substantial investment with longer timelines for return. They also entail managing technical debt—the cost of adapting and upgrading AI models as the market and technology evolve rapidly.

Managing AI Investment Portfolios: Risk Tolerance and Ambition

Organizations need to embed risk tolerance into their AI investment strategy. Boards and executives must be comfortable allocating a portion of the AI portfolio to transformative, high-risk projects while balancing more conservative investments that yield quicker wins.

This portfolio approach allows companies to experiment, learn, and accelerate through the AI maturity curve. Early adopters who built proprietary large language models (LLMs) may have incurred technical debt but gained invaluable experience that accelerates future innovation.

Getting Started: Crafting a Strong AI Value Story

To successfully implement AI, organizations should begin by assessing their AI ambition and aligning it with strategic business priorities and KPIs. A clear value story connects AI technical outcomes to measurable business impact, enabling better investment decisions and prioritization.

Effective ideation involves both top-down and bottom-up approaches:

• Top-down: Strategic exploration of the art of the possible and benchmarking against other industries.
• Bottom-up: Engaging business units to identify tactical use cases that address immediate pain points.

This dual approach helps balance innovation with practical application, ensuring AI investments support competitive differentiation and operational goals.

Industry-Specific Considerations in AI Value Generation

AI value generation varies significantly by industry. For instance, life sciences companies may prioritize AI applications in clinical trials and drug discovery, requiring deeper, transformative investments. In contrast, mining companies might focus AI efforts on extending specific process differentiators.

Understanding industry dynamics and competitive positioning is essential for tailoring AI strategies that maximize value.

Prioritizing AI Use Cases: Key Factors to Consider

When ranking and prioritizing AI use cases, organizations should evaluate:

• Potential business impact and competitive advantage
• Alignment with defend, extend, or upend ambitions
• Complexity and risk of deployment
• Availability and quality of data
• Regulatory and compliance considerations
• Level of effort and required organizational changes
• People and change management challenges, especially when AI augments rather than replaces human roles

Neglecting these factors often leads to stalled AI initiatives or unrealized value.

Change Management: The Yeast Analogy for AI Adoption

Successful AI adoption requires creating the right organizational environment—much like the patience and conditions needed for yeast to make bread rise. Rushing AI deployment without preparing people, processes, and culture can prevent value realization.

Organizations must be patient and deliberate, ensuring the environment supports AI-driven change and that users are trained and incentivized to leverage AI effectively.

When to Build Your Own Models: The Role of Unique Data and IP

Some organizations, particularly those with proprietary data and intellectual property (IP), may benefit from building custom AI models. For example, pharmaceutical companies with exclusive molecular design data can develop AI models to predict drug efficacy or discovery outcomes, generating high-value results.

These investments resemble venture capital approaches, where multiple projects are seeded with the expectation that a few will yield transformative breakthroughs.

Tools and Frameworks to Support AI Value Management

To assist organizations in navigating AI value realization, several frameworks and tools are available to:

• Link technical AI outcomes to business KPIs
• Manage and minimize productivity leak
• Assess direct and indirect cost savings
• Prioritize use cases based on risk, complexity, and impact
• Support change management and user adoption strategies

These resources help organizations develop the competency to measure and harvest AI value effectively.

Upcoming Opportunities to Learn More

Data and analytics summits happening globally provide excellent opportunities for leaders to deepen their understanding of AI value generation. These events cover foundational topics such as AI-ready data, governance, people management, and strategic AI investments.

Final Thoughts: A Bullish but Realistic Outlook on AI Value

While the journey to realizing AI value is challenging, the outlook remains overwhelmingly positive. AI is not a magic bullet; it requires hard work, collaboration, and new skills. Success depends on a cross-functional effort involving business leaders, data scientists, IT, and change managers.

Organizations that develop a robust value management competency and embrace a pragmatic yet optimistic mindset will be best positioned to unlock the full potential of AI in recruiting and other domains.

Conclusion

AI in recruiting offers exciting opportunities to transform how organizations attract, engage, and hire talent. However, focusing solely on immediate cost savings or productivity gains risks missing the broader value AI can deliver.

By aligning AI investments with strategic ambition, managing productivity leak, embracing risk thoughtfully, and fostering organizational readiness, companies can create measurable, sustainable value from AI initiatives.

As AI technologies continue to evolve, leaders must remain patient, adaptable, and collaborative to navigate the complex landscape and ultimately realize AI’s promise.