Editorial
Every company is investing in AI. The momentum is undeniable. Organizations across industries are pouring resources into AI to enhance efficiency, productivity and innovation. In fact, 98% of senior data leaders at Fortune 1000 companies expect to increase their AI spending in 2025.
However, investing in AI is just the starting point. The real challenge — and opportunity — lies in ensuring that AI-driven efficiency translates into meaningful business impact and workforce transformation.
The AI Value Playbook
Too often, companies focus on AI’s ability to automate tasks and free up time but fail to establish a clear plan for how to use that newfound capacity. If AI saves ten minutes per employee, repeated thousands of times, the impact can be substantial. But without intentional reallocation, those efficiency gains risk being absorbed by low-value work (or no work at all) rather than driving strategic outcomes.
To bridge this gap, organizations must develop a structured AI value playbook that goes beyond implementation and focuses on execution to ensure AI investments yield tangible business returns:
- Identify Use Cases and Opportunities: Determine where AI can create efficiencies by analyzing tasks, workflows and benchmarks to understand effort, value and risks
- Select the Right Technology: Develop clear requirements and evaluate potential AI solutions to ensure they meet business needs. Prioritize AI platforms that allow you to combine data across the enterprise and knock down data silos.
- Implement AI With Change Management: Adoption doesn’t happen overnight. Enablement and training programs must accompany AI rollouts.
- Develop a Capacity Reallocation Plan: Measure the AI capacity gained and reallocate those resources through workforce planning, skill development and hiring strategies.
- Execute and Track Value Creation: AI is only valuable if organizations can track and measure how efficiency gains translate into business impact.
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Where to Start With AI Adoption — and What to Do After
The first step is identifying the right use cases and understanding where AI can create the most meaningful impact. This requires assessing the effort required to build a use case and the potential value. In addition, implementing “gates” to ensure ethical and responsible AI is crucial to ensure you don’t run into data privacy or AI regulations.
Once these opportunities are identified, selecting the right AI solutions becomes critical. Technology investments must be guided by clear requirements to ensure adoption. Companies then must implement technology thoughtfully and with the right enablement.
Although these steps are important, the real work begins after AI is implemented. Many organizations stop at deployment, assuming that the benefits will follow automatically. But without a clear plan for reallocation, the time savings or efficiencies AI generates end up scattered across countless small tasks that don’t meaningfully move the needle.
Companies must establish a systematic approach to capacity reallocation, ensuring that AI-driven efficiencies are directed toward the most critical business priorities.
AI-delivered capacity should not be viewed as a byproduct of automation but as a strategic asset that must be actively managed. Reallocating capacity to strategic priorities is more valuable to organizations than simply cutting costs, but it requires thoughtful workforce planning, identifying shifts in skill requirements, and determining whether to build, buy or borrow talent to align with evolving business needs.
The Smart Way to Redirect AI-Driven Efficiencies
For AI to become a long-term strategic advantage, companies must be thoughtful in designing and implementing capacity reallocation strategies.
Many organizations assume that employees will naturally fill their newfound capacity with higher-value work, but this assumption is flawed. Without clear direction, time savings often disappear into administrative tasks or minor productivity boosts rather than transformational outcomes.
For example, without proper upskilling and reskilling, employees may not have the right skills to apply their newfound capacity where it’s needed most. If an engineer gains 5% more time, but the business’s top priority is marketing content, that extra capacity may go unused. Organizations must be intentional about aligning capacity gains with both employee skills and strategic goals.
HR leaders must also take a proactive role in ensuring that AI-driven efficiencies align with key business priorities. This means working closely with department heads to understand where additional capacity can be best utilized, whether through reskilling, reallocating responsibilities or shifting resources to innovation-focused initiatives.
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Real Use Cases for Maximizing AI Time Gains
Reinvesting AI-driven capacity in the workforce can take many forms: upskilling employees for new roles, empowering teams to focus on higher-value tasks and allocating more resources toward innovation. AI should not be viewed as a tool that simply makes existing processes more efficient but as a catalyst for transformation.
For example, we’ve implemented a real-time tracking system through our AI Control Tower to measure the value created by AI, ensuring that every use case translates into a strategic advantage.
For any AI initiative, we evaluate three critical factors:
- How much is it being used
- How often it delivers valuable results
- How much time it actually saves employees
These insights help us continuously refine our AI strategy and ensure that capacity gains lead to measurable business impact.
Another example where we’ve seen significant impact is people operations. AI has fundamentally changed how our shared services function supports employees. We’ve implemented seven different technologies across a single platform that increase the number of deflected employee requests (i.e., AI solves the request without human intervention) and/or help agents handle cases more quickly or effectively.
Our measurement of shared services agent time saved from the adoption of these AI tools is approximately 25 FTEs in 2024. This is amazing, but the value is expanded when, rather than simply reducing headcount, we upskilled our shared service agents to support talent strategy work as People Partners.
Additionally, we redeployed several technically skilled resources to develop new AI use cases — essentially creating a flywheel of value. By freeing up capacity through AI, we reinvest that capacity into developing more AI-driven solutions, continuously driving additional value. This resource shift ensures that capacity gains from AI lead to meaningful improvements in workforce development and business outcomes.
Your AI Strategy Is Only as Strong as Your Leadership
Leadership commitment is the final and perhaps most crucial factor in making AI a true business transformation. Successful AI-driven transformation requires leaders to go beyond measuring adoption rates and instead embed AI into the broader business strategy. This involves treating AI-generated capacity as a strategic resource, just like acquiring additional headcount.
Imagine if a company were given the budget to hire a hundred new employees — there would be rigorous discussions about where these new hires would have the most impact. AI-driven capacity deserves the same level of strategic planning.
Leaders must engage in thoughtful allocation of the time and resources AI frees up, ensuring these gains are channeled into projects that drive innovation and strategic growth. Leadership must also reframe the narrative around AI implementation. Rather than focusing on cost reduction and workforce displacement, successful organizations recognize AI's potential to elevate their workforce capabilities. The goal should be to redirect employees toward more strategic initiatives that drive business value.
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For AI to be successful at the scale that many organizations expect, these gains have to be real and tangible. Measuring “hours saved” is not enough and organizations need to be thinking critically about how these use cases translate into ROI.
The future of AI in business isn’t just about automation — it’s about transformation. The companies that embrace this mindset shift will lead the way in defining what comes next.
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