The Productivity Shift
How Leaders Measure AI's Success
Introduction
AI adoption is accelerating across industries, yet many business leaders still struggle with a fundamental challenge: how to measure its success. While the potential of AI is widely acknowledged, quantifying its impact can feel like hitting a moving target. For our annual report, The Productivity Shift we surveyed over 250 business leaders to understand how organizations are evaluating AI’s return on investment (ROI). The findings reveal that while AI is driving meaningful outcomes, there is no single measure of success that captures AI’s full impact.
This lack of a standardized approach leaves many organizations uncertain about whether their AI investments are delivering real value. Some focus on workforce productivity, while others emphasize revenue growth or innovation. However, isolated metrics create an incomplete picture, making it difficult to determine which AI tools should be scaled, refined, or retired. To help leaders address this challenge, this report organizes top AI metrics into practical categories, provides insights on what each metric conveys, and shows how it fits into the larger ROI picture.
How Leaders Measure AI’s Success
No single metric captures AI’s full impact. Because of this, business leaders look at a range of key indicators to measure the success of their AI deployments. From cost reduction and revenue growth to productivity, innovation, and customer engagement, no single metric dominates as the standard. The only wrong approach is not measuring AI’s ROI at all—yet 11% of leaders report having no formal measurement in place. Without a clear framework, organizations risk missing critical insights into AI’s true value and its impact on the business.
This report provides that framework, breaking down key metrics to explain what each reveals, what it doesn’t, and how to connect them for a more complete assessment.
Top indicators leaders use to measure the ROI of their AI deployments
The Four Pillars of AI Success
The key success indicators used to measure AI’s impact can be grouped into four categories, each offering a different perspective on performance and value. By understanding the insights each indicator conveys, you can more quickly assess which initiatives to invest in and pinpoint why initiatives may not be translating into business impact.
Each of these pillars tells a different part of the story, and only by looking at them together can organizations get a complete picture of AI’s value. The next sections will break down each one, exploring how to measure success and where common oversights can occur.
Compliance is not traditionally seen as an ROI measure as it’s hard to quantify the value of risks avoided. That is likely why only 13% of leaders report it as a measure of AI success. However, compliance is the first critical checkpoint in evaluating any AI tool or initiative. Before an AI tool can deliver value, it must first meet an organization’s security, privacy, and regulatory standards.
Compliance
Pillar 1
Any tool that fails compliance poses risks that outweigh any potential benefits, making compliance a necessary gate before considering quality, productivity, or impact. Without proper safeguards, AI can introduce vulnerabilities, such as data breaches and regulatory fines, undermining trust and exposing the business to unnecessary risk.
Compliance and risk reduction
Common measures
- Data encryption, examinations (e.g., SOC 2), and security certifications (e.g., ISO 27001)
- Regulatory compliance (e.g., GDPR, California Consumer Privacy Act [CCPA], HIPAA)
- Access controls and user permissions
- Incident response and breach history
Larger context
Compliance alone doesn’t indicate effectiveness or usability. A tool can be secure but still fail in quality, adoption, or impact.
What this metric tells you
Ensures an AI tool meets security, privacy, and regulatory requirements before it is deployed. This is typically measured as a pass-fail, determining whether the tool is safe to use within your organization. A pass means it meets security standards and legal requirements.
An AI tool is only as valuable as the quality of its outputs. Whether generating content, analyzing data, or automating processes, AI must consistently produce accurate, relevant, and trustworthy results to drive real business impact.
Quality is one of the most closely watched indicators of AI success, with 40% of leaders using
Quality
Pillar 2
these metrics to evaluate performance. This focus is well-founded, as a similar percentage of leaders (41%) express concerns about AI’s potential to produce outdated, inaccurate, or misleading content. Without reliable outputs, AI can introduce risk rather than value, making quality a critical pillar in assessing AI’s true impact.
Quality improvements
Larger context
Quality alone doesn’t measure efficiency, adoption, or business impact. A highly accurate tool that is slow, difficult to use, or poorly integrated may still fail to deliver ROI.
What this metric tells you
Measures the consistency, accuracy, and relevance of AI-generated outputs, as well as the quality of work produced by employees using the tool. Strong performance here indicates AI is effective in supporting—rather than hindering—communication, decision-making, and workflows.
Common measures
- Error rates (e.g., grammatical, factual, or analytical mistakes)
- Communication scores (e.g., clarity, readability, effectiveness)
- A/B testing results for AI vs. human performance
Many AI ROI metrics—like productivity, innovation, and efficiency—are realized at the individual employee level. But the real value of AI emerges when usage scales beyond individuals to entire teams and functions. When AI becomes embedded in daily workflows across departments, its impact compounds—driving collaborative efficiency, knowledge sharing, and organization-wide improvements.
Employee Experience
Pillar 3
Utilization is a leading indicator of AI ROI and a measure leaders can look to when considering scaling a tool or initiative. Despite over half of leaders saying they have invested in AI tools to foster innovation (50%) and improve productivity (63%), less than one-third actually measure these metrics—leaving potential blind spots in assessing AI’s true impact on workforce efficiency and creativity.
Productivity gains
Larger context
Productivity gains don’t necessarily mean better outcomes—faster work isn’t always more effective. This metric should be assessed alongside quality and impact to ensure AI is driving both effectiveness and efficiency.
What this metric tells you
Measures AI’s impact on time savings, process automation, and output rate and efficiency. With 96% of leaders reporting productivity gains from AI, this is one of the clearest indicators of AI’s success.
Common measures
- Weekly hours saved
- Increased output per employee
- Task automation rates
Enterprise-wide AI success must be measured by tangible business outcomes. Whether through cost savings, increased revenue, stronger customer relationships, or greater operational speed, AI should influence the bottom line.
Impact
Pillar 4
Yet, while many leaders recognize the potential AI can have on these metrics, less than one-third actually have tracking measures in place to prove it. By tracking AI’s influence on key business metrics, organizations can determine whether their investments are truly driving bottom-line value.
Cost reduction
Common measures
- Reduction in operational costs (e.g., labor, infrastructure)
- Tech stack reduction or consolidation
- Standard operating procedures streamlined or automated
Larger context
Cost reduction alone doesn’t account for the strategic value AI may bring. A tool that cuts costs but diminishes quality or experience may not deliver long-term benefits.
What this metric tells you
Measures AI’s ability to lower costs, streamline operations, and improve resource allocation. This tells you whether AI is reducing manual effort, minimizing mistakes, and driving operational efficiencies that lead to cost savings.
There is no single metric that fully captures AI’s return on investment, as its impact spans across teams, workflows, strategic decision-making, and the customer experience. Without clear tracking mechanisms, organizations risk investing in AI tools that fail to deliver meaningful value—or, worse, overlooking AI solutions that could be transformative. The ROI of AI is not determined by a single metric but by a combination of factors across compliance, quality, productivity, and business impact. A nuanced, flexible approach to measurement allows leaders to assess AI’s effectiveness holistically, ensuring that investments align with strategic goals.
For organizations looking to put this framework into action, our AI ROI Playbook provides a practical scorecard to help start measuring AI’s impact with confidence. AI is no longer a future investment—it’s here, reshaping the way businesses operate. But without a clear way to measure success, even the most advanced AI tools risk becoming underutilized or misaligned with business goals. Leaders who take a structured, data-driven approach will be the ones who maximize AI’s potential.
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