Measuring What Matters: A Comprehensive Framework for AI-Assisted Workflow Metrics

This is the fifth in the series on AI-assisted workflow series. In our first article, "Beyond Automation: The Evolution and Promise of AI-Assisted Workflows," we outlined the transformative potential AI holds for businesses beyond mere automation, emphasizing augmentation and innovation. The second article, "Human in the Loop: Designing Effective Human-AI Systems," focused on practical design strategies for effectively incorporating human judgment within AI processes. We dived deeper into identifying the optimal integration points and maintaining a sustainable balance between human expertise and AI efficiency in our third article, “The Workflow Integration Spectrum: Finding the Right Human-AI Balance”. The fourth article, “Building Blocks of Effective Hybrid Workflows: People, Process, and Technology” explored the critical building blocks that make hybrid workflows successful and provides actionable insights for implementation. This last article focuses on how to measure the performance of AI-Assisted workflows.

Introduction

In today's rapidly evolving technological landscape, the integration of artificial intelligence into business workflows has revolutionized how organizations operate. However, as these hybrid human-AI systems become more prevalent, traditional performance metrics often fall short in capturing their true effectiveness.

Traditional metrics typically focus on isolated aspects of performance - productivity, quality, or cost - without accounting for the unique dynamics created when humans and AI systems collaborate. These conventional measurements were designed for workflows where tasks flow linearly and responsibility is clearly delineated, but they struggle to evaluate the complex interplay between human judgment and machine capabilities.

A multidimensional measurement approach is essential for understanding and optimizing AI-assisted workflows. By examining efficiency, quality, adaptability, human factors, and business impact simultaneously, organizations can develop a holistic view of system performance. This comprehensive perspective enables leaders to make informed decisions about where to invest resources, how to adjust processes, and how to balance workloads between human and AI components.

Most importantly, robust metrics drive continuous improvement in hybrid systems. When intelligently designed, measurement frameworks not only evaluate current performance but also highlight opportunities for enhancement. They create feedback loops that help both human team members and AI systems adapt, learn, and evolve together, fostering a culture of ongoing optimization that keeps pace with changing business needs and technological capabilities.

Establishing Measurement Foundations

Defining Success for Different Types of Hybrid Workflows

Before implementing any metrics, organizations must clearly define what "success" means for their specific AI-assisted workflows. Success criteria vary widely depending on the workflow's nature, context, and objectives:

• For decision support systems, success might center on improved decision quality and reduced deliberation time.

• In customer service applications, success could involve resolution rates alongside customer satisfaction.

• Within content creation workflows, success might balance productivity with creativity and originality.

• For predictive maintenance systems, success often combines accuracy of predictions with prevention of costly downtime.

Each organization must engage in thoughtful consideration of their unique goals to avoid measuring what is merely convenient rather than what is truly valuable.

Balancing Quantitative and Qualitative Approaches

Effective measurement frameworks incorporate both quantitative metrics that provide precise, comparable data points and qualitative assessments that capture nuanced aspects of performance:

• Quantitative metrics offer objectivity, consistency, and scalability.

• Qualitative assessments provide context, depth, and insights into user experience.

• Mixed-method approaches combine the strengths of both, using qualitative findings to explain quantitative patterns and guide metric refinement.

This balanced approach ensures that measurement efforts capture both the easily quantifiable aspects of performance and the more subtle dimensions of effectiveness that often prove most valuable.

Setting Baselines and Comparative Benchmarks

Meaningful evaluation requires appropriate reference points:

• Internal historical baselines establish starting points and track improvements over time.

• Pre-implementation vs. post-implementation comparisons highlight the specific impact of AI integration.

• Human-only vs. AI-only vs. hybrid performance benchmarks reveal where collaboration creates value.

• Industry benchmarks provide context for relative performance, though differences in implementation and context must be considered.

Without these reference points, raw metrics lack the context needed for proper interpretation and decision-making.

Involving Stakeholders in Metric Development

The most effective measurement approaches engage diverse stakeholders in metric design:

• End users provide invaluable perspectives on practical usefulness and experience.

• Technical teams offer insights into system capabilities and limitations.

• Business leaders ensure alignment with strategic objectives.

• Customers or clients help define ultimate value creation.

• Cross-functional collaboration leads to more robust, balanced frameworks.

This participatory approach not only improves metric quality but also builds understanding and buy-in among those who will be evaluated by and make decisions based on these measures.

Performance Metrics Framework

Efficiency Metrics

Efficiency metrics evaluate how effectively the hybrid workflow utilizes resources and time:

Throughput and Processing Time

• Units processed per time period (documents reviewed, cases handled, decisions made).

• End-to-end completion time for workflows.

• Cycle time for specific process stages.

• Time distribution across human and AI components.

Resource Utilization

• AI system utilization rates (percentage of available capacity used).

• Human time allocation (time spent on different task categories).

• Load balancing between AI and human resources.

• Peak vs. average utilization patterns.

Cost Per Transaction

• Direct costs per unit processed.

• Labor costs for human-handled components.

• Computing and infrastructure costs for AI components.

• Cost comparison to previous or alternative workflows.

Automation Rate and Human Intervention Frequency

• Percentage of tasks that are fully automated without human involvement.

• Frequency of human override or intervention.

• Distribution of effort between AI and human components.

• Trend analysis of automation rates over time.

Quality Metrics

Quality metrics assess the accuracy, reliability, and value of workflow outputs:

Error Rates and Types

• Overall error frequency.

• Error categorization (false positives, false negatives, etc.).

• Error severity classification.

• Error source analysis (human vs. AI components).

Accuracy and Precision

• Match rate to established ground truth.

• Confidence scores for AI-generated outputs.

• Consistency of accuracy across various categories.

• Precision-recall balance for classification tasks.

Consistency Across Cases

• Variance in quality metrics across similar cases.

• Reliability in edge cases and unusual scenarios.

• Performance stability over time.

• Consistency across different users or teams.

Outcome Quality Compared to Human-Only or AI-Only Approaches

• Comparative quality assessments across different workflow configurations.

• Unique quality improvements enabled by hybrid approaches.

• Trade-offs between different quality dimensions.

• Expert evaluation of output quality beyond simple accuracy.

Adaptability Metrics

Adaptability metrics evaluate how well the hybrid system handles change and improves over time:

Time to Respond to Exceptions

• Detection time for anomalies or edge cases.

• Resolution time for exceptions.

• Path to resolution (automated vs. human escalation).

• Exception categorization and trend analysis.

Learning Curve Measurements

• Time to proficiency for human users.

• Improvement rate in AI system performance.

• Knowledge transfer effectiveness.

• Training efficiency metrics.

Improvement Rates Over Time

• Performance trend analysis across key metrics.

• Learning from feedback mechanisms.

• Iteration cycles and improvement frequency.

• Diminishing returns analysis.

Resilience During Disruptions or Unusual Scenarios

• Performance degradation under stress.

• Recovery time after disruptions.

• Adaptability to changing conditions.

• Robustness in unexpected situations.

Human-Centered Measurement

Human Performance Metrics

Human performance metrics assess how the AI integration affects human contributors:

Productivity and Effectiveness

• Output per hour compared to pre-implementation baseline.

• Task completion rates.

• Focus time vs. interruption patterns.

• Value-added activities vs. administrative overhead.

Decision Quality and Speed

• Decision accuracy with AI assistance vs. without.

• Time to decision.

• Decision confidence levels.

• Decision consistency across similar scenarios.

Skill Development and Learning

• Growth in technical capabilities.

• Development of complementary skills.

• Knowledge acquisition curves.

• Versatility and adaptability measures.

Job Satisfaction and Engagement

• Self-reported satisfaction scores.

• Engagement levels with various aspects of work.

• Perceived value of AI assistance.

• Retention and recruitment effects.

Interaction Quality Metrics

Interaction metrics evaluate the effectiveness of human-AI collaboration:

Handoff Smoothness Between Human and AI

• Transition time between AI and human components.

• Information completeness during handoffs.

• Contextual awareness maintenance.

• Friction points in workflow transitions.

Communication Effectiveness

• Clarity of AI-generated explanations.

• Human understanding of AI outputs.

• Information exchange efficiency.

• Feedback incorporation rates.

Trust and Reliance Appropriateness

• Calibration of trust to actual AI capabilities.

• Over-reliance vs. under-reliance patterns.

• Appropriate skepticism and verification behaviors.

• Trust development over time.

Human Override Patterns and Justifications

• Frequency of AI recommendation overrides.

• Justification analysis for overrides.

• Pattern recognition in override scenarios.

• Learning from override instances.

Business Impact Evaluation

Linking Workflow Metrics to Business Outcomes

Effective measurement frameworks connect operational metrics to strategic business outcomes:

• Revenue impact analysis.

• Customer retention and satisfaction effects.

• Market responsiveness improvements.

• Competitive advantage creation.

• Innovation acceleration metrics.

ROI Calculation Approaches for Hybrid Workflows

ROI assessment for AI-assisted workflows requires comprehensive accounting of both costs and benefits:

• Implementation and maintenance costs.

• Training and change management investments.

• Productivity gains and quality improvements.

• Risk reduction and compliance benefits.

• Strategic capability development.

• Opportunity cost considerations

Customer/End-User Experience Measurement

The ultimate test of hybrid workflow effectiveness often lies in customer impact:

• Customer satisfaction scores.

• Experience continuity measures.

• Resolution completeness.

• Effort reduction metrics.

• Personalization effectiveness.

• Problem anticipation and proactive resolution.

Long-term Value Creation Assessment

Beyond immediate operational gains, organizations must evaluate strategic value creation:

• Organizational capability development.

• Knowledge creation and retention.

• Adaptability to market changes.

• Scalability without proportional cost increases.

• Innovation enablement metrics.

• Talent attraction and development.

Implementing a Measurement Program

Metric Selection and Prioritization

Successful measurement programs start with careful selection of metrics. Metrics should be:

• Aligned with strategic objectives.

• Actionable and influence decision-making.

• Cover key performance dimensions.

• Balanced between leading and lagging indicators.

• Prioritized by business goals and leading indicators to provide early view into performance.

Data Collection Approaches and Tools

Robust data collection is the foundation of effective measurement. Organizations must develop comprehensive strategies that capture both quantitative and qualitative dimensions without creating excessive overhead.

• Modern AI-assisted workflows should incorporate instrumentation that automatically captures key performance indicators.

• Semi-automatic techniques such as surveys, feedback loops and annotation systems provide insights into AI’s performance.

• Qualitative data captured through contextual inquiry, experience sampling, etc. helps with building trust in AI-assisted workflow by identifying pain points and bottlenecks.

• Effective measurement requires bringing disparate data sources together.

• Responsible data collection requires careful attention to transparency, privacy, and security.

Reporting and Visualization Best Practices

How metrics are presented significantly impacts their utility. Effective visualization transforms raw data into actionable insights that drive improvement.

• Well-designed dashboards balance comprehensiveness with clarity.

• Use-case relevant visualizations can help identify trends and patterns .

• Understanding performance over time is critical for improvement.

• Organizations should design views that align with decision-making authority and provide each audience with actionable insights relevant to their role.

• Modern visualization approaches leverage interactivity to help stakeholders get performance patterns and for scenario evaluation or modeling.

Continuous Improvement & Feedback Loops

Measurement programs should drive ongoing optimization:

• Regular review cycles for key metrics.

• Action planning based on measurement insights.

• Experimentation frameworks to test improvements.

• Metric refinement and evolution over time.

• Knowledge sharing across teams and functions.

Common Measurement Pitfalls to Avoid

Despite best intentions, organizations frequently encounter obstacles when measuring AI-assisted workflows:

Overemphasis on Easily Measured Dimensions

• Focusing excessively on efficiency metrics while neglecting quality and human experience.

• Measuring what is convenient rather than what is important.

• Allowing available data to dictate measurement priorities rather than strategic needs.

Solution: Regularly audit your measurement framework against strategic objectives and ensure balanced coverage across all critical dimensions.

Failure to Account for System Maturity

• Applying the same metrics to nascent and mature systems.

• Not adjusting expectations based on implementation stage.

• Missing opportunities to measure learning and improvement.

Solution: Develop stage-appropriate metrics that evolve with system maturity, with greater emphasis on learning metrics in pilot stages.

Attribution Errors

• Incorrectly attributing outcomes to either human or AI components.

• Failing to recognize emergent properties of the combined system.

• Creating counterproductive competition between human and AI elements.

Solution: Design metrics that acknowledge shared outcomes while maintaining appropriate accountability for each component.

Metric Proliferation

• Creating too many metrics, diluting focus, and creating measurement fatigue.

• Collecting data without clear action plans.

• Failing to distinguish between primary KPIs and diagnostic metrics.

Solution: Implement a tiered metric system with a small number of strategic KPIs supported by diagnostic measures.

Static Measurement Approaches

• Not evolving metrics as systems mature and objectives change.

• Failing to incorporate new measurement techniques as they emerge.

• Measuring against outdated baselines or benchmarks.

Solution: Schedule regular measurement framework reviews and deliberately evolve your approach as systems mature.

Navigating Inherent Tensions in Measurement Design

Effective measurement frameworks must acknowledge and balance several inherent tensions:

Efficiency vs. Quality

Tension: Optimizing for speed and resource utilization often creates pressure on quality dimensions.

Navigation Approach:

• Implement guardrails that prevent efficiency improvements at the expense of quality.

• Develop composite metrics that reward balanced improvement.

• Create visualization tools that highlight trade-off relationships.

Standardization vs. Customization

Tension: Organizations need comparable metrics across implementations but also require context-specific measurements.

Navigation Approach:

• Establish a core measurement framework that applies universally.

• Allow for domain-specific extensions with clear documentation.

• Create translation mechanisms between custom and standard metrics.

Short-term vs. Long-term Optimization

Tension: Immediate performance improvements may come at the expense of system adaptability and long-term capabilities.

Navigation Approach:

• Include leading indicators of future performance alongside current metrics.

• Explicitly measure learning and improvement capacity.

• Balance operational metrics with strategic capability development measures.

Autonomy vs. Control

Tension: Greater AI autonomy can increase efficiency but may reduce human engagement and oversight.

Navigation Approach:

• Measure appropriate reliance and intervention patterns.

• Track capability boundaries and exception handling.

• Monitor for skill atrophy or enhancement.

Visibility vs. Overhead

Tension: Comprehensive measurement creates valuable insights but can impose significant collection and analysis burdens.

Navigation Approach:

• Design passive collection mechanisms embedded in workflows.

• Prioritize high-value metrics over comprehensive coverage.

• Balance measurement depth with practical resource constraints.

Successful measurement programs explicitly acknowledge these tensions and design frameworks that navigate them intentionally rather than allowing them to create hidden biases in performance assessment.

Industry-Specific Measurement Considerations

Healthcare-Specific Metrics for Hybrid Clinical Systems

Healthcare implementations require specialized measurement approaches:

• Clinical outcome improvements.

• Diagnostic accuracy and safety metrics.

• Provider adoption and satisfaction.

• Patient experience and trust measures.

• Regulatory compliance and documentation quality.

• Integration with evidence-based practice.

Financial Services Evaluation Frameworks

Financial service applications focus on different priorities:

• Risk assessment accuracy.

• Fraud detection effectiveness.

• Regulatory compliance assurance.

• Customer personalization metrics.

• Transaction processing efficiency.

• Decision explanation adequacy.

Manufacturing and Operations Measurement Approaches

Manufacturing contexts emphasize operational excellence:

• Predictive maintenance effectiveness.

• Quality control precision.

• Production optimization metrics.

• Supply chain integration measures.

• Resource utilization improvements.

• Safety incident prevention.

Customer Service and Experience Metrics

Customer-facing implementations require experience-centered metrics:

• First contact resolution rates.

• Customer effort scores.

• Sentiment analysis metrics.

• Personalization effectiveness.

• Channel transition smoothness.

• Problem anticipation and prevention.

Future Directions in Hybrid Workflow Measurement

Emerging Approaches to Evaluating Collaborative Intelligence

The field continues to evolve with new measurement paradigms:

• Symbiotic performance indicators that only apply to human-AI collaboration.

• Dynamic allocation optimization metrics.

• Emergent capability measurements.

• Collective intelligence assessment frameworks.

• Multi-agent system evaluation approaches.

Beyond Productivity: Measuring Augmented Human Capabilities

Advanced frameworks examine how AI enhances human potential:

• Cognitive extension metrics.

• Creativity enhancement measures.

• Decision quality under complexity.

• Knowledge accessibility improvements.

• Skill development acceleration.

Ethical and Responsible AI Measurement Frameworks

Responsible implementation requires ethical measurement:

• Fairness and bias detection metrics.

• Transparency and explainability measures.

• Privacy protection effectiveness.

• Value alignment assessment.

• Accountability and oversight metrics.

• Long-term impact evaluation.

Predictive Metrics for System Optimization

Forward-looking organizations implement anticipatory measures:

• Early warning indicators for performance degradation.

• Future enhancement prioritization frameworks.

• Capability gap identification metrics.

• Emerging risk detection.

• Opportunity recognition measures.

Phased Implementation Guide

To effectively translate these insights into actionable outcomes, we recommend a phased approach:

1. Planning and Stakeholder Engagement: Define Success: Collaborate with stakeholders (end users, technical teams, business leaders, and customers) to establish tailored success criteria for your specific workflows. Select Core Metrics: Prioritize a small set of strategic KPIs that align with your business objectives while planning to support diagnostic metrics.

2. Pilot Phase: Implement on a Small Scale: Roll out the measurement framework in one department or workflow area. Set Baselines: Use internal historical data and industry benchmarks to establish reference points. Feedback Loop: Collect both quantitative data and qualitative feedback to validate initial metrics.

3. Full-Scale Deployment: Integrate and Scale: Gradually expand the measurement framework across the organization, adjusting metrics as needed based on the pilot phase insights. Training and Change Management: Provide training sessions, create a measurement learning community, and establish clear communication channels to ensure adoption. Continuous Review: Set regular review cycles to refine metrics, ensuring they remain aligned with evolving business needs and technological capabilities.

Case Studies: Measurement in Action

Financial Services: Enterprise Risk Assessment Transformation

A global financial services firm transformed its risk assessment processes through a hybrid human-AI approach:

The organization began by defining success across multiple dimensions:

• 30% improvement in risk assessment accuracy.

• 40% reduction in processing time.

• 90% analyst satisfaction with new tools.

• Zero increase in regulatory findings.

They established a cross-functional metric development team including:

• Risk analysts (end users).

• Data scientists (AI developers).

• Compliance specialists.

• Customer representatives.

• Executive sponsors.

The resulting measurement framework integrated:

• Efficiency metrics: processing time, analyst capacity, exceptions handled per hour.

• Quality metrics: false positive/negative rates, risk score accuracy, documentation completeness.

• Adaptability metrics: new risk pattern identification speed, model improvement cycle time.

• Human-centered metrics: analyst satisfaction, skill development, decision confidence.

• Business impact metrics: regulatory finding reduction, customer onboarding improvements, risk-adjusted return.

Challenges Encountered and Solutions Developed

The implementation revealed several measurement challenges:

1. Challenge: Initial metrics overemphasized speed at the expense of quality. Solution: Rebalanced scorecard with weighted quality metrics and implemented guardrails against optimization for speed alone.

2. Challenge: Analysts felt evaluated unfairly when AI components failed. Solution: Separated performance attribution between human and AI components while maintaining joint outcome metrics.

3. Challenge: Data collection created significant overhead. Solution: Implemented passive collection methods and integrated measurement into existing workflows.

4. Challenge: Metric proliferation caused focus issues. Solution: Created tiered metric system with five primary KPIs and supporting diagnostic metrics

Key Insights and Lessons Learned

The case revealed several valuable insights:

• Metrics that encouraged appropriate trust calibration drove better outcomes than those focused solely on efficiency.

• Human-AI handoff quality emerged as a critical success factor not initially measured.

• Measurement transparency significantly improved adoption and trust.

• Comparative metrics (showing human-only, AI-only, and hybrid performance) provided valuable context.

• Regular metric review and evolution was essential as the system matured.

Business Outcomes Achieved

The measurement-driven approach delivered substantial results:

• 42% reduction in risk assessment processing time.

• 35% improvement in risk score accuracy.

• 67% reduction in regulatory findings.

• 28% increase in analyst job satisfaction.

• 15% improvement in customer onboarding completion rates.

• $18.5M annual cost savings through improved efficiency and reduced remediation.

The organization now uses its measurement framework to guide ongoing investments, prioritize enhancements, and demonstrate value to stakeholders.

Healthcare: Clinical Decision Support Measurement

A major healthcare system implemented an AI-assisted clinical decision support system for diagnosis and treatment recommendations, focusing on complex cases where standard protocols provided insufficient guidance.

Their measurement approach centered on:

Core Metrics:

• Diagnostic accuracy improvement (compared to pre-implementation baseline).

• Time to treatment decision.

• Provider adoption and adherence rates.

• Patient outcome improvements.

• Documentation quality and completeness.

Implementation Approach: The organization took a phased approach, beginning with high-volume but lower-risk conditions before expanding to more complex cases. Their measurement program differentiated between:

• System performance (technical accuracy, response time).

• Clinical workflow impact (integration smoothness, interruption patterns).

• Provider experience (cognitive load, satisfaction, trust calibration).

• Patient outcomes (both clinical and experience measures).

Key Findings:

1. Measurement revealed that provider trust dynamics significantly impacted system effectiveness.

2. Integration quality metrics proved more predictive of outcomes than raw AI accuracy.

3. Contextual factors influenced system performance more than anticipated.

4. Time-to-value varied dramatically across specialties and provider experience levels.

The organization established a "Measurement Learning Community" where clinical teams shared insights from their metrics and collaboratively developed measurement innovations. This community became instrumental in spreading effective measurement practices across the healthcare system.

Manufacturing: Predictive Maintenance Optimization

A global industrial manufacturer implemented an AI-assisted predictive maintenance system across twelve facilities, with a measurement approach focused on operational and financial outcomes.

Measurement Framework:

• Equipment uptime improvement.

• Maintenance labor optimization.

• Parts inventory reduction.

• Mean time between failures.

• False alarm reduction.

• Technician skill development.

Implementation Insights: The manufacturer discovered that facility teams were responding differently to AI recommendations based on local maintenance cultures. They developed a "recommendation response pattern" metric that tracked how different teams utilized AI inputs, which revealed that:

• Some teams were over-relying on AI recommendations without appropriate verification.

• Others were systematically ignoring certain classes of recommendations.

• The most successful teams developed structured collaboration patterns that combined AI insights with technician expertise.

By measuring these interaction patterns and correlating them with outcomes, the organization was able to identify best practices and develop targeted training that improved performance across all facilities.

These additional case studies demonstrate how measurement approaches must be tailored to industry context while maintaining core principles of comprehensive, balanced assessment focused on both technical and human factors.

Conclusion

The Strategic Importance of Robust Measurement

As AI-assisted workflows become central to competitive advantage, measurement approaches that capture their multidimensional nature are increasingly strategic. Organizations that develop sophisticated, balanced measurement frameworks gain several critical advantages:

• Clearer insight into performance drivers and limitations.

• More effective resource allocation decisions.

• Enhanced ability to communicate value to stakeholders.

• Stronger foundation for continuous improvement.

• Better alignment between technology investments and business outcomes.

Balancing Standardization with Customization

Effective measurement approaches balance standardization for comparison and customization for relevance:

• Core metric sets that apply across implementations.

• Industry-specific overlays that address sector needs.

• Organization-specific components that align with strategic priorities.

• Workflow-specific metrics that capture unique value drivers.

This balanced approach enables both internal and external benchmarking while ensuring measurement relevance to specific contexts.

Building a Measurement-Driven Improvement Culture

Beyond tools and frameworks, successful organizations foster cultures where measurement drives improvement:

• Leaders who model data-informed decision making.

• Safe environments for identifying and addressing shortcomings.

• Recognition for measurement-driven improvements.

• Investment in measurement capabilities and literacy.

• Clear connections between metrics and strategic priorities.

This cultural foundation transforms measurement from a reporting exercise to a catalyst for ongoing enhancement.

This comprehensive framework for metrics and measurement of AI-assisted workflows provides organizations with a structured approach to evaluating and optimizing their hybrid human-AI systems. By implementing these multidimensional measurement strategies, leaders can ensure their investments in AI technologies deliver maximum value while maintaining human-centered work environments.

Additional Resources

For organizations looking to deepen their understanding of AI-assisted workflow measurement, these resources provide valuable insights:

Key Research

• Davenport, T. H., & Ronanki, R. (2023). "The AI Advantage: How to Put the Artificial Intelligence Revolution to Work"

• Brynjolfsson, E., & McAfee, A. (2022). "Machine, Platform, Crowd: Harnessing Our Digital Future"

• Daugherty, P., & Wilson, H. J. (2023). "Human + Machine: Reimagining Work in the Age of AI"

Industry Standards and Frameworks

• ISO/IEC 25000 series (Software Quality Requirements and Evaluation)

• IEEE 7010-2020 (Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being)

• The Partnership on AI's ABOUT ML (Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles)

Practical Tools

• Google's People + AI Guidebook (Measurement section)

• Microsoft's Responsible AI Assessment Guide

• The Open Group AI Measurement Framework

Communities of Practice

• AI Measurement Consortium

• Association for the Advancement of Artificial Intelligence (AAAI)

• International Association for Human Resource Information Management (IHRIM) AI Working Group

These resources represent a starting point for organizations seeking to develop or enhance their AI-assisted workflow measurement capabilities.