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INSIGHTS
8 min read
Published on 08/20/2024
Last updated on 08/20/2024
Measuring GenAI adoption in the enterprise: Key performance metrics
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Generative artificial intelligence (GenAI) is still relatively new to most enterprises. As a result, companies might not be familiar with AI performance metrics and how to track them. Moreover, issues like neural network opacity and shadow AI can make measuring model reliability or workforce adoption difficult.
However, as with any new enterprise technology or process, business leaders must monitor the effects of AI initiatives with reliable metrics. This is key for aligning GenAI with a business’s values and goals and maximizing its return on investment (ROI). Many enterprises have already invested in GenAI innovations, yet measuring their impact is a common challenge. According to Cisco’s AI Readiness Index, only 41% of business and IT leaders involved in AI integration have defined performance standards.
In the race to innovate, focusing on technology alone isn’t enough to stay competitive. Capturing the right GenAI evaluation metrics is necessary for building agile solutions and optimizing value in this rapidly evolving field.
Why measuring GenAI adoption drives value
By tracking GenAI usage, an enterprise can understand how well the technology delivers on strategic goals. Key performance indicators (KPIs) offer an objective source of truth and make it easier for organizations to communicate AI’s impact to users, leadership, and stakeholders. KPIs are crucial for monitoring ROI, aligning AI initiatives to business objectives, and adapting to market demands. A global executive AI survey by the Massachusetts Institute of Technology (MIT) found that “7 out of 10 respondents agree that enhancing KPIs — not just improving performance — is critical to their business success.”
While leveraging business metrics isn’t new, KPIs are fundamental to GenAI success. GenAI performance indicators, such as accuracy rate, are valuable sources of feedback for diagnosing underperformance and refining model behavior. Frequent evaluation and refinement create a more robust and agile model, iteratively improving output reliability. This is especially crucial for mitigating issues like bias, which models can magnify or reinforce over time, or data drift, which can lead to inaccurate outputs as training data becomes outdated.
Strategies and AI performance metrics for measuring GenAI adoption
Organizations often struggle to track GenAI usage because they’re unsure which metrics are relevant to their goals. There are four main areas enterprises can focus on, which cover business and workforce impacts as well as the reliability of the technology itself.
Metrics for GenAI workforce adoption and user experience
GenAI applications that meet user needs are more likely to be adopted and support business objectives. By measuring how many users are actively using GenAI tools, you can gauge the extent of GenAI integration, understand how tools are actually used in inference, and identify functional gaps. These metrics also indicate workforce buy-in and user competence, which can inform AI upskilling and education programs.
Apply the following AI metrics to evaluate how GenAI tools are used across the enterprise:
- Adoption rate:
- Active user volumes over a specific period
- Active user volumes per tool
- Shadow AI adoption rates
- Session behavior:
- Average session length
- Query volumes per session
- Character or word count per user query
- Abandonment rate (sessions ended before resolving tasks)
- User experience:
- Qualitative user feedback from surveys
- Employee sentiment
- User skill level:
- Prompt engineering efficacy
- Training evaluation scores
Metrics for business alignment and outcomes
GenAI can support a variety of enterprise use cases, such as creating marketing content, writing code, or generating reports and legal documents. Measuring outcomes for these types of applications helps you evaluate whether GenAI tools directly support key business targets and goals.
In terms of specific metrics, track those relevant to the GenAI-supported department or process in question. For example, the following AI measurements can help assess business impacts in development, finance, customer experience, or marketing use cases:
- Coding and development
- Volume of AI-generated code
- Reduction in code errors and bugs
- Code quality improvements
- Finance
- Efficiency gains in financial analysis and report generation
- Improvements in budgeting and forecasting accuracy
- Overall ROI for GenAI initiatives
- Customer experience
- Support resolution times
- NPS
- Conversion rates
- Cost per customer interaction
- Marketing
- Content generation productivity
- Website traffic volumes
- SEO performance
- Email open rates
Metrics for model reliability and trustworthiness
Models must instill confidence if enterprises are to meet adoption targets. Continuously evaluating GenAI models for output accuracy and issues like bias, data disclosure, and AI hallucinations can help you assure users and stakeholders that models are behaving as expected. Even if models perform effectively on training data, quality benchmarks give developers a clearer view of how GenAI tools function against real-world queries. Measuring model safety and transparency is also necessary for building reliable and trustworthy AI.
Output accuracy and quality
There are several measurements your enterprise can use to evaluate GenAI accuracy. The baseline metric is expressed as a percentage, representing the proportion of a model’s correct predictions. Experts cite the human baseline is about 80%, so an accuracy rate of 80% or higher would be considered strong in comparison. Error rates capture the inverse, indicating the total percentage of incorrect predictions. Loss functions calculate the margin of error, a key metric developers use to adjust model parameters for enhanced performance.
Consider using a quality index framework to assess a model’s overall reliability. For instance, the Bilingual Evaluation Understudy (BLEU) algorithm is commonly used to evaluate translation performance. In 2019, researchers developed the Super General Language Understanding Evaluation (SuperGLUE), a benchmark designed to score models based on their average performance across different tasks.
Safety
GenAI models can expose users to hazards such as hate speech, criminal activity, or sensitive data disclosure. This can occur if models respond to user prompts requesting private information or instructions on how to commit financial fraud. To mitigate harm, routinely test AI system trustworthiness against unsafe prompts. You can develop safety benchmarks independently or use third-party solutions like the MLCommons AI Safety Benchmarks.
Transparency
Due to their opaque algorithms and internal reasoning processes, it can be difficult to understand how GenAI tools make decisions or use data. This is a barrier to building trustworthy AI since model interpretability is often necessary to comply with data privacy regulations and diagnose performance issues. There’s no standardized way to measure model transparency, but you can use a guideline like the Foundation Model Transparency Index as a starting point. The Index, developed by AI researchers, scores models on 100 transparency indicators, from a model’s environmental footprint to the clarity of its data lineage.
Metrics for end-to-end lifecycle performance
Evaluating GenAI performance doesn’t end with the model itself. Transformation involves an entire lifecycle of processes, including data governance, AI security, training and deployment infrastructure, and continuous feedback and development. Enterprises can measure performance in this complex landscape by gathering metrics in the following areas.
Data quality and management
Assess the quality of your GenAI training data. For example, calculate the proportion of enterprise data that is accurate, relevant, complete, discoverable, and has sufficient data provenance. Develop standards for evaluating diversity within datasets, a subjective benchmark that depends on your use case and values. You can also determine compliance with enterprise data policies and regulations, such as the General Data Protection Regulation (GDPR).
Security
Closely track GenAI security incident rates, including the number of malicious prompts, data disclosures, or unauthorized access attempts over a set period. With help from ethical hacking teams, you can also test AI systems and document vulnerabilities and anomalies.
Compute demands and efficiency
GenAI development and inference are resource-intensive, requiring significant investments in computing infrastructure and power consumption. Save costs and streamline development by tracking resource consumption and efficiency throughout the GenAI lifecycle. This can facilitate more informed decisions when upgrading hardware, partnering with cloud service providers, or developing machine learning operations (MLOps) strategies. These metrics are also valuable for ensuring alignment with ethical AI frameworks or environmental initiatives, which often outline sustainability targets. Consider measuring:
- Network latency
- GenAI emissions (using tools like Code Carbon or ML CO2 Impact)
- Computational throughput
Kickstart GenAI development with AI performance metrics
GenAI solutions are unlocking new capabilities for a variety of enterprise applications. By harnessing GenAI adoption metrics, business leaders gain a valuable opportunity to optimize their investments. Insights surrounding user behaviors, business impacts, model performance, and the end-to-end AI lifecycle are all necessary for quantifying GenAI’s enterprise value and identifying areas for improvement.
Because there’s no universal guide to measuring GenAI adoption, organizations must establish KPIs and processes best suited to their goals and use cases. Regardless of which approach you use, the most effective strategy is the one that starts now. The earlier you receive feedback, the more efficiently you can refine your enterprise’s GenAI solutions and gain a competitive advantage.
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