Rethinking SLAs for AI-Based Software: Beyond Uptime and Refresh Rates
In the rapidly evolving landscape of enterprise software, artificial intelligence has fundamentally changed what we expect from our tools. Yet many organizations continue to structure their Service Level Agreements (SLAs) around metrics designed for traditional software systems.
As Artificial Intelligence (AI) transforms business operations, it's crucial to reimagine how we define, measure, and guarantee service quality.
The Limitations of Traditional SLAs
For decades, SaaS service agreements have centered around a familiar set of metrics traditionally originated from Infrastructure As A Service (IaaS) agreements :
System uptime (the classic "five nines" or 99.999% availability)
Response time (milliseconds to process a request)
Refresh/update frequency (how often systems receive patches)
Issue resolution timeframes (hours to fix bugs)
While these metrics remain relevant for infrastructure components, they fail to capture what actually matters in SaaS solutions — a real problem commonly referred to as Watermelon-Effect —: the Quality, Reliability, Performance and Trustworthiness of machine-generated outputs.
As one VP of Customer Success recently shared with me:
“Our AI chatbot was technically ‘up’ 100% of the time last quarter, but it was giving nonsensical answers to 35% of customer queries. During our meetings with this AI chatbot supplier, the signed SLA considered such results a success, but our business users considered it a failure. ”
A New Framework for AI Service Guarantees
A Forward-thinking mindsets are now looking at defining and implementing AI-specific SLAs that address the unique characteristics of these AI powered systems. Here's what that looks like in practice:
1. Output Quality Metrics
The heart of any AI system's value proposition lies in the quality of its outputs. Unlike traditional software that follows deterministic rules, AI systems make probabilistic judgments that require nuanced quality measurement.
The following are what we believe elementary metrics that establish clear standards for what constitutes "good" AI performance, creating accountability for the fundamental capabilities that drive business value.
Accuracy and Precision
Percentage of outputs matching human-verified ground truth
F1 scores for classification tasks
Error rates for numerical predictions
Hallucination rates for generative content
Consistency
Variance in outputs for identical inputs
Stability across different user segments
Performance consistency across edge cases
Relevance
Percentage of outputs directly addressing user queries
User satisfaction scoring
Task completion rate
2Performance Boundaries
2. Performance Boundaries
While traditional performance metrics remain somehow relevant, AI systems demand contextual understanding of performance expectations. Different AI tasks require different performance profiles, and SLAs must reflect these nuances. By establishing clear performance boundaries rather than one-size-fits-all metrics, organizations can ensure AI systems deliver appropriate responsiveness without unnecessary cost premiums. (Reader shall note that values depend on the industry and AI field application)
3. Continuous Improvement Guarantees
Unlike static software systems, AI solutions should become more effective over time through ongoing learning and optimization. This evolutionary capability represents a fundamental difference from traditional systems and requires specific contractual guarantees. By formalizing improvement expectations, organizations protect against model degradation and ensure their expected AI-generated outcomes mature alongside their clients' business needs.
Model Refresh Cycles
Regular retraining schedule
Performance improvement targets
Fine-tuning response timeframes
Feedback Integration
User feedback collection requirements
Timeframes for addressing systematic errors
Continuous learning benchmarks
Practical Steps for Implementing Value-Based AI SLAs
If you're transitioning to value-driven AI service agreements, consider these steps:
Baseline your expectations: Work with vendors to establish realistic performance metrics based on your specific use cases, and critically, quantify the current business cost/value of these processes.
Implement tiered performance standards with value alignment: Different AI applications require different standards with corresponding value metrics – a customer-facing chatbot might tie to satisfaction scores and retention rates, while an internal data analysis tool connects to decision quality and time savings.
Include human oversight provisions with cost accounting: Specify when and how human reviewers will audit AI outputs, and track the fully-loaded cost of this oversight to measure ROI.
Create feedback mechanisms with value capture: Establish formal channels for users to report problematic outputs, and implement systems to quantify the business impact of identified issues.
Implement value sharing mechanisms: Structure compensation models where vendors participate in created value, not just deliver functioning systems.
Plan for evolution with performance tiers: Include procedures for adjusting metrics as technology and expectations evolve, with corresponding value thresholds that trigger contract adjustments.
Value-Based KPIs: Tying AI Performance to Business Outcomes
The most sophisticated AI SLAs now directly connect technical metrics to business value creation. This approach ensures technology investments deliver measurable ROI rather than just meeting technical benchmarks.
Mapping AI Metrics to Business Value
Revenue Impact Metrics
Conversion rate improvements attributable to AI recommendations
Revenue per interaction for AI-assisted processes vs. traditional approaches
Customer lifetime value changes for AI-enhanced service paths
Efficiency Metrics
Cost reduction per automated decision
Time savings per AI-assisted workflow
Resource reallocation opportunities created
Innovation Acceleration
Time-to-market reduction for AI-assisted product development
Successful feature implementations driven by AI insights
Patent opportunities identified through AI analysis
The Future of AI Service Agreements
As AI capabilities continue to mature, expect SLAs to evolve toward:
More sophisticated Fairness guarantees across intersectional demographics
Explainability requirements for high-stakes decisions
Environmental impact considerations (carbon footprint per inference)
Cross-system Compatibility standards
Progressive Value-Sharing models based on documented business outcomes
The organizations succeeding with AI implementation are those treating these systems not as magical black boxes but as sophisticated tools requiring thoughtful governance. By reimagining SLAs around meaningful outcomes rather than simple availability metrics, they're ensuring AI delivers on its transformative potential.
Key Takeaways
Traditional uptime and refresh metrics remain necessary but insufficient for AI systems
Effective AI SLAs focus on Output Quality, Performance boundaries, Ethical guardrails, and Improvement cycles
Establish clear escalation and remediation process, including a rule-based system as fallback mechanism
The goal remains more strict alignment between technical performance and business outcomes
As you navigate AI vendor relationships, remember that the right metrics drive the right behaviours. With thoughtfully constructed SLAs, you can ensure your AI investments deliver measurable value while maintaining appropriate safeguards.
“By focusing on outcomes rather than just system availability, we’ve created meaningful accountability,” explains S., a CTO of a Canadian voip provider. “Our vendor is now aligned with our business goals, not just technical checkboxes.”
About the Author: Amine Ati is the Managing Director of Rivvalue, a consultancy firm specializing in SaaS strategy, customer experience, and value optimization. With deep expertise in improving customer experience, reducing Total Cost of Ownership (TCO) and maximizing ROI, he helps B2B organizations navigate the complexities of the SaaS decision journey, ensuring sustainable growth and measurable business impact.
