Wasting Millions? Start Measuring AI Success With 7 Metrics
Cash-Flows Meet Stubborn Data-Points: AI pulled in 46.4% of last year’s venture capital funding, yet proving return on investment remains elusive for many businesses implementing it.
Trust Drives AI Success: Trust issues contribute to poor AI returns. Distrust in AI leads to negative ROI, highlighting the importance of an organization's confidence in AI technology.
Rethinking AI with Google: Google suggests that AI's unique nature necessitates a re-evaluation of traditional ROI metrics, given its accessibility and result-transforming potential.
Darkness of Undefined Success: Businesses average 37 AI projects annually but lack clarity on success, as current metrics inadequately link AI initiatives to clear outcomes.
The Soft ROI Conundrum: Soft ROI metrics like innovation culture and competitive edge, though valuable, complicate clear quantification, challenging CTOs to develop comprehensive ROI frameworks.
AI is raking in cash: 46.4% of last year’s VC funding, to be exact. But even with money flowing in, the C-suite struggles to see how AI drives business transformation.
More than half of IT leaders say proving ROI is their biggest challenge, especially since RAND Corporation forecasts that 80% of AI projects will fail, burning billions in the process.
Stephen Mann, principal analyst and content director at ITSM.tools, points to a bigger issue: trust. “Teams that don’t trust AI are the ones to report negative ROI from their AI investments. It appears to be a chain where AI adoption and trust (or lack thereof) fuel each other,” he says. Stephen’s not the only one noticing this.
Google’s AI team has also pointed out that generative AI is unlike past tech. It’s more accessible, and both technical and non-technical teams can use it. This is why we must rethink how we measure AI performance, where traditional ROI frameworks often fall short.
Let’s take a closer look at why old approaches don’t work and discuss the seven metrics that matter when evaluating AI ROI.
AI ROI Measurement Challenges
Businesses are launching an average of 37 AI POCs, yet over 30% of CIOs admit they have no clear idea which ones succeed. They’re operating in the dark without robust metrics linking AI efforts to tangible results.
Isolating AI’s impact can be tricky because it’s often deployed alongside other tech or process changes, such as an AI-driven inventory system rolled out with a new store layout. Traditional A/B testing struggles when multiple factors (AI, marketing, redesigns) affect outcomes simultaneously.
Organizations also grapple with “soft ROI,” things like a culture of innovation, employee retention, competitive edge, and better risk management—valuable but hard to quantify. Factor in the time to train, refine, and maintain AI, and it’s clear why CTOs hesitate to build an ROI framework. The fear of short-term negative returns under investor pressure compounds the challenge.
7 Metrics to Measure the Success of AI Investments
Harvard's Michael Porter introduced a strategic triage that enterprises can use to assess their AI projects’ competitive advantage. His model is built on cost leadership, differentiation, and focus, which help organizations deliver highly specialized products and services in niche markets.
For CTOs, understanding how their AI projects align with these levers is critical to justifying investment and driving measurable results. Here are seven metrics and OKRs to define AI success:
1. Faster Time-to-Market (TTM)
Time to market is about getting your product or service to market faster, from initial concept to final deployment, by using AI to accelerate each step. The quicker you launch, the sooner you start earning revenue: If AI helps reduce the development time from 18 months to 12, that’s an extra 6 months of potential revenue. And that’s a huge win when it comes to boosting financial performance and gaining market share.
How to Measure TTM
- Concept-to-launch duration: Track how long it takes to go from idea approval to market launch.
- Iteration cycle time: Measure design, testing, and refinement speeds.
- Time-to-value ratio: Compare how quickly a product or feature starts generating returns.
- Resource efficiency index (REI): Evaluate person-hours saved or redeployed.
2. Process Throughput
Tracking process throughput is essential for measuring how efficiently tasks are processed within a given time. It reflects your system’s ability to handle workloads and impacts overall capacity and efficiency.
How to Measure Process Throughput
- Volume per time unit (VPTU): Track completed tasks or transactions pre- and post-AI.
- Cost per transaction: Analyze how AI impacts resource spending at higher volumes.
- Peak performance sustainability: Monitor how long the system maintains maximum throughput.
- Recovery time: Evaluate how quickly processes bounce back after disruptions.
3. Employee & Customer Experience
Every AI initiative ultimately serves one of two groups: employees or customers. Their retention, productivity, satisfaction, and engagement are the real indicators of success and whether AI gets greenlighted by the board and the C-suite.
For employees, AI should automate repetitive tasks and streamline workflows, giving them more time to focus on strategic and high-value work. AI can also optimize workload distribution – like when an AI-led management tool flags that employees are spending too much time in meetings and not enough on focused work.
On the customer side of projects, AI projects need to center on creating a frictionless experience. The payoff comes in the form of increased loyalty, longer customer lifetimes, and a steady flow of referrals – all critical components of long-term business growth. When these elements come together, the result is a positive ROI, with an organization that’s agile, customer-centric, and capable of empowering all stakeholders involved.
How to Measure Employee Experience (EX) to Determine AI Success
- Compare retention rates pre/post AI implementation
- Track retention in AI-augmented vs. non-augmented roles
- Monitor voluntary departure reasons
- Employee net promoter scores (eNPS)
- Time to productivity for new hires
How to Measure Customer Experience (CX)
- NPS and CSAT scores
- First contact resolution (FCR) and response time to customers
- Sentiment and emotion analysis on socials
- Customer retention and churn rates
- Incremental changes in customer lifetime value pre-/post-AI
4. Technical Debt Impact (TDI)
Technical Debt Impact (TDI) measures how accumulated shortcuts and inefficiencies affect an AI system’s performance, maintainability, and scalability or, conversely, how AI itself adds to an enterprise’s growing technical debt.
It’s a critical metric, given that tech debt already consumes 30% of IT budgets and 20% of human resources, and with AI, it’s only getting worse.
In 2025, over 50% of technology leaders expect their tech debt to reach moderate to severe levels, with AI among the most significant contributors. Measuring TDI will enable CTOs to cut these hidden costs by justifying model refactoring, optimizing resource allocation, and striking the right balance between maintaining legacy systems and delivering new AI-driven capabilities.
How to Measure Technical Debt Impact
- Data pipeline latency: Assess delays in data ingestion and preprocessing.
- Time-to-production for model updates: Check how quickly models adapt to new data.
- Inference cost per prediction: Compute resource costs for each AI inference.
- Bug fix rate: Monitor how often data or model issues emerge and require fixes.
5. Data Asset Utilization
AI is only as good as the data it’s built on. Whether you’re working on predictive intelligence or autonomous systems, data asset utilization tells you how effectively AI uses available data.
The stronger the utilization, the greater the AI’s reach, breaking down silos, mitigating dataset biases, and delivering enterprise-wide intelligence with data integration.
If models aren’t fully leveraging high-value datasets or, worse, are overfitting on redundant ones, it directly impacts accuracy, strategic relevance, and, ultimately, the success of your AI project.
How to Measure Data Asset Utilization
- Data access frequency: Measure how often AI models tap into each dataset.
- Data processing latency: Track retrieval and processing times in AI workflows.
- Storage vs. active utilization ratio: Compare stored data with actively used data.
- Feature usage rate & data lineage: Identify which features drive performance and ensure data quality.
6. Error Rate Reduction (ERR)
Getting data right is only half the battle. The true challenge is ensuring that data is clean and structured and delivering precise, updated, and unbiased results at scale. Error rate reduction is key to this process. It reflects how well AI evolves by reducing errors and quality issues and signals the model’s ability to learn and adapt over time.
Fewer errors in AI models mean less need for IT teams to step in, making it easier for AI to scale across departments as it serves domain-specific needs. Monitoring this progress ensures that AI doesn’t degrade with growth, giving CTOs the confidence that AI is future-proof, aligned with business goals, and can consistently deliver customer satisfaction and operational efficiency.
How to Measure Error Rate Reduction?
- Baseline vs. current error rate: Compare initial vs. present levels of inaccuracy.
- False positive rate: Pinpoint incorrect positive classifications.
- Error distribution: Identify biases or weak spots across data segments.
- Drift in error rate: Ensure models don’t degrade post-deployment.
- Corrective iteration efficiency: Measure how quickly retraining cuts errors.
7. Scalability Coefficient
The scalability coefficient is the difference between an AI initiative that thrives and one that collapses under its own weight and becomes a money pit. It measures how well an AI solution can expand across an organization without skyrocketing costs or complexity.
AI models may start small, but as adoption grows, so do the demands on networking, storage, and processing power. What starts as a hybrid cloud setup can quickly become a costly overhaul—think cold storage expansions, on-prem hardware upgrades, and rising per-deployment costs. Before you know it, AI’s potential ROI is offset by infrastructure sprawl.
How to Measure Scalability Coefficient
Scalability has to be measured across both technical and business dimensions. Here’s what CTOs should be tracking:
- Computational efficiency (FLOPS/watt) and training time scalability
- Inference latency and cost per inference
- Infrastructure overhead and time to value for deployments
- Ensuring growth doesn’t skyrocket costs helps AI remain a growth driver, not a cost sink.
Growth without a scalable foundation leads to inefficiencies, making AI more of a cost center than a growth driver.
Future-Proof Your AI Investments
AI-led disruption isn’t slowing down, and CTOs must prioritize agility to make a compelling business case for AI. That means shifting from pilot projects to enterprise-wide adoption, where agentic AI drives cross-functional impact.
Secure buy-in by aligning strategic goals with execution via scalable infrastructure, disciplined MLOps, and governance frameworks that enable growth without sacrificing control.
Use these seven key metrics as a structured roadmap to justify AI investment, monitor adoption, and optimize performance, potentially delivering up to 3.5X returns.
Until then, subscribe to The CTO Club's newsletter for more growth plans about AI and CTO-driven insights.
