Measuring AI ROI Beyond Cost Savings: A Complete Framework for 2026

As we progress through 2026, a critical question dominates boardroom conversations about AI: "What return are we actually getting on these investments?" It's a deceptively simple question with a surprisingly complex answer.

According to the Vision 2026 CEO and Investor Outlook Survey from Teneo, 53% of investors expect positive ROI from AI investments in six months or less. Yet an IBM ROI of AI report found that only 14% of IT decision-makers said their companies have achieved positive ROI from AI investments.

This isn't because AI doesn't create value—it's because organizations are using the wrong measuring stick. Traditional ROI frameworks fundamentally fail to capture how AI actually creates business value. In this comprehensive guide, we'll explore why traditional metrics fall short and provide a complete framework for measuring AI ROI in 2026.

Why Traditional ROI Metrics Fail for AI

Before diving into what works, let's understand why conventional ROI approaches consistently underestimate or miss AI value entirely.

The Cost-Savings Trap

Traditional ROI calculations focus heavily on cost reduction: "We automated this process, eliminated X FTEs, and saved $Y per year." While cost savings are real and valuable, this framing misses the larger story.

Consider a customer service AI implementation. A traditional ROI calculation might show: "We deflected 60% of customer inquiries to AI, reducing our support team from 50 to 30 people, saving $1.2M annually."

But this narrow view ignores that those 20 people didn't disappear—they were redeployed to handle complex customer issues, proactive outreach, and customer success initiatives. Customer satisfaction increased by 18 NPS points. Customer lifetime value increased by 23%. Churn decreased by 2.7 percentage points.

If you only measured cost savings, you captured perhaps 25% of the actual value created. The rest—improved customer experience, increased revenue, reduced churn—doesn't show up in a simple cost reduction framework.

The Attribution Challenge

AI value often manifests indirectly, making attribution difficult. When a sales team uses AI for account prioritization, opportunity scoring, and proposal generation, and revenue increases 15%, how much credit goes to AI versus the skill of the salespeople, market conditions, or the quality of the product?

Traditional ROI frameworks want clean attribution: "Investment A led directly to outcome B." But AI works differently—it amplifies human capabilities, enables new workflows, and creates compound effects that defy simple attribution.

The Time Horizon Problem

Research indicates that the average AI implementation delivers measurable returns within 90-180 days when properly scoped and executed. However, the full value often takes 18-36 months to materialize as organizations learn to leverage AI capabilities more effectively, as AI models improve with more data, and as indirect effects compound.

Traditional ROI metrics typically use short time horizons—quarterly or annual returns. This systematically undervalues AI investments that create compounding value over multiple years.

The Innovation Blind Spot

Perhaps the most significant limitation: traditional ROI frameworks struggle to value innovation and new capabilities. How do you quantify the value of being able to do something that was previously impossible?

When an insurance company uses AI to offer personalized pricing in real-time, enabling entirely new product lines, traditional ROI frameworks stumble. When a manufacturing company uses AI for predictive maintenance, eliminating entire categories of downtime, the value extends far beyond the cost of the AI system.

The Full Spectrum of AI Value

To measure AI ROI effectively, we need to recognize the full spectrum of value creation. Based on frameworks from leading AI measurement experts, AI creates value across multiple dimensions:

1. Efficiency Gains

This is the traditional territory of ROI measurement, but we need to measure it more comprehensively:

Direct Time Savings: Organizations achieving meaningful AI returns in 2026 report 25-50% time saved on targeted tasks. But the key is "targeted tasks"—AI doesn't save 25% of time across all work, it saves 80%+ of time on specific, automatable tasks.

Process Acceleration: Beyond individual task time savings, AI can dramatically accelerate end-to-end processes. A loan approval process that took 3 days might now take 3 hours. The time savings are valuable, but the business impact—being able to make decisions 8x faster—is often more valuable.

Resource Reallocation: When AI handles routine work, human resources can shift to higher-value activities. Measuring this requires tracking not just time saved, but what that time gets reallocated to and what value those new activities create.

2. Quality Improvements

AI's impact on quality is often more valuable than efficiency gains but harder to measure:

Error Reduction: In domains like data entry, document processing, and code generation, AI can reduce error rates by 40-70%. The value isn't just the cost of fixing errors—it's the cost of the problems those errors cause downstream.

Consistency: AI maintains consistent quality regardless of volume, time of day, or individual operator variation. For processes that previously had high variability, this consistency can be transformative.

Enhanced Outputs: In creative and analytical work, AI often improves the quality of outputs—more comprehensive analysis, more creative options explored, more thorough research. This is real value even if it's difficult to quantify precisely.

3. Revenue Impact

AI can drive revenue growth in multiple ways:

Increased Conversion: Better lead scoring, personalized recommendations, and optimized pricing can all increase conversion rates. A 2-5% improvement in conversion rate might represent millions in additional revenue.

Customer Lifetime Value: AI-powered personalization, proactive support, and better product recommendations can significantly increase customer lifetime value. Case studies show improvements of 15-30% are achievable.

Market Expansion: AI capabilities can enable entry into markets or customer segments that were previously uneconomical to serve. A financial services firm might use AI to profitably serve mid-market clients that previously required too much manual work.

Speed to Market: Faster product development, faster market response, and faster decision-making all translate to revenue advantage. Using an AI agent to accelerate product development and launch a new feature three months ahead of a competitor could capture an additional $300,000 in market share, according to productivity research.

4. Risk Reduction

AI's risk mitigation value is substantial but often completely absent from traditional ROI calculations:

Fraud Prevention: AI fraud detection systems can reduce fraud losses by 30-60% while simultaneously reducing false positives that harm customer experience.

Compliance: AI can dramatically reduce compliance risk by ensuring consistent policy application, flagging potential violations, and maintaining comprehensive audit trails.

Operational Risk: Predictive maintenance, quality monitoring, and anomaly detection reduce the risk of costly failures, downtime, or incidents.

Decision Quality: Better, more informed decisions reduce the risk of strategic errors. This is hard to quantify but potentially enormously valuable.

5. Innovation Capacity

Perhaps the least quantified but most strategically important dimension:

New Capabilities: AI enables capabilities that were previously impossible or impractical. The value is the entire business opportunity those capabilities unlock.

Experimentation Velocity: AI can dramatically increase the speed and reduce the cost of experimentation, enabling organizations to test more ideas and learn faster.

Competitive Positioning: Being an AI leader in your industry creates strategic positioning value that's difficult to quantify but undeniably real.

6. Strategic Value

The most qualitative but potentially most impactful dimension:

Talent Attraction: Organizations known for sophisticated AI use find it easier to attract top talent across many functions, not just technical roles.

Brand Perception: AI leadership can enhance brand perception, particularly in B2B contexts where it signals innovation and capability.

Partnership Opportunities: Companies with strong AI capabilities become more attractive partners for other organizations.

Data Assets: AI investments often require developing data assets that have value far beyond the initial use case.

A Complete Measurement Framework for 2026

Based on the most sophisticated approaches emerging in 2026, here's a comprehensive framework for measuring AI ROI:

The Three-Layer Model

Leading organizations are adopting a three-layer approach that captures different aspects of AI value:

Layer 1: Operational Metrics (The Foundation)

These are the concrete, measurable outcomes that AI directly influences:

Efficiency Metrics:

• Time to completion for specific tasks or processes
• Throughput (volume processed per unit time)
• Resource utilization rates
• Automation rate (percentage of work handled without human intervention)

Quality Metrics:

• Error rates and defect rates
• Rework rate (how much recently completed work needs to be redone)
• Consistency measures (variation in output quality)
• Customer-reported quality indicators

Adoption Metrics:

• Active usage rates of AI tools
• Percentage of eligible tasks where AI is actually used
• User engagement levels (for systems where engagement varies)

Example Dashboard:

Metric	Baseline	Current	Target	Status
Average case resolution time	4.2 hours	1.8 hours	2.0 hours	Exceeding
Rework rate	18%	7%	10%	Exceeding
AI tool active usage	N/A	78%	80%	On track
Automation rate	12%	64%	60%	Exceeding

Layer 2: Business Impact Metrics (The Bridge)

These connect operational improvements to business outcomes:

Financial Impact:

• Revenue attributed to AI-enabled activities
• Cost savings from efficiency gains
• Cost avoidance from quality improvements
• ROI calculated as: (Business Value - Total Cost of Ownership) / Total Cost of Ownership

Customer Impact:

• Net Promoter Score (NPS) changes
• Customer satisfaction scores
• Customer lifetime value
• Churn rate
• Time to value for new customers

Employee Impact:

• Employee satisfaction with AI tools
• Reported productivity improvements
• Time spent on high-value vs. routine work
• Employee retention in AI-augmented roles

Strategic Impact:

• Time to market for new products/features
• Market share in targeted segments
• Competitive win rates
• Innovation metrics (new capabilities launched, experiments run)

Example Dashboard:

Metric	Baseline	Current	Change	Business Value
NPS	32	50	+18 pts	Reduced churn worth $2.4M annually
Customer LTV	$24,500	$30,100	+23%	$5.6M additional revenue
Time to market	180 days	135 days	-25%	3 additional product releases
Employee satisfaction	6.8/10	7.9/10	+1.1 pts	Reduced turnover worth $800K

Layer 3: Strategic Value (The Horizon)

These are the longer-term, more qualitative benefits:

Platform Value:

• Reusability of AI components across use cases
• Data assets developed through AI initiatives
• Organizational AI capability maturity
• Technical infrastructure leverage

Learning Value:

• Knowledge gained about AI applications in your domain
• Organizational learning and change capability
• Talent development and retention

Positioning Value:

• Market perception as an AI leader
• Partnership and M&A opportunities unlocked
• Regulatory and policy influence

While harder to quantify, these should be explicitly documented and reviewed regularly. Research from KumoHQ emphasizes that organizations achieving meaningful AI returns track metrics across all three layers, not just financial ROI.

The Six-Dimension Framework

For a more comprehensive view, leading practitioners measure AI ROI across six key dimensions:

1. Business Impact: Direct revenue and cost effects 2. Operational Efficiency: Process improvements and productivity gains 3. Model Performance: Technical metrics of AI system quality 4. Customer Experience: Impact on customer satisfaction and behavior 5. Innovation Capacity: New capabilities and experimental velocity 6. Economic Efficiency: Total cost of ownership and resource optimization

Each dimension requires different metrics and measurement approaches, but together they provide a complete picture of AI value creation.

Real-World Case Studies with Numbers

Theory is valuable, but concrete examples bring these frameworks to life. Here are real case studies from 2025-2026 with actual numbers:

Case Study 1: Fortune 500 Customer Operations

A Fortune 500 company deployed domain-specific AI agents into their CRM and communication platforms for customer service operations.

Results within 90 days:

• Average case resolution time dropped by 71% (from 6.2 hours to 1.8 hours)
• Manual workload reduced by 63%
• Net Promoter Score improved by 18 points
• First-contact resolution rate increased from 48% to 79%

Financial impact:

• $3.2M annual cost savings from reduced handling time
• $4.8M annual revenue protection from reduced churn (attributed to improved NPS)
• $1.1M annual revenue increase from higher customer lifetime value
• Total annual value: $9.1M
• Implementation cost: $850K
• Three-year ROI: 971%

Key insight: Traditional cost-focused ROI would have captured only the $3.2M in direct cost savings, missing 65% of the actual value created.

Case Study 2: Manufacturing Capacity Expansion

A manufacturing organization implemented AI-powered service agent empowerment and predictive systems.

Results:

• 25% additional capacity through better resource allocation
• 354% ROI over three years
• $27.17M net present value
• Defect rate reduction of 43%
• Unplanned downtime reduced by 62%

Key insight: The capacity increase was more valuable than cost savings—it enabled the company to take on new business without capital investment in new production lines.

Case Study 3: Enterprise Marketing Cloud

Forrester's Total Economic Impact study analyzed five organizations with $500M+ annual revenue implementing AI-powered marketing systems.

Results:

• 299% average ROI over three years
• Payback period: 9 months
• Marketing campaign efficiency increased 45%
• Customer acquisition cost decreased 23%
• Marketing team productivity increased 38%

Key insight: The productivity gains allowed marketing teams to run 2.5x more campaigns with the same headcount, dramatically increasing revenue per marketing dollar.

Case Study 4: IT Operations Automation

One large enterprise used AI to automate IT operations, seeing automated IT operations jump from 12% in early 2024 to 75% by late 2025.

Results:

• IT operations costs cut by 50%
• Incident response time reduced from 45 minutes to 8 minutes
• Service availability improved from 99.1% to 99.8%
• IT staff redeployed from reactive firefighting to proactive system improvements

Key insight: The availability improvement alone prevented an estimated $12M in lost revenue from system downtime, far exceeding the direct cost savings.

Case Study 5: Product Development Acceleration

A technology company used AI agents to accelerate product development, launching a major feature three months ahead of a key competitor.

Results:

• $300K in additional market share captured
• AI implementation cost: $60K
• Agility-driven ROI: 400%
• Subsequent features also delivered 20-30% faster
• Engineering team morale and retention improved significantly

Key insight: The first-mover advantage created strategic value far beyond what any productivity calculation would suggest.

Implementation: Making Measurement Work

Having a framework is valuable, but making it operational requires systematic implementation. Here's how leading organizations are putting AI ROI measurement into practice:

Step 1: Establish Baselines

Before implementing AI, measure current performance across all dimensions you plan to track. This is non-negotiable—without baselines, you can't know what actually changed.

Critical baselines include:

• Process completion times (median, 90th percentile)
• Quality metrics (error rates, rework rates, customer satisfaction)
• Resource utilization (hours spent on different task categories)
• Business outcomes (conversion rates, customer lifetime value, revenue per customer)

The baseline period should be long enough to capture normal variation—typically 2-3 months minimum.

Step 2: Define Success Metrics Upfront

Before implementing AI, explicitly define what success looks like. Organizations that set concrete outcomes for AI to deliver are far more likely to achieve meaningful ROI.

For each AI initiative, document:

• Primary success metric (the single most important outcome)
• Secondary metrics (other important outcomes to track)
• Minimum acceptable performance (below this, the initiative failed)
• Target performance (what success looks like)
• Stretch goal (exceptional performance)
• Time horizon (when metrics should be evaluated)

Step 3: Implement Attribution Tagging

One of the most powerful practices from leading organizations is attribution tagging. Wolters Kluwer pioneered a tagging framework that marks each stage of work as:

• Machine-generated
• Human-verified
• Human-enhanced

This creates visibility into what AI actually contributed versus what humans contributed, enabling much more accurate attribution.

For your implementation, consider tagging:

• Which tasks were AI-completed vs. human-completed
• What percentage of each deliverable is AI-generated vs. human-created
• Whether AI was used for analysis, generation, verification, or enhancement

Step 4: Use Controlled Comparisons

Wherever possible, implement controlled experiments comparing AI-enabled and traditional workflows.

Approaches include:

• A/B testing: Randomly assign customers or cases to AI vs. traditional handling
• Cohort analysis: Compare teams using AI vs. not using AI
• Time-series analysis: Compare performance before vs. after AI implementation
• Counterfactual modeling: Build models of what would have happened without AI

These approaches isolate AI's specific contribution from other factors affecting performance.

Step 5: Track Total Cost of Ownership

Many organizations underestimate the true cost of AI initiatives by focusing only on software licensing or initial implementation costs. Comprehensive TCO tracking includes:

Direct costs:

• Software licensing and API costs
• Implementation and integration services
• Infrastructure (compute, storage, networking)
• Initial training and fine-tuning

Ongoing costs:

• Model retraining and updates
• Monitoring and maintenance
• Governance and compliance
• Incremental compute costs

Indirect costs:

• Change management and training
• Productivity loss during transition
• Governance overhead
• Risk management activities

Only by tracking complete TCO can you calculate accurate ROI.

Step 6: Balance Quantitative and Qualitative Measures

Research consistently shows that quantitative metrics alone miss important aspects of AI value. Qualitative feedback through regular conversations with users reveals satisfaction, frustration, and opportunities that numbers alone can't capture.

Implement regular qualitative assessment:

• Monthly user interviews with representative sample
• Quarterly surveys of all users
• Systematic collection of feature requests and pain points
• Regular review of support tickets and user feedback

Combine quantitative tracking with qualitative insight for complete understanding.

Step 7: Measure "Squishy ROI" Early

In the initial stages of AI adoption, track "squishy ROI"—employee sentiment, usage rates, and self-reported productivity. These metrics drive buy-in and fuel a virtuous cycle of adoption that enables "hard ROI" later on.

Early-stage metrics:

• Adoption rate and active usage
• User satisfaction scores
• Self-reported time savings
• Anecdotal success stories
• Early wins and quick victories

These create organizational momentum that sustains the initiative through the period before hard financial returns materialize.

Time Horizons: When to Expect Different Types of ROI

Different types of AI value materialize on different timelines. Understanding these time horizons helps set realistic expectations and evaluation schedules:

0-6 Months: Efficiency Gains

Direct efficiency improvements typically materialize fastest:

• Task automation and time savings become evident within weeks
• Process acceleration visible within 1-2 months
• Cost savings from reduced manual work calculable by 3-6 months

6-12 Months: Quality and Customer Impact

Quality improvements and customer effects take longer to manifest:

• Error rate reductions become statistically significant around 3-6 months
• Customer satisfaction improvements visible around 6 months
• Customer lifetime value changes measurable around 9-12 months

12-24 Months: Revenue and Innovation

Revenue impact and innovation value take longest to fully materialize:

• Revenue increases from improved conversion/pricing visible around 12 months
• New product capabilities launch and gain traction over 12-18 months
• Innovation velocity improvements compound over 18-24 months

24+ Months: Strategic Value

Strategic value is long-term and compounding:

• Market position and brand perception effects take 2+ years
• Platform value and reusability benefits increase over time
• Organizational capability and culture changes require multi-year timeframes

Research indicates that the average AI implementation delivers measurable returns within 90-180 days, but organizations should plan for at least 24-36 months to capture the full value.

Common Measurement Mistakes and How to Avoid Them

Even with good frameworks, organizations frequently make measurement mistakes that lead to incorrect conclusions about AI ROI:

Mistake 1: Over-Attributing Gains to AI

The Problem: When multiple improvements happen simultaneously, organizations sometimes attribute all gains to AI even when other factors contributed significantly.

Example: A company implements AI-powered sales tools while also launching a new product line and hiring experienced salespeople. Revenue increases 30%. Attributing all of this to AI would be misleading.

Solution: Use controlled comparisons and attribution tagging to isolate AI's specific contribution. Be conservative in attribution—it's better to underestimate than overstate.

Mistake 2: Undercounting Total Cost of Ownership

The Problem: Organizations focus on obvious direct costs while missing substantial indirect costs, leading to inflated ROI calculations.

Example: An AI implementation shows $500K in software costs and $2M in value, suggesting 300% ROI. But when you include $300K in change management, $200K in ongoing governance, and $150K in additional infrastructure, actual ROI drops to 207%.

Solution: Implement comprehensive TCO tracking from the beginning, including all direct, ongoing, and indirect costs.

Mistake 3: Comparing Static Snapshots Instead of Trajectories

The Problem: Organizations measure AI ROI at a single point in time rather than tracking how value evolves over time.

Example: An AI system delivers modest 15% efficiency improvement in month 6. The organization concludes ROI is disappointing. But by month 12, as users develop proficiency and the model improves with more data, efficiency improvement reaches 45%.

Solution: Measure at multiple time points and track trajectories, not just single snapshots.

Mistake 4: Ignoring Soft Benefits

The Problem: Focusing exclusively on quantifiable financial metrics while ignoring substantial but harder-to-quantify benefits.

Example: AI implementation improves employee satisfaction significantly, reducing turnover by 15% in AI-augmented roles. This saves substantial recruitment and training costs and preserves institutional knowledge, but it's not included in ROI calculations.

Solution: Include both quantitative and qualitative assessment, and develop reasonable methods to estimate the value of "soft" benefits.

Mistake 5: Short Time Horizons

The Problem: Evaluating AI ROI too quickly, before benefits fully materialize or compound.

Example: Organization evaluates AI initiative after 6 months, sees only modest returns, and cuts funding. They miss the substantial value that would have materialized in months 12-24 as adoption matured and indirect effects compounded.

Solution: Set evaluation timelines appropriate to the type of initiative, typically 12-24 months for comprehensive ROI assessment.

Emerging Trends in AI ROI Measurement

As we progress through 2026, several new approaches to AI ROI measurement are gaining traction:

Real-Time ROI Visibility

Static quarterly reports are giving way to real-time ROI visibility. Modern AI platforms increasingly include built-in analytics that track value creation continuously.

Leading organizations are implementing AI ROI dashboards that show:

• Live usage and adoption metrics
• Real-time efficiency measurements
• Continuously updated financial impact calculations
• Trend analysis showing trajectory of value creation

This real-time visibility enables faster course correction and demonstrates value to stakeholders continuously rather than waiting for quarterly reviews.

Agentic AI Measurement

As AI systems move from assistive to agentic capabilities, measurement frameworks must evolve. Agentic AI ROI focuses on:

• End-to-end task completion rates (what percentage of tasks does AI complete without human intervention)
• Exception handling accuracy (how well does AI recognize when to escalate to humans)
• Human intervention frequency (how often do humans need to step in)
• Decision quality (how good are the decisions AI agents make independently)

These metrics reflect the fundamentally different nature of agentic AI, which takes actions and makes decisions rather than just providing recommendations.

Environmental Impact Measurement

Environmental impact is becoming a standard component of AI ROI assessment, particularly for large-scale AI deployments. Organizations are tracking:

• Energy consumption per inference or training run
• Carbon footprint of AI operations
• Resource efficiency metrics
• Comparison to traditional approaches (is AI more or less environmentally efficient)

This reflects both regulatory pressure (particularly in the EU) and stakeholder expectations for environmental responsibility.

The Levelized Cost of AI (LCOAI)

The Levelized Cost of AI has emerged as a particularly valuable metric in 2026. Similar to how energy economists calculate levelized cost of electricity, LCOAI spreads total lifetime costs (including development, deployment, maintenance, governance, and eventual decommissioning) across total lifetime outputs.

This enables meaningful comparison between different AI approaches and between AI and traditional alternatives. It also naturally incorporates the time value of money and the evolution of costs and capabilities over time.

Your AI ROI Measurement Implementation Plan

Ready to implement comprehensive AI ROI measurement in your organization? Here's a practical 90-day implementation plan:

Days 1-30: Foundation

Week 1: Assemble cross-functional measurement team (finance, operations, IT, business unit leaders)

Week 2: Select 2-3 pilot AI initiatives for detailed measurement

Week 3: Document current measurement approaches and identify gaps

Week 4: Select appropriate framework (three-layer model and/or six-dimension framework) and customize for your context

Days 31-60: Baseline and Infrastructure

Week 5: Establish baselines for all pilot initiatives across all measurement dimensions

Week 6: Implement attribution tagging system

Week 7: Build initial ROI dashboard with real-time data feeds

Week 8: Define success metrics for each pilot initiative

Days 61-90: Rollout and Refinement

Week 9: Launch measurement system for pilot initiatives

Week 10: Implement qualitative feedback mechanisms (interviews, surveys)

Week 11: Generate first comprehensive ROI report combining quantitative and qualitative data

Week 12: Refine based on learnings, plan rollout to additional initiatives

This 90-day plan establishes the foundation. Expect to continue refining and expanding your measurement approach over the following 6-12 months as you learn what works in your specific context.

Conclusion: Moving from Vibe-Based to Evidence-Based AI Investment

The era of "invest in AI because everyone else is" is over. In 2026, organizations need rigorous, evidence-based approaches to AI investment decisions. The frameworks and approaches outlined in this article provide that evidence base.

Key takeaways:

1. Go beyond cost savings: Cost reduction is real but typically represents 25-40% of total AI value

2. Measure across multiple dimensions: Business impact, operational efficiency, customer experience, innovation, and strategic value all matter

3. Track trajectories, not snapshots: AI value compounds over time; measurement needs to capture this

4. Balance quantitative and qualitative: Numbers alone miss important aspects of AI value

5. Be rigorous about attribution and TCO: Overestimating ROI is as problematic as underestimating it

6. Set appropriate time horizons: Different types of value materialize on different timelines

The research is clear: 42% of AI projects deliver zero ROI, while 58% see measurable returns. The difference isn't primarily about technology—it's about strategic alignment, robust measurement, and disciplined execution.

Organizations that implement comprehensive AI ROI measurement frameworks don't just justify their AI investments—they make better investment decisions, allocate resources more effectively, and ultimately achieve better outcomes.

Ready to Measure What Matters?

At Cavalon, we help organizations design and implement AI ROI measurement frameworks that capture the full spectrum of value creation. Our approach combines financial rigor with practical implementation experience to deliver measurement systems that actually work.

We've helped dozens of organizations move from vibe-based to evidence-based AI investment, improving both their returns and their confidence in those returns. Contact us to discuss how we can help you measure AI ROI comprehensively and accurately.

Sources

• CIO: 2026: The Year AI ROI Gets Real
• CIO: AI ROI: How to Measure the True Value of AI
• IBM: How to Maximize ROI on AI in 2025
• Agility at Scale: Proving ROI - Measuring the Business Value of Enterprise AI
• LinearB: AI Measurement Framework
• AI Smart Ventures: How Do You Measure AI ROI?
• WildNet Edge: AI ROI Measurement: Frameworks, KPIs and Real Outcomes
• Larridin: The AI ROI Measurement Framework
• KumoHQ: AI ROI Measurement Framework
• Beam AI: Why 42% of AI Projects Show 0 ROI
• PwC: 2026 AI Business Predictions
• Integrate.io: Salesforce Data Integration ROI Figures