How to Calculate ROI for AI Investments (2026) – Shopify

Simply implementing artificial intelligence (AI) in your business is no longer a competitive advantage. 

Although BCG reports just 5% of companies are “future-built,” 35% are scaling AI and beginning to generate value. The differentiators are your AI operating model, measurement, and adoption—not just access to AI tools.

But while AI spending is rising across the board, and lagging can mean losing your competitive edge, the return on investment (ROI) for AI is hard to isolate, slow to prove, and frequently measured inconsistently. 

Deloitte’s 2025 research highlights that typical AI ROI can take between two and four years, with only 6% of brands seeing payback in under a year. 

If you don’t have time to waste, this article shows how to shorten time to value and demonstrate the impact of AI credibly. 

What AI ROI means in 2026

The ROI of AI measures how much value your business gains from artificial intelligence compared to the cost of building, deploying, and maintaining the technology. 

AI ROI formula: (Net benefit ÷ total cost of ownership (TCO)) x 100 = AI ROI

The challenge: AI ROI is not as simple as other ROI calculations that look at the direct financial returns compared to the cost of building it. If you buy a software subscription, for example, the costs are predictable, and value is easy to track.

For artificial intelligence, a range of indirect costs impact the payback period:

• Data preparation
• Integration
• Training
• Quality assurance
• Governance 

Plus, because a single AI implementation can affect multiple areas of a business, other factors come into play that make the reward side of ROI calculations more complex:

• Financial returns: What are the direct financial gains associated with AI implementation? These are specific to the AI use case. If you’re using AI in your enterprise resource planning (ERP) system to improve demand forecasting, for example, you might consider metrics like inventory carrying cost reductions in your calculation. 
• Operational returns: How much time have you saved since implementing AI? How many errors has it prevented? Again, this can be difficult to track if you don’t have the infrastructure and team buy-in to measure time savings. 
• Risk: What risks have you avoided with AI? On the flip side: What risks does the technology now impose? The AI industry is still largely unregulated—risk adjustments account for the potential downside costs of implementation. 

Why AI ROI feels elusive

Investment in AI is rising, but measurement is lagging and returns take longer. According to Deloitte, almost two-thirds of organizations say AI is part of their corporate strategy but recognize that not all returns are immediate or financial. 

Culprits contributing to why AI ROI gets stuck include:

• Dirty product data and weak tagging: AI doesn’t “know” anything; it predicts based on patterns. Poor data quality and inconsistent formatting hurt time to value because you’ll spend time fixing mistakes.
• Lack of testing discipline: Without rigorous testing and benchmarking, you can’t prove the AI is better than the old manual approach—especially for indirect gains like time savings. 
• Isolating AI value is hard: AI rollout is often bundled with other transformations. How do you put a dollar value on better employee morale because AI handles repetitive tasks like data entry, while sales revamped comp plans at the same time? 
• Resistance to organizational change: Some 52% of workers say they’re worried about the future impact of AI use in the workplace; 32% think it will lead to fewer job opportunities for them in the long run.
• Privacy/security concerns: Executives’ top AI concern is sharing data with large language model (LLM) providers. Governance can slow rollout and delay time to value, but it prevents costly failures.
• Fragmented customer experience (CX) tooling: The data required by AI often sits on different platforms. This isolation, or lack of real-time synchronization, can raise TCO and cause delays.

💡Tip: Unified commerce solves fragmented tooling by merging your data into one centralized business “brain” with no patchy middleware or custom integrations. 

Shopify is the only platform that does this natively. An independent research firm found that Shopify merchants benefit from up to 37% better performance than competitors, 20% faster implementation times, and 89% lower annual third-party support costs.

The AI ROI formula for ecommerce (with benchmarks)

Here’s a reminder of the AI ROI formula for ecommerce:

AI ROI = (Net benefit ÷ total cost of ownership) x 100

Let’s put this into practice for an ecommerce brand implementing onsite search optimization. The AI tool uses natural language processing (NLP) and machine learning to predict what a customer is most likely to buy, then surfaces those products in personalized site search results. 

Start by calculate the total cost of ownership. Initial development, API tokens, and human labor to implement the system total $70,000.

Next, calculate the total gains with an A/B test. The variant group that saw the AI-generated suggestions had a 0.5% increase in conversion rate. The brand usually generates $40 million from search-driven revenue, so the revenue lift attributed to the AI rollout is $200,000. 

Add the operational savings, like removing 1,000 “Where can I find this product?” tickets. These cost about $10 each to answer manually, meaning cost savings of $10,000. Total gains equal $210,000. 

Plug these figures into the formula to calculate the ROI of AI:

($140,000 ÷ $70,000) x 100 = 200% ROI

💡Benchmark: Half of organizations surveyed by Google Cloud expect gains of 6% to 10%.

However, exact AI ROI benchmarks differ by use case. For instance, the same report found brands that use AI agents—a specialized solution that can plan, suggest, and perform tasks—have the highest ROI across the board. Of early adopters, 88% report ROI from generative AI in at least one use case. 

Payback period and average time to value 

The payback period is the time it takes for the net benefits of your AI strategy to outweigh the total cost of ownership. Calculate it with this formula:

Total cost ÷ Monthly net benefit = Payback period (in months)

Let’s say you’ve invested $120,000 into data preparation, AI integration, and initial training. There’s a monthly operating cost of $2,000 to cover API tokens and maintenance. But you save roughly $2,000 in labor costs and can attribute a $10,000 revenue lift per month to the AI. The monthly net return would be $10,000.

In this case:

$120,000 ÷ $10,000 = Payback period of 12 months 

Deloitte found the average payback period is two to four years—much longer than the typical 7 to 12 months for other technology investments. Just 6% of businesses see payback in under a year; only 13% even saw returns within 12 months, even among the most successful projects. 

A practical AI ROI measurement framework

Accurate measurement of AI ROI requires a three-pronged approach: better scoping + better measurement + better rollout. Organizations with this type of visible AI strategy are twice as likely to experience revenue growth from AI adoption as ad hoc approaches.

Here’s a repeatable measurement workflow you can apply to calculate the return on any AI project:

1. Set the baseline: Workflows and KPIs

The easiest approach is to isolate a specific workflow so you aren’t overwhelmed by the complexity of AI throughout your entire business. Be specific—don’t measure “AI for marketing”; measure the impact of a particular workflow (like AI-generated product descriptions) instead.

Once you’ve isolated a workflow, set the baseline:

• How much money do you currently spend doing this manually?
• How much time do you spend on it?
• How often does the manual work need a redo? 

Next, choose a KPI to anchor post-AI implementation measurement against. In the context of AI-generated product descriptions: revenue lift on product pages, the time to publish new product pages, and specification accuracy are good choices.

Here are some more examples:

2. Track fully loaded costs

The sticker price of an AI tool is a small percentage of the total cost you’ll spend on it. Create a system to track the fully loaded cost—the entire amount you’ve spent on the AI initiative—to accurately calculate ROI.

Factors that make up AI’s total cost of ownership include:

• Licenses
• Implementation
• Internal labor 
• QA
• Governance
• Ongoing monitoring

💡Tip: Rushing implementation can create technical debt that inflates costs later. If you spend $50,000 now but have to spend $200,000 next year to rebuild the system because the data structure was poor, your TCO just quadrupled. This is why it’s best to start small with a pilot project before a widespread AI rollout. 

3. Monitor adoption

It’s great to implement AI, but if your team is resistant to using it, costs increase without the operational efficiency gains to make the investment worthwhile. 

For example, if your AI generates 1,000 optimized product descriptions but your merchandising team continues to manually write their own because they don’t trust the tool, your ROI is negative. You’ve paid for the AI tool as well as the manual work.

To increase buy-in:

• Explain the benefits to practitioners of using AI versus manual labor.
• Create a culture of experimentation. 
• Identify “super users” of AI on your team and have them run peer-to-peer training sessions.

4. Draft an attribution plan

You can’t look at a dashboard and trust the “revenue generated” number—you must prove the money wouldn’t have been made without the AI to demonstrate ROI. 

Draft an attribution plan that helps uncover this with:

• A/B tests: Divide the workflow into two groups: one using AI, the other without. Remember to isolate the variable to see the true impact. If you’re launching an AI shopping assistant, for example, halt other major site changes during the test period. 
• Control groups: If you can’t isolate the AI implementation, use a control group—people, tasks, or pages excluded from the experiment—to track the impact.
• Matched-market: Compare two similar segments and apply AI to one while keeping the other as is. If you’re calculating the impact on stockouts with AI demand forecasting, for example, you might compare the KPI for one geographic region that used AI against another that forecast demand manually.
• Time-boxed pilots: Set a baseline, freeze other campaigns, and run a pilot. Compare pre-implementation benchmarks after the pilot ends. It’s the easiest way to set up attribution, but the hardest to prove—you’ll need to clean the data to ensure outside factors (like a holiday sale) didn’t skew the results.

💡Tip: Customer journey maps can show whether AI actually influenced the customer’s journey or just sat in the middle of it. If an AI assistant suggests a product the user was already going to buy through search, for example, the AI didn’t earn that sale. 

Convert time saved into dollars

Productivity gains are the biggest value drivers of generative AI, according to a recent Google Cloud study.

A Thomson Reuters report of legal/tax/accounting professionals backs this up: respondents expected to free up nearly 240 hours per year (up from 200 in 2024)—worth $19,000 per professional annually. But while time saved significantly impacts ROI, it’s not tangible and difficult to track.

To do this, put a dollar value on the time bought back. Then apply the employee’s fully loaded cost—usually 1.25x to 1.4x their base salary—to the time-saved KPI.

💡Tip: Account for avoided costs in your calculation. If customer ticket volume grows by 20% but your support staff stays the same size because AI handled the surplus, your ROI is the fully loaded cost of the support rep you didn’t have to hire.

Fastest paths to AI ROI in ecommerce

Pressure to generate AI ROI—and prove it—raises two questions: Which ecommerce AI use cases deliver ROI fastest? And which ones require longer timelines but create bigger advantages?

Customer service and experience

Customer service and experience are a top use case of AI agents. Almost half of organizations surveyed by Google Cloud deploy them in their business. 

This use case has some of the fastest paths to AI ROI: Chatbots can quickly answer queries that support reps would spend hours researching and answering. They pull data from store policies to give answers within a matter of seconds, and can optimize return workflows—for example, generating a return shipping label to divert products to a location running low on stock of that particular SKU. 

KPIs to track:

• Cost per ticket
• Deflection rate
• Customer satisfaction score (CSAT)
• Time to first response
• Refund rate

Marketing and creative ops

Generative AI has a low barrier to entry, as almost every technology provider offers some kind of GenAI within their product suite. For example, Shopify Sidekick—the suite of AI tools built into Shopify—can handle marketing tasks like:

• Generating product descriptions
• Drafting email copy
• Writing email subject lines
• Altering product imagery 
• Redesigning your storefront 

KPIs to track:

• Content cycle time
• Customer acquisition costs (CAC) 
• Conversion rate
• Return on ad spend (ROAS) variance
• Creative output per week 

Merchandising and onsite search/personalization

Customers demand personalized experiences. Onsite search is the perfect way to deliver that; AI can do it at scale with machine learning to detect what a customer is most likely to buy. It can even combine this with other information—for example, stock levels at retail locations—to offer “buy now, pick up today” options within search recommendation widgets. 

KPIs to track:

• Search conversion rate 
• Zero-result rate 
• Average order value 
• Product discovery metrics
• Return rate

Operations (inventory, forecasting, fulfillment)

Moving from manual work to AI assistance within operations can free up time and help you make better decisions. 

Take the use of AI for predictive stock leveling, for example. Traditional inventory management relies on predetermined safety stock levels. AI replaces this with dynamic levels that adjust daily based on factors like lead times, supplier reliability, and localized demand. It can also tell you which warehouse should hold the stock to speed up customer shipping and cut costs. 

KPIs to track:

• Stockouts
• Overstocks
• Inventory holding cost
• On-time and in-full (OTIF)
• Picking accuracy

Risk reduction and security

The net benefit of AI in risk reduction and security is difficult to measure because gains are often measured in loss avoidance, rather than direct revenue. The “return” is avoiding a catastrophic event through implementation of tolls like automatic fraud detection and compliance monitoring.

KPIs to track:

• Fraud loss rate
• Chargebacks
• Time to resolution
• Compliance incidents