AI and Efficiency: How Businesses Use AI To Save Time and Scale Smarter

AI tools can help ecommerce teams automate repetitive work, support decision-making, and improve customer service.

In McKinsey’s State of AI: Global Survey, 88% of respondents reported using AI in at least one business function, up from 78% the year before. But only one-third of respondents reported using AI at scale. 

The figures suggest that while AI might be common, it’s not yet delivering consistent efficiency gains across teams and workflows. 

AI supports efficiency when AI tools take on repetitive tasks with clear workflows, with humans still reviewing and guiding the output. This guide explains where AI can improve efficiency in commerce, which workflows are better candidates for automation, and how to measure the results.

Defining AI and efficiency

In this context, AI efficiency refers to measurable gains in time, cost, output, accuracy, or capacity after a business adds AI to a workflow. Those gains can show up in both back-office and customer-facing work, from inventory reporting to support responses. This comes in a few forms:

• Automation: AI handles repetitive, rules-based tasks that previously required manual work, such as routing support tickets, updating inventory records, or flagging orders for review.
• Augmentation: A team member completes a task faster with AI assistance, such as drafting product descriptions, summarizing reports, or analyzing campaign performance.
• Agentic execution: AI systems complete multiple steps within a workflow, but still operate within defined controls and escalation rules, such as triaging returns and routing exceptions to the right team.

Efficiency needs a measurable definition tied to a workflow. Teams can track it through metrics such as:

• Speed
• Output
• Cost reduction
• Accuracy
• Capacity expansion

Where AI improves efficiency

AI tends to improve efficiency most in structured, repeatable tasks with clear rules and consistent inputs. Examples include summarizing reports, drafting product description copy, processing inventory updates, routing support tickets, and surfacing fraud signals for review.

The more ambiguous the work is, or the more it depends on context, judgment, or brand nuance, the more AI performance can fall off. A common approach for more complex tasks is AI-assisted execution with human review for exceptions and approvals.

Benefits of AI systems for efficiency gains

AI can improve different workflows in different ways.

A practical starting point is workflows with high volume, clear rules, and measurable outcomes. McKinsey reports 80% of organizations set efficiency as an objective of AI initiatives. Here are some specific categories to measure those benefits—and where human review still matters.

Time efficiency

Teams start with repetitive tasks that follow clear rules. AI models perform well with simple tasks because these have clear rules. It’s also easy to share examples of how your team executed these workflows in the past. 

Here are a few examples:

• Drafting emails or product descriptions
• Summarizing reports, like inventory reports
• Running internal search across documents like PDFs
• Creating support replies for common questions
• Pulling highlights from campaign or sales reports

One way to measure time efficiency is in time saved per task or per week. In 2025, an NBER field experiment across thousands of knowledge workers showed AI saved them an average of two hours on email each week. These gains are most useful in workflows where speed matters more than originality, but a person should still review customer-facing messages before they are sent.

Process efficiency

When AI reduces manual steps in a workflow, teams spend less time on handoffs and routine checks. 

For example, when AI-powered tools automatically update inventory numbers after a customer order and trigger alerts to improve inventory visibility, these are tasks a human no longer has to take care of. A team member can review alerts and respond in real time. The same logic can apply to order routing, support triage, and return processing, where AI helps move work forward faster across systems.

Process automation works best when teams define both the rules and the review process. With those in place, processes can get an instant boost thanks to AI’s ability to handle digital processes. Without those controls, automation can move errors through the workflow faster.

Decision efficiency

AI can process large data sets faster than a manual review, which makes it useful for tasks such as:

• Forecasting customer demand
• Identifying customer segments from behavioral or purchase data
• Detecting anomalies related to fraud or security events
• Prioritizing service tickets based on urgency or likely next steps

Business leaders should still check the logic behind these recommended decisions. For example, is there really a customer segment in the data the way AI tools suggest? Does the recommendation reflect a real pattern?

When the data and review process are sound, AI can help identify patterns for teams to evaluate. But decision support works best when people treat AI as a helper, not the final decision-maker.

Customer efficiency

AI can also support customer-facing workflows when the task is structured and the escalation rules are clear.

Automating repetitive tasks with AI doesn’t have to refer to only back-end workflows. AI can also support:

• Faster responses for customer support, including faster handoff of complex cases to the right teams
• Better, more data-driven product recommendations
• Shorter wait times for checking on product inventory on a customer’s behalf
• Quicker answers to order-status or return-policy questions

AI chatbots can handle routine support questions, while teams route complex or sensitive cases to a person. Customer interactions go well when the AI satisfies a customer’s need for a quick response. But speed alone isn’t enough. Brands still need human review for sensitive issues, exceptions, and cases where tone or judgment matter.

AI efficiency by the numbers:

• For organizations using AI, 49% in service operations report cost savings, per Stanford HAI’s AI Index Report 2025.
• 71% of respondents using AI in their marketing and sales reported revenue gains. (Stanford HAI)
• For enterprises already using generative AI, 65% reported it helped increase employee performance, according to research by OECD.

Together, these numbers show that AI can create real efficiency gains, but results depend on workflow and implementation.

How artificial intelligence improves efficiency in commerce

AI works best when applied to the right workflows, supported by clean, consistent data and a unified system. In commerce, efficiency starts with fixing workflows first, then layering in AI to automate work and improve decisions. Platforms like Shopify already embed many of these capabilities through automation, analytics, and AI assistants.

AI models and fixing operational bottlenecks

Efficiency gains tend to show up first in operations. Here, AI can remove bottlenecks before improving the customer-facing experiences.

In many cases, that starts before AI is deeply embedded, by reducing manual work, improving data visibility, and connecting workflows across systems. These are the conditions where AI can later add the most value.

POLYWOOD

POLYWOOD, North America's largest direct-to-consumer outdoor furniture brand, faced a classic scaling bottleneck: their heavily customized Magento platform. With over 150,000 product variations, their engineering team was trapped in a cycle of platform maintenance rather than innovation.

By migrating to Shopify, POLYWOOD reclaimed their most valuable resource: time. This allowed them to embed AI across every facet of their business, from manufacturing to customer discovery. Engineering resources were redirected from platform maintenance to higher-value work.

POLYWOOD applied AI to high-impact workflows across the business:

• 100% AI-assisted coding across the development team, reducing time spent on routine engineering work
• Conversational product discovery across a 150,000-SKU catalog, helping customers find products faster
• AI forecasting and workflow analysis in manufacturing, improving planning and operations

Instead of adding AI on top of existing workflows, POLYWOOD redesigned how work got done. Routine tasks were automated, decision support was faster, and teams had more capacity to focus on higher-value work.

Venezia FC

The Venetian Football Club was dealing with surging sales and clogged omnichannel operations that made handling inventory a difficult, time-consuming process. With Shopify POS automating many of their inventory updates, Venezia FC saved 48 hours of manual inventory work each week.

Their efficiency gains came from improving workflows and system visibility first. Inventory became easier to manage, workflows were more consistent, and the team had more time to focus elsewhere.

This kind of operational foundation is what allows AI tools to work effectively—by providing clean data, connected systems, and repeatable processes.

Using AI performance to scale and improve customer-facing efficiency

AI adoption often starts in back-end workflows, but it drives real competitive advantage when those connected systems enable faster, more personalized customer experiences.

Girls with Gems

High-end fashion retailer Girls with Gems needed a way to make back-end efficiency show up in their customer experience, particularly during a Christmas sale in December 2024. Using Launchpad to schedule product pricing, they created a more personalized experience for shoppers, while also implementing same-day delivery and improving management of their gift card program. 

More automation during a 107% YoY surge in online sales revenue showed that it was possible to improve both the front end and back end at once.

As workflows became more automated and predictable, the business created the conditions for AI-assisted tools—such as reporting, inventory insights, and customer segmentation—to scale more effectively.

AG Jeans

Cost savings aren’t always the name of the game. For AG Jeans, front-end operational issues made it difficult to deliver the level of service their clientele expected.

The brand used Shopify as its single source of truth: they moved to a full Shopify adoption across the business. This made it easier to deploy Shopify Plus partners and Shopify POS—a process that was as simple as shipping each of their 15 stores an iPad and telling them how to turn it on. The in-store experience had sales associates hopping on the app and ringing customers up with ease. 

With systems unified and workflows simplified, the brand is better positioned to layer in AI-powered clienteling and personalization tools that depend on consistent data and connected customer interactions.

How to use AI efficiently for a competitive advantage

AI adoption often produces better results when teams start with structured workflows and measurable goals. Adding generative AI to an unclear workflow rarely fixes the underlying process. In most cases, it just adds more work to review.

Finding a way to reduce operational costs, for example, starts with redesigning how the work itself gets done. Tools alone don’t create efficiency—better workflow design does. Here’s what that might look like in practice.

1. Have business leaders audit repetitive workflows 

Start with high-volume tasks that follow clear rules and take significant time. Start with lower-risk workflows rather than complex tasks. This matters because repetitive, lower-risk work is usually where teams can get early wins.

Here are a few examples of repetitive workflows in most organizations:

• Support tickets, including setting escalation rules
• Inventory reporting, as well as setting up automated inventory updates
• Product content, where generative AI tools can specifically help
• Order-status questions and other routine support requests
• Recurring reporting tasks for sales, merchandising, or campaign performance

2. Rank operational efficiency tasks by impact and risk 

After brainstorming the tasks above, start prioritizing them. From the risk management standpoint, which tasks have the lowest costs if AI makes mistakes? 

And then consider which tasks are the easiest to repeat and standardize, and which workflows have predictable inputs and measurable outcomes. This step matters because not every repetitive task is a good automation candidate. Good starting points are tasks that are frequent, predictable, and low risk.

3. Start with assistive use cases

Ask what AI adoption can do to assist current use cases, even if it’s not a replacement of any specific workflow, before going into full-on automation. This gives teams a safer way to test AI inside existing work before handing off more responsibility.

For example, having AI predict customer demand can offer insights to boost a company’s operational efficiency. But it doesn’t replace any particular workflow. The same applies to data summaries, or having generative AI start drafting responses to customer queries without clicking “send.”

These are just short-term AI solutions. But they’re also ones that will produce immediate efficiency gains. For many teams, this is the clearest path: start with augmentation, then automate once the workflow is stable and the review rules are clear.

4. Define rules for human review of AI solutions

AI’s impact on the daily life of a business starts becoming obvious when it does work more independently. Before that happens, there may be key areas humans still need to review. Decide on two things:

• What can AI do independently right now?
• What workflows still need approval?

For example, having AI generate and send responses to customer queries may be too aggressive an approach, without at least starting with a stage in which you review and improve those messages first. Similarly, you could have AI start automating repetitive tasks while still flagging edge cases (i.e., inventory alerts) without making inventory decisions just yet.

5. Create measurements for “before” and “after” 

To evaluate AI, teams need a baseline before rollout. Create KPIs from the outset as your “before AI” snapshot. These measures will be different for every company, but they require tracking some variables:

• Time saved (manual hours removed from a workflow)
• Output volume
• Customer conversion boosts
• Error rate
• Resolution time

As companies plan for AI investment, they need a clear picture of the kinds of gains they can expect from incorporating more AI into their business. The goal is not to prove that AI is impressive. It is to prove that the workflow now performs better. 

AI agents and the future of efficiency

AI agents can complete multiple steps within a workflow, such as answering a question, updating a record, and routing an exception. They can do more than generate a response—they can take action across a defined process. 

For instance, an AI agent might be able to take on more customer service queries—even to the point of resolving them and updating records—outside the business’s usual hours of service operations.

The practice of using AI agents is growing, but it is still early for most organizations. Per McKinsey, 23% of respondents saw their organizations scaling an agentic AI system in their enterprise in the near future, with another 39% already experimenting with them.

But a careful approach works best. AI agents are best for structured and repeatable workflows, such as internal operations like inventory reporting, record updates, and customer support triage, where steps and escalation paths are already clear. For fraud-related use cases, teams can use AI to flag anomalies while routing final decisions to a human reviewer.

That’s why guardrails are critical. AI agents need clear rules, approval or escalation paths, and visibility into what they are doing. Without those controls, they can create faster workflows—but also faster mistakes.

Start with one internal workflow. Add one customer-facing workflow. Measure the time saved, along with error rate and output quality, and once the workflow is more consistent, you can start to scale AI’s role.