A practical guide to AI transformation for retail and ecommerce brands – from automating operations to selling through AI-powered channels.
AI transformation is the redesign of a business, including its workflows, roles, data, and operating model, to make AI a structural part of how work gets done. For commerce businesses, that means moving beyond isolated tools and redesigning how products are created, priced, sold, and supported.
There’s more to that shift than just adopting a few AI tools, and many companies are struggling to make it work. Bain & Company found that roughly 80% of CEOs are dissatisfied with the progress of their AI programs, despite their investment. Boston Consulting Group’s (BCG) research finds that only about 10% of AI’s value comes from the algorithms, with another 20% from the technology. This leaves 70% potentially riding on how well companies redesign their workflows around AI.
Ahead, we’ll walk through how enterprise retailers can move beyond disconnected pilots and build the foundations for AI that scales across their operations.
What does AI transformation mean in 2026?
AI transformation is the systematic embedding of AI into how a business operates, including its workflows, decisions, and customer-facing moments. Unlike isolated AI adoption, it changes how work is organized and carried out across the business.
McKinsey frames it as a rebuild of the operating model itself, which is exactly where it can be confused with two other things: AI adoption and digital transformation.
The below table outlines some of the differences between these three related concepts.
| AI adoption | Digital transformation | AI transformation | |
|---|---|---|---|
| Definition | Introducing AI into selected tools, tasks, or use cases | Modernizing how a business operates through digital technologies and digital-first processes | Redesigning core workflows, decisions, roles, and customer experiences around AI |
| Scope | Tool-level, often team-by-team | Broad and enterprise-wide, potentially covering any digital technology | Enterprise-wide, but focused specifically on where AI changes how work gets done and how business value is created |
| Typical trigger | A team identifies a task it wants to automate or complete faster. | Legacy systems, disconnected processes, or changing digital customer expectations. | AI pilots are producing isolated gains but failing to scale into measurable business results. |
| Commerce example | A copywriter uses AI for product descriptions. | Connecting ecommerce, stores, inventory, and fulfillment systems. | Redesigning merchandising so AI coordinates assortment, pricing, promotions, and inventory within defined human guardrails. |
For commerce, that redesign tends to show up in four places: content and merchandising, pricing and inventory, customer experience, and sales channel strategy.
Polywood shows what that broader redesign can look like in practice. After migrating from a heavily customized Magento environment to Shopify, the outdoor furniture brand redirected development resources from platform maintenance toward customer experience and AI innovation.
They then established a weekly cross-functional AI standup, introduced governance for testing new tools, and embedded AI across development, customer service, product discovery, demand forecasting, production scheduling, and warehouse operations.
Polywood’s Shopify development team now uses AI-assisted coding throughout their work, while customers can explore more than 150,000 product variations through conversational discovery.
“Our entire development team today is using AI-assisted coding 100%,” says Benjamin Spiegel, chief digital officer.
“Now all of these people are working on new initiatives—the commercial portal, process improvements for customer service agents, self-service returns, self-service AI agents for customer questions.”
How are commerce businesses approaching AI transformation?
David’s Bridal carries roughly 200 data points on every bride who walks through their door. But an enterprise IT team modernizing a supply chain dashboard doesn’t have a customer standing in a fitting room while the system decides what she sees next. The immediacy is one reason AI transformation looks different in commerce.
BCG’s research on retail AI shows that it touches four core dimensions at once, from product discovery to where value gets created in the business:
- Customer journeys: More consumers are approaching retailers with broader “missions” than looking for a single product at a time.
- Channels: AI assistants are becoming part of product research and discovery, while stores play a greater role in reassurance, service, and fulfillment.
- Profit pools: Retailers with their own customer relationships may be able to protect stronger margins, while those dependent on third-party AI recommendations will face increased operational pressure.
- Differentiation: As forecasting, replenishment, and promotions get more automated, brands’ advantage comes from their customer proposition, proprietary data, rules, and human judgment.
The first difference is the breadth of the transformation.
A commerce business may be running AI across product content, pricing, customer service, fulfillment, and the sales channel itself, as AI-native storefronts begin contributing measurable sales. Those systems also operate alongside fast-moving catalogs, seasonal demand, live inventory, and customer expectations that change in real time.
Bain projects agentic commerce in the US alone could reach $300 to $500 billion by 2030, creating a channel most retail organizations didn’t have on their roadmap three years ago.
The second difference is capacity.
Most mid-market and enterprise retailers run lean relative to the operational complexity they carry, and David’s Bridal’s pre-transformation operation is a case study in what that bottleneck looks like. Their CIO described technology running on languages nobody supported anymore, amassing technical debt that consumed the team’s time and capacity. Once that constraint was lifted, the transformation timeline collapsed: the brand completed a full ecommerce replatforming, a new Canadian site, and a first-of-its-kind concept store, all within nine months.
“It’s hard to find a technology partner where you could actually change your corporate business strategy based on the functionality they have, but that’s where we are with Shopify,” says Elina Vilk, president and CBO.
The third difference is cultural.
For instance, Polywood’s chief digital officer says that employees will experiment with AI on their own devices whether or not there’s a sanctioned process, so the real choice is governance versus blindness.
“The reality these days is that if you don’t do that, people that want to will do it anyway,” says Benjamin Spiegel.
“They’re at home and they’re going to use their own installation of ChatGPT and upload your company data… So you have to figure out how to adapt quickly but keep governance active.”
BCG describes lasting AI transformation as a workforce shift requiring reskilling, human-AI workflows, employee involvement, and continued adaptation. The cultural work is practical: equip employees with sanctioned tools, involve them in redesigning workflows, and make sure that they’re allowed to experiment safely.
The three pillars of AI transformation for commerce
The World Economic Forum (WEF) names three enablers for AI transformation to succeed: people, digital infrastructure, and responsible AI governance.
The pillars, however, cannot be treated independently. A strong platform, for example, will not rescue the wrong use case, just as a valuable workflow will not scale if employees avoid it. And even enthusiastic teams will struggle when AI sits outside the systems and data they use to run the business. Combined, these pillars create the conditions for AI to move from isolated experimentation into everyday operations.
People
You can deploy capable AI systems and still see little impact if merchandisers, marketers, service teams, and store employees don’t use them in their daily work. According to Deloitte’s 2026 survey of 200 retail and consumer products executives, broad AI adoption never exceeded 36% outside of IT departments, even though 75% considered AI a top strategic priority.
The number of enterprise-wide deployments remained much lower at 7% to 10%.
Your teams need more than formal training; they need permission to experiment, clear boundaries, and evidence that AI will improve recognizable parts of their work.
Polywood, for instance, calls their approach “freedom within a framework.” Their employees bring new tools and use cases to a weekly cross-functional AI standup, where IT can assess them and create an approved path forward. The company also gives teams access to sanctioned AI tools and isolated environments where they can test safely.
Digital infrastructure
The ALDO Group built a fully composable commerce stack in 2017 because nothing off the shelf could handle their needs. But eight years later, maintenance and technical debt consumed more engineering capacity than the stack returned in business value.
After moving to Shopify, ALDO rebuilt three brands in under nine months while integrating their enterprise resource planning (ERP), product information management (PIM), search-and-discovery solution, and loyalty program. Deployments went from hours-long, infrastructure-coordinated releases to instant, one-click updates. Marketing and merchandising teams also gained direct autonomy over content and merchandising changes that once needed an IT ticket.
“ [Shopify] is already ready for what’s next—contextual commerce, conversational AI, advanced personalization. These aren’t future possibilities; they’re working now,” says Gregoire Baret, VP of digital product.
While Shopify didn’t remove ALDO’s need for an enterprise technology ecosystem, it reduced the custom infrastructure the team had to build and maintain around it. That gave the business access to AI-native tools and emerging commerce channels through the same foundation.
Responsible AI governance
Polywood’s approach shows how governance can support experimentation without slowing it down. That includes repurposed hardware on isolated networks for experimentation, rapid IT review, and funded access to multiple approved AI tools rather than forcing one. AI governance, in their model, helps the business adopt AI quickly while maintaining control.
Aviator Nation makes the case at the data layer instead. Before consolidating onto one platform, the brand ran separate systems for ecommerce and in-store point of sale (POS), so a customer’s purchase history lived in two places. Staff also couldn’t process an online return in a physical store.
Once they unified onto Shopify POS across 17 locations, they got a single view of every customer. That single view is what makes their AI tooling trustworthy. Their director of ecommerce, Curtis Ulrich, uses Shopify Sidekick to check the business’s assumptions against that unified data, which depends on having connected, reliable information.
“Over 2,000 transactions, when you’re saving 20 to 30 seconds by me and you not having to yell letters back and forth at each other, that’s huge,” says Curtis.
Together, people, infrastructure, and governance establish the foundation for scaling AI. The roadmap below applies that foundation in stages.
A phased roadmap for commerce AI transformation
The following roadmap moves from low-risk automation to operational intelligence, and finally, new forms of commerce execution. Each phase builds on the controls, team experience, and infrastructure established in the one before it.
Phase 1: Automate high-volume, low-risk workflows (Weeks 1-4)
Start where errors are easy to catch and correct, and the volume makes even small time savings compound: content. This includes product descriptions, category pages, blog posts, and email copy. These workflows are high-volume, repetitive, and low-risk because a weak first draft often only costs an edit. But content is only one starting point. The same criteria can help teams identify other repetitive workflows that consume more staff time.
Shopify Sidekick helps brands generate content directly within the Shopify admin. Sidekick can generate text for product descriptions, blog posts, pages, email campaigns, Shopify Inbox replies, and storefront themes. The media tools can also remove or generate image backgrounds without requiring staff to move assets into separate software.
Once content is moving, look for other operational tasks—the rule-based work that doesn’t need generation so much as consistent execution. Shopify Flow can support these workflows. You can unpublish an out-of-stock product and notify the team, flag a high-value order for review before it ships, or tag a customer for a loyalty milestone.
Plus, Shopify Flow’s Sidekick integration lets you describe the workflow you want in plain language rather than building the trigger-condition-action logic by hand.
Before you automate anything, do two things:
Identify 2-3 workflows where staff time is disproportionate to output value
Look for tasks nobody questions because they’ve always been done manually. This could be bulk product description rewrites for a seasonal catalog refresh or drafting responses to the ten questions customer service answers most.
Establish a review protocol before the first output goes live
Your review protocol should cover:
- Accuracy: Are specifications, prices, materials, claims, and policies correct?
- Brand: Does the language sound like the business and suit the customer?
- Compliance: Does the output introduce unsupported health, performance, sustainability, or promotional claims?
- Context: Is the content right for the product, market, language, and channel?
- Approval: Who must review it, and what can eventually be published with lighter oversight?
- A shortlist of AI-assisted workflows worth continuing
- A documented review and approval protocol
- A baseline measurement of time saved per output
- A record of the errors and exceptions that still require human judgment
Your 24/7 Shopify expert is here
From daily tasks to strategic planning, Sidekick delivers instant help the moment you ask. Learn how your AI assistant makes it easier to start, run and grow your business on Shopify.
Phase 2: Extend AI into operational decision-making (Weeks 5-12)
Once Phase 1’s automated content and workflows are running with a review protocol behind them, the next step is handing AI’s role in decisions that move revenue and cost.
The goal during weeks 5 through 12 is operational intelligence. Here, AI helps your teams interpret commerce data, identify the next action, and automate the repeatable steps surrounding that decision.
Build repeatable reporting around business decisions
Start with a report the team already uses to make a recurring decision. That could be a weekly inventory review, promotion analysis, returns report, or comparison of product sales and profit.
Shopify Sidekick works within the context of the Shopify admin. You can ask questions in plain language, generate ShopifyQL queries, create data visualizations, and explore measures such as sales, costs, profits, returns, and discounts.
Choose one report and turn it into a repeatable operating rhythm:
- Define the decision the report should support.
- Specify the products, locations, channels, or customer groups it should cover.
- Ask Sidekick to surface the relevant measures and exceptions.
- Compare its findings with the team’s existing report.
- Document which follow-up actions happen when a threshold is reached.
Take Snocks. The brand’s data requests from finance, sales, logistics, and creative teams once went through one ecommerce leader, turning routine questions into a capacity bottleneck. With Shopify Sidekick, employees can now ask questions in plain language and build their own reports directly from Shopify data.
Previously, reports would take up to 30 minutes; now they take seconds, coming up to 98% faster.
“Everyone has worked with AI by now. But having it sit right inside Shopify, connected to your own data, makes it so much more usable,” says Kevin Foitzik, group head of ecommerce and IT.
Introduce an AI-assisted pricing review
A good next step is pricing, which moves AI from reporting to recommendations while still letting humans retain control of the final decision.
The Shopify Smart Pricing app uses machine learning to generate product-level markdown and markup recommendations. The tool’s price tips draw on store sales and inventory data, along with seasonality and market trends. You can review and apply every change, so the tool supports the pricing decision rather than changing prices autonomously.
Then, you can build a review process around those recommendations:
- Define which product groups are eligible for AI-assisted price review.
- Establish minimum margin and brand-positioning guardrails.
- Assign an owner to review each recommendation.
- Record why recommendations are accepted or rejected.
- Monitor conversion, units sold, inventory movement, and profit after implementation.
Automate customer service triage and escalation
The customer service department offers another clear path from AI assistance to controlled automation.
Shopify Inbox connects online-store conversations with the customer, product, cart, and order context available in Shopify. When the Inbox agent is enabled, the AI can respond to customers automatically using approved the retailer’s knowledge base
Start with a narrow routing protocol:
- Let the agent handle defined, repetitive questions such as order status, shipping, return policies, and basic product information.
- Identify topics that should always move to staff, such as complaints, unusual refund requests, sensitive product questions, or high-value sales opportunities.
- Assign ownership for each escalation category.
- Review completed AI conversations for factual accuracy and customer feedback.
- Update the underlying product details, policies, pages, or knowledge base when the same error appears repeatedly.
Put one AI-built customer segment into active use
The final step in this phase is applying AI to campaign targeting. Sidekick can generate a customer segment from a plain-language description. You might ask for customers who purchased from a particular category more than once or customers who haven’t ordered in 90 days.
Shopify converts that request into a dynamic segment that teams can test and review before saving.
Each segment should begin with a specific commercial hypothesis. For instance, recent first-time customers may respond to a second-purchase campaign, or high-value lapsed customers may need a different message from frequent discount shoppers.
Once saved, Shopify’s dynamic customer segments automatically add or remove customers as they begin or stop meeting the rules. The segment can then be used for targeted marketing through Shopify Messaging.
- A repeatable Sidekick-assisted reporting process tied to a real operating decision
- An AI-assisted pricing review with documented commercial guardrails
- Automated customer-service triage and a defined human escalation path
- At least one AI-built customer segment in active campaign use
Phase 3: Expand into AI-native sales channels (Weeks 13 and beyond)
The last phase extends AI transformation beyond internal operations and into new places to sell. AI-native sales channels allow shoppers to research, compare, and, in some cases, purchase products without beginning on the retailer’s website.
On Shopify, AI-referred orders grew nearly 13 times year over year in Q1 2026. Plus, shoppers arriving on product pages from AI search converted at nearly 50% higher rates and carried 14% higher average order values (AOV) than organic search traffic. These signals suggest AI is becoming a meaningful discovery and sales channel, not just an internal productivity tool.
“That growth rate mirrors the early-stage signals we saw in mobile and social. Those channels went on to reshape commerce entirely,” says Kyle Risley, a senior SEO lead at Shopify.
Monos, Gymshark, and Everlane are moving to sell directly inside AI Mode in Google Search and the Gemini app, through the Universal Commerce Protocol (UCP) Shopify co-developed with Google.
“It’s a new way for our story and product details to show up at the exact moment someone is asking real questions with real intent, in a format that feels helpful, not intrusive,” says Victor Tam, CEO and cofounder of Monos.
Shopify Agentic Storefronts lets eligible brands manage their presence across AI channels through the Shopify admin. Products are made available through Shopify Catalog and connected sales channels, while eligible platforms can support Shopify-powered direct checkout.
- At least one active AI-native discovery or sales channel
- A catalog reviewed for AI interpretation and product comparison
- Clear ownership of product, policy, and brand-data accuracy
- Channel-level attribution and performance reporting
- A process for correcting how the business appears in AI conversations
- Baseline conversion, order-value, and revenue-per-session data for AI-referred shoppers
Tip: Read the full guide on how to get your product data ready for agentic commerce.
At this point, the roadmap has moved from automating low-risk work to supporting operational decisions and, finally, opening new sales channels. Let’s look at some commerce use cases where that transformation can create value.
The highest-impact AI use cases for retail and ecommerce
AI transformation creates value across the commerce operation, but the outcome depends on where the technology is applied. The strongest use cases either return meaningful capacity to lean teams, improve recurring commercial decisions, or open up a new way to reach customers.
The examples below show what those outcomes look like in practice across five Shopify brands.
Product content and merchandising
Maggy London‘s four-person ecommerce team used Sidekick to audit 199 collections and produce a file for systematically cleaning up underperforming pages. The team also built a Shopify Flow workflow that identifies the 20 bestselling products each week, tags them as trending, and removes the tag after seven days.
“Sidekick turned our ecom team into a strategic intelligence hub for the whole company. The insights we pull don’t just improve our website—they inform design and product development,” says Sara Bako, president.
The result is merchandising that reflects current demand without weekly manual updates. Across their wider use of Sidekick, the team saves at least one full day of work per week.
Inventory forecasting and operational intelligence
Doe Beauty automated 80% of their operational tasks using Shopify Flow, freeing their six-person team to focus on strategy rather than manual work. As a result, inventory placement decisions run on better data access, with demand forecasting supported by automated alerts rather than manual tracking.
“Since switching to Shopify, we’ve seen our operations run smoother,” says founder Jason Wong.
Customer service automation and triage
Health and beauty brand Redmond built a production AI commerce agent with a two-person team in 10 weeks. The agent answers routine product and order questions using approved information and hands conversations to employees when judgment or care is required.
The team now has faster responses, higher self-service resolution, fewer repetitive tickets, and more staff time for complex conversations.
“I was just so surprised how easy it was to get started and wire it up in the development store,” says Phillip Hinson, an applied AI developer who had been building internal knowledge tools at Redmond. “It’s like a USB-C. It should work almost everywhere.”
Tip: Shopify Knowledge Base app lets you review and customize the store facts and FAQs AI shopping agents use when answering customer questions. The tool also shows which questions agents are receiving and whether the available information can answer them.
AI-native discovery and sales
Jewelry brand Pura Vida, is using Microsoft Copilot Checkout, allowing customers to discover products and move toward purchase within an AI conversation.
Shopify connects the channel to the brand’s existing catalog, checkout, and order infrastructure. Pura Vida can test a new sales channel without building a separate transaction system.
How to build the team and culture for a successful AI transformation strategy
When Maggy London’s founder told all 60 of the company’s employees that AI fluency was now expected of everyone, he built a weekly Friday speaker series where anyone could demo a workflow they’d built, plus a Slack channel for sharing what was working. The four-person ecommerce team went from a handful of curious early attempts, to using Sidekick 21 out of 30 days a month.
“What we learned is that people need to see it in action to be able to use it,” says president Sara Bako. “The demos are what unlock adoption.”
Google’s Workspace teamuses chess as a model for how AI fluency develops. After Deep Blue beat Garry Kasparov in a chess match in 1997, some predicted that AI would kill interest in human chess. Instead, AI-powered analysis tools helped players improve their skills and raised the level of competition.
The framework Google draws from this is:
- Positional play: The daily, incremental gains that come from equipping people with tools that make existing work faster
- Tactical play: The rarer, bigger moves that come from reinventing how work happens rather than optimizing it
According to Google’s research, only a small fraction of organizations reach that tactical stage. But the ones that do report more new ideas and faster time to market than the rest.
That maps directly onto team adoption. Most of what a person needs from AI at first is positional: small, low-stakes wins that build the confidence to try something bigger later.
Organizations that successfully build AI fluency tend to follow a few consistent practices:
- Start where the work is least creative. Choose repetitive, time-consuming tasks that are easy to verify, such as summarizing recurring reports or checking an existing assumption against commerce data.
- Turn curious employees into internal champions. Look for employees who are curious, willing to test different approaches, and able to explain what worked to their colleagues.
- Keep employees inside the review loop. At first, employees should review every output before it is published or acted upon. Over time, adjust oversight based on the task’s risk:
- Draft: AI produces an output for an employee to rewrite or approve.
- Recommend: AI proposes an action, but a person makes the decision.
- Act with review: AI completes a defined task and flags it for inspection.
- Act by exception: AI handles routine cases and escalates unusual ones.
- Make experimentation part of the operating rhythm. Give champions time to test workflows, create a shared place for prompts and lessons, and regularly review which experiments should be stopped, improved, or rolled out more widely.
Polywood’s Benjamin Spiegel offers a useful starting rhythm.
“Here’s a fun thing: look at your own firewall data,” says Benjamin. “Who pings ChatGPT 275 times a day? That person should probably be on your AI council.”
The goal is to build enough confidence and oversight for teams to expand AI use responsibly over time.
What are common mistakes that slow AI transformation in commerce?
BCG’s 2026 survey of senior consumer packaged goods (CPG) and retail executives, run with The Consumer Goods Forum, found that roughly 75% of CPG companies are still stuck in pilot mode with their AI efforts. Retail tells a different story, splitting almost evenly between the 45% of businesses scaling AI and a similar share that’s barely started. More than half of all companies surveyed don’t formally measure AI’s return on investment (ROI) at all.
The most common mistakes reverse the principles established earlier in the roadmap: starting without a defined business problem, investing without redesigning the work, and automating without the right controls.
Starting with the tool instead of the business constraint
An AI initiative needs a workflow, accountable business owner, and measurable outcome. Yet Deloitte found that technology leaders owned 54% of AI strategies rather than the profit-and-loss leaders responsible for producing commercial results. Without business ownership, pilots often stay disconnected from the work they’re meant to improve.
Treating infrastructure as the outcome
Connected data and modern architecture are necessary, but they do not produce value on their own. Deloitte found that although 82% of retail and consumer products executives planned to increase AI investment over the next year, most of that spending was still directed toward IT and data infrastructure rather than use cases that generate business value. Infrastructure should support a specific workflow or decision, not become the transformation strategy itself.
Treating it as a technology purchase
Rand’s analysis found AI projects fail at roughly double the rate of conventional IT projects, and BCG’s 2025 survey found only about 5% of organizations generating value from AI at scale. Buying the technology alone doesn’t change how the business operates.
Automating before establishing control
Customer-facing and operational AI needs defined data sources, permissions, human escalation paths, and accountable owners. Deloitte found that 29% of retailers still had no defined approach to agentic commerce even as the sector moved into active experimentation. As AI takes on higher-risk tasks, review processes should evolve with it.
Start with a business problem, measure the outcome, and scale what works.
How to measure AI transformation progress in commerce
Every phase so far has produced an output, whether that’s a time-savings baseline or a live sales channel. This section is about turning those outputs into a repeatable measurement process.
Measure at three levels. A workflow can look successful at one level while underperforming at another:
- Efficiency: Measures hours saved or tasks completed without a person.
- Quality: Measures whether the AI-assisted output holds up against what a person would have produced, including accuracy, tone, and error rate
- Growth: Measures changes in revenue, conversion, or retention
| Measurement level | The question to answer | The commerce metrics to track |
|---|---|---|
| Efficiency | Is AI returning meaningful capacity to the team? | Staff time per output, cost per task, cycle time, reporting time, tickets handled per agent, throughput per employee, percentage of cases completed without escalation |
| Quality | Is the work at least as accurate and useful as it was before? | Approval rate, edit rate, factual-error rate, first-contact resolution, customer satisfaction, forecast accuracy, pricing-recommendation acceptance, returns or complaints caused by incorrect information |
| Growth | Is the improved workflow affecting a business outcome? | Conversion rate, gross margin, average order value, repeat purchase rate, revenue per recipient, sell-through, stockout rate, markdown exposure, revenue from AI-referred or AI-native channels |
The three levels should be read together. Industry data can provide useful context.
In Nvidia’s 2026 survey of hundreds of retail and consumer products professionals, 54% of respondents reported improved employee productivity from AI, 52% reported greater operational efficiency, and 41% reported improved customer service.
Another 37% said AI had reduced annual costs by more than 10%, while 30% reported revenue increases above 10%.
Your results will depend on the use case, baseline performance, implementation maturity, and how much of the wider workflow has been redesigned.
Build a repeatable AI scorecard
For each AI-assisted workflow, create a compact scorecard that tracks:
- Intended outcome: What problem is the workflow supposed to solve?
- Baseline: How did it perform before AI?
- Efficiency measure: How much time or cost has been saved?
- Quality guardrail: What must not deteriorate?
- Growth measure: Which commercial result should eventually move?
- Owner: Who reviews the results and decides whether to scale, change, or stop the workflow?
Where possible, compare AI-assisted work with a control group or the previous process.
Set the review cadence before launch
Different measures move at different speeds:
- Weekly: Review workflow health, time saved, errors, overrides, escalations, and unusual outputs.
- Monthly: Compare quality and commercial performance with the baseline or control group.
- Quarterly: Decide which workflows to scale, redesign, or retire. Review whether the capacity created is being reinvested where the business intended.
Don’t wait until the quarterly review to investigate a sudden quality or compliance issue. But don’t abandon a workflow because a long-term growth measure hasn’t moved after one week.
Use Sidekick to shorten the reporting cycle
Shopify Sidekick can create new reports or modify existing reports from plain-language prompts. Sidekick can also generate line, bar, or donut charts. It can also export reports in formats including CSV, Parquet, JSONL, and XML.
This reduces the work required to produce a consistent view of performance. The workflow owner can then review the same measures over time.
Looking for the best Shopify enterprise plan for your long-term growth?
Transformation AI FAQ
What are the 6 pillars of AI transformation?
While there’s no universally accepted six-pillar model, a practical framework includes: business strategy, workflow redesign, people and skills, data and technology infrastructure, responsible governance, and measurement.
Together, these pillars help businesses redesign how work gets done and connect AI use to measurable outcomes.
What role does AI play in digital transformation?
AI adds new capabilities to digital transformation. It can use advanced analytics, generative AI, and intelligent automation to interpret data, generate content, support decisions, and redesign existing business processes.
The goal is to integrate AI into how the business operates, rather than adding disconnected AI-powered tools to an unchanged workflow.
How can a company begin an AI transformation?
Start with two or three high-volume, low-risk tasks tied to a clear business constraint. Set a baseline, assign an owner, and test the workflow with human review before scaling it.
How long does AI transformation take?
A focused workflow can be tested within weeks, but broader AI transformation generally unfolds over months or years. It is an ongoing process because systems, team skills, risks, and AI tools continue to change.
What does AI transformation lead to?
Done well, AI transformation can increase capacity, improve decision-making, personalize customer experiences, and support new products, channels, and business models. The result is a business that can use AI consistently across its workflows while maintaining clear ownership, measurement, and control.
Get news, trends, and strategies for unlocking new growth.
Unified commerce for the world’s most ambitious brands
