Agentic Commerce Cheat Sheet for Ecom Leaders

AGENTIC COMMERCE

CHEAT SHEET FOR ECOM LEADERS

By Stefan Hamann – May 16th, 2025

Introduction

Agentic Commerce is

emerging as the next major

shift in online retail, where AI-

driven agents act on behalf of

customers to find and

purchase products.

This cheat sheet provides a

strategic overview and tactical

guide for CEOs and decision-

makers to quickly grasp this

trend and its implications.

It’s designed to be read in

under 30 minutes, balancing

big-picture narrative with

actionable insights.

Agentic – Commerce - Cheat Sheet

What will you learn?

Why Pay Attention? AI agents are

moving beyond chat – they’re now

capable of completing transactions

end-to-end for shoppers . Industry

leaders from payments to retail are

already investing here, signaling that

agentic commerce could redefine how

customers shop.

What’s Inside: You’ll learn what agentic

commerce is, why it matters now, and

how it differs from previous eCommerce

shifts. We highlight key performance

metrics (both current and emerging),

outline a phased implementation

roadmap, identify new revenue

opportunities it unlocks, and share early

data points from the field – all with a

neutral, fact-based tone.

Shopware’s Perspective: Throughout,

the guide aligns with an open, API-ready,

and merchant-first approach. Adopting

flexible technology and a customer-

centric mindset will be crucial for

merchants to ride this wave.

Agentic – Commerce - Cheat Sheet

Agentic Commerce 1x1

– What It Is & How It’s Different

Definition: Agentic Commerce refers to online shopping

tasks performed by AI agents on behalf of users – from

product discovery all the way to checkout . In this model, a

customer can simply tell an AI assistant what they want

(e.g. “find me a laptop under $1000 and buy it”), set any

preferences or limits, and the agent handles the rest:

searching options, making recommendations, and

completing the purchase autonomously .

How It Differs from Past

eCommerce Shifts: This

isn’t just another

iteration of online

shopping or a new

marketplace – it’s a

fundamental change in

who performs key

actions:

Agentic – Commerce - Cheat Sheet

Agentic Commerce 1x1

– What It Is & How It’s Different

Traditional eCommerce: The

customer navigated websites or

apps, compared options, and

manually made the purchase (even

as we moved from desktop web to

mobile apps to voice assistants).

Decisions and checkout were user-

driven.

Agentic Commerce: Now an AI

agent can execute those steps.

Once the user sets the goal (what to

buy and constraints), the agent

takes over the heavy lifting . It’s a

leap similar to the shift from physical

to online shopping, or from online to

mobile, but now from manual to

autonomous. As Visa’s product chief

noted, this is “a new standard for a

new era of commerce” – akin to how

prior eras moved to online and then

to mobile .

Agentic – Commerce - Cheat Sheet

Agentic Commerce 1x1

– What It Is & How It’s Different

Why It Matters: With AI agents able to act 24/7 and handle

complex comparisons instantly, this trend promises

unprecedented convenience. Shoppers could soon have

personalized “auto-buy” experiences (imagine never running

out of household essentials because your AI reorders for

you). For merchants, it means sales can happen through

conversations and intelligent intermediaries, not just through

traditional web storefronts. It also raises the bar for data

quality and system agility – because if your product data

isn’t AI-ready or your updates are slow, these new agents

might bypass your offers. In short, agentic commerce has the

potential to streamline the customer experience even further,

but it requires rethinking how we present and syndicate

product information, and how we build trust in AI-driven

decisions.

Agentic – Commerce - Cheat Sheet

Why Now?

Recent Developments & Industry Momentum

Agentic commerce isn’t some far-off idea – it’s unfolding right now, driven

by breakthroughs in AI and a push from major players in tech, payments,

and retail. Key developments in the last months underline the urgency:

Perplexity × PayPal (May 2025): Perplexity AI (a chat-based answer engine) partnered with PayPal

to enable direct purchases inside a chat. Consumers can now ask Perplexity to find a product or

book a service and check out instantly with PayPal or Venmo – never leaving the conversation . The

entire flow, from payment through invoicing, happens behind the scenes via PayPal’s APIs, making

it nearly frictionless . This move extends Perplexity’s shopping assist capabilities to PayPal’s 430

million+ active accounts , showing how AI platforms and payment networks are converging.

Visa’s “Intelligent Commerce” Rollout (Apr 2025): Visa unveiled an API platform to let AI

assistants transact on users’ behalf . Visa is working with OpenAI, Microsoft, Perplexity and others

to embed payments directly into AI agents . The idea: soon people will have AI agents “browse,

select, purchase, and manage on their behalf” (as Visa’s Chief Product Officer put it) . To support

this, Visa introduced AI-ready payment credentials (tokenized cards that an authorized agent can

use securely) and tools for setting spending limits and rules . In short, one of the world’s largest

payment networks is preparing for a future where your AI assistant might be swiping your card

(securely) for you.

Agentic – Commerce - Cheat Sheet

Why Now?

Recent Developments & Industry Momentum

Surging Interest & Investment: The numbers speak volumes

about momentum:

Enterprise Pilots: 65% of organizations said they were piloting AI agents in Q1 2025 – up from

37% just one quarter earlier . This explosion in trials indicates that companies feel pressure to

experiment with agent-driven commerce now, or risk falling behind.

Consumer Curiosity: Shoppers are increasingly open to the concept. Two-thirds of

consumers expressed interest in using AI agents to secure limited-stock items (getting that

hot product before it sells out) and to auto-buy products once they hit a target price . In

fact, by late 2024 about 24% of consumers said they’d be comfortable letting an AI agent

shop for them outright – a figure likely to grow as successful use cases build trust.

Market Entry of Tech Giants: OpenAI’s ChatGPT, Microsoft (via Bing Chat and plugins),

Google, and others are all enabling transactions in their AI platforms, either through plugins

or partnerships. This broad tech ecosystem support means agentic commerce isn’t

confined to a single app – it’s becoming a capability that could exist wherever AI assistants

interact with users.

Bottom line: Advances in generative AI (which can understand intent and converse naturally)

combined with new payment infrastructures (from PayPal, Visa, Mastercard, etc.) have created

a perfect storm for agentic commerce to take off. Both supply (technology capabilities) and

demand (consumer expectations) are aligning in 2024–2025, making now the time for

eCommerce leaders to pay close attention.

Agentic – Commerce - Cheat Sheet

Key Performance Indicators – Measuring

Agentic Commerce Readiness

As you embrace agentic commerce,

traditional eCommerce KPIs (like

conversion rate, AOV, etc.) remain

important, but new metrics of

readiness and performance come

to the forefront. Here are four

essential KPIs to monitor, explained in

plain terms:

Let´s have a look on those Indicators:

Data Fill Rate: Are your product data and content fully populated for AI consumption? This KPI measures

the completeness of your product information that an AI agent would use (descriptions, specs, pricing,

inventory availability, images, metadata, etc.). A high data fill rate means your catalogue is richly

described and up-to-date, so an AI can confidently understand and compare your products. In practice,

if your data fill rate is 95%+, an agent is less likely to skip your item due to missing info. If it’s low, you risk

the AI overlooking or misrepresenting your offerings.

Real-world context: Think of an AI shopping assistant trying to find “a red running shoe, size 11, under

$100” – if your product listing lacks color or size info, it won’t get picked. Aim for near-100% fill on all key

attributes.

Agentic – Commerce - Cheat Sheet

Key Performance Indicators – Measuring

Agentic Commerce Readiness

Update Speed: How fast can your systems update information across channels? In an agent-driven

world, data freshness is critical. This KPI tracks the latency between a change in your backend (price,

stock level, etc.) and that update reaching all consumer touchpoints (web, apps, and any AI agent

interfaces). For example, if an item sells out, how quickly will an AI agent “know”? If you can push

real-time updates (or within minutes) to wherever agents fetch data, you prevent scenarios where

the AI tries to buy something that’s no longer available or misprices an offer.

Real-world context: An AI agent might scan dozens of sites for the best deal. If your price dropped

an hour ago but your feed to the agent platform hasn’t updated, you might be passed over or the

agent might present an outdated price – costing you the sale or causing customer frustration.

Update Speed is typically improved by robust, well-documented APIs and event-driven integrations

(hint: ensure your platform is API-ready and supports webhooks or real-time feeds).

Agent Uptake Rate: What share of your customers (or orders) are leveraging AI agent

assistance? This is a new twist on adoption rate – it gauges how many users opt into or actively

use agentic commerce features. For instance, if you offer a “personal shopping bot” on your site or

integration with external agents (like Alexa or Google assistant shopping), what percentage of

total transactions or customers go through these agents? A rising agent uptake rate indicates

growing trust and reliance on AI-driven shopping among your customer base.

Real-world context: Perhaps you launch a feature where customers can reorder via a chat

assistant or your mobile app’s AI. If 15% of monthly orders start coming through that assistant,

that’s your agent uptake. Compare it over time or against benchmarks – it reflects customer

willingness to delegate tasks. Low uptake might mean you need to educate users or improve the

agent experience (ease of use, trust signals, etc.).

Agentic – Commerce - Cheat Sheet

Key Performance Indicators – Measuring

Agentic Commerce Readiness

Agent Conversion Rate: How effectively does the AI agent turn opportunities into actual sales? This

measures the success rate of agent-driven interactions. For example, if an AI agent recommends 10

products to users or initiates 10 purchase processes and 4 result in completed orders, that’s a 40%

agent conversion rate. It’s analogous to traditional conversion rate but focused on scenarios where

an agent is involved in the shopping journey. This KPI helps you understand how “persuasive” or

efficient the agent is in meeting customer needs.

Real-world context: Imagine your AI chatbot suggests a product during a conversation or

proactively puts an item in the cart for subscription renewal – do customers go along with it and

complete checkout? A high agent conversion means the agent’s choices align well with customer

intent and trust. If it’s low, you may need to refine the agent’s recommendations (better

personalization, more accurate suggestions) or troubleshoot where in the process users drop off

(maybe the agent-added item wasn’t truly wanted, or users got cold feet letting the bot finalize the

purchase).

Agentic – Commerce - Cheat Sheet

Emerging Metrics – Looking Ahead to New

Success Measures

Beyond the core KPIs above,

forward-looking teams are

beginning to consider new

metrics that capture the unique

challenges of agent-led

commerce. These metrics may

not yet be standard, but they

could soon inform strategy as

agentic commerce matures:

Uniqueness Index: How distinct is your catalog and content in an AI-driven marketplace? This

metric reflects the originality or exclusivity of what you offer – essentially, how “replaceable” are

your products or information when an AI agent is hunting for solutions. A high uniqueness index

means your products (or their descriptions) stand out and aren’t simply commodities among

dozens of similar options.

Why it matters: AI agents excel at comparison shopping. If you and your competitors all

provide similar items with similar descriptions, an agent might default to the cheapest or the

one it can get fastest. But if your product has unique attributes or your content highlights a

niche value (and thus the agent recognizes it as a distinct option), you escape the commodity

trap. Real-specs, the other offers an exclusive bundle or custom support plan. An AI agent with

a user who values that bundle will flag the second retailer’s offer as unique. Measuring your

Uniqueness Index could involve tracking the percentage of products that have at least one

differentiating feature or content versus the market, or an SEO-like score indicating how unique

your descriptions are compared to others. Over time, boosting this index (through exclusive

products, richer content, or unique services) can make your offerings more attractive to

automated agents filtering through options.

Agentic – Commerce - Cheat Sheet

Emerging Metrics – Looking Ahead to New

Success Measures

Bot Trust Score: How much do your customers trust AI agents to act on their behalf

through your services? This is an emerging gauge of consumer confidence in letting bots

handle shopping. It can be measured via surveys (e.g. asking customers “How comfortable

are you letting our AI assistant make a purchase for you?” on a scale) or behavior (e.g. the

ratio of fully automated orders vs. those that required human approval). A higher Bot Trust

Score means users are confident the agent will get it right – choosing suitable products,

getting the details correct, and not making mistakes.

Why it matters: Trust is the currency of agentic commerce. If users don’t trust the bot,

they’ll double-check everything or simply opt out of using it, negating any convenience

gains. Real-world context: Suppose your store’s app has an “auto-buy for me” feature for

repeat purchases. If only 5% of users enable it, or many still click “Review before purchase,”

your Bot Trust Score is low. But if, after a few successful orders, 50% of those users are now

letting the AI auto-purchase without intervention, trust is growing. Factors that can improve

this score include transparency (showing the user what the agent is doing and why),

reliability (few errors or surprises), and clear safeguards (easy refunds or cancellations if

something goes wrong). Merchants should monitor this sentiment metric closely – it’s a

barometer of how ready customers are to embrace fully automated commerce.

Agentic – Commerce - Cheat Sheet

Implementation Roadmap – Adopting

Agentic Commerce Step by Step

How can your commerce team

practically implement agentic

commerce? A phased, time-based

roadmap can help turn this big idea into

actionable steps. Here’s a high-level

timeline for rolling out agentic commerce

capabilities in a controlled, strategic way:

Next 0–3 Months: Preparation and Assessment – Get your house in

order.

o Audit Data & Platforms: Evaluate your product data quality (fill rate) and integration

capabilities. Identify gaps in descriptions, inventory accuracy, or API endpoints that an AI

agent would use. Start initiatives to clean and enrich data (e.g. ensure every product has

complete attributes, up-to-date stock info, and SEO-friendly content that an AI can parse).

Ensure your eCommerce platform is open and API-ready – you’ll likely need to expose real-

time inventory, pricing, and ordering functions to external AI services. If your current systems

are not flexible (e.g., if they can’t easily share data or accept orders via API), plan upgrades or

use middleware.

o Internal Alignment: Bring your team up to speed. Host an “AI Commerce 101” session for key

departments (merchandising, IT, customer service, marketing) to explain what agentic

commerce is and why you’re exploring it. Encourage brainstorming of potential use cases

relevant to your business (for example, could an AI handle part of customer support, or assist

in reorders for your product line?). Establish a small cross-functional task force or working

group to lead the pilot efforts.

o Quick Wins (if any): If you have simple chatbot or automation tools already, experiment with

enabling purchasing in a limited way (for instance, adding a “reorder last purchase” via chat

on your site as a mini agent experience). This helps test the waters internally and with

customers, and surfaces any immediate technical hurdles on a small scale.

Agentic – Commerce - Cheat Sheet

Implementation Roadmap – Adopting

Agentic Commerce Step by Step

3–6 Months: Pilot an Agentic Experience – Build and launch a pilot

project.

o Choose a Use Case: Select one practical scenario to automate with an AI agent. This

could be smart reordering for subscription items, a personal shopping assistant on your

website/app for a specific category, or integration with an existing AI platform (e.g.

creating a plugin for ChatGPT or integrating with Alexa’s shopping list). Focus on a use

case with clear customer value but limited risk. For example, allow an AI to handle a

portion of customer service inquiries like “Where is my order?” or to assemble a

personalized bundle for holiday gift shopping that the user can approve.

o Develop & Integrate: Work with developers to implement the necessary connections. If

it’s an internal chatbot agent, integrate it with your backend so it can fetch product info,

place orders, and apply any business rules (like not selling below cost, respecting

inventory limits). If it’s an external agent (like a voice assistant skill or partnering with

Perplexity’s commerce program), leverage their APIs/SDKs to list your products and

enable transactions. This is where an API-first architecture pays off – it will significantly

speed up integration if your platform (such as Shopware or similar) is built for modular

connectivity.

o Test & Train: Before public launch, test the agent thoroughly. Have team members

simulate user requests to see how the AI performs – does it pick suitable products? Does

it follow rules (e.g., only ship within areas you serve)? Are there failure modes (like it can’t

find an item and responds poorly)? Refine based on these tests. Also, prepare customer-

facing messaging: make it clear what the agent can do and set expectations. Provide an

easy way for users to give feedback or get human help if needed (this safety net will build

trust). Soft-launch the pilot to a small segment or during a quiet period to monitor

performance closely.

Agentic – Commerce - Cheat Sheet

Implementation Roadmap – Adopting

Agentic Commerce Step by Step

6–12 Months: Expand and Iterate – Learn from the pilot, iterate, and

broaden scope.

o Measure & Optimize: Once the pilot is live, track the KPIs discussed (data fill, agent

conversion, etc.) along with traditional metrics. How many people are using it? Are

transactions completing smoothly? Gather qualitative feedback too – do customers feel

it’s helpful? Use these insights to fine-tune the agent’s logic or the user experience. For

instance, if you see a lot of agent-initiated carts not checking out, investigate why

(maybe the agent’s suggestions need improvement or users need more transparency to

trust the process).

o Gradual Rollout: If the pilot shows promise, consider expanding the agentic features to

more users, product categories, or additional tasks. For example, if you started with

reorders in one category, extend it to more products; or if the AI was only answering

questions, let it start placing orders for those who opt in. Ensure your infrastructure scales

– more usage means your APIs and systems will handle higher load in real-time.

Continue training your AI models or rules with new data coming from actual usage (many

agent systems improve with feedback loops).

o Training & Change Management: As AI agents take on more tasks, re-train your staff on

new workflows. Customer support should know how to handle issues from AI-placed

orders; merchandisers might need to learn how to feed the AI new promotions or content;

IT will need to monitor integrations for uptime. Maintain a merchant-first approach: use

the tech to assist your team, not just as a black box. For instance, your salespeople might

get alerts from the agent about what customers are asking for – enabling them to act

proactively. This human-AI collaboration can be a competitive advantage if managed

well.

Agentic – Commerce - Cheat Sheet

Implementation Roadmap – Adopting

Agentic Commerce Step by Step

Beyond 12 Months: Scale & Innovate – Integrate agentic commerce

into core strategy.

o Scale Successful Use Cases: By now, you’ll know which agentic commerce applications

are delivering value. Scale those across your entire customer base or across channels.

For example, if your AI personal shopper is boosting conversion for online store users,

consider bringing the same capability in-store via mobile app or kiosk, creating a unified

experience. Negotiate partnerships with major AI platforms if relevant (perhaps your own

“agent” can plug into popular assistant ecosystems to reach more customers).

o Stay Adaptive: The AI field is evolving rapidly. Plan for continuous updates – both in

technology (new AI model improvements, security measures, etc.) and in business

strategy. Regularly revisit your KPIs and customer feedback. Be ready to adjust rules – for

instance, if customers start trusting the agent massively, you might allow it more

autonomy (higher spending limits maybe); if a new risk emerges (fraud pattern or error),

implement stricter controls or additional validation steps. Keeping an open, adaptable

architecture is crucial here: it will let you swap in new AI services or integrate new data

sources with minimal friction as the landscape changes.

Throughout all phases, keep a customer-centric and merchant-controlled mindset. Use

agents to enhance convenience, not to remove the human touch entirely. The

implementation should be incremental – building trust at each step with both your team

and your customers. By moving methodically, you reduce risks and ensure that when

agentic commerce reaches mainstream adoption, your business is not only prepared to

support it technically but has earned a reputation as trusted and innovative in this new era.

Agentic – Commerce - Cheat Sheet

New Revenue Levers - Unlocked by Agentic

Commerce

Agentic commerce isn’t just about automation for its own

sake – it opens up tangible opportunities to boost revenue

and customer lifetime value. By allowing AI to handle certain

tasks, businesses can capture sales that might otherwise be

lost or not even attempted.

Here are practical revenue levers that agentic commerce

can pull, along with examples of how they work:

Agentic – Commerce - Cheat Sheet

New Revenue Levers - Unlocked by Agentic

Commerce

Smart Reordering & Subscription Autofill: AI agents can track when customers are likely

to need a repurchase and automatically reorder on their behalf. This reduces friction for

the customer (no more forgetting to buy) and increases retention for the merchant.

Example: A customer normally buys pet food every 4 weeks. An agent can monitor usage

and order a new bag right before the old one runs out, billing through a saved method.

This “never run out” service not only secures repeat revenue but can be a loyalty

differentiator. Many subscription businesses already use simple reminders; an AI takes it

further by placing the order for the customer (with an option to cancel if needed). Smart

reordering ensures you capture the refill sales instead of a competitor or not at all, and

can even smooth demand planning on the back end.

Bot-Built Bundles & Upsells: AI agents excel at analyzing customer preferences and

basket patterns to suggest bundles or add-on products that a user might not have

thought of – and even assembling them into one convenient purchase.

Example: A shopper tells an AI, “I’m looking to start a home gym.” The agent picks a set of

products – a yoga mat, dumbbells, resistance bands – and offers it as a bundle with a

slight discount. The customer, with one click, buys the whole package. Similarly, during a

regular purchase, an AI agent might automatically include a complementary item

(“You’re buying a DSLR camera, shall I add a memory card and case? Here’s the bundle

price.”). This increases Average Order Value (AOV) and can move more inventory.

Importantly, because it’s personalized and timely, it feels helpful rather than pushy – a

well-trained agent will only suggest relevant combinations. Retailers are already seeing

early signs of this; for instance, internal data from pilot programs indicate higher basket

sizes when AI curates the cart contents versus customers shopping solo.

Agentic – Commerce - Cheat Sheet

New Revenue Levers - Unlocked by Agentic

Commerce

B2B AI Negotiation & Quick Quotes: In B2B commerce, where purchases often involve quotes and

negotiations, agentic systems can streamline deal-making and lock in sales faster.

Example: A procurement AI for a business buyer communicates with your sales agent AI to request

a quote for 100 units of a product. Your AI, following rules you set (pricing floors, volume discounts),

instantly provides a tailored quote and even negotiates within allowed parameters – all in seconds,

not days of back-and-forth emails. Once terms are agreeable, the agent executes the order. This

speed can win business; being the first with a quote (especially if an AI can respond 24/7) might

secure the deal. It also frees your human sales team from handling small or routine negotiations,

letting them focus on complex, high-value relationships. Early signals: companies using AI in sales

processes have reported faster close rates and higher quote conversion, because the AI can

respond nearly instantly to inquiries while interest is hot.

24/7 Personal Shopping Concierge: Perhaps the most far-reaching lever is creating an always-on

AI personal shopper that proactively engages customers. Instead of waiting for the customer to

come to you, an AI agent can continually work in the background to find opportunities to delight

them – which translates into new purchases.

Example: A customer has been browsing camping gear but hasn’t purchased yet. Your AI agent

notices a price drop on a high-rated tent that fits their need and pings the customer: “The tent you

liked is now 20% off, shall I grab it for you?” Or it notices that with their hiking boots purchase, they

might need compatible trekking poles, and it suggests those (perhaps even auto-ships a

recommendation with easy return if not wanted, for a truly frictionless try-and-buy). By being

proactive and context-aware (using browsing history, wishlists, past purchases, seasonal trends),

the agent can unlock revenue that might have been missed – it’s like having a sales associate who

never sleeps and knows each customer intimately. Retailers leveraging AI for personalized

outreach have seen significant upticks in engagement and conversion, as the agent can catch the

right moment to make an offer.

Agentic – Commerce - Cheat Sheet

New Revenue Levers - Unlocked by Agentic

Commerce

Each of these levers is enabled by

giving more agency to the

technology in service of the

customer. Crucially, they should all

be implemented with clear

customer permission and the ability

to opt out – making sure these

conveniences are viewed as value-

add, not intrusion. When done right,

agentic commerce features not

only boost revenue but also

increase customer satisfaction

(because the shopping experience

becomes easier and smarter). They

embody a merchant-first ethos by

using tech to serve genuine

customer needs in real time, while

keeping the merchant in control of

the strategy (you decide the rules

the AI follows, the bundles it offers,

the limits on auto-discounts, etc.).

Agentic – Commerce - Cheat Sheet

Early Outcomes & Benchmarks – Proof Points

and Takeaways

Agentic commerce is still in its early days,

but we’re already seeing compelling

benchmarks and signals that indicate its

potential impact. Here are some data

points and early examples to benchmark

against and inspire your strategy:

• Revenue Uptick for Early Adopters: 69% of retailers leveraging AI

agents report significant revenue growth driven by more personalized

shopping experiences . In fact, companies that have begun weaving

agentic AI into sales report an average 6–10% increase in revenue as a

direct result . This suggests that even modest implementations (like

better recommendations or automated reorders) can lift the top line. It’s

a strong early validation that investing in these capabilities pays off.

• Efficiency and Conversion Boosts: Businesses are noting efficiency gains

that translate to sales. For example, Verizon saw nearly a 40% increase

in sales productivity after deploying an AI assistant to help its human

reps with customer interactions . While that case is about AI assisting

staff, not customers, it underlines how quickly AI can influence

conversion rates by providing timely, smart support. We can expect

similar or greater uplift when AIs directly assist customers in making

purchases (imagine that level of responsiveness applied to every online

shopper in real time).

Agentic – Commerce - Cheat Sheet

Early Outcomes & Benchmarks – Proof Points

and Takeaways

• Consumer Readiness is Rising: Consumer trust in AI for commerce is no

longer a major barrier for a sizeable segment of the market. Surveys show

about 1 in 4 consumers (24%) are already comfortable letting an AI agent

shop for them , and this comfort level will grow as positive experiences

accumulate. Moreover, consumers specifically appreciate use-cases like

deal-finding and convenience: 66% want AI agents to snag high-demand

items or auto-purchase at a target price on their behalf . These early signals

indicate a pent-up appetite for agentic services – if they prove reliable.

Monitoring your own customer base for willingness (e.g., uptake rates,

feedback) will be key; you might be surprised how many are ready to try an

AI-powered option, especially younger digital-native shoppers.

• Projected Scale of Impact: Experts forecast that AI agents could handle up to

20% of all eCommerce tasks by 2025 – including browsing for products,

answering questions, personalizing offers, and even completing checkouts. If

that comes to pass, it means a significant portion of the customer journey

might be influenced or managed by AI. Early movers in agentic commerce

will have an edge in shaping how those agents perceive and prefer their

products. Benchmark yourself against industry predictions: for example, if by

next year 10% of your orders are agent-initiated, you’re on track with (or

ahead of) the broader trend; if it’s 0%, it’s time to accelerate experiments or

risk playing catch-up later.

Agentic – Commerce - Cheat Sheet

Early Outcomes & Benchmarks – Proof Points

and Takeaways

Key Takeaway: Agentic commerce is

quickly moving from concept to reality,

with measurable benefits in conversion,

revenue, and customer engagement. The

competitive benchmarks are being set

now – retailers who successfully harness

AI agents are seeing more sales and

stronger loyalty. To ride this wave, focus

on being prepared and adaptable:

ensure your systems are open and API-

driven for easy integration, keep your

data quality high, and maintain a

merchant-first mindset so that as you

adopt automation, you continue to

control your brand experience and

customer relationship. Those who

combine the power of AI with the agility

of a modern commerce platform will be

best positioned to thrive in this new era

where machines as buyers (and buying

assistants) become part of the

commerce landscape.

Agentic – Commerce - Cheat Sheet

Sources:

•

•

•

•

•

•

Agentic – Commerce - Cheat Sheet