AI Agents in 2026: Make Your SME Discoverable

Most SMEs are still optimising for human shoppers. That’s the wrong buyer to focus on in 2026.

AI agents are increasingly acting on behalf of customers: they search, compare, check delivery promises, apply vouchers, choose payment options, and place orders. The customer still has preferences—but the agent does the work. If you sell online (or even just generate leads online), this changes what “good digital marketing” looks like.

This post is part of our “AI dalam Peruncitan dan E-Dagang” series, where we look at practical ways AI improves retail and e-commerce in Singapore—from personalisation to demand forecasting. Agentic commerce is the next step: AI doesn’t just recommend products; it increasingly executes the purchase journey.

Agentic commerce is replacing “omnichannel” as the real battleground

Answer first: Omnichannel matters less when an AI agent is doing the browsing; what matters is whether your business is machine-readable, API-accessible, and trusted.

For years, marketers invested in omnichannel: consistent branding across web, marketplaces, social, email, and physical stores. That work isn’t wasted, but the bottleneck has shifted. Autonomous agents don’t get impressed by your homepage design or your app’s animation. They want structured information they can parse quickly.

The source article quotes Silicon Foundry’s Mark Menell: retail is moving “from omnichannel to agentic commerce,” and the winners will be retailers who expose catalogue and loyalty data via APIs. Here’s the blunt translation for SMEs: if your product data is messy, incomplete, or trapped inside a platform your agent can’t access, you’ll be invisible at the moment of purchase.

What changes in the funnel when agents do the shopping?

Your funnel becomes more “data-first” than “creative-first.” Creative still matters—humans build preference—but conversion pathways will increasingly be decided by agents.

A practical way to view it:

• Humans create intent: “I want a healthier snack”, “I need a laptop bag under $80”, “I prefer local brands.”
• Agents execute intent: filter options, evaluate shipping/returns, check stock, compare ratings, apply membership perks, and pay.

So marketing in 2026 isn’t just about persuading a person. It’s about ensuring an agent can confidently choose you.

“Agents don’t shop—they decide”: visibility becomes interpretability

Answer first: To win agent-driven purchases, your brand must be easy for AI to interpret and rank—through structured data, consistent claims, and clear proof.

A key point from Silicon Foundry’s Eii Promisel: shopping and payments move to autonomous agents, and even banks compete less on app UX and more on API access and trust. That’s not just a finance story—it’s a marketing story.

Here’s what I’ve found SMEs often miss: they treat product pages as “sales copy.” Agents treat them as evidence.

What agents look for (and what they penalise)

Agents optimise for reducing customer regret. That means they prioritise clarity and reliability over hype.

Agents tend to reward:

• Specificity: dimensions, materials, compatibility, expiry dates, warranty terms
• Operational truth: real stock status, real delivery windows, real return rules
• Comparable data: consistent attributes across products (size charts, variants, bundles)
• Trust signals: verified reviews, recognised payment methods, transparent policies

Agents penalise:

• Missing attributes (“Size: standard”, “Delivery: fast”)
• Contradictory info (shipping policy differs on different pages)
• Dark patterns (“free trial” with unclear cancellation)
• Vague claims (“premium quality”) without measurable detail

Snippet-worthy stance: If your product information can’t survive a spreadsheet, it won’t survive an AI agent.

Agent-to-agent customer service will be the new conversion rate optimiser

Answer first: Speed and system integration will differentiate brands because agents will negotiate returns, delivery, and availability in seconds.

The article highlights agent-to-agent interactions: shoppers use assistants to confirm delivery times, verify return eligibility, or check stock—and brands respond with their own agents that can read order data and act instantly. Conversations that used to take minutes collapse into a single automated exchange.

For Singapore SMEs, this lands in a very practical place: your “marketing” includes your ops stack.

A Singapore SME scenario: the agent asks, your system answers

Imagine a customer’s agent message:

• “Need this delivered to Tampines by Tuesday 6pm.”
• “Returnable within 14 days? Who pays return shipping?”
• “Does this model fit a 16-inch laptop?”

If your business can respond instantly and consistently (because your stock, delivery SLA, and policies are unified), you win.

If the answer requires a staff member to cross-check a Google Sheet, WhatsApp a courier, and manually inspect a SKU list, you lose—not because your product is bad, but because the agent will choose a vendor with lower transaction friction.

What to fix first (without buying a massive enterprise stack)

You don’t need an “Agent Factory” to start. You need your basics connected:

1. One source of truth for inventory (even if it’s a simple OMS)
2. A clean delivery promise by zone/time (don’t overpromise; be consistent)
3. Returns and warranty rules written clearly and mirrored everywhere
4. Customer service macros rewritten into structured, reusable answers

This is directly aligned with our “AI dalam Peruncitan dan E-Dagang” series theme: AI improves customer experience most when inventory, fulfilment, and product data are reliable.

Make your marketing stack “agent-ready”: a 2026 checklist for SMEs

Answer first: Agent-readiness is mostly about structured data, APIs, and measurable trust—not fancy AI tools.

The source outlines requirements like real-time responsiveness, structured content, interoperability, and new KPIs such as “Share of Model” (how often an AI recommends your brand). For SMEs, you can translate that into an achievable roadmap.

1) Structure your product and service data like you mean it

If you sell products, standardise attributes across your catalogue:

• Title format (brand + model + key spec)
• Variant clarity (size, colour, pack size)
• Price rules (bundles, tiers, membership pricing)
• Availability (in stock/backorder, lead times)

If you sell services (common among Singapore SMEs), do the same:

• Service scope and exclusions
• Turnaround times and capacity
• Location coverage
• Pricing ranges and what changes the quote

2) Expose data through platforms and APIs you already use

“API” doesn’t have to mean custom engineering on day one.

Start with what you control:

• Keep your website CMS data clean (consistent fields, not free-text chaos)
• Ensure marketplace listings match your site (avoid contradictions)
• Use a commerce platform that supports integrations (orders, inventory, shipping)

When the time comes to integrate with agent protocols (the article mentions OpenAI’s ACP and Google’s AP2), you’ll be grateful your data wasn’t a mess.

3) Create content that agents can quote accurately

Agents summarise. They compare. They cite.

So write content with extractable statements:

• “Ships next business day for orders before 3pm.”
• “14-day returns for unopened items; return shipping paid by customer.”
• “Fits laptops up to 16 inches (max width 36cm).”

This doesn’t make your site boring. It makes your business easier to buy from.

4) Adopt a new KPI: “Share of Model” (and measure the inputs)

“Share of Model” is the idea that you track how often an AI recommends you.

SMEs can approximate this without enterprise tooling:

• Track brand search lift after content updates
• Add “How did you hear about us?” options like “ChatGPT/AI assistant”
• Monitor customer service logs for “my assistant said…” language
• Compare conversion rate changes when you improve delivery/return clarity

The win isn’t a dashboard. The win is noticing patterns early.

Don’t build a frankenstack: AI only helps when it’s placed correctly

Answer first: Random AI tools create cost and chaos; you need a rollout plan tied to specific revenue or service outcomes.

The article includes a sharp warning from Kate Frost (IMG): marketers should stop assuming “any AI anywhere” adds value. I agree. I’ve seen SMEs burn weeks wiring chatbots, content generators, and automation tools—then discover the real issue was inaccurate stock, unclear policies, or inconsistent pricing.

Here’s a disciplined way to avoid that:

Pick one use case per quarter (and tie it to a number)

Examples that actually map to revenue or cost:

• Reduce “Where is my order?” tickets by 30% through proactive shipping updates
• Improve product page completeness (missing attributes) from 40% to 5%
• Cut response time for delivery/returns questions from hours to <2 minutes

Then choose the smallest toolset that can do the job.

SaaS isn’t going away—and SMEs should be happy about it

Another strong point from the source: AI won’t replace SaaS; it extends it. SMEs benefit here because SaaS provides the guardrails—permissions, workflows, audit trails—while agents provide speed.

If you’re choosing systems in 2026, prioritise:

• Reliability and integrations over fancy UI
• Data exportability (you can move your data)
• Clear governance (who can change prices, policies, refunds)

A practical 30-day plan for Singapore SMEs

Answer first: You can become “agent-ready” in 30 days by cleaning data, tightening policies, and connecting your core systems.

Use this as a fast start—especially relevant after the year-end rush and ahead of Lunar New Year demand spikes, when fulfilment promises and stock accuracy get tested.

1. Week 1: Data audit

   • List your top 50 SKUs/services by revenue
   • Identify missing specs, inconsistent titles, unclear variants
   • Fix the top 20 first

2. Week 2: Policy clarity

   • Rewrite delivery, returns, warranty pages in plain language
   • Mirror the same rules on marketplaces and FAQs

3. Week 3: System connection

   • Ensure inventory updates in one place
   • Align courier SLAs with what you promise on-site

4. Week 4: “Agent-friendly” content upgrades

   • Add 5–10 extractable statements per top product/service page
   • Add a short comparison table where relevant (sizes, materials, bundles)

If you do only one thing: make your delivery and returns rules unambiguous. Agents love clarity because clarity reduces risk.

What this means for the “AI dalam Peruncitan dan E-Dagang” roadmap

AI in retail isn’t just personalisation and forecasting anymore. Agentic commerce forces a tighter link between marketing, operations, and customer experience.

McKinsey numbers cited in the source show 62% of organisations are still experimenting with agentic AI, and only 23% are scaling. That’s good news for SMEs: the window to act is still open. But it won’t stay open for long—once agent ecosystems standardise how they discover and transact, late movers won’t just be “less competitive.” They’ll be less visible.

If you want help prioritising what to fix first—product data, integrations, content structure, or automation—this is exactly the kind of work we do in Singapore SME digital marketing. The right goal isn’t “add AI.” The goal is be easy to choose when an AI agent is buying for your customer.

What part of your buying journey would an agent get stuck on today: product clarity, delivery promise, returns, or payment?