AI Agents in 2026: What Singapore SMEs Must Fix Now

A big chunk of your next “customer” won’t be a person scrolling Instagram or comparing tabs on Chrome. It’ll be an AI agent acting on someone’s behalf—finding options, checking stock, applying vouchers, choosing delivery, and paying in seconds.

That sounds futuristic until you look at how quickly behaviour is shifting. Research cited by Incubeta shows 70% of consumers say they’d welcome AI agents helping them shop. Meanwhile, McKinsey reports 62% of organisations are still experimenting with agentic AI and only 23% are scaling. That gap is the opportunity—and the risk.

This post is part of our “AI dalam Peruncitan dan E-Dagang” series, where we look at how AI changes retail and ecommerce: personalised recommendations, demand forecasting, inventory management, and customer behaviour analysis. The next step in that story is simple: AI won’t just recommend products. It will increasingly execute the purchase. For Singapore SMEs, the winners won’t be the loudest brands. They’ll be the most agent-readable brands.

Agentic commerce is here, and it doesn’t browse like humans

AI agents don’t “shop” the way people do; they decide and execute. Humans still form preferences and set constraints (“under $80”, “deliver by Friday”, “cruelty-free”), but agents do the mechanical work—search, compare, verify policies, apply loyalty benefits, and complete checkout.

That changes the marketing playing field in a blunt way: if your product info can’t be understood quickly by an AI system, you’re not competing. You’re not even being considered.

For Singapore SMEs, this is especially relevant because many businesses still rely on:

• Instagram DMs as a “catalogue”
• PDFs for menus or product lists
• Inconsistent SKU naming across Shopee/Lazada/Shopify
• Manual stock updates (often “best effort”)

Humans will tolerate that. Agents won’t.

A practical example: the Friday-night purchase

Think about a common scenario in Singapore: someone needs a last-minute gift for a weekend housewarming. In the past, they’d browse a few stores, compare delivery times, and make a call.

In 2026, the prompt might be: “Get a housewarming gift under $60, deliver tomorrow, not too generic.” The agent then checks availability, ratings, return policy, delivery SLA, and payment options—then buys.

If your store doesn’t expose accurate product details and delivery rules clearly, the agent moves on. No drama. No second chance.

Marketing’s new battleground: structured data, APIs, and trust

The brands that win with AI agents are the brands that are easy to verify. That means structured data, reliable policies, and consistency across channels.

Industry voices are already pointing here: retailers who expose catalogue and loyalty data via APIs become “agent-friendly storefronts”. The same logic applies to SMEs—even if you’re not building custom APIs tomorrow.

What “agent-readable” actually means for an SME

You don’t need an enterprise data team to get started. You need discipline.

Your product and policy information must be:

1. Structured (clear fields like price, size, colour, warranty, ingredients)
2. Consistent (same names, same variants, same SKUs across platforms)
3. Current (stock, delivery windows, promo validity)
4. Verifiable (return policy, guarantees, contact and escalation paths)

A useful mental model: If a spreadsheet can represent it cleanly, an AI agent can usually interpret it cleanly. If it lives in a designer PDF, it’s a problem.

“Trust” becomes measurable (and not just branding)

One quote from the source hits hard: banks will compete less on app UX and more on API access and trust. Retail will follow.

For SMEs, “trust” in an agentic environment often comes down to:

• Clear delivery SLAs (and actually meeting them)
• Transparent returns/exchanges
• Reliable product quality signals (authentic reviews, consistent descriptions)
• Fast resolution when something breaks

Agents are optimisers. They select the option with the least risk for the buyer.

Agent-to-agent customer service will raise the bar overnight

Here’s the reality: customer interactions will increasingly be agent-to-agent. Customers will use assistants to ask “Is this in stock?”, “Can it arrive by Tuesday?”, “What’s the return policy on opened items?” Brands will answer using their own AI systems connected to order and inventory data.

The competitive shift is brutal: speed becomes a feature. Conversations that took minutes collapse into one automated exchange.

What this means for Singapore ecommerce operations

If you’re running on disconnected tools (inventory in one place, orders in another, WhatsApp support on phones), your response times will stay human-speed. Agents will rank you lower.

You don’t need to automate everything. But you do need a single source of truth for:

• Inventory
• Order status
• Delivery promises
• Returns workflow

That foundation supports the AI layer later.

A simple KPI that will matter more than “response time”

Start tracking first-contact resolution rate for common questions:

• “Where’s my order?”
• “Can I change delivery date?”
• “Is this authentic / certified?”
• “Do you have size M in black?”

If your team can’t answer these instantly today, an agent won’t wait tomorrow.

The new SEO: “Share of Model” and being recommended by AI

Traditional SEO is about ranking on search engine results pages. In agentic commerce, a major goal becomes: getting recommended by the model.

The source calls this emerging KPI “Share of Model”—how often an AI recommends your brand when users ask for a product like yours.

How do you increase “Share of Model” without guessing?

You influence it the same way you influence human decisions—just more systematically.

Focus on signals an AI can extract and compare:

• Detailed product specs (not marketing fluff)
• Stable pricing and promo rules
• Delivery accuracy (promise vs actual)
• Return/refund clarity
• Category authority content (guides, comparisons, care instructions)

For our AI dalam Peruncitan dan E-Dagang series, this connects directly to personalisation and behavioural analysis: the more consistent your catalog + customer outcomes are, the more confidently an agent can choose you.

Singapore-specific content ideas that help agents and humans

If you sell retail products, publish content that answers high-intent constraints common in Singapore:

• “Same-day vs next-day delivery: which areas are supported?”
• “What to buy under $50 for CNY visiting (and why)”
• “Humidity-proof storage tips for skincare/supplements”
• “HDB-friendly appliance sizing guide”

This isn’t content for content’s sake. It’s decision support, which is exactly what agents package into recommendations.

AgentOps for SMEs: don’t build a frankenstack

Most companies get this wrong: they add random AI tools and call it “transformation”. The source warning is on point—AI only helps when placed at the right point in the process, with a rollout plan and change management.

Enter AgentOps—the operational layer for managing AI agents reliably (cost, compliance, monitoring). SMEs may not need “Agent Factories”, but the mindset still applies.

The SME version of AgentOps (what I’ve seen work)

Start with one workflow that’s repetitive and measurable. Examples:

• Auto-replies for delivery/returns questions, pulling from a policy page + order status
• Product recommendation assistant for your top 50 SKUs
• Promo eligibility checker (voucher rules, minimum spend, exclusions)

Then set three guardrails:

1. Accuracy threshold: define what the agent must never guess (refund policy, medical claims, delivery guarantees)
2. Escalation path: when it’s uncertain, it hands off to a human with context
3. Cost controls: cap usage, monitor failure cases, review weekly

This avoids the “frankenstack” problem—lots of tools, no reliability.

A 30-day checklist to become “agent-ready” (without a rebuild)

You can make meaningful progress in a month if you treat this like operations, not hype.

Week 1: Fix your catalogue hygiene

• Standardise product titles, variants, and SKUs across channels
• Ensure every product has: price, dimensions, materials/ingredients, warranty, FAQs
• Remove contradictory claims between listings

Week 2: Make policies machine-readable

• Create one clear returns/exchange page (plain language, bullet rules)
• Publish delivery SLAs by region (even if it’s just “West / Central / East / North”)
• Document “edge cases” (opened items, perishable goods, customised products)

Week 3: Connect the basics

• Ensure inventory updates flow to your main storefront reliably
• Centralise order status so support can reference one system
• Create canned answers tied to policy links (not free-typed messages)

Week 4: Pilot one agentic workflow

Pick one:

• An AI chat assistant limited to order tracking + returns policy
• A product finder for 3-5 categories (“gift”, “office”, “kids”, etc.)

Measure:

• Deflection rate (how many queries resolved without human)
• Resolution quality (spot-check transcripts)
• Time-to-answer for common intents

If it improves outcomes, expand slowly.

Where Singapore SMEs should place their bets in 2026

Agentic commerce will reward boring excellence. Clean data. Fast fulfilment. Clear policies. Consistent customer outcomes. Those are “marketing assets” now because agents use them to decide.

If you’re in retail or ecommerce, treat 2026 as the year you stop optimising only for clicks and start optimising for decision systems. That’s the practical bridge between AI personalisation (recommendations, demand forecasting, customer behaviour analysis) and revenue.

If you want help translating this into an execution plan—catalogue cleanup, structured content, marketing automation, and an agent-ready funnel—start with a focused audit and a 90-day roadmap. The SMEs that act early won’t just get more leads; they’ll be the default option agents keep picking.

What part of your customer journey is still “human-only” because your data is too messy to automate—catalogue, support, delivery promises, or returns?