HashMicro vs Infor: Comparing What Each Says About AI

Both vendors will arrive with AI on the slide deck. Scores, summaries, forecasts, recommendations. The vocabulary is almost identical across the market, which makes it useless for choosing between two systems.

What can be compared is narrower and far more useful: what each vendor states in writing, what data a feature reads, who can switch it off, who supports it in Singapore hours, and whether the records underneath are connected enough for any of it to work. AI features are only as good as the records they read, so most of the comparison is really about foundations.

This guide shows how to score your own foundations, works through a sales example, lists three Singapore considerations, gives six questions that force a like-for-like answer, summarises what each vendor states, and ends with a two-week bake-off for the shortlist.

The Foundation Both Vendors Depend On

AI features read your records, so the records decide the result. Score your own system Green, Amber, or Red on five points before either demo:

1.     Clean master data. Customers, items, and suppliers appear once. Test: can you list your top 20 customers with no duplicates?

2.     One connected record. Sales, stock, purchasing, and finance share the same customer and item. Test: can a salesperson see stock and payment history on one screen?

3.     Usable history. Several years of transactions sit in one place, because scores learn from the past. Test: can you pull a customer's full order history in minutes?

4.     Capture at the source. Data is entered when the event happens, not retyped the next day. Test: how many hours pass between a delivery and its record?

5.     Control and review. Permissions, an audit trail, and a named person who checks a suggestion before it changes a price or a message. Test: which actions can the system take without approval?

Score Yourself

Rate each point Green (done and reliable), Amber (partly in place), or Red (not yet). Then count your Greens:

•    4 to 5 Green: you are ready to pilot one feature on a small, measurable task.

•    2 to 3 Green: master data comes first, starting with the Amber and Red points.

•    0 to 1 Green: the connected system comes before any AI conversation, whichever vendor wins.

This is a rule of thumb, not a standard.

A Worked Example: Follow-Up Time

A distributor has six salespeople and 40 open quotes. Following up today means opening the customer file, reading old emails, and writing a summary, which takes roughly 20 minutes. Suppose a generated summary with a suggested next step takes 5 minutes to review.

•    Time saved per quote: 20 − 5 = 15 minutes

•    Across 40 quotes: 15 × 40 = 600 minutes, or 10 hours a week

•    Per salesperson: 10 ÷ 6 = about 1.7 hours a week

The saving appears only if the quote, the history, and the stock position live in the same system. If quotes sit in one tool and history in another, the summary has nothing to read. Run the sum with your own numbers, then ask each vendor to produce that summary from your exported records while you time it. The figures are illustrative, so replace them with your own.

Three Traps That Skew an AI Comparison

•    Comparing presentations, not answers: a confident demo is not a commitment, so compare written replies to the same questions.

•    Judging on tidy demo data: a feature that shines on sample records may stumble on your real ones.

•    Skipping the baseline: measure how long the task takes today, or you cannot prove the saving later.

Three Singapore Considerations

•    Personal data: CRM records usually contain personal data, so ask how each vendor handles consent and purpose under the Personal Data Protection Act.

•    Where data sits: Infor says its cloud ERP is hosted through Amazon Web Services. Ask every vendor, HashMicro included, where your data would be stored and who can access it.

•    Real records: customer data here mixes languages, name orders, and formats. A feature tested on demo data tells you nothing about yours.

Six Questions That Force a Like-for-Like Answer

Ask both vendors the same six, in the same order, and compare the written replies rather than the presentations:

1.     Which features are in the base price, and which are extra? A weak answer names a bundle without naming a price.

2.     What data does each feature read, and where is it processed and stored? Ask for the country, the provider, and who at the vendor can access it.

3.     Can a feature be switched off or limited by role? If a suggestion can reach a customer without a human in between, you need that control on day one.

4.     Can you see why a score or recommendation was produced? A number with no reasoning cannot be coached, defended, or improved.

5.     Who supports the feature in Singapore working hours? Ask whether that is the vendor or a partner, and which hours.

6.     What happens to your data after a feature runs? Retention and reuse belong in the contract, not the demo.

What Each Vendor States

Infor's side. Infor sells a family of products through partners. Reseller and review guides describe platforms such as LN, M3, and CloudSuite Industrial, each aimed at different industries, and Infor's own materials describe its cloud software as hosted on Amazon Web Services. For anything AI-related, pin down specifics rather than categories: which product and edition the feature belongs to, whether it is included in the subscription, which partner configures it, and who answers when a score looks wrong. Answers vary by product and by partner, so ask for them in writing. Infor's scale tends to matter when:

•    the group already runs Infor and has a partner who knows its industry

•    requirements sit squarely inside its target verticals

•    there is an IT team able to administer a deeper platform and its options

•    procurement policy favours a global vendor's hosting posture and roadmap

HashMicro's side. For mid-sized Singapore businesses whose sales data is scattered across quoting tools, spreadsheets, and inboxes, the first win is not a cleverer model. It is one record that an AI feature can read. According to HashMicro's Infor comparison page, its AI summarises sales activities, prioritises leads, provides closing-opportunity insights, and generates follow-up recommendations. The listed capabilities include automated pipeline summaries, customer meeting summaries, sales performance analysis, follow-up recommendations, AI lead scoring, and closing opportunity predictions. The same page shows an ISO 27001 certification and says implementation is handled by HashMicro's own team, with unlimited users at no cost.

Those are the vendor's statements, so test them with your own records. The vendor's own side-by-side view is set out in the HashMicro and Infor comparison. Put the six questions above to it as firmly as you would to Infor or its partner, so the comparison stays like for like.

A Two-Week Bake-Off

Before committing, ask each finalist to prove one feature on your own data:

•    the same 50 real records, untidied, and one measurable task

•    a baseline: how long that task takes your team today

•    the written answers to all six questions, before the pilot starts

•    a log of every question raised during the two weeks and how fast it was answered

•    one decision metric, such as hours saved per week per salesperson

Two weeks of your own data settles arguments that months of demos cannot.

Judge the Plumbing, Not the Slides

AI makes a connected, well-kept system faster. It does not repair a messy one, and no vendor's feature list changes that. Score the five foundations, put the six questions to both sides, then run the bake-off on your own records.

When you are ready to look at the product side, you can review the HashMicro Infor AI readiness details on its comparison page, then ask Infor or its partner the same six questions.