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The most common EDG application mistakes on AI projects are starting work before approval, vague or tool-first scoping, missing documentation for the claim, and underestimating what the business needs to actually run the solution after go-live. None of these are exotic problems, they are avoidable process issues that show up repeatedly when AI projects are scoped in a hurry.

This is not a guaranteed checklist to approval, since only EnterpriseSG assesses and approves applications. It is a practical list of the mistakes we see businesses make when preparing an AI project for EDG, drawn from how grant-funded projects are generally structured and where they tend to go wrong.

Why these mistakes are so common with AI projects specifically

AI projects tend to attract a particular kind of urgency that more conventional IT projects do not. A business owner sees a competitor using a chatbot, or reads about what AI can do, and wants to move fast. That urgency is understandable, but it is also exactly what produces the mistakes below: skipping the scoping step, assuming the technology alone is the value proposition, and treating the grant application as a formality to get through quickly rather than a reflection of how well-planned the project actually is.

1. Starting the project before applying

This is the single most common and most costly mistake. EDG generally requires approval before project work begins, including signing contracts on the assumption that grant support will follow. If you have already started, that portion of the work is typically not eligible for support. Fix: treat "apply and get approval" as the first step of the project timeline, not a parallel administrative task.

2. Scoping the tool, not the capability

Applications that lead with "we want to buy an AI chatbot" are weaker than applications that lead with "our customer response process is slow and inconsistent, and here is how AI automation restructures it." Fix: define the business problem and the before-and-after first, then bring in the tool as the mechanism. Our article on EDG process redesign for AI automation covers this in more detail.

3. No clear baseline or success measure

Without a baseline (how the process works today) it is difficult to demonstrate the capability upgrade the grant is meant to support. Fix: document the current process, even roughly, before writing the application. Time taken, people involved, error rates if known, or simply the number of manual steps are all useful baselines.

4. Choosing the wrong grant track

Some businesses apply through EDG for something that is really a PSG-eligible off-the-shelf tool, or vice versa, which can mean the application does not fit the criteria of the scheme applied to. Fix: check whether a pre-approved solution already covers your need before assuming a custom EDG build is the only path. Our PSG vs EDG comparison walks through how to tell these apart.

5. Underestimating internal readiness

An AI project that looks good on paper can still struggle if the business is not ready to use it, for example if the underlying data is messy, or if no one owns the process of maintaining and improving the system after launch. Fix: an honest readiness check before applying. Our earlier post, is your business AI ready, is a useful starting point.

6. Weak or missing vendor documentation

A costed, specific proposal from your implementation partner is typically part of what supports an EDG application. A vague scope of work, or a quote with no detail on what will actually be built, makes it harder to demonstrate the project is real and well-planned. Fix: insist on a proper written proposal before submitting, not a one-line quote. If you need a properly scoped, costed proposal to work from, request a quote and we will put one together with you.

7. Poor documentation during and after the project

Because EDG pays out on a reimbursement basis, the claim stage requires evidence: invoices, contracts, proof of payment, and proof the deliverables were completed. Businesses that treat documentation as an afterthought often find the claim process slower and more painful than it needed to be. Fix: keep a dedicated folder of every invoice, contract, and delivery milestone from day one of the approved project.

8. Assuming the grant covers everything

EDG can support up to 50% of qualifying costs for eligible SMEs, not the full project cost, and only for costs that qualify. Businesses sometimes plan a budget assuming full or near-full coverage, which creates a cash flow problem when the actual reimbursement comes through lower than expected. Fix: budget the project assuming you will pay the non-supported portion plus the full amount upfront before reimbursement arrives. Our piece on budgeting an AI project when grants reimburse later walks through this in detail.

A ninth mistake worth calling out separately: treating the vendor relationship as transactional

Beyond the eight mistakes above, there is a subtler one that affects both the application and the delivery: choosing an implementation partner purely on price, without checking whether they actually understand grant-funded project structures. A vendor who has never scoped a project against EDG or PSG criteria before may write a proposal that is technically fine but does not map cleanly to what the application needs, missing details on the baseline, the qualifying cost breakdown, or the expected capability outcome.

This does not mean the cheapest vendor is automatically the wrong choice, but it does mean it is worth asking directly: has this partner scoped grant-eligible projects before, and can they produce the kind of costed, specific proposal an application benefits from? Our article on red flags when hiring an AI vendor in Singapore covers this evaluation in more depth, and it applies just as much to grant-funded projects as to any other AI engagement.

What does a well-prepared application actually look like end to end?

Pulling the fixes above together, a well-prepared AI project for EDG typically has:

• A documented current-state process, even if informal, showing where the friction is today
• A clearly defined target state describing what changes and what capability the business gains
• A costed, specific proposal from an implementation partner who understands what is being built and why
• Confirmation that the grant track (PSG or EDG) actually matches the project's scope and customisation level
• An honest internal assessment of whether the business is ready to use and sustain the solution after go-live
• A documentation plan in place before the project starts, not improvised at claim time
• A realistic budget that assumes the full cost upfront, with the grant treated as a later, partial rebate

None of this guarantees approval, since that decision sits entirely with EnterpriseSG. What it does is remove the avoidable reasons an application might be weak, which is the part actually within your control.

Quick reference: mistake vs fix

Mistake	Fix
Starting work before approval	Apply first, treat approval as the project's true start date
Tool-first scoping	Lead with the process problem and capability gap
No baseline	Document the current process before applying
Wrong grant track	Check PSG-eligible options before assuming EDG
Low internal readiness	Run an honest AI-readiness check first
Weak vendor proposal	Get a full, costed, specific scope of work
Poor documentation	Keep invoices and proof of delivery from day one
Assuming full cost coverage	Budget for the non-supported portion and the reimbursement gap

The pattern behind most of these mistakes

Almost every item on this list traces back to the same root cause: treating the grant application as paperwork instead of treating it as an extension of proper project planning. A well-scoped AI project, one with a clear problem, a clear plan, and a realistic view of what it takes to run afterward, tends to produce a stronger application almost as a side effect. This is the same discipline behind our AI opportunity and ROI mapping and AI implementation roadmap services: get the plan right, and the grant application becomes a much smaller lift.

How do these mistakes typically surface, in what order?

In our experience helping SMEs scope AI projects, these mistakes tend to surface in a fairly predictable sequence if nobody catches them early. First, a business gets excited about an AI idea and starts talking to vendors before thinking through the actual process problem (mistake 2). This leads to a vague or generic proposal (mistake 6), which produces a weak application, or worse, the business starts the project before realising an application was even needed (mistake 1). If the project does proceed, documentation often gets treated as a low priority during delivery (mistake 7), only to become a scramble at claim time.

Breaking this chain early, at the point where the AI idea first takes shape, is far more effective than trying to fix any single mistake in isolation later. This is why we push clients towards proper scoping before anything else, through our AI opportunity and ROI mapping work, rather than jumping straight into a vendor conversation.

Ready to see what AI can do for your business?

If you are preparing an EDG application for an AI project and want a second pair of eyes on the scoping before you submit, request a quote and we will help you tighten it up. WhatsApp us at +65 9184 9908, email glenn@freemansland.co, or reach out through our contact page.

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