Budget season is approaching, and if you’re responsible for AI investments, you need numbers that survive scrutiny. Here’s a framework for building an AI budget that’s defensible and realistic.

The Budget Categories

AI spending typically falls into several categories. Getting the categorisation right makes the numbers easier to track and defend.

Category 1: Productivity AI Licensing

This includes Microsoft Copilot, Google Workspace AI, and similar per-seat productivity tools.

How to estimate: Current user count x per-seat cost x expected adoption rate. Factor in tiered licensing if applicable.

Watch out for: License shelfware. If 70% of your seats see meaningful use, you’re doing well. Budget for realistic adoption, not theoretical full deployment.

2026 guidance: Expect per-seat costs to decline 10-15% as competition increases. But don’t assume aggressive vendor discounts – they have shareholders too.

Category 2: Cloud AI Services

Consumption-based AI services including inference APIs, managed ML platforms, and AI-enabled cloud services.

How to estimate: This is the hardest category. Start with current consumption, project growth based on planned use cases, and add contingency.

Watch out for: Usage-based pricing surprises. A successful pilot can generate unexpected production costs. Build in buffer.

2026 guidance: Expect 40-60% growth over 2025 spending if you’re scaling from pilots to production. More if you’re launching new applications.

Category 3: AI-Enabled Applications

Software purchases where AI is a key capability – intelligent document processing, AI-powered analytics, customer service platforms.

How to estimate: Standard software procurement. Vendor pricing plus implementation costs plus internal resources.

Watch out for: Hidden consumption fees layered on subscription pricing. Read contracts carefully.

2026 guidance: This category grows as point solutions mature. Budget for 2-3 new applications if you’re in growth mode.

Category 4: Custom Development

Building bespoke AI solutions, whether internally or with AI consultants Sydney as external AI implementation partners.

How to estimate: Project-based. Detailed scope and resource planning required. Add 30% contingency minimum.

Watch out for: Scope creep and the sunk cost fallacy. Build stage gates into budgets with genuine decision points.

2026 guidance: Only budget custom development for clear competitive advantage. Be ruthless about build vs. buy decisions.

Category 5: Infrastructure and Data

Data infrastructure, compute resources, and foundational investments that enable AI but aren’t AI-specific.

How to estimate: Often shared with broader technology budgets. Allocate proportion based on AI workload share.

Watch out for: Underestimating data preparation costs. Data quality work is expensive and essential.

2026 guidance: If your data foundations are weak, this should be a significant budget line. AI without good data is waste.

Category 6: People and Skills

Training, hiring, contractors, and upskilling investments.

How to estimate: Hiring costs (if applicable), training program costs, contractor rates for skills gaps.

Watch out for: The AI talent market has cooled but remains competitive. Budget realistically for market rates.

2026 guidance: Internal capability development should be a budget priority. Dependency on external resources is expensive and risky.

Sample Budget Allocation

For a mid-sized Australian enterprise with moderate AI maturity, a reasonable 2026 allocation:

Category	% of AI Budget
Productivity AI Licensing	25-30%
Cloud AI Services	15-20%
AI-Enabled Applications	20-25%
Custom Development	10-15%
Infrastructure and Data	15-20%
People and Skills	10-15%

Adjust based on your maturity and priorities. Less mature organisations should weight infrastructure and skills higher; more mature organisations can allocate more to applications and custom development.

Questions Finance Will Ask

Be prepared for these:

“What’s the ROI?” Have specific business cases for major investments. Vague “productivity improvement” won’t cut it. Quantify where possible.

“Why this and not that?” Be ready to explain prioritisation decisions. What’s in the budget represents choices; be able to defend them.

“What happens if we cut 20%?” Know what you’d sacrifice. Having a tiered proposal (minimum, target, stretch) shows maturity.

“How does this compare to peers?” Benchmarking data helps. Tech spending as percentage of revenue varies by industry – know your comparables.

“What did we get for last year’s spend?” The hardest question. If you can’t demonstrate value from prior investments, new requests face scepticism.

Building the Business Case

Each significant budget item needs a supporting business case:

Problem statement: What business problem does this solve?

Current state: How are we handling this today? What’s the cost?

Proposed solution: What are we investing in?

Expected benefits: Quantified where possible, qualified where not.

Investment required: Total cost including internal resources.

Timeline: When will we see value?

Risks: What could go wrong? How will we mitigate?

Template business cases make this process efficient. Don’t start from scratch each time.

The Approval Process

AI budgets often require multiple approval layers. Navigate this by:

Building coalition before formal submission. Socialize with key stakeholders informally. Understand concerns and address them before the formal process.

Speaking the language of your audience. Technical details for IT governance, business outcomes for executive leadership, financial metrics for finance.

Providing appropriate detail. Executive summaries for senior leadership, detailed breakdowns available if requested.

Anticipating objections. Think through likely pushback and prepare responses.

Multi-Year Considerations

AI investments often span multiple years. Consider:

2026 as year two or three of multi-year initiatives. What’s committed from prior decisions?

Foundation investments that enable future work. Data infrastructure investments may not show ROI immediately but unlock future possibilities.

Avoiding cliff edges. If a major project ends in 2026, what fills the gap? Budget cycles should smooth, not spike.

Final Thought

AI budget planning isn’t fundamentally different from other technology budget planning. It requires clear categorisation, realistic estimation, defensible prioritisation, and compelling business cases.

What makes AI budgeting challenging is immaturity – in the technology, the market, and organisational understanding. This means more uncertainty, more questions, and higher burden of proof.

Build budgets that acknowledge this uncertainty while still committing to specific investments. That’s the balance good AI budget planning achieves.