Most organisations start the AI vendor selection process backwards. They watch a demo, get excited about the interface, and then try to reverse-engineer whether the technology actually fits their problem. Months later, they're locked into a contract with a system that works beautifully in controlled conditions and falls apart against their real data, their actual workflows, and their specific compliance requirements.
This article is about doing it the other way around.
Define the Problem Before You Look at Any Vendor
The single most expensive mistake in AI procurement is starting with a shortlist instead of a problem statement. Before you open a browser or take a vendor call, you need a written definition of what you're trying to solve - specific enough that you can use it as a scoring rubric later.
A useful problem statement answers four questions:
- What decision or task is currently slow, expensive, or error-prone?
- What does good output look like, and how will you measure it?
- What data does the system need to work with, and where does that data live?
- What happens downstream when the AI gets it wrong?
That last question matters more than most people acknowledge. If your AI-assisted system misclassifies a customer support ticket, the cost is a delayed response. If it misclassifies a medical record or a financial transaction, the consequences are categorically different. Your tolerance for error should shape your entire vendor evaluation framework before you've spoken to a single sales team.
Document this. A one-page brief that your internal stakeholders have agreed on is worth more than any vendor comparison spreadsheet you build later.
Understand What You're Actually Buying
The AI vendor landscape is genuinely confusing because the same label - "AI platform," "AI solution," "AI assistant" - gets applied to products with completely different architectures. You need to know what category of system you're evaluating.
The main categories you'll encounter:
- Foundation model APIs (OpenAI, Anthropic, Google Gemini) - you're accessing a general-purpose model and building application logic around it
- Fine-tuned or domain-specific models - a base model adapted for a particular industry or task type
- RAG-based systems (Retrieval-Augmented Generation) - models that query your documents or databases at inference time rather than having knowledge baked in
- ML platforms - infrastructure for training, deploying, and monitoring your own models
- Vertical AI applications - end-to-end products built for a specific workflow (contract review, invoice processing, customer service automation)
Each category has different evaluation criteria. A RAG-based document system needs to be assessed on retrieval accuracy and chunking strategy, not just on the quality of its generated text. A vertical application needs to be assessed on workflow fit and integration capability, not on the underlying model architecture.
When a vendor is vague about which category their product falls into, that's a signal worth noting.
Run a Structured Technical Evaluation
Demos are not evaluations. A vendor demo uses curated data, optimal prompts, and a controlled environment. Your production environment is none of those things.
A structured technical evaluation has three components:
1. Proof of Concept on Your Own Data
Ask every shortlisted vendor to run a proof of concept (POC) using a sample of your actual data. Define the success criteria in advance - not vaguely ("it should work well") but specifically ("it should correctly extract the counterparty name from 95% of these 200 contracts").
Set a time limit. A POC that takes longer than four weeks is either a scope problem or a capability problem.
2. Failure Mode Testing
Deliberately test the edges. Feed the system ambiguous inputs, incomplete data, and edge cases you know exist in your environment. Ask:
- What does the system return when it doesn't know the answer?
- Does it hallucinate, or does it abstain?
- How does output quality degrade as input quality drops?
A system that confidently returns wrong answers is more dangerous than one that says "I don't have enough information to answer this."
3. Integration and Latency Testing
If the AI needs to fit into an existing workflow, test the integration under realistic conditions. Check:
- API response times under load
- How the system handles authentication and session management
- Whether the vendor's rate limits will constrain your use case
# Simple latency test pattern
import time
import requests
def test_api_latency(endpoint, payload, iterations=100):
times = []
for _ in range(iterations):
start = time.time()
response = requests.post(endpoint, json=payload)
elapsed = time.time() - start
times.append(elapsed)
avg = sum(times) / len(times)
p95 = sorted(times)[int(0.95 * len(times))]
return {"average_ms": avg * 1000, "p95_ms": p95 * 1000}Even a basic latency test like this, run against a vendor's API with your actual payload sizes, will tell you more than their marketing documentation.
Scrutinise Data Handling and Compliance
Australian organisations operating under the Privacy Act 1988, the Australian Privacy Principles, and sector-specific regulations (APRA CPS 234 for financial services, for example) need to treat data handling as a non-negotiable evaluation criterion, not an afterthought.
Ask vendors directly and get written answers to:
- Where is data processed and stored? Many AI vendors process data in US or European data centres by default. Depending on your data classification, this may create compliance obligations.
- Is your data used to train their models? Some API agreements include clauses permitting training on customer inputs. Read the terms carefully.
- What is their data retention policy? How long are your prompts and outputs stored? Can you request deletion?
- Do they have a current SOC 2 Type II report? This is a baseline security assurance standard. Ask for it.
- How do they handle a data breach? What are their notification timelines and your obligations downstream?
A concrete scenario: a mid-sized Australian law firm shortlisted three AI contract review vendors. Two had strong product demos. Only one could confirm that data was processed within Australian borders and provide documentation showing they did not retain client data for model training. The technical performance difference between the three was marginal. The compliance difference was decisive.
This is how vendor selection should work.
Evaluate Commercial Terms and Lock-in Risk
The commercial structure of an AI vendor relationship deserves the same scrutiny as the technical evaluation. Several patterns create risk that isn't obvious at signing:
Pricing tied to consumption without caps - token-based or usage-based pricing can scale unpredictably. Model what your actual usage will look like at 50%, 100%, and 200% of projected volume. Ask for a committed spend model with overage rates, not open-ended consumption pricing.
Proprietary data formats - if your data, fine-tuning work, or workflow configurations are stored in vendor-specific formats, migration becomes expensive. Ask what a migration off the platform looks like and how long it would take.
Model versioning and stability - foundation model providers regularly update their models. An update that improves average benchmark performance can degrade performance on your specific task. Ask whether you can pin to a specific model version and for how long.
Contract length versus technology maturity - the AI vendor landscape is moving fast. A three-year contract signed today may lock you into a platform that's significantly behind the market in 18 months. Favour shorter initial terms with renewal options, even if the per-unit cost is slightly higher.
Build an Internal Evaluation Committee
AI vendor selection decisions made by a single person - whether that's a CTO, a procurement manager, or a business unit head acting alone - tend to optimise for the wrong things. A CTO may over-index on technical elegance. A procurement manager may over-index on price. A business unit head may over-index on the demo experience.
A functional evaluation committee for AI procurement typically includes:
- A technical lead - assessing architecture, integration, and security
- A business process owner - assessing workflow fit and output quality
- A legal or compliance representative - assessing data handling and contractual risk
- A finance representative - assessing total cost of ownership, not just licence fees
Each committee member scores vendors against criteria relevant to their domain. The aggregate score surfaces trade-offs that would be invisible to any single evaluator.
This structure also creates accountability. When a vendor relationship goes wrong - and some will - a documented evaluation process shows that reasonable due diligence was performed.
What to Do Next
If you're currently in or about to start an AI vendor selection process, here's a practical sequence:
- Write your problem statement first. One page, agreed by all stakeholders, before any vendor contact.
- Categorise the vendors you're considering. Foundation model API, RAG system, vertical application - each needs different evaluation criteria.
- Build a POC brief. Define your test data, your success metrics, and your four-week time limit before you invite vendors to respond.
- Prepare your compliance questions. Draft your data handling questions now, not after you've fallen in love with a product.
- Assemble your committee. Identify the four stakeholders who need to be involved and get time in their calendars.
If you're not sure where to start, or if you're dealing with a complex environment where the problem statement itself isn't clear, that's worth getting external help on. The cost of a few days of structured scoping is trivial compared to the cost of a failed implementation or a compliance incident.
Exponential Tech works with Australian organisations on exactly this kind of structured AI vendor selection and strategy work. If you'd like to talk through your situation, get in touch.
