AI project proposal evaluation means comparing bids on the same criteria rather than on the demo or headline price: the business step being automated, data preparation, a measurable accuracy threshold, pilot and acceptance terms, integration, model and data ownership, and support after go-live. A good proposal answers "what, to which standard, with whose data" rather than "how clever".
Why do AI proposals look so different from one another?
Say you have asked three firms to automate one process, such as reading supplier invoices or answering routine customer questions. One suggests a few weeks' work wired to an off-the-shelf language model, another proposes training a model on your own data, and the third is selling a platform licence plus consultancy days. All three presentations are polished, yet their scope, assumptions and risks have little in common.
With ERP or web software you can at least compare feature lists. AI is different because the output is probabilistic: the model will sometimes be wrong and its performance depends on your data, so the most important part of the proposal is often the part nobody wrote down. The weighted scoring approach from our guide to evaluating ERP proposals still applies, but the questions change.
This article is about the decision at the proposal stage. How to model the payback is covered in our piece on measuring AI project ROI, and choosing the first use case in where to start with AI in business.
What choosing the wrong proposal costs
When an AI project fails to deliver value, the cause is often not the technology but the fact that nobody defined success before the contract was signed. That is a proposal-stage problem. How common it is to see no tangible value at all is shown here:
In BCG's 2024 survey of 1,000 executives in 59 countries, 74% of companies surveyed had yet to show tangible value from AI, and just 4% had cutting-edge AI capabilities. (BCG — Where's the Value in AI?)
The buyer's own expertise is often the bottleneck. According to TurkStat's AI Usage Statistics 2025, among Turkish enterprises that considered but did not use AI, 74.2% cited a lack of expertise and 67.4% high costs. Without in-house expertise, a proposal gets judged on the vendor's own narrative, and cost anxiety makes the cheapest bid look safest.
Data readiness is the line item proposals skip most often. A survey of 565 data and analytics professionals by Drexel University and Precisely, a data management software vendor, 2025 Outlook: Data Integrity Trends and Insights, found a lack of data governance (62%) to be the top data challenge holding back AI initiatives, and only 12% said their data was AI-ready. A proposal that leaves data preparation out of scope simply hands that work back to you, or to a later change request.
Security belongs in the proposal too. In IBM's 2025 Cost of a Data Breach announcement, 13% of organisations reported breaches of AI models or applications, and 97% of those lacked proper AI access controls. A bid that says nothing about access rights, logging and data flows quietly transfers that risk to you.
Eight sections every AI project proposal should contain
Before scoring anything, check whether each proposal covers the sections below. A missing section tends to come back later as extra work, delay or a dispute.
- Target process step and baseline. Which step will be automated, and how many items a month, at what time per item and with what error rate is it handled today? Without this, success cannot be measured.
- Approach and rationale. Off-the-shelf model, retrieval over your documents (RAG), fine-tuning or training from scratch: which was chosen, and why? We compare these in our custom AI model training guide.
- Data requirements and preparation. How many records, which fields, who labels them and who cleans them? The labelling effort is explained in our data labelling guide.
- Measurable success criterion. Accuracy, precision or error rate, measured on which test set and accepted at which threshold, all in writing.
- Pilot and decision point. Pilot length and scope, and the figure that will decide "continue, adjust or stop" at the end.
- Integration. Which system the model connects to, through which interface, and where uncertain outputs go for human review.
- Security, data protection and data location. Does data leave your organisation, in which country is it processed, who has access and how long are logs kept? If you sell into the EU, also ask which risk class the system falls into under the EU AI Act.
- Ownership, maintenance and exit. Who owns the trained model, code, prompts and labelled data? Who retrains the model if performance drifts, and what is handed over if the contract ends?
AI project proposal evaluation scorecard
Having the sections is not enough; their content has to be compared. Fill in the table below for each bid, adjust the weights to your project and score each criterion from 1 to 5.
| Criterion | What to look for | Red flag | Suggested weight |
|---|---|---|---|
| Business goal | One measurable process step with today's baseline | "We transform your processes with AI" | High |
| Data preparation | Record counts, labelling and cleansing as work items | Data work missing or marked "provided by client" | High |
| Success criterion | Threshold on a held-out test set from your data | A generic promise of "high accuracy" | High |
| Pilot | Short, narrow, with a written decision rule | No pilot, or an open-ended one | Medium–High |
| Integration | Target system, interface, human approval flow | A separate screen that doubles staff workload | Medium |
| Security and data protection | Data location, access rights, logging | No data flow diagram | High |
| Ownership and exit | Handover of model, code, prompts and data | Model lives only on the vendor's platform | Medium–High |
| Maintenance | Performance monitoring and retraining terms | "The model learns by itself", no monitoring | Medium |
Multiply scores by weights and add them up, keeping the price in a separate column. That way you can see whether the cheapest bid is actually the most expensive once its missing items are counted.
Evidence on your data, not a demo
An AI demo nearly always looks good, because the vendor chose the examples. Before deciding, ask each vendor for results on a small sample of your real data that they have not seen in advance. Give the same sample to every bidder and the results become directly comparable.
This matters even more for generative AI. The Stanford AI Index 2026 reports that on Vectara's document-summarisation benchmark, even the top 15 models introduced unsupported information at rates between 1.8% and 5.4%. If the proposal does not say how such errors will be caught, put the controls from our article on reducing AI hallucinations to the vendor.
Ask one more question: does this job need AI at all? For a task with clear rules and tidy inputs, rule-based automation can be cheaper and more predictable; we set out the decision criteria in AI vs rule-based automation. A vendor who raises this unprompted is usually a good sign.
Terms to carry into the contract
The acceptance threshold, pilot decision rule and list of deliverables should move into the contract word for word. Settle who owns the code, trained model and labelled data using the clauses in our guide to software contracts and source code.
In Türkiye, personal data is governed by the KVKK (Law No. 6698, the country's data protection law, broadly comparable to the GDPR). If personal data is processed, the vendor usually acts as a data processor, and under the KVKK you share liability for its security measures. Purpose, security measures and what happens to the data at the end should therefore be set out in a written data processing agreement, and sending data to a model hosted abroad brings separate cross-border transfer rules into play. See AI and KVKK for detail, and take legal advice on your specific case.
To probe the vendor's own security practices, use a third-party vendor security assessment, and check whether the proposal addresses access rights, logging and subcontractors in writing.
If the current state of your data is unclear, ask for a short data review before signing. A basic data governance framework that assigns ownership of each field is as decisive for an AI project as the model itself.
How we approach this at Digital Bridge
Whether we are asked to quote for an AI project or to read competing proposals alongside you, we follow the same sequence:
- Process step and baseline first. Through our consultancy services we record the target step together with you: today's volume, time per item and error rate. Every line in the proposal ties back to that baseline.
- Choosing the right approach. AI integration that adds one capability to your existing software, an enterprise LLM assistant grounded in your own documents, or custom AI model training on your data: we write down which one is needed and why. If AI is not needed, we say so.
- A pilot with a written acceptance threshold. We agree the accuracy threshold with you before go-live, run the pilot on your data and define the decision rule up front.
- Integration with human review. We connect the model to your ERP, CRM or bespoke software through system integration, routing uncertain records for approval on the screens your staff already use.
- A written proposal. We do not sell packaged products; after a needs analysis we prepare a written proposal setting out scope, phases and fee. You can score it with the table above like any other bid.
Next step
If you have one or more AI proposals on the table, run each through the eight sections and the scorecard above; the empty cells become your list of questions for the vendor. Then get in touch with us and we will help you pin down the target step, the success criterion and the pilot scope. For more use cases and guides, visit our Artificial Intelligence topic page.