What is Proof of Concept?
A Proof of Concept is a small-scale, time-limited project designed to validate whether a proposed AI solution can technically work and deliver the expected results, typically completed in four to eight weeks before committing to a full-scale implementation.
What Is a Proof of Concept?
A Proof of Concept (PoC) is a focused experiment that tests whether an AI solution can actually work for your specific business context. It is deliberately small in scope, short in duration — typically 4 to 8 weeks — and designed to answer a fundamental question: "Can this AI approach solve our problem with our data?"
A PoC is not a finished product. It is not meant to be deployed to customers or integrated into production systems. It is a learning exercise that helps you make an informed decision about whether to proceed with a larger investment.
Why Proof of Concepts Matter
AI projects carry inherent uncertainty. Unlike traditional software where requirements can be precisely specified upfront, AI systems depend on data quality, model performance, and real-world conditions that are difficult to predict in advance. A PoC reduces this uncertainty by testing the riskiest assumptions before you commit significant resources.
The financial logic is compelling:
- A typical PoC costs USD 10,000 to 50,000 and takes 4 to 8 weeks
- A failed production AI project can cost USD 200,000 to 1,000,000+ and take 6 to 12 months
- The PoC helps you avoid the expensive failure by testing feasibility early
What a Good PoC Includes
Clear Hypothesis
Define exactly what you are testing. Example: "We hypothesize that a machine learning model trained on our past 24 months of sales data can predict next-month demand with at least 80 percent accuracy."
Defined Scope
Limit the PoC to one specific use case, one dataset, and one success metric. Resist the temptation to expand scope during the PoC — that is what the full project is for.
Representative Data
Use real business data, not synthetic or demo data. The PoC must validate that AI works with your actual data, including its imperfections. If your data is messy, the PoC will reveal that, which is valuable information.
Success Criteria
Establish specific, measurable criteria before starting. These might include:
- Model accuracy thresholds (e.g., 85 percent classification accuracy)
- Processing speed requirements (e.g., under 2 seconds per prediction)
- Cost targets (e.g., processing cost below USD 0.10 per document)
- User satisfaction thresholds (e.g., 70 percent of test users find it useful)
Evaluation Framework
Plan how you will evaluate results. Who will review the output? What decisions will be made based on the PoC outcomes? What are the criteria for moving forward versus stopping?
PoC Process: Step by Step
- Define the hypothesis — What specific assumption are you testing?
- Prepare the data — Gather, clean, and format the relevant dataset
- Select the approach — Choose the AI technique and tools to test
- Build the prototype — Develop a minimal working model
- Test with real data — Run the model on your actual business data
- Evaluate results — Compare outcomes against your pre-defined success criteria
- Document findings — Record what worked, what did not, and what you learned
- Make a go/no-go decision — Decide whether to proceed to a pilot phase
Common PoC Mistakes
Scope Creep
The PoC expands from "can we predict demand?" to "let us build an entire demand planning platform." Keep it focused on validating the core hypothesis.
Using Demo Data Instead of Real Data
A PoC that works with clean demo data but has never touched your actual messy data proves nothing about real-world feasibility.
No Success Criteria Defined Upfront
Without pre-defined success criteria, teams tend to rationalize whatever results they get. Define what "good enough" looks like before you start.
Confusing PoC with Pilot
A PoC tests whether something can work. A pilot tests whether it does work in a real operational environment. They are different phases with different goals.
Ignoring Negative Results
A PoC that proves the approach does not work has saved you a much larger investment. Treat negative results as valuable information, not failure.
PoC Considerations for Southeast Asian SMBs
- Budget efficiency — PoCs are especially valuable in markets where investment capital is limited, allowing you to test before committing
- Vendor evaluation — Use PoCs to evaluate AI vendors and consulting partners before signing long-term contracts
- Data reality checks — Many ASEAN businesses discover during PoCs that their data needs significant cleanup, which is better to learn early
- Stakeholder buy-in — A successful PoC is one of the most powerful tools for convincing boards and investors to fund AI initiatives
- Local context — Ensure your PoC tests AI performance with local data, languages, and business conditions, not just global benchmarks
For CEOs managing limited budgets, the proof of concept is the most risk-efficient way to explore AI opportunities. Rather than committing hundreds of thousands of dollars to a full AI implementation based on vendor promises, a PoC lets you validate the approach with a fraction of the investment. If the PoC fails, you lose weeks, not months. If it succeeds, you have concrete evidence to support a larger investment.
The PoC also serves as a critical decision-making tool for the leadership team. It transforms abstract AI discussions into concrete demonstrations with real data and measurable results. This is far more persuasive than slide decks and vendor demos when making investment decisions.
For CTOs, the PoC is where technical assumptions meet reality. It reveals whether your data is actually good enough, whether the AI approach is technically feasible, and whether integration with existing systems is practical. These insights are essential for accurate project planning, budgeting, and timeline estimation. A CTO who skips the PoC phase is essentially estimating costs and timelines blindly.
- Define specific, measurable success criteria before starting the PoC — never evaluate results without pre-set benchmarks
- Use real business data, not synthetic or demo data, to validate that AI works in your actual context
- Keep the scope narrow and resist feature creep — the PoC should test one hypothesis, not build a product
- Budget 4 to 8 weeks and USD 10,000 to 50,000 for a typical PoC, depending on complexity
- Treat a negative PoC result as valuable information that saved you from a larger failed investment
- Include business stakeholders in PoC evaluation, not just technical teams
- Document all findings thoroughly so they inform the pilot and production phases
Frequently Asked Questions
What is the difference between a proof of concept and a pilot?
A proof of concept tests whether an AI approach can work technically with your data. It is a short, controlled experiment. A pilot tests whether the solution works operationally in a real business environment with real users. The PoC answers "can it work?" while the pilot answers "does it work in practice?" A successful PoC leads to a pilot; a successful pilot leads to production deployment.
How do we know if a PoC is successful?
Success is determined by the criteria you defined before starting. If the AI model meets your accuracy, speed, and cost thresholds using your real data, the PoC is successful. However, partial success is also valuable. For example, if the model meets accuracy targets but requires more data than expected, the PoC has successfully identified both the opportunity and the additional requirements.
More Questions
For most SMBs without dedicated AI teams, engaging an AI consulting partner or vendor to run the PoC is more efficient. They bring expertise, tools, and experience that accelerate the process. However, ensure your internal team is involved and learning throughout the PoC. The worst outcome is a successful PoC that nobody internally understands. If you do use an external partner, require knowledge transfer as a deliverable.
Need help implementing Proof of Concept?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how proof of concept fits into your AI roadmap.