Hook

You've narrowed down to two vendors. Now you need to run a proof-of-concept to decide between them. Your team allocates 3 people for 4 weeks.

After 4 weeks, you have two POCs that "look good." Both vendors improved their accuracy. Both integrated with your systems (with some work). Both seem viable. So you pick one based on price.

Six months into implementation, you realize you picked the wrong one. The platform doesn't scale the way you expected. The operational burden is higher than anticipated. You're locked in by then.

The problem: your POCs didn't actually test what matters. You tested happy paths, not real-world conditions. You didn't measure operational effort honestly. You didn't simulate actual scale.

This lesson teaches you how to design a POC that gives you real answers, not just feel-good results.

Purpose

A POC should answer three questions:

• Does this tool work on my problem? (Accuracy, functionality)
  - Can my team operate this long-term? (Operational burden, scalability)
  - What will implementation actually cost? (Effort, timeline, risk)

Most POCs only answer question 1. That's why they fail to predict real-world success.

This lesson teaches you how to design a POC that answers all three questions and gives you confidence in your vendor choice.

Why This Matters

A bad POC leads to a bad vendor choice, which leads to a bad implementation, which leads to a failed project. Poor POC design is one of the top reasons AI projects fail.

Good POC design takes discipline and rigor:

• Define success criteria upfront (before starting)
• Test on real data and conditions (not ideal scenarios)
• Measure operational effort honestly (not just model accuracy)
• Simulate production scale (not just 1% of expected volume)
• Document findings clearly (make the decision easy)

Organizations that do this get honest POC results and make better vendor choices.

Core Concepts

Key Insight: POC Must Be Time-Bounded and Scope-Limited

A POC is not a pilot. Don't try to solve everything in 4 weeks.

Scope your POC carefully:

What to include:

• Single, well-defined problem (not 5 use cases)
• Real data (not a cleaned, normalized sample)
• Integration with one or two key systems (not all systems)
• Realistic volume (or at least, measure what happens at realistic volume)
• Operational testing (not just model training)

What to exclude:

• Scaling to production volume (test methodology instead, measure extrapolation)
• Beautiful dashboards (focus on functionality, not appearance)
• Every feature the vendor offers (test what you'll actually use)
• Custom integrations (if vendor won't support out-of-the-box, note it)

A good POC takes 4-6 weeks with 2-3 people full-time. If it takes longer, your scope is too large.

Key Insight: Success Criteria Must Be Defined Upfront

Before starting, define what "success" looks like. Not vague success (model works), specific success (accuracy >90%, integration takes <3 weeks, operational overhead <10 hours/month).

Success criteria should cover:

Functionality:

• Model accuracy (precision, recall, F1, or your metric)
• Latency (p95 or p99, measured in production environment)
• Throughput (requests per second or inferences per day)

Operational:

• Training time (hours to train on 1M records)
• Retraining frequency (can retrain weekly without manual effort?)
• Monitoring (can you see when model is degrading?)
• Failure handling (what happens when model crashes?)

Integration:

• Data pipeline setup time (how long to connect data sources?)
• API integration (how hard to integrate with your systems?)
• Data format support (does it handle your data format natively?)

Cost:

• Implementation effort (contractor hours needed?)
• Operational effort (hours/week to maintain?)
• Infrastructure cost (compute, storage?)
• Licensing (cost at your expected scale?)

Team:

• Learning curve (how long for team to be productive?)
• Support needed (can your team do this, or do you need vendor support?)
• Customization effort (estimate of customization work if needed)

If any success criterion isn't met, you note it and decide: "Is this a dealbreaker or just a trade-off?"

Key Insight: Stakeholder Alignment and Governance

Before you start a POC, align on what success looks like with all stakeholders. Not just your IT team, but business leaders, process owners, and anyone who will be affected by the decision.

Stakeholders to Include:

• IT Operations leadership (you): responsible for implementation, ongoing operations, and vendor management
• Process owners (business teams): understand current process, know what success looks like operationally
• Finance/Procurement: understand cost, legal, licensing implications
• Data governance/Security: understand data handling, compliance, risk implications
• Executive sponsor: has budget authority, aligned with business goals

Alignment Activities Before POC Starts:

1. Baseline Current State (Weeks 0-1)

• Document current process and performance (how well does it work today?)
• Identify pain points (what specifically will improve with this tool?)
• Measure business impact (how much does current process cost? How much time does it take?)

Example for demand forecasting:

• Current: Manual forecast + trend analysis, takes 40 hours/week of analyst time
• Current accuracy: MAPE 12%
• Current cost: $250K/year in analyst salaries
• Pain point: Can't respond to market changes quickly; forecast lags reality by 1-2 weeks

2. Define Business Success Metrics (Not Just Technical Metrics)

• Reduce analyst time from 40 hours/week to 10 hours/week (supervision only)
• Improve forecast accuracy from 12% MAPE to <8%
• Reduce inventory holding costs by $100K/year
• Enable real-time response to market changes

Your technical success criteria should directly map to business success:

• Technical criterion: "Model retraining daily without manual intervention" maps to business goal "real-time response to market changes"
• Technical criterion: "Operational burden <5 hours/week" maps to business goal "reduce analyst time to supervision only"

3. Get Stakeholder Sign-Off on Success Criteria

• Document success criteria in a shared document
• Have each stakeholder (IT, Business, Finance) review and sign off
• Include weighted importance: which criteria matter most?

Example weighting (total = 100%):

• Model accuracy: 30% (directly impacts business value)
• Operational effort: 25% (determines cost to operate)
• Integration complexity: 20% (determines implementation timeline and risk)
• Cost: 15% (must fit budget)
• Team learning curve: 10% (determines support need)

4. Establish Decision Authority and Process

• Who makes the final decision? (Usually: CIO or IT Director, with CFO/Finance input)
• What's the decision process? (See "Go/No-Go Decision Framework" below)
• What if vendors tie? (Predetermined tie-breaking criteria: e.g., "pick vendor with better support")

5. Regular Stakeholder Communication During POC

• Weekly status updates to steering committee (15 min)
• Monthly deep-dive reviews with key stakeholders (1 hour)
• Highlight findings that affect business or cost early, don't wait until end

This prevents surprises. If vendors are tracking poorly on a critical criterion, stakeholders know by week 2, not week 6.

Key Insight: Test on Real Data, Real Problems

The difference between a useful POC and a useless POC:

Useless POC:

• Use vendor's sample dataset
• Clean and prepare data for them
• Run on laptop/small server
• Test in ideal conditions

Useful POC:

• Use your actual data (same format, quality, scale as production)
• Measure time to prepare data (this is often 50%+ of project)
• Run on infrastructure similar to what you'll use
• Test on realistic problems with realistic edge cases

Example: Churn prediction POC

Useless approach:

• Vendor provides cleaned dataset of 100K customers
• You train model on it
• Model achieves 92% accuracy
• Conclusion: "Works great, let's buy it"

Useful approach:

• Use your actual customer data (300K customers, includes inactive, recently joined, high-churn segments)
• Data is messy (30% missing values, inconsistent formats)
• Measure time to clean and prepare (usually 2 weeks, not 2 days)
• Train on data from 2 years ago, test on recent 3 months
• Measure accuracy per segment (works well for segment A, poorly for B)
• Evaluate: "Acceptable for segment A, need customization for segment B"

Key Insight: Measure Operational Effort Honestly

Most POCs measure model accuracy but not operational effort. Then during implementation, the team discovers operating the platform is much harder than expected.

Components of operational effort:

Training

• Time to run end-to-end training pipeline (manual? automated?)
• Effort needed to tune hyperparameters (1 hour? 1 week?)
• Frequency of retraining (weekly? monthly?)

Monitoring

• Time to set up monitoring (1 day? 2 weeks?)
• Effort to investigate alerts (automated? manual?)
• Ability to detect model degradation (yes/no?)

Maintenance

• Time to deploy new model version
• Time to rollback if model is bad
• Effort to handle data issues (missing values, schema changes)

Support

• Response time for vendor support (4 hours? 24 hours? no SLA?)
• Ability to debug issues (good error messages? black box?)

Measure these during POC. Don't guess.

Key Insight: POC Structure and Timeline

A well-structured POC has phases:

Phase 1 (Week 1): Setup

• Get access to vendor platform
• Set up development environment
• Secure necessary data (with privacy protections)
• Document baseline (current performance without platform)

Phase 2 (Weeks 2-3): Implementation

• Build data pipelines (connect data sources, transform data)
• Train initial models (get something working)
• Measure accuracy and latency
• Document implementation effort

Phase 3 (Week 4): Operational Testing

• Run retraining cycle (does it work without manual intervention?)
• Test monitoring and alerting
• Simulate failure scenarios (what happens if data is delayed?)
• Measure operational burden

Phase 4 (Week 4-5): Scale Testing

• Test at 10x the data volume (measure how latency/cost scale)
• Measure cloud infrastructure cost at that scale
• Identify bottlenecks

Phase 5 (Week 6): Analysis and Recommendation

• Measure all success criteria
• Score against weighted importance
• Document findings
• Make clear recommendation: recommend, recommend with caveats, or don't recommend

Key Insight: Common POC Failure Modes and How to Avoid Them

Even well-designed POCs fail. Here are the most common failure modes and how to prevent them:

Failure Mode 1: Hidden Integration Complexity

What happens: Vendor's API works fine in lab. In week 3, your team discovers that integrating with your legacy authentication system requires 2 weeks of work. Suddenly your vendor is months away from production-ready.

IT Operations impact: You lose 1-2 months of implementation timeline. You're now deciding between vendors with incomplete information.

How to prevent:

• Do integration testing in week 1 or 2, not week 5
• Actually integrate with your real systems (authentication, logging, monitoring) during POC, not just APIs
• Have your IT architect review vendor architecture by week 1
• Test against real data source systems, not mocked data
• Document integration blockers as you find them

Failure Mode 2: Model Degrades on Out-of-Distribution Data

What happens: POC tests on historical data (2020-2024). Model achieves great accuracy. You deploy in production on 2025 data (which is different from historical data due to market changes, seasonality, or new customer segments). Model accuracy drops to 60%.

IT Operations impact: You're now managing a model that doesn't work. Credibility with business is damaged. You consider rolling it back, which wastes the implementation investment.

How to prevent:

• In week 4, simulate out-of-distribution data (new customer segments, recent data patterns, edge cases)
• Test retraining on recent data only (does model adjust when it sees new patterns?)
• Measure accuracy by customer segment, product category, time period (not just overall accuracy)
• Include automated monitoring of accuracy degradation in POC (can you detect when model stops working?)
• Plan for periodic retraining in production (don't assume static model will work forever)

Failure Mode 3: Operational Burden Underestimated

What happens: POC says "operational effort: 5 hours/week." In production, it's 20 hours/week because:

• Daily data quality issues (missing data, late arrivals, format changes)
• Model debugging when accuracy dips (why did this prediction change? need investigation)
• Stakeholder requests (urgent exceptions, one-off analyses, edge cases)
• Monitoring/alerting setup that wasn't planned

IT Operations impact: You now have a system eating up 20 hours/week of team capacity. The team is burned out. You consider decommissioning it.

How to prevent:

• In week 4, simulate realistic operational scenarios
• Run the platform for a full month (or 4 weeks) during POC if possible, don't just test it once
• Track every operational task (time, reason, complexity)
• Ask: "Will this happen in production?" Document it as operational overhead
• Build data quality monitoring into POC (measure how often data is invalid, how long it takes to fix)
• Establish SLAs during POC: "This model will need investigation if accuracy drops >5% or latency exceeds 500ms"

Failure Mode 4: Hidden Licensing Costs

What happens: Vendor quotes $100K/year. Six months in, they add charges for: extra API calls, additional compute resources, premium support, or "enterprise features" you need. Actual cost is now $250K/year.

IT Operations impact: You're over budget. Finance is unhappy. You're locked in and can't easily switch.

How to prevent:

• In week 1, get written quote detailing all costs (licensing, compute, support, add-ons)
• During POC, measure actual usage (API calls, compute hours, data ingestion volume)
• Ask vendor: "What would these usage levels cost at production scale?"
• Get written confirmation of pricing model (per-user? per-inference? flat fee? how does it scale?)
• Review contract terms (are there cost escalators? hidden minimums?)

Failure Mode 5: Vendor Support is Unavailable

What happens: During POC, you hit a blocker (model not converging, data loading fails, API returns errors). You reach out to vendor support. Response time: 48 hours. Investigation time: another 48 hours. By then, your POC timeline is blown.

IT Operations impact: You don't get answers during POC. You make decision based on incomplete information. In production, same support issues happen monthly.

How to prevent:

• Establish vendor support SLA in writing before POC (response time, hours of availability, escalation path)
• Test vendor support early (ask a real question in week 1, measure response time)
• Have vendor's technical architect available during POC (not just support)
• Document vendor responsiveness as part of POC findings

Practical Use Cases

Use Case 1: POC for Demand Forecasting Platform

Scenario: Retail company evaluating two forecasting vendors.

Success Criteria:

Criterion
Target

MAPE on test data
<8%

Time to train (daily)
<1 hour

Automatic retraining
Yes, unsupervised

Setup time
<2 weeks

Operational effort
<5 hours/week

Cost at scale
<$50K/year

POC Design:

Week 1: Setup

• Get historical sales data (5 years, 500 stores, daily granularity)
• Understand data quality (missing values? outliers?)
• Set up cloud environment similar to what you'll use
• Establish baseline: current forecasting method (trend + seasonality heuristic) has 12% MAPE

Week 2: Vendor A Implementation

• Load data into vendor's platform
• Train initial model (time it)
• Measure MAPE on recent 3 months
• Document implementation effort and decisions

Week 3: Vendor B Implementation

• Repeat for Vendor B
• Compare implementation effort

Week 4: Testing Both

• Run automatic retraining (measure if it works without manual intervention)
• Simulate real operational scenario: add new store, platform should auto-update
• Measure inference time (how long to get forecasts for 500 stores?)
• Document operational tasks and effort

Week 5: Scale Testing

• Measure performance at 10x stores (if you wanted to scale)
• Measure cost at that scale
• Identify bottlenecks

Week 6: Analysis

Vendor
MAPE
Setup Time
Operational
Cost
Recommendation

Vendor A
7.5%
10 days
3 hrs/wk
$40K/yr
Yes

Vendor B
6.8%
14 days
8 hrs/wk
$35K/yr
Conditional

Recommendation: Vendor A. Slightly higher MAPE (acceptable), much lower operational burden. Vendor B is cheaper but requires more ongoing effort, which isn't worth the savings.

Use Case 2: POC for Model Serving Platform

Scenario: Company evaluating KServe vs. Seldon for model serving.

Success Criteria:

Criterion
Target

Deployment time
<2 days

P99 latency
<150ms

Throughput
>1000 req/sec

Operational effort
<10 hrs/week

Model versioning
Multiple versions in parallel

Cost per inference
<$0.0001

POC Design:

Week 1: Setup

• Deploy Kubernetes cluster
• Deploy KServe and Seldon on same cluster
• Load test model (XGBoost, ~100MB)

Week 2: KServe Testing

• Deploy model to KServe
• Measure deployment time (from code to live prediction)
• Load test (send requests, measure latency and throughput)
• Measure Kubernetes infrastructure cost
• Deploy multiple versions, test canary deployment

Week 3: Seldon Testing

• Repeat for Seldon
• Compare ease of use, operational complexity

Week 4: Operational Testing

• Simulate model upgrade (deploy new version without downtime)
• Simulate failure (kill pod, measure recovery time)
• Monitor CPU/memory usage
• Measure cost per inference (GPU hours ÷ predictions)

Week 5: Analysis

Platform
Deploy Time
P99 Latency
Throughput
Operational
Cost
Winner

KServe
1 day
120ms
1200 req/s
8 hrs/wk
$0.00008
Yes

Seldon
2 days
140ms
1000 req/s
5 hrs/wk
$0.00009
Conditional

Recommendation: KServe. Better performance, comparable operational effort, lower cost. If your team has Seldon expertise, Seldon is acceptable.

Examples

Example 1: POC Success Criteria Template

Project: [Project Name]

Vendor: [Vendor Name]

POC Duration: [Duration]

Team: [Team Members]

Functional Requirements

• [ ] Model accuracy target: ***_* (metric: *****)
• [ ] Latency requirement: ***__ ms (p95 or p99)
• [ ] Throughput requirement: ***_* predictions/sec
• [ ] Integration points: **_____ (what systems must connect?)

Operational Requirements

• [ ] Training time acceptable: <***_* hours
• [ ] Automatic retraining: Yes/No
• [ ] Monitoring and alerting: ***** (what must be visible?)
• [ ] Operational effort: <***__ hours/week

Integration Requirements

• [ ] Data sources can connect: Yes/No
• [ ] API works with our stack: Yes/No
• [ ] Data format supported natively: Yes/No
• [ ] Custom integration needed: Yes/No (estimate effort if yes)

Cost Requirements

• [ ] Licensing cost acceptable: <$***_*/year
• [ ] Implementation effort: <***** weeks
• [ ] Operational effort cost: <$***__ /year (labor)
• [ ] Infrastructure cost: <$_____ /year

Go/No-Go Criteria

• [ ] All functional requirements met
• [ ] Operational requirements met
• [ ] Total cost below budget
• [ ] Team confident in long-term operation

If any criterion not met, document impact and decide: Is it a dealbreaker?

Example 2: POC Weekly Status Template

Week 2 Status: Vendor A Implementation

Task
Status
Finding

Data loading
Complete
Took 3 days (expected 2) due to schema validation

Model training
Complete
Time: 45 min (target: <60 min). Acceptable.

Accuracy measurement
Complete
MAPE: 7.2% (target: <8%). Exceeds requirement. ✓

Integration testing
In Progress
API works well, but error messages are unclear

Operational testing
Not started
Planned for Week 4

Key Learning:

• Schema mismatch caused delay in data loading (will add day to implementation timeline)
• Model accuracy is better than expected
• Need to understand error handling better

Next Week:

• Complete integration testing
• Start operational testing (retraining, monitoring)
• Measure full end-to-end time

Anti-Patterns

Anti-Pattern 1: POC Scope Creep

Week 1: "We'll test Vendor A on churn prediction."

Week 2: "While we're at it, let's test on recommendation too."

Week 3: "Let's also test integration with our entire data warehouse."

Week 8: POC is still running, no decision made.

Instead: Define scope upfront. One problem, limited scope. If you want more, run a second POC.

Anti-Pattern 2: Using Vendor's Demo Data

Vendor provides a beautiful, clean dataset. You train model on it. It works great. Then on real data, it doesn't.

Instead: Use your actual data, as-is (with privacy protections). This is the only test that matters.

Anti-Pattern 3: Not Measuring Operational Effort

You measure accuracy but not how much work it takes to operate the platform. During implementation, surprise: operational burden is huge.

Instead: Schedule operational testing in your POC. Retrain models, deploy new versions, handle failure scenarios. Measure effort honestly.

Anti-Pattern 4: POC Success Criteria Too Vague

"The model should work well."

"Integration should be reasonable."

"Cost should be acceptable."

These are too vague. You'll rationalize any result as acceptable.

Instead: Define specific, measurable criteria upfront. "Model accuracy >90%." "Setup time <2 weeks." "Cost <$50K/year."

Anti-Pattern 5: Not Documenting POC Findings

POC ends, and now there's disagreement about what you learned. Did Vendor A really work better?

Instead: Document findings clearly. Spreadsheet comparing vendors on each criterion, with score and justification.

Go/No-Go Decision Framework

After the POC, you need a clear decision process. Most organizations use gut feel ("feels right") or politics ("vendor we already use"). This framework gives you structure.

The Decision Matrix: Weighted Scoring

Step 1: Score Each Vendor Against Each Criterion

Use a 1-5 scale:

• 1: Does not meet requirement (dealbreaker)
• 2: Barely meets requirement (significant risk)
• 3: Meets requirement (acceptable)
• 4: Exceeds requirement (nice to have)
• 5: Significantly exceeds requirement (competitive advantage)

Step 2: Weight Each Criterion

Not all criteria are equal. From your stakeholder alignment phase, you should have weights. Example:

Criterion
Weight
Vendor A
Vendor B
A Score
B Score

Model Accuracy
30%
4
5
1.2
1.5

Operational Effort
25%
4
2
1.0
0.5

Integration Speed
20%
3
2
0.6
0.4

Cost Fit
15%
4
3
0.6
0.45

Team Learning Curve
10%
4
3
0.4
0.3

Total Score
100%

3.8
3.15

Vendor A wins: 3.8 vs 3.15.

Step 3: Check for Dealbreakers

If any criterion scored 1 (does not meet requirement), escalate. This is a conversation with stakeholders: "Vendor failed on [criterion]. Is this a dealbreaker, or do we override it?"

Example: Vendor B has integration speed of 1 (would take 6+ weeks to integrate). Decision: "Integration timeline is critical (Q2 deadline). Vendor B is disqualified."

The Financial Viability Check

Even if a vendor scores high, it must be financially viable.

Check 1: Does the Implementation Cost Fit Your Budget?

POC estimated $X in implementation costs. Do you have budget for $X + 50% contingency?

Example: POC estimated $150K implementation cost. Your approved budget is $180K. You can proceed (with tight margin).

If not: Go back to Procurement. Negotiate with vendor, reduce scope, or eliminate vendor from consideration.

Check 2: Do the Operating Costs Scale Linearly?

During POC, you measured costs at test scale (1% of production volume). Extrapolate to production scale.

POC measured: $10K compute cost for 1% of volume

Production scale (100x): $1M compute cost/year

Question: Does this fit your IT budget for AI infrastructure? If not, this vendor is not viable long-term.

Check 3: What's the Total Cost of Ownership (TCO) vs. Value?

This is covered in detail in the TCO lesson. For now: Does the 3-year cost produce 3x value? (basic ROI rule of thumb)

Example:

• 3-year TCO: $500K
• Expected 3-year value: $1.2M (reduction in analyst time + accuracy improvements)
• ROI: $700K net value, payback in year 2
• Decision: Financially viable

The Risk Assessment Check

Finally, evaluate risk of each vendor choice.

Risk Category
Vendor A
Vendor B
Mitigation

Vendor Viability
Stable, 10+ years
Startup, 2 years
Vendor A: lower risk

Data Security
SOC 2 Type II certified
Pending certification
Vendor B: higher risk; need compliance assessment

Integration Risk
APIs well-documented
Limited documentation
Vendor B: higher risk; need architect review

Support Risk
24/7 support SLA
Business-hours support
Vendor A: lower risk; better for production support

Lock-In Risk
Standard data formats
Proprietary formats
Vendor B: higher risk; harder to migrate away

Decision Rule: If Vendor B has multiple high risks, the risk-adjusted cost is higher, even if upfront cost is lower.

The Final Decision Template

Document your decision clearly for stakeholders:

DECISION: RECOMMEND VENDOR A

Recommendation Summary:

• Vendor A has highest weighted score (3.8 vs 3.15)
• Financially viable (TCO $500K, ROI payback Year 2)
• Lower implementation risk (well-documented APIs, proven integrations)
• Better ongoing support (24/7 SLA vs business hours)

Trade-offs:

• Vendor B is 15% cheaper on licensing ($85K vs $100K/year)
• Vendor B has slightly better model accuracy (6.8% MAPE vs 7.2%)
• Not material: higher operational burden for Vendor B (8 hrs/week vs 4 hrs/week) offsets accuracy gain

Conditions (if any):

• Confirm DataRobot support can provide technical architect during first 2 weeks of implementation
• Confirm licensing cost includes compute resources (vs. charged separately)

Next Steps:

• Procurement: Finalize contract with Vendor A (net-30 terms, 3-year commitment, performance SLAs)
• Implementation: Begin implementation planning (4-week timeline per POC findings)
• Finance: Approve $500K budget (implementation + 3-year operations)

This structure makes the decision clear, defensible, and documented. Your CFO and CIO can see the logic and support it.

Human Judgment Checkpoints

Checkpoint 1: Are Your Success Criteria Specific and Measurable?

Not "model should work," but "MAPE <8% on 2024 test data."

Checkpoint 2: Are You Using Your Actual Data?

Not vendor's demo data. Your data, in its real state (messy, incomplete).

Checkpoint 3: Have You Measured Operational Effort?

Actually ran retraining, deployment, failure scenarios. Not guessed.

Checkpoint 4: Have You Tested at Your Expected Scale?

Or at least measured how costs/performance extrapolate to your scale.

Checkpoint 5: Can You Make a Clear Recommendation?

After the POC, can you say "recommend Vendor A because [reasons]" or "don't recommend because [reasons]"? If you're torn, the POC didn't answer enough questions.

Key Takeaways

Scope POCs carefully: one problem, limited scope, 4-6 weeks. Don't try to solve everything. You want answers, not beautiful polish.

Define success criteria upfront, before starting. Make them specific and measurable. Use them to guide the POC and decide the outcome.

Test on your actual data and problems. Vendor demo data is not representative. Use real data, even if it's messy.

Measure operational effort honestly. Training accuracy is 20% of the decision. How hard is it to operate long-term matters just as much.

Simulate production conditions. Test at realistic scale (or measure extrapolation). Test failure scenarios. Test real integrations.

Document findings clearly. A comparison table with scores and justification makes the vendor choice obvious.

Make the decision based on criteria, not gut feel. Weighted scoring helps tie-break when multiple vendors seem good.

Remember: POC is a decision tool, not a pilot. The goal is to answer "should we use this vendor?" not "let's build it and see if it works."