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Executive’s Guide to Industrial AI Platform Selection

A Structured Framework for Evaluating Industrial AI Vendors Across Accuracy, Deployment Speed, Integration Depth, and Total Cost of Ownership

Published by Sensfix Inc. — San Francisco | St. Petersburg, FL | Łódź, Poland | Seoul, South Korea

$184B

industrial AI market projected by 2030

questions in the RFP template

evaluation dimensions in the framework

EXECUTIVE SUMMARY

Executive Summary

Industrial AI is no longer a pilot program. The market is projected to reach $184 billion by 2030, and enterprises across manufacturing, transportation, utilities, and facilities management are moving from evaluation to procurement. The challenge has shifted from “should we deploy AI?” to “which platform should we choose?”

This guide provides a structured framework for evaluating industrial AI vendors — covering the dimensions that matter most for production deployments. It includes a 40-question RFP template, a vendor evaluation scorecard, and a 3-year TCO calculator that accounts for the hidden costs most vendor presentations omit.

CHAPTER 1

Why Platform Selection Matters More Than Algorithm Selection

The industrial AI vendor landscape is fragmented. Hundreds of companies offer computer vision, predictive maintenance, or workflow automation — each claiming superior accuracy. The buyer faces a paradox: the more vendors they evaluate, the less clarity they achieve.

Platform vs. Point Solution: The Fundamental Choice

Point Solutions

Address one problem well. A dedicated bird detection system. A shelf monitoring robot. Each excels in its domain but creates vendor proliferation when you need five or ten capabilities.

Platforms

Address multiple problems from a unified architecture. Computer vision, audio AI, IoT integration, and workflow automation share the same data layer and rule engine. Adding a new use case is a configuration change — not a procurement event.

For enterprises with 3+ operational challenges to address, the platform approach almost always wins on TCO — even if individual capabilities are comparable rather than superior.

CHAPTER 2

The Evaluation Framework — Seven Dimensions

Detection Accuracy

20%

What to measure: Not the number the vendor quotes, but how that number was derived.

What good looks like: Sub-1% error rate on cargo counting in production at an operational port terminal, measured over months of continuous operation.

Red Flags

Accuracy claimed only on proprietary benchmarks
No false positive rate disclosed
Demo accuracy significantly higher than production

Deployment Speed

15%

What to measure: Calendar time from contract signing to first production value — not "time to install."

What good looks like: Camera-based deployment on existing infrastructure delivering production results within 30–60 days.

Red Flags

Custom model training requiring 6+ months of data collection
Professional services costing more than first-year licensing
No reference customer confirming deployment timeline

Integration Depth

15%

What to measure: How deeply the platform connects with existing enterprise systems — and whether integrations are production-tested.

What good looks like: Bidirectional integration with CMMS/TOS/ERP at named reference customers.

Red Flags

"We integrate with everything" without naming specific systems
Integration requiring months of professional services
Customer required to build and maintain integration

Multimodal Capability

15%

What to measure: Whether the platform genuinely processes multiple data types through a unified architecture.

What good looks like: Production deployment where video AND audio data produce results neither modality achieves alone.

Red Flags

"Multimodal" means video + images from same camera
Audio AI recently added with no production reference
IoT requires separate middleware

Scalability

15%

What to measure: How cost and complexity scale from pilot to enterprise.

What good looks like: Unlimited users, cameras, data nodes under a single annual platform fee.

Red Flags

Per-camera pricing making large deployments unfeasible
Each new use case requires new module purchase
Performance degrades as camera count increases

Operational Reliability

10%

What to measure: How the system performs under real-world conditions — not demo conditions.

What good looks like: Edge-first architecture with graceful degradation during network outages.

Red Flags

No uptime SLA offered
Cloud-dependent with no edge fallback
No production uptime data available

Total Cost of Ownership

10%

What to measure: The full 3-year cost including everything the vendor presentation doesn't mention.

What good looks like: Transparent pricing with no hidden professional services, storage, or escalation costs.

Red Flags

Professional services doubling first-year cost
Annual price escalation clauses buried in contract
Data storage costs growing with deployment scale

CHAPTER 3

40-Question RFP Template

Accuracy & Performance (Questions 1–8)

1What is the measured detection accuracy in production (not demo) environments?
2What is the false positive rate in production?
3How was accuracy measured? (Independent validation, customer-reported, internal benchmark)
4How does accuracy vary across lighting conditions, weather, and camera angles?
5What is the inference speed (time from image capture to detection result)?
6How does performance scale with the number of simultaneous camera feeds?
7What is the minimum camera resolution required?
8Provide accuracy metrics and false positive rates from 3 reference customers.

Deployment & Implementation (Questions 9–16)

9What is the typical timeline from contract to first production value?
10Does deployment require new hardware? If so, itemize.
11How much customer data is required before the system is operational?
12What professional services are required for deployment? Itemize and price.
13Can deployment proceed without access to customer's internal network?
14What is the deployment architecture (edge, cloud, hybrid)?
15Provide 3 reference customers with confirmed deployment timelines.
16What happens if the pilot doesn't meet agreed success criteria?

Integration (Questions 17–22)

17Which enterprise systems have you integrated with in production?
18Describe the integration architecture (API, webhook, file, streaming).
19Is integration bidirectional? Provide examples.
20Who builds and maintains integrations — your team or ours?
21What is the typical integration timeline?
22How are integrations affected when either platform is updated?

Multimodal Capability (Questions 23–28)

23What data modalities does the platform natively process?
24Demonstrate a production use case using 2+ modalities simultaneously.
25Does the rule engine support cross-modal triggers? Provide examples.
26Is audio AI native or third-party? When was it added?
27How many IoT protocols are natively supported?
28Can the platform process unstructured text (maintenance logs, complaints)?

Scalability & Pricing (Questions 29–34)

29What is the pricing model? (Per-camera, per-user, per-site, platform fee)
30Are there limits on cameras, users, data volume, or API calls?
31How does adding a new use case work? (New license, configuration, or development?)
32Provide pricing for: 10, 50, 500, and 1,000 cameras.
33What is the annual price escalation policy?
34Are there minimum commitment periods or early termination fees?

Reliability & Support (Questions 35–40)

35What is the uptime SLA? What are the remedies for SLA breaches?
36How does the system handle network outages and camera feed drops?
37What is the support model? (Dedicated account team, ticket-based, community)
38What is the average response time for critical issues?
39Provide uptime metrics from 3 production deployments running 6+ months.
40How is system health monitored? Who is alerted when the AI system has issues?

CHAPTER 4

Vendor Evaluation Scorecard

Dimension	Weight	Vendor A	Vendor B	Vendor C
Detection Accuracy	20%	/10	/10	/10
Deployment Speed	15%	/10	/10	/10
Integration Depth	15%	/10	/10	/10
Multimodal Capability	15%	/10	/10	/10
Scalability	15%	/10	/10	/10
Operational Reliability	10%	/10	/10	/10
Total Cost of Ownership	10%	/10	/10	/10
Weighted Total	100%	/10	/10	/10

Scoring Guide

9–10ExceptionalProduction-proven with multiple references, best-in-class metrics

7–8StrongProduction-proven with at least one reference, competitive metrics

5–6AdequatePilot-proven but limited production references

3–4ConcerningDemo-proven only, claims not independently validated

1–2UnacceptableNo production evidence, significant capability gaps

3-Year TCO Calculator

Category	Year 1	Year 2	Year 3
Software licensing	$	$	$
Hardware (cameras, sensors, edge)	$	$0	$0
Professional services (deployment)	$	$0	$0
Custom model training	$	$ (retrain)	$ (retrain)
Integration development	$	$ (maint.)	$ (maint.)
Staff training	$	$ (turnover)	$ (turnover)
Internal IT support	$	$	$
Data storage and compute	$	$	$
Annual price escalation	—	$	$
Total	$	$	$

CHAPTER 5

Making the Decision: The 90-Day Evaluation

The most reliable way to evaluate an industrial AI platform is not an RFP process — it’s a structured 90-day evaluation on a real use case with measurable success criteria defined before the pilot begins.

Week 1–2

Platform Deployment

Deploy on existing infrastructure — 2–3 cameras, one use case.

Week 3–4

AI Models Configured

Models generating results in production environment.

Week 5–8

Production Operation

Continuous accuracy and false positive measurement against real conditions.

Week 9–12

Integration Testing

Bidirectional integration with enterprise systems validated.

End of Pilot

Comparative Report

AI performance vs. current manual process — with dollar-value impact.

Warning Signs During Evaluation

The vendor resists defining measurable success criteria before the pilot

Professional services costs for the pilot exceed the first year of licensing

The "pilot" requires 6+ months of data collection before producing results

Reference customers were reluctant to speak or gave vague endorsements

Integration with your enterprise systems was deferred to "post-pilot"

The vendor's technical team is unavailable during the pilot period

CONCLUSION

Conclusion

Industrial AI platform selection is ultimately a bet on which vendor will be a reliable long-term partner — not which one has the most impressive demo. Demos are optimized. Production is messy. The gap between the two is where vendor promises either hold or collapse.

The evaluation framework in this guide — seven dimensions, forty questions, a scoring model, and a structured pilot process — is designed to close that gap. It forces vendors to provide production evidence, name reference customers, disclose hidden costs, and commit to measurable outcomes.

The industrial AI market is maturing fast. The vendors that survive the next three years will be the ones that deliver measurable production value — not the ones with the best slide decks. Choose accordingly.

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