The real reason agent AI can't be used isn't the model—it's the data.
Generative AI adoption is accelerating. But results on the ground aren't keeping up. The root cause is the absence of a trusted data foundation.
About 90% of large domestic companies feel challenged by AI agent adoption
Overview of Challenges
Why does AI stall on the front lines?
Generative AI is deployed but doesn't deliver results. The top challenge is concentrated on 'data.'
| Challenge Category | Content | Response Rate |
|---|---|---|
| Confidentiality & Privacy | Concerns about handling confidential and personal information | 55% |
| System Integration | Complexity of integrating with existing systems | 51% |
| Data Quality | Not getting expected responses (data quality issues) | 46% |
| Accountability | Unclear output basis and inference process | 40% |
These appear as separate challenges, but the root converges on one point: 'Data that AI can trust and use is not prepared.'
Structural Barriers
Three Data Problems Agent AI Faces
Problem 01
Authenticity Problem
Sensor values, business logs, and contract records are exposed to loss and tampering risks as they pass through multiple points. Feeding them directly to AI induces hallucinations and distorts business reasoning.
Problem 02
Privacy Problem
Handing over all data necessary for business automation to external parties is not permitted under personal information protection laws and confidentiality management. The contradiction of 'wanting to prove but not show contents' blocks AI utilization.
Problem 03
Accountability Problem
If agent AI executes autonomously, humans must be able to verify and explain 'why that decision was made.' Traceability of processing grounds becomes a prerequisite for AI adoption.
Solution
What is Lemma Oracle
A 'data refinery infrastructure' that collects, verifies, and delivers real-world data to AI in a trusted form. Three functions—Normalize, Commit, Prove—provide a foundation where AI can safely execute business operations.
Function 01
Normalize
Normalize
Extract only attributes from encrypted documents via ZKP. AI agents can perform conditional reasoning, search, contracts, and payments without touching raw data.
Function 02
Commit
Commit
Identify issuers via DID and permanently record provenance information on-chain. Maintain a state where both AI and humans can audit and re-verify at any time.
Function 03
Prove
Prove
Prove only the 'fact that conditions are met' via ZKP without disclosing any confidential information. Can be safely presented to trading partners, audit bodies, and government agencies.
Implementation Results
What changes before and after implementation
Data Verification
Manual visual inspection and manual matching
Approval Process
Multiple confirmations, seals, email exchanges
Audit Response
Manually digging up records
AI Utilization
Stalled due to data quality concerns
External Proof
Either disclose confidential info or give up proving
Loyal Customer Authentication
Manual community management and authentication
Data Verification
Oracle automatically collects and verifies
Approval Process
Automatically record condition achievement, human final confirmation
Audit Response
Instantly provide timestamped provenance
AI Utilization
Safely deploy AI on verified foundation
External Proof
Prove only 'facts' via ZKP, keep confidentiality
Loyal Customer Authentication
VC auto-issuance and status proof (confidentiality protected via ZKP)
Self-Assessment
Do you have these challenges?
If even one applies, Lemma Oracle is effective. Click to confirm.
- Have operations that proceed based on external fact verification for approval, payment, or next process
- Spending manpower, time, and costs on that verification work
- Considering AI adoption but concerned about internal data quality and confidentiality management
- Need to prove traceability across supply chains
- Required to prove 'who did what when' for audit and compliance
- Reluctant to disclose confidential information when proving to trading partners or government
Download the Whitepaper for Free
From ZKP, DID, provenance management technical specifications to PoC design steps that can start in as little as a few weeks. We've compiled 'next actions' for those considering adoption.