This is Part 2 of our series on why enterprise AI fails. Read Part 1 if you missed it.
Enterprise teams aren’t blocked by model quality anymore. GPT-4, Claude Sonnet 4, Gemini Pro – pick your favorite, they’re all remarkably capable. The problem is that having a powerful engine doesn’t matter if you can’t get it approved, can’t switch when you need to, and can’t explain what it’s doing when regulators ask.
MIT’s 2025 research on AI project failures paints a clear picture: It’s not the models. It’s everything around AI.
The messy reality of deploying AI in the real world creates six systemic problems that kill projects faster than any technical limitation ever could.
Problem #1: The Make-or-Buy Paradox
Building for auditability and governance is slow; buying for speed creates governance gaps you can’t explain later.
When Air India needed to scale customer service, they identified a specific constraint: their contact center couldn’t grow with passenger volume. Instead of buying a black-box solution, they built AI.g, their generative AI virtual assistant. Result? Over 4 million queries processed with 97% full automation.
The lesson: They prioritized understanding what the system was doing over speed of deployment. McKinsey’s November 2025 AI survey confirms this pattern: organizations reporting “significant” financial returns are twice as likely to have redesigned end-to-end workflows before selecting modeling techniques.
The hidden cost of buying: IBM’s 2025 Cost of Data Breach Report reveals that 97% of AI-related security breaches involved organizations that lacked proper AI access controls. When you buy a black box, you can’t inspect it. When you can’t inspect it, you can’t secure it.
What Chiri does differently: Bring your own models, or use ours. Deploy cloud or self-host. Every action logged, every decision traceable. You get the speed of a managed service with the auditability of an in-house build, without having to choose one or the other.
Problem #2: Vendor Lock-In
Switching models often means rebuilding workflows and integrations from scratch.
The AI landscape is creating what multiple 2025 industry reports call “a new era of cloud vendor lock-in.” Organizations are either sticking with their current provider and only using the AI tools it offers, or spending significant money and time ensuring data is optimized for migrating between clouds.
The financial reality: According to cloud security analysts in 2025, switching cloud providers isn’t just technically complex, it’s financially punishing:
- Egress fees for moving data out
- Migration engineering time (6-12 months typical for complex applications)
- Parallel running costs during transition
- Lost optimizations when APIs don’t have direct equivalents
What Chiri does differently: Any model. One interface. No lock-in. Run models in parallel, switch mid-conversation, or compare outputs side-by-side (Council Mode). Use the best model for each job. Change your mind later without re-platforming. Your workflows aren’t tied to a vendor’s API, they’re portable by design.
Problem #3: Knowledge Fragmentation
Working solutions live inside teams, not as shared, reusable building blocks.
MIT’s 2025 research identified misaligned expectations and vague objectives as leading causes of AI failure. When knowledge is trapped in individual implementations, organizations can’t scale what works.
The pattern: A data science team builds a brilliant RAG implementation. It works perfectly for their use case. Six months later, another team tries to solve a similar problem and starts from scratch because:
- The solution isn’t documented as a reusable pattern
- The prompts and configurations aren’t version-controlled
- There’s no shared library of what works
- Every team becomes their own prompt engineering department
What Chiri does differently: Task Personas turn best practices into versioned, shareable, enforceable AI behaviors. System prompts + task definitions, allowed tools, output formats, guardrail constraints, and Git-like version history. When one team solves a problem well, every team benefits. RAG collections are shareable across the org, knowledge stops being trapped in private setups.
Problem #4: No Standardization
A dozen tools, a dozen ways of working, no collaboration layer.
MetricStream’s 2025 GRC Practitioner Survey found that only 13.76% of organizations have actually integrated AI into their GRC frameworks. The gap? Lack of standardization across teams and tools.
The compound effect:
- Team A uses one vendor’s API structure
- Team B uses a different approach entirely
- Team C has built custom tooling
- GRC can’t audit any of them consistently
- Security can’t enforce policies across all three
- Nobody can share learnings or compare results
Integration challenges reported in 2025: 47.75% of GRC professionals cite “integration with existing systems and workflows” as their top AI challenge, while 45.95% report a “lack of skilled talent to manage AI systems.”
What Chiri does differently: One interface for all models. One approach to RAG. One way to define tasks and guardrails. One audit trail format. One permission model. This isn’t about constraining creativity, it’s about creating a foundation that lets teams innovate without fragmenting governance.
Problem #5: Enterprise Security Gaps
Weak isolation, coarse permissions, insufficient controls.
IBM’s August 2025 Cost of Data Breach Report contains devastating statistics:
- 63% of breached organizations had no AI governance policies
- 97% of AI-related security breaches involved systems lacking proper access controls
- Shadow AI (unauthorized AI use) added an average of $670,000 to breach costs
- One in five organizations reported breaches involving shadow AI
The shadow AI problem: When employees use unauthorized AI tools with corporate data because approved tools don’t work, security gaps compound. IBM found that breaches involving shadow AI were more likely to result in compromise of personally identifiable information (65%) and intellectual property (40%).
Real-world impact: The average global data breach cost in 2025 was $4.44 million, but for organizations with high levels of shadow AI, costs were significantly higher. In the US specifically, breach costs hit $10.22 million, the highest anywhere.
What Chiri does differently: Hybrid RBAC + ABAC policy engine with context-aware decisions. Tenant isolation including database-level controls. Shareable document collections with granular access control. PII guardrails that detect and protect sensitive data in both input and output, with customizable strategies (redaction, masking, hashing, blocking). This is enterprise-grade security, not retrofitted access controls.
Problem #6: Compliance Nightmares
Missing audit trails and execution visibility when it matters most.
The EU AI Act is now enforceable with fines up to €35 million or 7% of global revenue. The regulatory landscape in 2025 is accelerating:
- 480+ state-level AI bills enacted in the US as of 2025
- EU AI Act enforcement beginning 2026 with risk-based classifications
- 38 states have enacted approximately 100 AI-related laws
- New requirements for hiring bias audits, algorithmic discrimination prevention, and data privacy
The compliance disconnect: NAVEX’s September 2025 research shows that while regulations multiply, organizational readiness lags. Only 18% of organizations have an enterprise-wide AI governance council, and compliance teams can’t audit what they can’t see.
The black box problem: When security, legal, and audit ask questions, “the model decided” isn’t an acceptable answer. You need to show:
- What data did it retrieve?
- What tools did it run?
- Which persona/guardrails applied?
- Which model was called, and when?
- What happened step-by-step?
What Chiri does differently: Every interaction produces execution traces you can review, search, and export. Answer the questions security, legal, and audit will ask, before they ask them. Immutable logs, scoped access to audit events, compliance review interfaces, and retention controls built in.
The Result Is Predictable
Pilots proliferate. Risk piles up. The organization can’t scale what works.
The 2025 reality check:
- MIT: 95% of generative AI pilots fail
- S&P Global: 42% of companies abandoned most AI initiatives (up from 17% in 2024)
- McKinsey: Only 6% of organizations qualify as “AI high performers”
- IBM: 63% lack AI governance policies
When 42% of companies abandoned their AI initiatives in 2025, it wasn’t because the AI was bad. It was because everything around the AI made it impossible to deploy responsibly at scale.
What Actually Works
McKinsey’s 2025 survey identifies what separates the 6% of high performers from everyone else:
- They target enterprise-wide transformation, not incremental efficiency
- They redesign workflows before selecting models (2x more likely than low performers)
- They set growth and innovation objectives, not just cost reduction
- They invest substantially and systematically in AI capabilities
- They implement disciplined management practices with clear KPIs
WorkOS’s July 2025 analysis confirms: organizations that succeed “begin with unambiguous business pain, invest disproportionately in trustworthy observable data pipelines, choreograph human oversight as a feature, and operate AI as living products with on-call rotations and success metrics tied to real dollars.”
In other words: they didn’t just deploy AI. They built the infrastructure to govern it.
In Part 3 of this series, we’ll show you what that infrastructure looks like when it’s built into the platform from day one, not bolted on as an afterthought.
The bottom line: Your AI isn’t failing because the models aren’t good enough. It’s failing because you’re solving six problems individually that should be solved systemically. The winners will be the organizations that stop optimizing for “fast” or “safe” and start building for both.