95% of GenAI Projects Fail — and How Process Fixes It
Tuesday, September 16, 2025
Most GenAI tools can talk. Few can execute. Here’s why structured process modeling is the missing link — and how crismo helps bridge it.
The AI Boom — and the Great Stall
Generative AI is everywhere — but impact is scarce.
A recent MIT report found that 95% of enterprise GenAI pilots fail to deliver measurable business results. Not because the models are weak — but because the systems around them are. Most companies don’t have the process awareness or structure to plug AI into anything meaningful.
Executives often blame regulation or model performance. But the real issue?
AI can’t help if it doesn’t know how your business works.
The Missing Ingredient: Process Context
AI doesn’t create clarity — it requires it.
Most enterprise workflows are undocumented, fragmented, or scattered across tools and teams. That’s why GenAI stalls in real-world applications: there’s no shared understanding of what the process actually is.
That’s where process modeling comes in.
Enter BPMN (Business Process Model and Notation) — a visual way to define how work flows:
- It makes the invisible visible
- Clarifies roles, handoffs, and dependencies
- Creates a structure AI tools can operate within
BPMN is how you operationalise AI — not just experiment with it.
From Chatbot to Copilot — If You Feed It Right
GenAI tools are powerful — but without structure, they tend to improvise.
And while that flexibility works in casual use, it falls apart in enterprise environments where roles, responsibilities, and outcomes must be clearly defined.
That’s the disconnect: AI can generate answers, but it doesn’t know when to act, what steps to follow, or who to notify.
It lacks the operational logic.
This is where process modeling comes in.
What turns a chatbot into a useful copilot isn’t just a smarter prompt — it’s context:
- What is the intended flow of work?
- Who owns which step?
- What counts as “done”?
- When should the AI act — and when should it escalate or wait?
With BPMN in place, AI can:
- Navigate real business workflows instead of open-ended guesses
- Stay within role-specific boundaries
- Trigger actions or surface insights at the right time — without overstepping
Companies succeeding with GenAI aren’t just experimenting with it — they’re embedding it into structured, well-understood processes.
A Quick Example: AI in Onboarding
Imagine a customer onboarding process modeled in crismo:
- A BPMN diagram clearly shows the handoff between sales, operations, and support
- AI is connected to the process model and knows when a deal is closed
- Instead of sending a generic email, the AI checks the process, sees which checklist item is next, and triggers the right internal task or follow-up
No ambiguity. No improvisation. Just smart execution aligned with the way your business actually runs.
crismo: Where AI Meets Structure
At crismo, we believe AI is only as powerful as the process context it’s grounded in.
That’s why we help teams map their workflows clearly — not just for alignment, but for action. crismo gives you:
- Lightweight BPMN modeling, without the complexity
- Real-time collaboration across roles and tools
- A foundation for AI-native workflows, copilots, and agents
Whether you want to export models, embed them into your systems, or prepare them for automation —
crismo helps you move from modeling to operationalisation.
From onboarding and approvals to escalations and orchestration —
crismo is where structure meets scale.
Final Thought: If You Want Smart AI, Show It the Rules
The next generation of AI won’t just answer questions —
it’ll participate in workflows. But only if it understands them.
That means giving AI access to the same logic your teams follow every day —
documented, structured, and ready to act on.
That means turning process knowledge into models —
and models into systems AI can actually work with.
That means capturing how work really gets done —
not just how we wish it worked — and letting AI plug into that reality.
🌀 Start building that foundation — in crismo.
Try it free →
Resources
Challapally, Aditya. The GenAI Divide: State of AI in Business 2025.
MIT NANDA Initiative, Massachusetts Institute of Technology, August 2025.
Download the full report (PDF)