Aerogram : https://www.aerogram.ai/
Off-the-shelf AI Fails In Enterprises.
88% of enterprises have already deployed AI in at least one function [1]. But only 5% of those AI pilots are actually extracting meaningful value [2].
Read that again.
Almost every large company has AI running somewhere. Almost none of them are getting real results from it.
This isn't a technology problem. It's an strategy problem. Most AI fails because it lives in brittle workflows, lacks contextual learning, and sits completely misaligned with day-to-day operations [2].
At Cleverse, we've been studying how the best companies are navigating this AI transformation. The pattern is clear: enterprises are stuck between two paradigms. The ones that win will be the ones who stop choosing between them.
Chat-based and Autonomous Agent Collapse
Every enterprise AI project falls somewhere on a spectrum between two approaches:
Chat on one end. Tools like ChatGPT or Claude where employee prompt to do work. The human stays in control.
Fully autonomous agents on the other. AI systems that take actions end-to-end with minimal human input.
Here's the problem with each:
Chat UI Problems:
- Works great for simple tasks, struggles with complex workflows
- Requires prompting skill
- Context/Knowledge lives in private conversation, not shared systems
Agent Problems:
- Do end-to-end work, but quality is inconsistent and unpredictable
- Behavior is hard to control in production environments
- Removes humans entirely, which creates trust and governance issues
Notice what's missing? There's no middle solution. No place where AI and humans collaborate on complex workflows with appropriate oversight.
AI Initiatives Stall After Pilots
Here's the pattern: A team runs many pilots. one of them works. Leader tell this quick win story. 6 months later, nothing has changed in the whole business. Why?
A pilot that impresses executives is not the same thing as a system that transforms operations. Most enterprises confuse the two.
Every Enterprise Will Soon Run 100+ AI Apps
Different teams use different models. Different tools. Different approaches. No governance. No standards. No way to build on what others have done.
This is the infrastructure fragmentation problem.
Here's the uncomfortable truth: Every enterprise will soon need to run 100+ AI applications. Not one super chatbot. Many specialized AI apps—each multi-step, workflow-bound, and context-aware.
The current fragmented infrastructure can't support this. You can't run 100+ apps when every team is building their own disconnected solution. You can't maintain quality when there's no shared knowledge layer. You can't ensure compliance when governance is an afterthought.
The Gap Between AI Skills and Core Business Tasks
Most enterprises respond to AI by training employees. "Everyone needs to learn prompting." "Our people need to skill up."
This sounds right. It's completely wrong.
Here's why: Most enterprise training teaches basic AI skills. But core business tasks require advanced AI skills to solve.
The gap between "I can use ChatGPT" and "I can build AI into my daily work" is wide.
This is why AI stalls. Employees take the training. They try a few things. The results are not helpful. They go back to their old workflows.
The solution isn't more training. It's better strategy that doesn't require advanced skills to use.
Aerogram : The New Enterprise AI Stack
As leaders, what can we do about it? This is an extremely hard task. But the companies getting this right are building a fundamentally different kind of stack.
Think of it in five layers:
We are still early in the enterprise AI shift. But the distance between leaders and laggards is growing.
The 5% extracting value today aren't running better pilots. They're building different infrastructure. They're solving the structural problems—the two-paradigm trap, the pilot-to-production gap, the fragmentation, the skills illusion—while everyone else keeps experimenting in circles.
Every workflow automated, every knowledge layer connected, every AI app deployed creates compounding advantage. Meanwhile, companies stuck in pilot mode keep restarting from scratch.
Aerogram : https://www.aerogram.ai/
References
[1] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
[2] https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
