AI Agents That Deliver Where Demos Can’t
How inGenious AI and AWS Nova turned pilot hype into production outcomes
From Pilots to Production: Why Most Fail
80% better comprehension. 33% fewer support tickets.
These are not pilot stats. They are production outcomes. And they are the reason AWS chose to feature inGenious AI in a new case study.
MIT’s 2025 GenAI Divide report found that 95% of enterprise pilots fail to deliver business impact. The issue is not that LLMs do not work. It is how they are deployed.
Putting an LLM in front of a knowledge base is the easy bit. It looks great on the happy path but it misses the point. The real challenge is staying compliant, accurate, and safe when things get messy.
CBA found that out the hard way. They cut staff, grabbed the AFR headline, then had to backtrack when the system failed in production (AFR).
The problem is that executives see the demo and think it is amazing. But all the hard work is not in showing what the tech can do when it is right. It is in making sure it does not do the wrong thing.
The demos impress, but production proves customers have no patience for mistakes. It is a classic case of building for the demo, not for reality. And reality always wins.
The Right Way: Building on Strong Foundations
That is why inGenious AI partnered with AWS to take a different approach.
By integrating Amazon Nova via Bedrock into our AI Agent Management platform, we built agents that:
- Understand the customer (TrueIntent): Accurately capture the true intent of an enquiry, no matter how it is phrased. This creates a structured request that downstream systems can act on with precision.
- Provide and personalise answers with guardrails (TrueAnswer): Because TrueIntent identifies the correct fixed and validated answer, TrueAnswer does not need to search an entire knowledge base. Instead, it generates a clear and compliant response directly from the approved answer, adding personalised context while keeping accuracy and safety intact.
- Automate with agentic AI (TrueAction): Nova Act plus AgentCore takes what used to require a human agent hand-off and turns it into a safe, fully automated action.
The outcomes are clear:
- 80% better comprehension of complex queries
- 33% fewer support tickets in production
As our CEO Mark Chatterton put it:
“Using Amazon Bedrock, we built an AI agent that understands the nuance and unpredictability of human language better than ever before.”
The Hard Truth: Enterprises Underestimate the Messy Middle
MIT’s research is blunt. 95% of pilots fail not because leaders ignore the messy edge cases, but because they underestimate them. They know the risks are there. What they do not grasp is how brutally hard it is to solve them in the real world.
That is why so many projects stall. The demo impresses, but customers are less forgiving than slides. Without observability, governance, and human-in-the-loop controls, AI agents cannot scale safely.
Our platform lets enterprises design AI agents that do not just survive under pressure. They thrive by delivering accuracy and compliance even when the stakes are highest.
The Enterprise Playbook for AI Agent Management
Here’s what separates our AWS case study from the 95% of failed pilots:
- Accuracy that holds up under pressure
- Zero-risk deployment, not hope-for-the-best
- Continuous improvement baked in
- Multi-model orchestration that scales with you
Dropping Gen AI into a demo is easy. Making it bulletproof in production is what defines success.
Closing the Gap Between Hype and Reality
CBA’s backflip showed what happens when AI is rushed in for the optics. The AWS case study shows the opposite. Put the tools in the hands of business users, give them the power to handle complexity safely, and the results stick.
Better comprehension. Fewer tickets. Outcomes that last.
👉 Read the full AWS case study
At inGenious AI, we are proving that AI Agents do not have to be risky experiments. With the right platform, they can be smarter, safer, and built for enterprise outcomes from day one.
