Webinar
Can you trust AI with enterprise-critical decisions?
Can you trust AI when 95% of enterprise AI projects fail to deliver measurable ROI? The reason is rarely the technology. It’s the absence of a framework for deploying agentic AI responsibly, with the right guardrails, the right data, and the right human oversight built in from day one.
The Problem
Enterprise AI is failing. Not because of the models, but because of how they are deployed. Organizations across the US and Europe are deploying large language models and agentic AI systems into business-critical processes, often without understanding the fundamental limitations of what they are building on. The results range from embarrassing hallucinations in client-facing outputs to, in documented cases, AI agents deleting production databases without consequence. The problem is not that you can't trust AI. The problem is that most organizations have not yet built the systems, data governance, and human oversight structures that make trustworthy AI deployment possible.
The Problem Quantified
95%
of enterprise AI projects fail to deliver measurable ROI (BCG / Gartner enterprise AI research)
11%
of organizations piloting agentic AI have reached production (TQA market analysis, 2026)
$40bn
invested in enterprise GenAI, with most returning near zero (McKinsey Global AI Report, 2025)
— Dillan Hackett, Director of Innovation, TQA
"There is no such thing as consequence for AI. It can give you wrong answers all day long and you will still go back to it tomorrow. That is a fundamental problem when you are deploying it into enterprise processes."
Four reasons agentic AI deployments fail and how to address each one
01
LLM hallucinations and AI confidence
Large language models are prediction machines, not fact engines. They produce confident-sounding outputs regardless of whether those outputs are accurate. AI hallucinations in enterprise settings are not edge cases. They are a structural feature of how LLMs are built.
02
Poor enterprise data quality
AI cannot distinguish between a confirmed case study and a discarded idea in a draft deck. Without clean, contextualized, and well-governed data, your agentic AI system will surface the wrong information with total authority. Data quality is not a precursor to AI deployment. It is part of the deployment.
03
No human-in-the-loop design
When an enterprise AI system produces a wrong output and there is no human-in-the-loop escalation path, the consequences fall entirely on your organization. Human oversight is not a workaround for AI limitations. It is a non-negotiable component of any responsible enterprise AI architecture.
04
Using frontier models for the wrong tasks
General-purpose frontier models like GPT-4 or Gemini Ultra are powerful but unpredictable. For most enterprise workflows, a smaller, task-specific model with defined AI guardrails will outperform a frontier model on reliability, cost, and speed. Match the model to the problem, not the other way around.
How to deploy trustworthy agentic AI in your organization
TQA has designed and deployed enterprise agentic AI systems for some of the world's biggest brands across the US and UK. This framework reflects what we have learned about what actually works in production, not just in proof of concept.
01
1. Establish enterprise data governance before deployment
Agentic AI does not understand context the way humans do. Outdated documentation, untagged draft files, and ambiguous naming conventions are all treated as equally valid inputs. Enterprise data quality and governance is the first and most important step in any trustworthy AI deployment. Without it, even the best models will produce unreliable results.
02
2. Design AI guardrails that permit the answer "I do not know"
LLMs are trained to produce an answer. They will hallucinate rather than admit uncertainty unless you explicitly design for that outcome. Effective AI guardrails must include a formal off-ramp: the system must be architected to return no answer when no reliable answer exists. This reduces AI hallucination rates and substantially increases the reliability of outputs.
03
3. Build adversarial multi-agent networks for critical outputs
Single-agent AI systems have a single point of failure. TQA always recommends multi-agent architecture with one agent for one purpose, content creation for example, and then a second agent for a complementary task, like finding errors. This adversarial approach yields more structurally reliable results and is the foundation of enterprise-grade agentic AI systems. The reviewing agent is rewarded for catching errors, creating a systematic quality loop.
04
4. Apply the 'perpetual trainee' principle to AI permissions
You would not grant a new trainee access to your production database on their first day. The same logic applies to agentic AI. Limit permissions, build hard guardrails at the system level rather than relying on prompt instructions, and define clear escalation paths to human oversight. Then back everything up. Always.
05
5. Benchmark continuously against defined success criteria
Trust in AI is not a destination. It requires ongoing measurement. Define what good looks like before deployment, build benchmarks for each process, and continuously evaluate AI agent performance against known outputs. Perfection is not achievable. Consistent, measurable improvement is.
Who should watch?
This session is designed for AI practitioners, business leaders, and product owners who need to validate to their peers and superiors that their AI implementation is trustworthy, safe, and ready for the enterprise.
Meet the Speakers
TQA: The Agentic AI Experts
TQA is an Agentic AI and Automation consultancy partnering with some of the world's biggest brands in the US and UK. We design and deploy enterprise agentic AI systems that work in the real world, on real business-critical processes, with the guardrails, data governance, and human oversight structures that make reliable deployment possible. We work with UiPath, ServiceNow, Microsoft, and leading AI model providers, including Anthropic (Claude), OpenAI (ChatGPT), and Google (Gemini). Our work spans enterprise automation, agentic AI system design, AI governance frameworks, and multi-agent architecture.
Ready to deploy enterprise AI your business can rely on?
TQA builds agentic AI systems that perform in production. If your organization is ready to move beyond proof of concept, we should talk. Schedule a consultation below.
Schedule a Consultation
We’re here to be your trusted partner in Agentic AI. You can schedule a meeting with us by using the form and we’ll be touch.
"*" indicates required fields
