Everybody’s “doing AI”. From Microsoft Copilot to Snowflake Cortex to OpenAI’s latest releases, generative AI (“gen AI”) is everywhere. The current narrative says these tools will magically unlock faster answers, sharper insights, and a new era of productivity. But will it though?
When finance and marketing operations teams start experimenting, a hard reality sets in. Gen AI is great at producing fluent text or writing code, but it often struggles with business intelligence. The models hallucinate, misinterpret context, and cannot show how they reached a conclusion. When a CEO asks, “Can I trust this number enough to make a decision?” the answer is often no.
As we work to to realize the value of gen AI and make it part of enterprise decision making, we need more than cool demos. We need AI we can trust.
Why generative AI falls short in business intelligence
Large language models are remarkable tools for content creation and communication. They can summarize reports, draft presentations, and speed up everyday tasks. Yet in business analytics, their limitations show up fairly quickly.
Yann LeCun, Chief AI Scientist at Meta, noted that LLMs have a very limited understanding of logic, do not understand the physical world, do not have persistent memory, cannot reason in any reasonable definition of the term and cannot plan hierarchically.
For business decision makers, this is more than an academic point. Business intelligence is not about wordplay. It is about accuracy, consistency, and decisions with a tangible bottom-line impact. If gen AI produces numbers that are unverifiable, the entire exercise becomes a liability rather than an advantage.
The risks of applying generic gen AI directly to enterprise data are meaningful:
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- Excessive system access. AI agents often need broad permissions that create security risks.
- Privacy and surveillance concerns. Sensitive company data may leave your control if it flows into a public cloud model.
- Breakdown of application-level security. Cross-app scraping and uncontrolled integrations can bypass established safeguards.
- Loss of human agency. AI may act without review or approval.
- Opaque reasoning. Leaders cannot see the logic behind the output.
- Data integrity drift. Without grounding in business logic, models can hallucinate or misalign metrics.
A CEO’s question: Can we trust LLMs with our data?
One of the first questions CEOs ask is direct: If we put sensitive company data into a generative AI tool, who else can see it?
This is the right concern to raise. Most off-the-shelf gen AI tools run in shared, multi-tenant environments. That creates uncertainty about where data lives, who has access to it, and whether it could be used to train models for other companies.
The solution is not to avoid gen AI altogether. The solution is to deploy it in a single-tenant, fenced-in environment that you control. Platforms such as Microsoft Azure AI Foundry allow enterprises to run generative AI within their own secure cloud tenancy. That means:
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- Isolation. Your data never co-mingles with other customers.
- Control. You define who can access which data and which models.
- Security. Data stays inside a controlled, compliant environment rather than a public LLM.
When CEOs hear that gen AI can be contained and governed like any other enterprise system, the conversation shifts. AI goes from being a perceived threat to becoming a trusted tool.
What trusted AI really means for enterprises
Trusted AI in business intelligence is not just about speed. It is about confidence. To be truly enterprise-grade, gen AI must deliver insights that are secure, explainable, and reliable.
Here are the pillars of trusted AI:
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- Secure by design. AI must operate in a single-tenant environment with no uncontrolled third-party exposure.
- Explainable. Every insight should include a reasoning chain so leaders can understand how the conclusion was reached.
- Grounded in your business data. Insights must come from your own semantic data layer and knowledge graph, not unverified text generation.
- Human in the loop. AI should suggest, but humans must make the decisions.
- Continuously accurate. Guardrails should prevent data drift and enforce alignment with agreed business definitions.
This is the standard leaders should hold to when evaluating any AI system for decision intelligence.
How to implement trusted AI in practice
There is a practical path forward for finance and data teams. It starts with the foundation and builds toward more advanced use cases.
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- Establish a single source of truth. Connect ERP, CRM, GA4, Snowflake, and other systems into a unified semantic data layer. This ensures every AI-generated insight is based on consistent definitions.
- Ground gen AI in business logic. Use knowledge graphs and driver trees so the AI understands how revenue, customers, channels, and operations are connected. This prevents hallucinations and ensures context.
- Start with controlled use cases. Focus on anomaly detection, KPI variance breakdowns, or channel optimization. These are high-impact areas where trust and accuracy can be proven quickly.
- Build governance from day one. Use role-based permissions, audit trails, and security controls to ensure accountability and compliance.
- Keep humans in the loop. Make sure AI does not act on its own. Require human review before decisions are executed.
From gen AI to large concept models
We at G2M and other AI practitioners are already acknowledging that gen AI by itself is not enough. Researchers argue that progress depends on combining LLMs with structured representations of the world (hint: knowledge graphs are a good place to start). This evolution leads toward Large Concept Models that do more than generate language. They understand how your business works, they understand cause and effect.
With this approach, AI can go beyond describing what happened. It can explain why it happened, simulate scenarios, and recommend actions that align with strategic goals. That is when AI becomes a true partner in decision intelligence.
The path to enterprise-ready AI
The companies that we see winning with best practices are not the ones that simply experiment with flashy gen AI demos. They build trusted AI systems. Trusted AI means insights in minutes instead of weeks. It also means security, transparency, and accountability. It is the difference between a chatbot that sounds convincing and a system you can rely on with actual business decisions.
At G2M Insights, we built Overwatch specifically for this purpose. Overwatch combines your data with a knowledge graph and a state-of-the-art generative model to produce insights that reflect the complexity of your business. It runs in a dedicated, single-tenant environment and ensures every conclusion is grounded in your own data and backed by transparent reasoning. For executives and data professionals, the question is no longer whether you will use AI. The question is whether your AI will be trusted enough to guide the business when it matters most.
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