Businesses must place trustworthiness and transparency at the heart of artificial intelligence deployment.
Artificial intelligence is making its mark at all levels of the economy, but its productive promises for businesses are slow to materialize. As its diffusion accelerates, one observation becomes clear: power is no longer enough. Behind the frantic race for models from the giants (OpenAI, Google, Anthropic, etc.) lies a more fundamental issue: that of trust. For Patrick Joubert, CEO of Rippletide, the next revolution in AI will be that of reliability: without transparency, no trust; without confidence, no productivity.
The hallucinations produced by Large Language Models (LLMs) go beyond the framework of technical dysfunction, they are emerging as a major industrial risk for players who integrate artificial intelligence into their processes. In Australia, Deloitte paid the price. The company was forced to reimburse the government after delivering an erroneous report via generative AI. Far from being anecdotal, this news is a signal. AI not only invented false credentials, it undermined a process that was supposed to embody rigor and compliance.
Behind this incident, one observation emerges: even the most structured organizations are not prepared to industrialize AI. The challenge is no longer to experiment, but to make it reliable. For the past year, the business and IT departments of large groups have been deploying language models at an unprecedented pace: to write, analyze, summarize, automate. But in most cases, these deployments rely on general-purpose models designed for language performance, not operational compliance.
However, in a large company, truthfulness is a regulatory requirement. An erroneous note can make the group liable, a biased report can invalidate an investment decision, a poorly generated sentence can expose a business secret. In an environment governed by ISO standards, ESG requirements, compliance constraints, the slightest hallucination becomes a governance incident.
The paradox is that LLMs are the first tools that companies do not really know how to audit. They produce content with high perceived value, but low verifiability. They give an impression of authority, without guarantee of truth. For a decision-maker, it is a double-edged sword: immediate productivity masks a growing debt of confidence.
The cognitive debt of large groups
Most managements that experiment with generative AI discover that it does not scale in complex environments. LLMs excel at demonstration, fail at production. Because as use cases become more complex – calls for tenders, due diligence, regulatory reporting, customer relations – tolerance for error collapses.
The hidden cost of hallucinations is colossal.
- Lost time: Up to 40% of productivity gains generated by AI are canceled out by human review tasks (Gartner).
- Legal risk: an incorrect report can cost several millions in litigation or non-compliance.
- Reputational risk: A single incorrect briefing note, made public, can erode years of investment in credibility.
This is the cognitive debt of large companies: having integrated AI tools before having put in place the reasoning, traceability and control infrastructures which guarantee the reliability of the results.
Large language models predict the most likely sentence. What businesses need are systems that justify their response — capable of reasoning, explaining and proving. As long as AIs remain black boxes, their use will remain confined to peripheral tasks.
The AI of tomorrow in business will rely on robust systems, capable of making decisions with complete autonomy and explaining their decisions with transparency. In other words, to anchor the deployment of AI in business in frameworks of trust, based on reliability, traceability and explainability of decisions.
Reliability as a competitive advantage
In a context where each general management seeks to translate AI into measurable ROI, the key is no longer power, but reliability. The companies that succeed in this transition will be those that are able to industrialize trust — not by limiting the use of AI, but by framing it with architectures of reasoning, validation and explainability.
The Deloitte incident does not reveal a technological failure, but a governance flaw. And this flaw, all large companies are exposed to it. The question is not whether AI will transform the company, but whether the company will be able to deploy it in a way that creates value that is both sustainable and responsible.
The organizations that succeed will make reliability a lever of sustainable performance. The others, sooner or later, will pay the bill for the hallucinations.
