By Tim Butler, CEO and Founder of Innovation Visual
Artificial intelligence is swiftly evolving from a supportive capability to a decision-making one. That shift raises a fundamental question for every senior leader: when an AI system makes a decision that carries risk, who is genuinely in control? The answer is becoming more complex as models develop, regulation lags behind, and organisations accelerate adoption in pursuit of efficiency and scale.
During a recent discussion with leaders from MyBuddyAI and Luminance , we explored these challenges in detail. What emerged was a clear message: AI can enhance decision-making, but only when companies understand the limits of AI autonomy and the importance of accountability.
All technology makes mistakes, but the nature of AI failure is different. Large language models (LLMs) can hallucinate, which means they might confidently generate information that is false or fabricated. Additionally, probabilistic reasoning in AI uses statistical likelihoods to make decisions, which can sometimes lead to incorrect conclusions despite appearing confident, and responsibility becomes diffused, spread across model creators, system integrators, and end users. AI algorithms are not explicitly programmed for every scenario; instead, they learn from training data, which enables them to recognise complex patterns and generate outputs.
Find out more about LLMs from AI expert Professor Paul Watson
Algorithmic bias is another critical concern. LLMs can perpetuate biases if they learn from biased data, which can lead to discriminatory behaviour in their output.
This creates a difficult governance question. When an AI agent triggers an action that affects revenue, safety, compliance, or customer experience, who holds the blame? The organisation deploying the system? The vendor that trained the model? The individual who interpreted the output?
Hallucinations remain the most significant threat. Mitigation requires deliberate architectural decisions, such as multiple-model strategies and well-defined guardrails. AI should not be treated as one entity but as a collection of capabilities that must be validated, cross-checked, and continually monitored
Despite rapid advances, AI is not yet ready for full autonomy in domains where error carries significant consequences. The example of emergency response illustrates this clearly. When there is urgency and risk, a person must remain in the loop. The nuance, judgement, and ethical reasoning required cannot be delegated to probabilistic systems alone.
In regulated sectors such as healthcare, energy, and financial services, the expectation of auditability and traceability remains. AI can accelerate processes, surface insights, and standardise quality, but it cannot be allowed to independently make irreversible decisions.
The most effective organisations are the ones that design AI systems with controlled autonomy. Automation, where it drives reliability and speed, paired with human oversight where ambiguity or risk is present. This hybrid model ensures progress without compromising responsibility.
One of the more sobering points raised in the session was the disparity in regulatory maturity across regions. Europe’s approach is slow and structured, the United States continues to take a more permissive stance, and many Asian markets move faster with fewer constraints.
For multinational organisations, this creates friction. Leaders need to navigate conflicting expectations, different compliance frameworks, and varying definitions of acceptable AI use.
However, this should not hold back adoption. Instead, it reinforces the need for organisations to build their own robust internal standards that exceed regulatory minimums. Trust, transparency, and accountable design must be driven from within, not outsourced to slow-moving legal systems.
Another significant insight from the conversation was the renewed focus on data. The effectiveness of AI is constrained not by the intelligence of the model but by the quality, structure, and integrity of the data feeding it.
Poor data leads to poor decisions. Strong data governance creates scalable advantage.
Interestingly, we also discussed the importance of user trust. An AI system can be accurate, but if users do not trust it, adoption fails. Conversely, over-trust is equally dangerous. Organisations must strike a balance where AI is seen as dependable but not infallible.
This alignment of data quality, user understanding, and system reliability ultimately determines whether AI becomes a driver of operational excellence or a source of organisational risk.
There is understandable concern that AI may reduce the need for human involvement. Yet, the reality is that this phase of AI transformation is creating new types of work, new roles, and new expertise.
As was highlighted in the discussion, the need for individuals who can design guardrails, evaluate model behaviour, interpret complex outputs, and architect multi-model solutions is growing rapidly. AI is not replacing human capability. Instead, it is elevating it and demanding higher levels of strategic oversight.
AI is not something to fear. It is something to understand, shape, and apply with discipline.
Organisations that thrive will be those that approach AI not as a shortcut, but as a structured capability that enhances the decision-making process while protecting accountability.
At Innovation Visual, our focus is to bring clarity to this complexity. We help organisations design AI systems that are transparent, effective, and aligned with their operational and regulatory needs. The goal is not autonomy for its own sake, but responsible automation that delivers measurable outcomes.
The answer is straightforward. The organisation deploying the AI system holds ultimate accountability. AI is a tool, not an independent actor. When an AI system makes a decision, it does so because someone authorised it to operate within defined parameters.
The critical distinction: you cannot outsource accountability. You can outsource technology and infrastructure, but the duty to ensure safe, ethical, and compliant AI use remains with the organisation. Leaders must be able to explain how an AI decision was made, why it was allowed to proceed, and what safeguards were in place.