Artificial intelligence is increasingly influencing how organisations make decisions. By processing large amounts of information quickly, AI can help businesses analyse financial information, identify security threats, assess customer risk and recommend next steps.
It is also increasingly being used to support more consequential decisions, including areas such as recruitment.
However, as AI becomes deeply embedded into business processes, an important question emerges: how much influence should organisations give AI over the decisions they make?
The risk is not limited to organisations allowing AI to make decisions autonomously. AI can influence a decision long before the final choice is made by gathering information, identifying patterns, recommending options and shaping how a problem is understood.
The World Economic Forum recently highlighted this emerging challenge, arguing that AI is increasingly moving into the thinking that takes place before decisions are made. This means organisations may still technically make the final decision themselves, while AI influences which information, possibilities and questions they consider in reaching it.
This article forms part of our The Dark Side of AI series, exploring the emerging security, governance and operational risks organisations should consider as AI adoption accelerates.
How Is AI Influencing Business Decisions?
AI does not need to make a decision completely autonomously to influence its outcome.
An organisation might use AI to analyse information, identify patterns, assess risk, prioritise cases or recommend a course of action that is then presented to an employee or leadership team.
For example, AI could help an organisation identify which sales opportunities to prioritise, assess potential financial risks, shortlist candidates during recruitment or recommend how a business should respond to changing market conditions.
The potential applications are significant, but not every AI-assisted decision carries the same level of risk.
Using AI to summarise a public document has very different consequences from using it to assess a job applicant, evaluate a customer’s financial risk or recommend a pricing decision.
As the potential impact of a decision increases, so too does the importance of understanding how AI has influenced it and what controls surround that process.
The Problem of Opaque AI Decision-Making
One of the biggest challenges associated with AI decision-making is explainability.
AI systems can analyse large volumes of information and identify relationships that would be difficult or time-consuming for a person to assess manually. However, this complexity can make it difficult to understand exactly why a particular recommendation has been made.
Consider an AI system that identifies a customer as high risk.
The organisation should be able to understand what information influenced that recommendation, whether the information was accurate and appropriate, whether bias could have affected the outcome and who is ultimately responsible for reviewing the recommendation.
This becomes particularly important when AI is used to support decisions affecting individuals.
The Information Commissioner’s Office (ICO) and The Alan Turing Institute have published guidance specifically addressing decisions delivered or assisted by AI. It highlights the importance of transparency and accountability, as well as considering the context in which AI is being used and its potential impact on individuals.
If an organisation cannot adequately explain why an AI system produced a particular recommendation, it becomes much harder to assess whether that recommendation should be trusted.
AI Can Shape the Options We Consider
There is another, less obvious risk.
Even when a human makes the final decision, AI may already have influenced the choices available to them.
Imagine a leadership team using AI to assess a potential new market. The AI analyses competitors, customer demand and internal information before presenting three potential strategies.
The leadership team might carefully evaluate all three recommendations before selecting one. On the surface, there is clear human oversight.
But what if there was a fourth strategy that the AI never presented?
The World Economic Forum describes this as “frame compression”, where AI can narrow the range of possibilities considered within an organisation. Once a plausible AI-generated analysis has been produced quickly, organisations may have less incentive to question its assumptions or spend additional time exploring alternatives.
This creates an important distinction between making the final decision and shaping the thinking that leads to it.
Human oversight therefore needs to involve more than simply approving or rejecting an AI recommendation. People must remain willing to question the assumptions, information and options AI presents.
The Risk of Bias and Unfair Outcomes
AI systems make recommendations based on the information, models and rules available to them.
If the underlying information contains historical biases, is incomplete or does not adequately represent the circumstances surrounding a decision, AI-assisted outcomes may also be unfair or inappropriate.
This becomes particularly important when AI is used to support decisions involving individuals.
A recruitment system, for example, could influence which candidates progress through an application process. A financial system might contribute to decisions around credit or customer risk. In these circumstances, automatically accepting an AI recommendation could have significant consequences for both the individual and the organisation.
The NIST AI Risk Management Framework identifies characteristics associated with trustworthy AI including reliability, accountability, transparency, explainability and fairness, with harmful bias appropriately managed.
AI can support human judgement, but an AI-generated recommendation should not automatically be assumed to be objective, fair or correct.
When AI Decisions Become High Risk
Not every use of AI requires the same level of oversight.
During our recent Security, Privacy & Compliance: Guardrails that Enable (Not Block) webinar with BloomAI, we discussed how organisations can consider AI use cases according to their potential consequences.
At the lower end, assisted use might include drafting internal notes, summarising public documents or supporting general research.
At a medium level, advisory use could involve analysing internal information, producing customer-facing content or providing recommendations that influence business activity.
At the highest level, consequential use could include recruitment, credit decisions, pricing or decisions involving personal information and people’s rights. These situations may require significantly stronger governance, including formal assessments, documented rationale, audit trails and senior approval.
This risk-based approach is also reflected in the UK Government’s AI Management Essentials, which covers areas including AI risk assessments, monitoring for errors and failures, fairness, bias mitigation and data protection.
The principle is relatively straightforward: the greater the potential consequences of an AI-assisted decision, the stronger the oversight should be.
Automated Decisions and Data Protection
Where AI is used to make decisions about individuals, organisations also need to consider their data protection responsibilities.
The ICO’s guidance on automated decision-making and profiling explains that automated individual decision-making occurs when a decision is made through automated means without human involvement.
Examples can include automated decisions about credit applications and recruitment assessments.
There are additional considerations where solely automated decisions have legal or similarly significant effects on an individual. Organisations using AI in areas such as recruitment, financial services, employee management or customer assessment should therefore understand where automated decision-making is taking place and ensure appropriate safeguards are in place.
This makes the distinction between AI supporting a person and AI effectively making the decision itself particularly important.
Is Keeping a Human in the Loop Enough?
Human oversight is frequently presented as one of the most important safeguards for responsible AI.
It is, but simply having a person approve an AI-generated recommendation does not necessarily provide meaningful oversight.
The World Economic Forum raises an important concern here. A person could carefully assess three AI-generated recommendations without ever questioning whether those were the right three options to consider in the first place.
This means effective human oversight should involve the ability to challenge AI, not simply approve it.
Employees responsible for higher-risk decisions should have sufficient knowledge, authority and information to question an AI recommendation, review the evidence behind it, consider alternatives and override the system where appropriate.
During our webinar, human input was identified as an important control alongside permissions, approved tools and auditability.
The objective is not to prevent organisations from using AI to improve decision-making. It is to ensure that responsibility does not quietly shift from people to technology simply because AI-generated recommendations appear credible.
Who Is Responsible When AI Gets a Decision Wrong?
The growing role of AI also creates an important question around accountability.
If an employee makes a significant business decision, organisations will typically have established processes for determining who was responsible, what information they used and how the decision was reached.
AI should not remove that accountability.
Organisations should be able to understand which AI system was used, what information it relied upon, who reviewed its output and how the final decision was made.
This is particularly important as organisations move beyond standalone generative AI tools and begin introducing AI agents and automated workflows capable of completing multiple steps within business processes.
Without appropriate auditability, organisations risk creating processes where AI has significant influence but responsibility for the resulting decisions becomes increasingly difficult to establish.
Building Guardrails Around AI Decision-Making
Managing the risks associated with AI decision-making does not mean applying the strictest possible controls to every AI use case.
Instead, organisations should establish proportionate guardrails based on the potential consequences.
This could include:
- Defining which AI systems and use cases are approved
- Establishing clear ownership and accountability
- Requiring meaningful human review for higher-risk decisions
- Maintaining appropriate audit trails
- Assessing the quality and appropriateness of the information being used
- Monitoring AI systems for errors, failures and unexpected outcomes
- Reviewing potential bias and unfair outcomes
- Ensuring employees understand when AI recommendations should be challenged
- Applying stronger controls as the potential impact of a decision increases
The UK Government’s AI Management Essentials similarly encourages organisations to maintain records of the AI systems they use, conduct risk assessments, monitor systems for errors and failures, consider fairness and bias, and establish appropriate data-management practices.
These controls can help organisations benefit from AI without surrendering visibility or control over how important decisions are reached.
Final Thoughts
AI has enormous potential to help organisations analyse information, identify risks and make faster, better-informed decisions.
However, the danger is not simply that AI might make the wrong decision.
As AI becomes embedded earlier in the decision-making process, it can influence which information people see, which possibilities they consider and how they understand the problem in the first place.
That makes effective oversight about more than keeping a human involved at the final stage.
Organisations need to retain the ability to understand AI recommendations, challenge their assumptions, explore alternatives and establish clear accountability for the decisions that follow.
As AI adoption continues, businesses should therefore ask a fundamental question:
Are we using AI to support better human decision-making, or are we gradually allowing AI to shape decisions we no longer fully understand?
If your organisation is exploring Microsoft Copilot, AI agents or wider AI adoption initiatives, now is the time to ensure the right governance, security and oversight are in place. At HAYNE.cloud, we help organisations assess their readiness for AI through governance reviews, Microsoft 365 security assessments and practical guidance designed to support secure, responsible and effective AI adoption.
AI decision-making is just one of several emerging risks associated with AI adoption.
As part of our The Dark Side of AI series, we will continue exploring the security, governance and operational challenges organisations should understand as AI becomes increasingly embedded within the modern workplace.
