The Blind Spots: What AI Risk Actually Looks Like for Your Business

The AI train is going fast. And whether your organization has jumped on board or is still watching from the platform, the risk implications are already arriving.

That is the part most business leaders and managers have not yet fully considered. AI risk is not something that only materializes once you have deployed a large language model or built an AI-powered product. It arrives the moment your employees start using AI tools to do their jobs, the moment a vendor you rely on starts embedding AI into their service, the moment an employee uses a public AI tool to draft a document containing confidential client information. You do not have to be an AI company to face AI-related risk.

This is the first in a three-part series looking at AI risk from a practical, non-technical perspective. It is written for the people who carry responsibility for organizational decisions – risk managers, CFOs, insurance managers, IT leads, CTOs, CISOs, and anyone else who has found themselves wondering whether their organization has really thought this through. If that question has kept you up at night, you are not alone. And by the end of this series, you will have a practical framework for understanding it more clearly.

Two kinds of concern

In conversations about AI risk, we often encounter two types of organizations and two related types of concern.

The first has moved quickly on AI. Tools are in use, processes have been automated, and there may even be AI embedded in products or services delivered to clients. The concern here is quiet but persistent: have we thought this through properly? Is there enough oversight? Do we know what the AI is actually doing? What happens if something goes wrong?

The second has moved cautiously or not at all. The concern here is different but equally uncomfortable: are we falling behind? And underneath that: if we do start rolling out AI, where do we even begin to think about the risks?

Both concerns are legitimate. And the good news is that they lead to the same place: understanding what AI risk actually looks like, and what effective governance involves. That is what this series is about.

Where the risk actually sits

AI risk is not one thing. It is a cluster of distinct exposures that show up differently depending on how your organization uses AI, the context in which it is used, and with what level of oversight. Here are the six areas we commonly see:

The first is outputs that people rely on. Whether it is a summary, a recommendation, a prediction, or a decision, if someone in your organization – or one of your clients – acts on an AI-generated output, you carry some responsibility for that output being fit for purpose. AI systems can be confidently wrong. They can produce plausible-sounding information that is factually incorrect, biased, or simply not appropriate for the context it is being used in. The fact that a machine produced it does not transfer liability. In the same way that a financial adviser cannot blame their spreadsheet for bad advice, an organization cannot point to the AI that generated it.

The second is automated or AI-assisted decisions. AI is increasingly being used to inform or replace human decisions – who gets hired, who gets credit, how risk is priced, and how customers are segmented. When those decisions produce differential outcomes across groups of people, they can attract regulatory scrutiny regardless of whether the discrimination was intentional. Again, the presence of an algorithm does not remove responsibility nor is a defense.

The third is your vendors and their AI. Most organizations are not building AI from scratch. They are using software that has AI built in, such as CRMs, HR platforms, financial tools, and analytics systems. If those vendors are using AI in ways that affect your clients or your data, the exposure does not stay with the vendor. It can travel back to you.

The fourth is data. AI systems are built on data, and that raises important data questions. Whose data was used to train the model? Was it obtained lawfully? Does it include personal information about your employees or clients? Are you sharing sensitive data with external AI tools without realizing it? Data is where AI risk and privacy risk converge, and that intersection is increasingly where regulators are looking.

The fifth is your employees and the tools they are already using. This is the exposure that most organizations underestimate. Employees across every function are already using AI tools to draft communications, summarize documents, analyze data, and write code. In many cases they are doing this without any formal policy governing what information they can share with those tools. A single employee pasting a client contract into a public AI tool can create a confidentiality breach that no security system would have caught.

The sixth is the regulatory environment. The EU AI Act is now in force and applies to any organization operating in or selling into European markets. It introduces binding obligations, particularly for organizations deploying AI in high-risk contexts such as employment decisions, credit assessments, and critical infrastructure. Non-compliance is not a future risk. It is a present one.

Why this matters beyond technology

There is a persistent assumption that AI risk is a technology problem, something for the IT department or the CTO to manage. That assumption is both understandable and dangerous.

The liability exposures from AI sit firmly in the business. A negligence claim arising from a flawed AI output lands on the organization, not the software. A regulatory investigation into discriminatory AI-assisted hiring lands on the employer. A data breach caused by an employee using an unauthorized AI tool lands on the data controller. These are legal, financial, and reputational consequences that belong to the boardroom, the risk function, and every manager who has signed off on an AI-related decision.

That is not an argument for paralysis. It is an argument for awareness – and for governance that matches the pace of adoption.

What comes next

In Part 2, we look at what happens when AI risk becomes an AI incident. Through five realistic scenarios, we examine how AI-related losses actually unfold, what the consequences look like, and what questions they raise for the organizations involved. Some of the scenarios may be more familiar than you would expect.

In Part 3, we move from diagnosis to action, and set out the practical framework for assessing readiness and strengthening governance.

About the Financial Lines Team

This series is written from within RiskPoint’s Financial Lines team, where we work with liability, governance, and emerging risk issues every day. The perspectives in this article are grounded in practical underwriting experience and close dialogue with brokers and clients navigating a changing risk landscape.

The Author

Erdal Erdogan is Technology Practice Lead at RiskPoint Group, where he focuses on insuring the tech industry across the Nordic and European specialty market. With over a decade of experience underwriting technology and software companies – including senior roles at large international insurance carriers – he specializes in Tech E&O/PI, Cyber, and from simple to complex technology risks. He works at the intersection of technology insight and insurance, helping brokers and clients navigate emerging and evolving risk landscapes.

About The RiskPoint Group

The RiskPoint Group is one of Europe’s largest Managing General Underwriters (MGUs), providing best in class insurance solutions to businesses and their advisors globally. The RiskPoint Group operates as RiskPoint in Europe and Asia and RP Underwriting in the rest of the world. With 325+ employees and 17 locations in Europe, North America, and Asia Pacific, the RiskPoint Group employs expert underwriting and specialized, in-house claims teams within Accident & Health, Transactional Risk, Property & Construction, Renewable Energy, and Liabilities, including Financial Lines, Cyber, Casualty, and Life Science. The RiskPoint Group is a Coverholder with Lloyd’s of London and is backed by a strong panel of well-reputed insurance companies.

Legal Notice & Disclaimer

The content published in this series – including all articles and accompanying materials – is produced for informational and educational purposes only. It does not constitute legal, regulatory, financial, or insurance advice, and should not be relied upon as such. The views expressed are those of the author in a personal professional capacity and do not necessarily represent the official position of RiskPoint Group or any of its affiliates, subsidiaries, or business partners.
References to the EU AI Act and other regulatory frameworks are provided for general informational purposes only. Regulatory obligations vary by jurisdiction, organization type, and the nature of AI systems deployed. Readers should seek independent legal advice regarding their specific compliance obligations.
Nothing in this content creates a client relationship, advisory relationship, or any other professional relationship between the author, RiskPoint Group, and the reader.

This content was accurate to the best of the author’s knowledge at the time of publication. AI regulation and market practices are evolving rapidly; readers should verify current requirements independently.