Generative AI is being adopted by the corporate world. Harvard Business School professor Joseph Fuller says “every big company now has multiple AI systems” and treats AI as part of their strategy. But experts warn that this fast adoption hides serious generative AI risks.
For example, consulting firm Oliver Wyman says “the unpredictability of generative AI systems is a big issue,” and questions AI system reliability. In practice, this means AI outputs can be erratic or surprising.
A Profound Logic analysis says “overreliance on AI can leave companies exposed when systems fail or produce unexpected results,” and potentially cause major operational disruption or bad strategic decisions. In short, even as companies rush to use AI, they must acknowledge these AI decision-making risks and build in safeguards.
What is Generative AI and Its Reliability Gaps
Generative models work very differently from traditional software. Instead of following rules, they “function like advanced autocomplete tools” – predicting text or images based on training data patterns.
In other words, generative AI is designed to predict the next word or outcome, not to verify that output as true. This means outputs are probabilistic. MIT Sloan researchers say AI’s goal is to generate plausible content, not factual truth. So AI tools may produce content that sounds reasonable but is wrong.
Moreover, these systems are trained on vast amounts of internet data, which is often wrong and biased. Any errors or biases in the training data will be amplified by the model. And generative AI is typically a “black box”: its internal logic is opaque.
As Oliver Wyman says, the decision-making process in these systems “is not always transparent, making it hard to trace the logic behind its outputs”. This lack of explainability means AI system reliability is compromised, and companies can’t trust or validate AI-driven recommendations.
Hallucinations and Inaccuracies in AI Outputs
In reality, these technical limitations lead to many generative AI risks in the real world. One big one is hallucination, where the AI generates false information. AI tools have been shown to produce completely made-up “facts” and citations.
For example, an AI chatbot once accused NBA star Klay Thompson of throwing bricks during games. In another case, ChatGPT invented entire quotes and sources about Michael Bloomberg.
Such hallucinations – outputs that are “confidently incorrect” – can fool users. As one legal analysis says, when generative AI “fills in the blanks with fiction, it does so with the tone and confidence of truth, which is what makes hallucinations so dangerous”.
In short, an AI-generated statement may look credible even if it’s false, which creates serious reputational risk.
Bias, Fairness, and Ethical Landmines
Bias and fairness are other landmines. Generative AI models can inadvertently perpetuate biases in their training data. Oliver Wyman says AI’s outputs can reflect “nuanced and multifaceted” discrimination not obvious in the training data.
For example, a model used to write job ads might learn to favor or exclude certain demographic groups, perpetuating inequality.
Similarly, image generators have been shown to amplify stereotypes (e.g., in gender or race) in their training datasets. Because AI is seen as “objective”, users may over-trust these outputs and ignore bias.
Intellectual Property, Legal, and Privacy Concerns
Another concrete risk is intellectual property and privacy. Generative AI uses copyrighted or private data to learn.
Lawsuits are already happening: creators are suing AI companies for using their copyrighted work without permission, and courts are ruling that AI-generated content (e.g., from a chatbot) can be legally binding on companies.
Similarly, private or sensitive information can leak. For example, in 2023, Samsung engineers accidentally shared confidential source code with ChatGPT.
In aggregate, these mean business risks of AI are broad – legal, financial, and ethical – and companies can be sued or fined by regulators if AI is misused.
Cybersecurity and Emerging Threats
Cybersecurity and privacy are also big concerns. AI can lower the barrier to entry for sophisticated threats. Malicious actors can use generative models to create highly convincing phishing emails, fake customer service bots, or even tailor-made malware. Meanwhile, as surveys show, data protection is top of mind for executives.
In one risk survey, 65% of business leaders cited data privacy and cybersecurity as their top concern for generative AI. Another PwC poll found 64% of CEOs ranked cybersecurity as the biggest generative AI risk.
AI’s ability to create deepfakes – fake audio or video that’s almost indistinguishable from real recordings – can damage brand and customer trust if used maliciously.
All of this adds to the business risks of AI on the security front.
The Business Risks of Overdependence
All these technical and external risks feed into business and strategic risk when firms over-rely on AI.
Many companies are tempted to automate decision-making at every level – from customer service chatbots to financial forecasting models. But too much dependence can backfire. If managers defer too much to AI recommendations, they may lose oversight.
In fact, a survey found 60% of risk professionals worry about “employees making decisions based on inaccurate information” generated by AI.
In other words, AI decision-making risks can translate into bad strategic choices. For example, acting on a faulty financial or market analysis from an AI could lead to bad investments.
Worse, if an AI system fails or goes down and a company has no human fallback, they may face operational disruption.
As Profound Logic so bluntly put it, “treating AI as a crutch can leave businesses exposed when systems fail or produce unexpected results, potentially leading to operational disruption or flawed strategic decisions”.
In short, over-reliance on AI exposes companies to a chain of failures: erroneous outputs can erode customer trust and brand value, and unchecked AI recommendations can undermine executive judgment and agility.
Mitigating Generative AI Risks Through Governance
In order to mitigate these risks, companies need to be in balance. Best practices demand human approval and rigorous control.
Absolutely no acceptance of AI-created material — particularly on legal, financial, or public issues should be done without confirmation.
That is, treat AI as a “creative assistant, not a source of truth”. Workers need to be trained to identify red flags (e.g., an overly confident tone or fabricated citations) and cross-check facts against reputable sources.
Companies need to take on human-in-the-loop processes such that any material AI output has to be cleared by experts or managers. Most experts stress that the “role of humans is never eliminated” in workflows powered by AI.
Businesses are also buying better tools: for example, retrieval-augmented systems combine in-real-time databases to decrease hallucinations, and watermarks or metadata can mark AI-created work for review.
At a governance level, companies need to put pen to paper and audit their AI systems. That involves keeping logs of prompts and outputs alongside monitoring error rates and running models periodically against bias or drift.
Regulatory and standards organisations advise treating AI with the same reliability engineering disciplines (e.g., monitoring mean time between failure against mission-critical technology) they would against other technology.
Companies can also create policies regarding the use of AI, such as what can be done with AI tools, who signs off on output, and how to default to human judgment.
By blending human experts and careful controls with AI insights, companies can harness the advantages of AI while limiting risks associated with AI decision-making.
Conclusion
Generative AI offers huge upside for innovation and efficiency, but uncontrolled use is a business risk of AI. Hallucinations, bias, IP/legal issues, and security flaws mean AI output can’t be blindly trusted. At the same time, loss of human oversight can amplify any single error into a crisis.
As the experts say, responsible AI adoption requires a dual approach: seize the strategic opportunities and build robust checks and balances to mitigate the dangers.
Only by embedding human judgment and strong governance into your AI initiatives can you avoid the pitfalls of overdependence and make generative AI drive value without putting the business at risk.
Featured Image – Freepik
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