AI Risk Management and Your Business  

The allure of AI is undeniable, for example:

– using AI-powered chatbots to provide 24/7 customer support, instantly answering queries and resolving issues

– leveraging AI to predict demand for consumer goods, minimizing waste and maximizing profits

– personalize the shopping experience for mall customers by analyzing their purchase history, browsing behavior, and even social media interactions (with permission, of course)

Pace of change

The breakneck pace of AI development creates a minefield for companies. New AI tools and platforms emerge daily, each promising revolutionary benefits. However, this rapid evolution makes it incredibly difficult for companies to assess the true impact and risks. The speed of change outstrips their ability to develop know-how and AI risk mitigation strategies, leaving them vulnerable to:

• Overhyped solutions: Distinguishing genuine innovation from marketing hype becomes a challenge, leading to potentially costly and ineffective investments.
• Hallucinations: A consistent problem of all Large Language Models/LLMs is their tendency to provide made-up facts and data to prove their point
• Security vulnerabilities: Rapidly developed AI systems may contain undiscovered security flaws. This exposes SMEs to data breaches and cyberattacks (example: prompt injection attacks)
• Ethical blind spots: SMEs may unknowingly implement AI systems that perpetuate biases or violate ethical guidelines.
• Scams and fraudulent offerings: The AI gold rush attracts opportunists peddling dubious or even fraudulent “AI solutions”. It’s hard to tell what’s genuinely beneficial and safe, and what is just a new flavor of scam. An executive eager to adopt AI might fall prey to a slick sales pitch for an AI tool that promises miraculous results but delivers nothing but empty promises and financial loss. Keeping up with the shifting landscape of AI requires dedicated effort and specialized knowledge, resources that many SMEs may not have.

AI risk examples

Security

One major concern is data security and privacy. SMEs often handle sensitive customer data, from addresses and payment information to personal preferences and buying habits. Feeding this data into AI systems, especially cloud-based ones, creates vulnerabilities. Data breaches, whether due to hacking or AI errors, can expose this information, leading to legal repercussions, fines, and damage to customer trust. Imagine a small business using an AI-powered CRM that suffers a data leak, exposing the personal details of thousands of customers. The fallout could be devastating.   

Deskilling

Dependence and deskilling are also worrisome. Over-reliance on AI tools can lead to a decline in essential skills within the workforce. If employees become accustomed to AI handling all analytical or creative tasks, they may lose the ability to perform these functions independently. This can make the business vulnerable if the AI system fails or becomes unavailable. Imagine a marketing team that relies entirely on AI for content creation and then finds itself unable to produce compelling copy when the AI tool is down.   

Opacity

Furthermore, the lack of transparency in some AI systems (“black box” problem), needs to be noted. It can be difficult to understand how an AI arrived at a particular decision, making it challenging to identify errors or biases. This lack of explainability can be a major issue in regulated industries or when dealing with sensitive decisions. Imagine an SME using AI to assess loan applications, only to discover that the system is denying loans to qualified applicants based on unclear and potentially discriminatory criteria.   

Some AI`s multimodal capabilities are impressive. But the potential for misuse of generated content, including deepfakes or misinformation, is a serious concern. Some LLMs can produce inaccurate or misleading information, requiring careful fact-checking and editing. AIs focusing on code generation can introduce security vulnerabilities if the output is not thoroughly reviewed. Relying on such tools without proper oversight and human intervention can have serious consequences. 

AI Risk mitigation

So, how can SMEs mitigate these risks? A proactive and strategic approach is crucial:

• Data Security: Invest in robust cybersecurity measures to protect sensitive data. Implement access controls, encryption, and regular security audits. Where in-house implementation is too difficult, ensure your procurement processes are mature enough to assess and select the right vendor.  
• Bias and Error Detection: Carefully evaluate the data used to train AI systems and look for potential biases or errors. The newest LLMs generate outputs that inspire confidence and prowess – they are not infallible though. Update your prompts, stress test the model and question the outputs at every turn.
• Human Oversight: Maintain human oversight of AI systems, especially in critical decision-making areas. Don’t blindly trust AI’s output. Use human judgment and expertise to validate AI recommendations. Whilst this sounds like defeating the whole purpose of AIs, the human-in-the-loop principle is the corner-stone for using AI appropriately and avoiding costly mistakes. 
• Transparency and Explainability: Choose AI systems that offer some level of transparency and explainability. Understand how the AI arrives at its decisions or at least ensure that you understand the governance processes around the AIs value chain (how was the data curated, what were the parameters, what was the level of human scrutiny in the training phases and who is accountable for the outputs of the AI in the case of SaaS vendors). Note: Unfortunately it is not possible to remove the “black box effect”, with most businesses lacking the means to train in-house LLM from scratch. One solution could be reinforcement learning to tweak relevant values, however the LLM will remain a black box, due to lack of transparency around the initial data-set used for training.
• Skills Development: Invest in training and development to ensure employees maintain essential skills, even with the use of AI tools. Don’t allow AI to completely replace human expertise. Increasing employee awareness around the advantages, common pitfalls and limitations of LLMs can be useful in preventing over reliance on such tools.
• Legal Compliance: Stay informed about relevant regulations and laws related to AI use, data privacy, and algorithmic bias.

Keep your strategy close, and suppliers closer

We appreciate that not all SMEs will have the necessary resources to tackle all AI risk. For these situations, it is advisable for companies to go back to their strategy, vision and mission statement, remember who they are, assess what their core competencies should be and focus on those – the rest should be outsourced to specialist companies who can provide superior service at lower costs due to their economies of scale and scope.