How to navigate the risks and limitations of third-party AI solutions
Left and right, companies are turning to third-party AI solutions to reap the benefits of AI without a major infrastructure overhaul. The benefits are clear: affordable entry points to advanced technologies and rapid integration.
However, these advantages often come with certain risks and limitations that can cause significant damage to your business operationally, financially, and technologically. By understanding the potential risks of using third-party AI tools, you can make better decisions about your next moves and learn how to prevent any issues from arising.
Data privacy & security concerns
Imagine a healthcare institution adopting a third-party AI system to enhance patient diagnostics. In this scenario, the AI solution requires access to the patient’s medical records, which contain highly personal information such as medical history, test results, and treatment plans.
Sharing such data with an external AI system can introduce risks of breaches, unauthorized access, or even data misuse. This can lead to severe consequences, including legal liabilities and reputational damage.
To mitigate these risks, you should ensure robust data protection measures. Regular security audits, encryption protocols, and continuous monitoring can help identify and address vulnerabilities before they are exploited. And, make sure to carefully assess any third-party vendor’s track record and reputation to ensure they share the same commitment to data security that you do.
Limited customisation and control
Third-party AI solutions are usually designed to cater to a wide range of businesses with varying needs. But they may lack the specificity required for individual businesses or industries. This lack of customisation and control can restrict the AI’s ability to adapt to unique challenges.
For example, let’s look at two banks looking to implement an AI solution for fraud detection. A bank in San Francisco and a bank in Berlin likely have different customer demographics, transaction patterns, and risk profiles. When they launch the same third-party AI solution, they will likely receive a general framework for fraud detection. However, it will have difficulty recognising the nuances and anomalies unique to each bank’s operations.
As a result, detection accuracy may be compromised. This could allow certain fraudulent activities to go undetected or lead to false positives.
To overcome these challenges, you need to conduct a thorough assessment of your AI requirements. This involves understanding the unique intricacies of your operations, industry regulations, and desired outcomes. Finding AI vendors that offer customisation options can also make a big difference.
Vendor lock-in and dependence
Implementing a third-party AI solution often creates a dependency on the vendor’s technology and services. Over time, you may find switching to an alternative AI vendor difficult due to integration difficulties, data migration, and other logistical obstacles.
This lock-in to a single vendor can lead to reduced bargaining power, increased costs, and limited innovation. This scenario limits the potential for process optimisation and improved results. It also stifles healthy market competition, as vendors have less incentive to innovate if their customers are tied to their platforms.
Vendors that offer robust data migration solutions should be favoured to effectively manage these risks. This means that data can be easily extracted and transferred to alternative systems if necessary.
It’s also smart to consider open-source AI alternatives. They provide greater flexibility, as the code behind the technology is accessible and can be customised to meet changing needs. This approach allows you to retain control over AI implementation and avoid being tied to a single vendor’s ecosystem.
Bias and fairness concerns
AI algorithms are only as objective as the data they are trained on. Third-party AI solutions may not be tailored to address specific fairness issues. They may also not adequately reflect the diversity of your target audience.
An example might be an e-commerce platform that uses a third-party AI solution to screen resumes during hiring. If the AI model was trained on historical hiring data, it could unintentionally favour certain demographics.
In this case, the model may perpetuate these biases by selecting candidates from these demographics more often. This can lead to continued social inequality, hindering a company’s efforts to build a diverse and inclusive workforce.
To avoid these potential pitfalls, it’s important to ensure that AI models are thoroughly tested for equality and inclusion. This consists of checking training data for potential biases and working to correct them before deployment.
It’s also important to evaluate any tool across different demographics to confirm that it works equally for all users.
Performance and reliability
The performance and reliability of third-party AI solutions can vary significantly. As with any SaaS product, you may experience delays, downtime, or unexpected errors that impact critical processes and productivity.
Conducting a thorough performance evaluation of potential AI vendors is critical in minimising these risks. You should evaluate the vendor’s track record regarding system uptime, response time, and error rate. You should also establish clear SLAs (service level agreements) with your AI vendor that define the expected level of performance, uptime guarantees, and response time in case of problems.
Recommendations from existing customers can also provide valuable insights into the performance and reliability of the vendor. You’ll be able to better understand potential pain points, the vendor’s responsiveness to issues, and overall satisfaction.
Should you consider Custom AI models instead?
Given all of these potential risks with third party solutions, we have to say we are big fans of custom AI models. They offer more room for adaptation and control and can be designed to meet your unique requirements and objectives. Custom AI models can also be fine-tuned and optimised to achieve the best performance for the specific task. This can lead to more accurate and efficient results than typical third-party AI.
However, the decision between custom and third-party AI always depends on your resources, budget, data privacy considerations, and long-term goals. It’s hard to deny the sophistication of third-party tools like ChatGPT and Dall-E. So, the best choice for your business requires thoroughly evaluating your requirements and priorities.
