Are there risks to AI? Absolutely. Can you avoid the risks it carries? For example, in the area of content creation, there are a variety of risks that you open yourself up to. One of the more significant is the trustworthiness of the content created. You rely on generative AI to create an accurate explanation or description of a subject matter, but can you, in fact, completely rely on it? The answer is a qualified no. The level of “qualified” depends on a variety of factors. Your AI generated content is only as good as its sources, and that can create real questions for readers. To explain further: AI is trained by exposing it to lots of data. Sources and content of that data has great impact on its reliability. Further, use of and corrections to that data further train the AI models. An organization using AI to create any type of video, text, image, or audio content needs to be concerned that it may include proprietary information that requires permission to use. Could material created by generative AI suddenly veer off into copyright infringement? You bet!
AI is also being used in areas such as recruitment. However, there has been research suggesting that bias can sneak into AI decisions as a result of the source data the tools are using. Bias is a concern not limited to the one example of recruitment. It can have consequences in areas where AI is making marketing decisions, and can taint medical and legal recommendations AI might provide. As a result, AI cannot go “unmonitored.” Review by humans and other tools is a best practice that is needed to improve accuracy and trustworthiness. This, in turn, may cut into the efficiencies that are perceived to be created by AI.
Additionally, most AI tools–Chat GPT to take just one example–are not going to necessarily incorporate consideration of regulation and compliance requirements. Many countries, individual States in the US, and US federal agencies are implementing data security regulations that are designed to protect the Personal Information of individuals. In many cases violations include civil penalties. In the case of the European Union’s General Data Protection Regulation, fines are significant.
If you are considering implementation of AI, your MSP can provide guidance. Here is a list of considerations for implementation of AI in your business.
- Assess your organization’s potential use of AI. Basically, with the holistic understanding of your business that an MSP has, they can help you evaluate where there is opportunity, and assist with prioritization of where to implement first.
- Understand your KPIs and organizational goals, from the top down. Before you do anything, it is essential to articulate your specific goals. What do you identify as the key strategies and how will you measure your success?
- Propose a possible range of AI solutions. Here is an area where your MSP can be of particular value. An MSP will be knowledgeable about the variety of applications out there and lead you to select those most appropriate for your goals.
- Estimate the solution’s ROI. Remember, measurement is important. And you cannot do everything. So identify each potential AI solution’s ROI. This will guide you to make the most effective use of your resources.
- Ensure compliance: For example HIPAA, PCI, HITRUST, ISO27001, SOC1, SOC2 all have very specific requirements. AI is a powerful and potentially intrusive tool. Compliance is critical, so ensure that your solutions meet necessary requirements.
- Implement the solution. An MSP can assist with implementation of the solution. There are some robust tools out there that make project implementation fairly simple for those with a technical bent and high-level guidance. For more complicated projects, you may not have the resources available for what can be a time-intensive project; an MSP can help.
- Manage tool-related risks. This is a post-implementation issue that most businesses do not take into account for the long term. Examples of ongoing tasks include documentation of configuration and access, access and password management, security patches and updates, as well as monitoring the function and deviations from expected operation.
- Ongoing evaluation for effectiveness and reliability. Remember, nothing is stagnant in business. Technologies change, the competitive environment changes. Your organization moves forward. Make sure you commit to ongoing reviews of the effectiveness of your chosen solution.
