Head of ProductNasuni,
Thank you for reading this post, don't forget to subscribe!The rapid progress in artificial intelligence (AI) has placed organizations in a challenging position. Enormous internal and competitive pressures exist to leverage large language models (LLM) or other AI solutions. However, the market is saturated and excessively hyped, making it extremely challenging to find the best solution for your enterprise.
AI introduces a set of sophisticated tools that require comprehensive evaluation. The first step involves clearly identifying the problems that AI might address and selecting potential solutions capable of addressing those issues. Afterward, these five broad questions should be taken into consideration as part of the assessment of these AI solutions for your organization.
1. How is privacy and security managed by the solution?
Data stands as a pivotal element in any AI strategy and is a crucial asset for enterprises. Any device deployed in your organization must secure and safeguard your data. Thus, it is imperative to commence by evaluating the public, private, and open-source LLM models available and identifying the most suitable variety that aligns with your privacy and security needs. Subsequently, delve into how the model utilizes customer data, personal information, and other sensitive data.
Determining whether your data will be safeguarded from inadvertent risks is imperative. Assess the terms and conditions or contract to ensure that your data cannot seep into the underlying model behind the tool.
As a data owner or steward, ensuring that users utilizing these tools are limited to accessing authorized data is crucial. In essence, preserving role-based access controls is vital to safeguard this critical asset.
Further, involve your security department in formulating policies regarding the utilization of data in these processes. If the solution does not adhere to your data privacy and security standards, exploration of alternative options becomes necessary.
2. Has bias evaluation been conducted for the solution?
If an LLM or AI tool complies with your data security requirements, the next step involves examining the presence of bias. It is essential to ascertain if the solution has been appraised for bias and/or how it has been enhanced or altered to mitigate the impacts of biased data. This assessment cannot rely on trust, as the potential adverse effects of biased AI tools on your brand and business are substantial. The AI vendor must demonstrate the measures taken to minimize the duration of training data and/or any associated model optimization to reduce bias.
3. Is the solution equipped for ensuring regulatory compliance?
Vendors must be equipped to adapt to new and evolving AI regulations. Navigating these regulations is complex as rules vary across regions, countries, and states. How will AI vendors adhere to these regulations? Who verifies compliance? Does the vendor provide standards support? These compliance-related questions hold particular significance for multinational corporations that must adhere to a range of regulations globally. It is advisable to inquire about the vendor’s approach to addressing these concerns, as well as how they will keep you and your legal and security departments informed of any future changes.
4. Is the solution designed to evolve over time?
Shifting the focus to time-related criteria, LLMs are trained on a specific set of data. However, as your business is dynamic and not static, relying on an AI tool incapable of learning from feedback and adapting as the data evolves over time is undesirable. It is essential to inquire about the provider’s strategy for enabling their model to adapt over time and ensuring the continual relevance of the data feeding their model. Understanding their approach to sustaining the solution’s functionality is equally important.
Furthermore, insights into how the solution handles so-called dirty data, both within the enterprise and model, are necessary. Dirty data can contaminate or impair the model’s performance, potentially leading to erroneous outcomes. Discretion is advised if your AI vendor asserts that any concerns about dirty data can be resolved through swift engineering. This implies the persistent presence of unsuitable data within the model, producing inaccurate or potentially harmful results. Such a solution is inadequate for enterprise use.
5. What level of support is necessary for implementing the solution?
Lastly, it is crucial to assess the internal expertise required to implement and leverage the solution. The vendor should provide a detailed response to this query. Additionally, inquire about the anticipated availability and responsiveness of these teams, as well as the type of qualified technical support contacts they will furnish.
While these five questions serve as a starting point, there are numerous other technical, regulatory, economic, and organizational factors to contemplate when evaluating an LLM-based tool or AI solution. It is advisable to request a comprehensive set of potential cost estimates. Companies transitioning data or services to the cloud may encounter unexpectedly high costs based on usage, and the same may apply to AI solutions. Enquiring about comprehensive estimates, encompassing potential unforeseen expenses such as operational risk insurance or cyber insurance, is recommended. Consider collaborating with a trusted, existing solution provider or AI consultant to facilitate the evaluation process.
Selecting an AI solution poses a complex undertaking for any enterprise. However, the complexity should not deter you. These are stimulating yet occasionally perplexing technological times. LLM and AI solutions will reshape how we derive value from data, and possibly how we conduct business in general. Therefore, it is advantageous to equip yourself with knowledge and commence training your team to discern solutions that offer the most benefits to your organization while safeguarding your critical assets.
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