Evaluating GenAI Solutions? Here Are 6 Things to Consider

Nearly every organization is evaluating generative AI (GenAI) solutions and trying to figure out how to leverage the technology to drive business value today rather than waiting for the future. While the technology is gaining traction in helping individuals with tasks such as drafting marketing and sales materials or answering travel-related questions, it has not achieved greater enterprise adoption given the number of significant limitations, including hallucinations, lack of insight into the original source of the answers, and an overall lack of data security.

Nevertheless, the integration of GenAI solutions has become a strategic imperative for organizations seeking to stay competitive and deliver exceptional customer experiences. GenAI, powered by sophisticated natural language processing (NLP) and machine learning algorithms, holds the potential to automate tasks, provide insights, and enhance decision-making processes.

It is imperative that organizations select the right GenAI tools to meet their critical business requirements. And by following proven best practices, organizations can benefit from advanced GenAI technology immediately.

But not all GenAI solutions are created equal. To ensure you invest in the right tool to meet your business needs, here are six critical considerations to make when evaluating GenAI solutions:

1. Accuracy and Reliability

One of the first factors to assess is the accuracy and reliability of the GenAI vendor, as many existing tools merely bolt on GenAI capabilities. Multiple vendors advertise retrieval augmented generation (RAG), an AI framework that works with pretrained large language models (LLM) and additional data to generate responses to a query. However, not all RAG is created equal. If RAG returns page after page of results and leaves it to GenAI to try and unscramble the correct answer, the results will be poor and unsuitable for enterprise use. Organizations need to scrutinize the RAG tool's track record for providing consistently accurate and dependable results. To do so, companies should examine the RAG tool’s ability to understand context, handle complex queries, and adapt to language or industry terminology changes. This recommendation is a consequence of the fact that the quality of any answer generated from the LLM is dependent upon the quality and length of the input documents (shorter, more concise, and more relevant are best) from the RAG tool. Additionally, organizations should test the tool on their own data and introduce changes on the fly versus letting the vendor spend weeks examining all possible examples to see how it responds to questions related to your specific business.

2. Explanation and Source Attribution

Transparency is key when evaluating GenAI solutions. How effectively does the GenAI explain the results and cite the sources of the answers it generates? A trustworthy GenAI tool should not only provide high-quality answers but also offer clear explanations of how it arrived at those conclusions. AI explainability is crucial for decision makers who need to understand the rationale behind the recommendations; it also ensures compliance with regulations that require traceability and source attribution.

3. Integration Requirements

Consider whether the GenAI offering is a standalone solution or if it needs to be integrated with other technologies such as vector databases, semantic search, and other natural language toolkits to deliver stellar results. Seamless integration is essential for maximizing the tool’s capabilities and enabling faster time to production. Evaluate the ease of integration and the completeness of a GenAI solution to avoid complex and time-consuming procurement and implementation processes due to integration issues.

4. Data Security Measures

Data security needs to be top of mind when considering GenAI solutions. Ask what enterprise-grade security measures does the GenAI solution offer to protect the company and its customers’ data? Even if there is a data protection measure in place, does the company have the controls in place to ensure that it adheres to these policies?  Assess the tool's compliance with data protection regulations such as GDPR and the ability to use open-source large language models (LLMs) that can be used in a virtual private cloud (VPC) without access to the internet at large. A strong GenAI solution should prioritize data privacy and provide options for customization to align with your organization's security policies and regulation mandates.

5. Domain-Specific Understanding

To be truly effective, a GenAI solution should be capable of understanding or quickly learning about the industry and domain-specific language relevant to your business. Data labeling and model training/retraining takes time and significant technical resources from organizations, and the costs can add up quickly to millions of dollars per year. Enterprises should evaluate the tool’s ability to quickly, efficiently, and cost-effectively adapt to industry jargon, technical terms, and context-specific nuances. A GenAI solution that can’t grasp your unique domain-specific requirements may provide generic or irrelevant answers, undermining its utility in your specific use cases.

6. Deployment Speed

Time-to-value is a critical consideration in today’s competitive landscape. How fast can the GenAI tool be deployed in your production environment? The deployment speed can significantly impact your organization’s ability to harness the benefits of GenAI quickly. Organizations need to assess the tool’s scalability, ease of setup, and ongoing support for rapid implementation. Ideally, the tool should offer a balance between speed and customization to align with your specific needs.

GenAI solutions hold immense potential to transform business processes and enhance customer experiences. However, to fully harness the power of GenAI and avoid potential pitfalls, organizations must carefully evaluate their options. By considering accuracy, transparency, integration, data security, domain-specific understanding, and deployment speed, enterprise leaders can evaluate and select the right GenAI tools that align with their critical business requirements. Making informed decisions in this rapidly evolving field is essential to stay ahead in the era of AI-driven innovation.

John Reuter is the chief strategy officer at Kyndi, a global answer engine provider that empowers people to do their most meaningful work. To learn more visit https://kyndi.com/ or follow them on LinkedIn and Twitter.