10 Essential Criteria for Evaluating AI Enterprise Search Tools in 2024

March 6, 2024
Sneh Ratna Choudhary
Sneh Ratna Choudhary
Table of Contents
What changed for sales productivity in 2023
What changed for sales productivity in 2023
Productivity Trend #1
Productivity Trend #2
What changed for sales productivity in 2023
What changed for sales productivity in 2023
Share on

Choosing the right solution among AI-powered enterprise search tools is not merely about deploying the latest technology — it's about finding a solution that aligns with your business goals, integrates seamlessly with your existing infrastructure, and upholds your data governance standards.

This guide will give you the necessary insights to make an informed choice in the age of AI, from understanding the critical role of connectors in data integration to assessing the depth of natural language processing capabilities for improved search relevance.

Related Read: Enterprise Search with AI Knowledge Assistants

Let’s dive into critical aspects you should evaluate to help your decision, ensuring that your AI Enterprise Search Tool enhances your enterprise search capabilities and strategically contributes to your organization's knowledge management and decision-making processes:

  1. Data Connectors: Evaluate how the enterprise search tools integrate with various data sources and applications to enhance data accessibility and workflow efficiency.
  2. Security and Privacy: Review the AI assistant's security measures, including data permissioning, encryption, and compliance with industry standards.
  3. Search Relevance: Verify that the AI assistant delivers accurate and up-to-date information, quickly adapting to data changes.
  4. Price: Considering pricing models and commitments, evaluate the cost relative to the solution's value.
  5. User Interface and User Experience: Consider the intuitiveness and user-friendliness of the interface, which can drive adoption and regular use.
  6. Analytics and Insights: Check for actionable analytics and insights that can drive improvements and inform strategic decisions.
  7. Customer Support and Success: Assess the vendor's support infrastructure, including training, documentation, and direct support channels, to facilitate effective enterprise search tool usage and problem resolution.
  8. Setup Time: Prioritize enterprise search tools designed for quick deployment and easy integration to minimize operational disruption.
  9. Uptime: Ensure the AI assistant supports high uptime, crucial for dependable access to information and organizational behavior change.
  10. Roadmap and Scalability: Consider the AI assistant's future development plans and scalability to ensure it will continue to meet evolving business needs and technological advancements.

1. Data Connectors

Teams leverage various applications in increasingly decentralized work environments to streamline their workflows - such as Salesforce for CRM, Google Drive for document storage, Slack for communication, and Asana for project management.

Data connectors are crucial as they connect the enterprise search tools and their data sources, such as internal databases and cloud services. This integration is critical for your search tool to unify data from various repositories, increasing workflow efficiency.

Support for Various Data Formats

Practical AI knowledge assistants and enterprise search tools should be capable of handling both structured and unstructured data and processing different object formats, ranging from documents to Jira issues and Slack messages.

They should also handle different file formats like PDF, Word, spreadsheets, and presentations. This ensures that no critical information is missed during the search process, regardless of format.

Freshness of Data

Effective enterprise search tools must quickly reflect document updates within source apps, particularly with permission changes or when real-time information is required. Take customer service, for example. If a rep is on a call with a customer, they need access to the latest account information to offer quality support.

For instance, owing to its unique real-time search API approach, Dashworks ensures users can access the most current information without typical delays experienced in traditional enterprise search systems.

Historical Data

Learn how your AI knowledge assistant handles large amounts of data — the volume of data processed at a time and how far back the search function goes.

Some indexing enterprise search tools may limit the amount of historical data they can access. Access to older data is critical if your business deals with historical data or long-term trends. On the contrary, Dashworks’ real-time search approach ensures no limit to historical data searched.

2. Security and Privacy

Data security is the bedrock of reliable AI enterprise search tools.

Here's a comprehensive overview of the security and privacy measures to expect in a solution:

No Data Indexing

Look for solutions like Dashworks that don’t need to index your data for search.

Traditional enterprise search tools, the predecessors to AI knowledge assistants, depend on indexing content to generate search results. All the information and documents to be searched are transferred to a central location or database on a vendor's cloud or on-premise.

Dashworks employs a unique no-indexing first approach that uses real-time API calls to fetch information relevant to user queries, minimizing the amount of data stored and reducing the exposure to security risks.

By not pre-indexing data, Dashworks significantly decreases the surface area for potential data breaches, ensuring higher security and privacy.

Document-level Access Permissions

Effective data permissioning is crucial for maintaining the integrity of access controls. Document-level permissions from connected applications should be synced automatically and reflect real-time changes so users only see what they're authorized to view.

Connecting to Single Sign-On (SSO) solutions like Okta or Azure AD can further streamline user access while maintaining data privacy.

Advanced Encryption

Foundational security best practices to look for in an AI-powered enterprise search tools include:

  • Use of advanced encryption standards, such as TLS and AES-256, that safeguard data in transit and at rest.
  • Regular penetration test assessments to identify and rectify vulnerabilities bolstering the system's defenses.
  • Proactive security measures, including fault-tolerant infrastructure and enhancing resilience and system integrity against potential threats.

Compliance with Industry Standards

Examples of industry-standard compliance to evaluate include:

  • SOC-2 Type 2: This standard ensures that a company's information security measures align with the high levels of oversight and monitoring.
  • GDPR: The General Data Protection Regulation compliance signifies that enterprise search tools uphold data privacy and protection for individuals within the European Union.
  • HIPAA: Compliance with the Health Insurance Portability and Accountability Act indicates adherence to stringent security protocols for protecting health information, especially if you’re in the healthcare industry.

AI Training

Modern AI systems rely on learning from user feedback to improve relevance. When it comes to training AI-powered enterprise search tools, there are two primary approaches:

  • Foundation Models: Some solutions use customer data to train broad foundation AI models. However, this can create risks regarding data privacy and potential misuse.
  • Federated Learning: Other solutions, like Dashworks, can employ federated learning. This approach ensures that no customer data is ever shared between workspaces while allowing customers the benefit of improved relevance. Furthermore, solutions like Dashworks include opt-outs for customers to completely disable by learning in their workspace for additional controls.

3. Search Relevance

Your AI knowledge assistant should deliver trustworthy and current information. It should be engineered to provide accurate responses and swiftly update its index to reflect changes in the source data, particularly when decision-making relies on timely and accurate information.

Natural Language Understanding

Natural Language Understanding (NLU) enables enterprise search tools to interpret complex questions and user intent accurately, providing precise and relevant responses.

  • Complex Query Interpretation: An AI assistant should interpret multifaceted queries involving several components. Dashworks, with its advanced NLU, can understand such complex questions and retrieve the needed information accurately and efficiently.
  • Intent Recognition: Recognizing the user's intent behind a query is another vital element of NLU. For instance, when a user inquires about a specific issue, Dashworks can retrieve information from a customer service tool like Zendesk and provide a comprehensive response.


Advanced AI-driven enterprise search tools should be capable of personalizing search results for a broad range of employees, including tailoring outcomes based on their department and role.

Plus, the AI enterprise search system should have location-based customization capabilities, delivering localized details like regional sales data or specific office information, depending on the user's geographical setting.

Response Customization

An enterprise search application needs mechanisms and features to improve accuracy. The AI-driven enterprise search tools must support should also allow response customization to align with your organization's needs.

At Dashworks, we've implemented mechanisms to allow swift corrections and improvements to the AI's responses.

For example, we offer intuitive options for users to give feedback on response accuracy. This direct feedback can be used to improve its future responses. Users can also provide specific instructions or corrections when the AI gives an incorrect answer, guiding the AI to understand better and respond to similar queries in the future.

Plus, Dashworks supports the integration of verified information. This permits the AI to reference and learn from accurate, authoritative content, thereby enhancing the reliability of its responses.

Multilingual Support

Consider how AI enterprise search tools process queries in multiple languages, rank information in different languages, and retrieve multilingual data while returning accurate, relevant results and adapting to different languages' linguistic nuances and idioms.

This multilingual capability ensures a seamless and inclusive user experience across different geographies and languages.

4. Price

When adopting an AI Enterprise Search solution, consider both the cost and the value it provides.

Evaluate the pricing in terms of initial investment, feature set, scalability, and support. Pricing could be based on seats, queries, data volume, or a mix and should align with your organization's needs. Also, check if there are minimum seat requirements to use the platform effectively.

Factor in deployment costs, noting that on-premise setups with traditional indexing can be pricier due to data storage demands. In contrast, Dashworks' real-time approach minimizes data storage needs, significantly lowering maintenance costs.

5. User Interface and User Experience

First impressions are paramount, especially for AI enterprise search tools.

As the main point of interaction between your team and the AI, the UI needs to be more than functional - it should be intuitive, responsive, and user-friendly.

Look for features like auto-suggestions and natural language processing that can significantly elevate the search experience.

Also, evaluate other add-on features, such as white-labeling and other customization options, that can improve user experience.

Ultimately, an AI assistant’s effectiveness is tied to its “stickiness"—the consistency and frequency of its use. So, the UI/UX should be a key consideration when assessing an AI knowledge assistant.

Integration of Enterprise Search Tools with Existing Workflows

When selecting from a variety of AI enterprise search tools, consider their ability to integrate seamlessly into existing workflows. This involves evaluating whether the tool offers a range of access and deployment options suitable for different user preferences and operational environments. These options include browser extensions, dedicated Slackbots, and web apps.

For instance, the Dashworks Slackbot lets users get answers to their questions directly within Slack channels, where they’re already used to asking questions to their co-workers. Similarly, the Dashworks browser extension can be easily installed on your preferred browser, enabling quick access to its search capabilities on a new tab page without disrupting your current workflow.

6. Analytics and Insights

Admin analytics and insights can greatly improve the usefulness of your AI assistant, improving searches, user experience, and informed decision-making.

The insights derived from enterprise search tools should be actionable, allowing you to make changes that boost the effectiveness and efficiency of your team. For example, analytics on Return on Investment (ROI) can be crucial in understanding the AI assistant's value, providing insights into its adoption rate, team usage, and overall productivity impact.

An ideal AI assistant identifies gaps in information that affect its response quality, whether from missing, outdated content or document access restrictions. By identifying these knowledge gaps, the AI assistant can help ensure that your team has access to the information they need when they need it.

7. Setup Time

You want to choose an AI assistant that deploys quickly and causes minimal disruption to daily operations.

However, traditional enterprise search tools may require more time due to complex indexing processes. This could cause considerable delays before the system is fully operational.

Prioritize solutions known for easy setup and seamless integration with existing systems, especially those involving OAuth or API key-based integrations.

Dashworks reduces setup time with its real-time search API. Once your apps are connected, they become instantly active. This quick deployment feature lets your team immediately use Dashworks' powerful functions without lengthy wait times.

8. Customer Support and Success

When choosing an AI enterprise search solution, consider the support and resources the vendor offers. The tool must be supported by a team that helps with adoption and addresses ongoing needs.

Training sessions and webinars are essential for your team to understand the tool's features and best practices, aiding smooth integration. Access to comprehensive documentation is also crucial for ongoing reference and support.

Immediate support through in-app or Slack chats can improve the user experience by providing quick answers and minimizing workflow interruptions.

9. Uptime

Uptime is essential for AI enterprise search tools, particularly for businesses needing fast and reliable information access. Dashworks focuses on high stability to handle large amounts of data effectively, ensuring dependable and efficient data access.

Dashworks uses a real-time search API, which is more stable and less prone to integration issues than traditional indexing methods. This approach helps maintain strong performance, even with significant data and reduces the chances of downtimes or technical issues.

Look for solutions like Dashworks that share their uptime status, allowing you to see system performance and transparency in the commitment to providing reliable service continuously.

10. Roadmap and Scalability

When selecting an AI Knowledge Assistant among the enterprise search tools on the market, consider its current capabilities and future growth potential.

The tool should satisfy immediate requirements and align with long-term business objectives and technological advancements. A detailed development roadmap can reveal the tool's dedication to continuous improvement and new features, indicating its ability to adapt and evolve.

Investigate what future functionalities the tool plans to add and how these align with emerging tech trends. This will give you insight into its potential to remain relevant and beneficial to your organization.

Choosing an AI assistant like Dashworks, which is equipped for both present demands and future developments, is vital for enhancing your organization's data utilization, productivity, and efficiency.

What Does the Future Hold for Dashworks?

Dashworks’ unique no-indexing first approach is crucial to enabling its vision of being the most powerful, personalized, and private AI knowledge assistant for work. In 2024, Dashworks is set to introduce key enhancements to increase its utility and integration capabilities:

  • Support for taking actions and executing workflows inside business applications.
  • Ability to ask questions with databases using natural language, removing the bottleneck of learning SQL, streamlining data analysis, and speeding up decision-making.
  • New API and web widget features are on the horizon, providing greater flexibility for utilizing the knowledge assistant as required and connecting it to new knowledge sources.
  • Multi-modal capabilities, such as image generation.

As the platform continues to evolve, Dashworks is committed to being an essential resource for businesses aiming to optimize their knowledge management and operational processes.

Want to explore Dashworks for yourself? Schedule a personalized demo today!


Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Sign up for Dashworks

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Schedule a demo
Book a demo
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Explore more posts

Book a Demo

Schedule time