Innovative organizations recognize how useful agentic AI can be and want to move quickly to take advantage of it—and search is one of the most obvious and immediately transformative benefits. Leading companies today are implementing internal AI search to solve some of the most basic and time-wasting aspects of work: searching for information, creating update memos, and getting the latest details about projects, clients, and teams.
An AI internal search tool makes information stored in various formats and databases searchable; it can then be prompted to answer questions based on the disparate pockets of information. Though simple in nature, it’s nonetheless novel as it requires the collection, retrieval, evaluation, citation, and translation of thousands of terabytes of data into a conversational and actionable answer. These actions are only possible thanks to the latest advancements in AI—and the functionality of these tools are getting faster and more impressive by the day.
For this reason, there is no question that internal AI search will be a standard requirement at nearly every organization that wants to compete in the age of AI. It facilitates the movement of information more quickly and eliminates silos, empowering teams to make faster progress and be more productive while also lessening the burden when employees depart.
As with all revolutionary technology, internal AI search introduces new considerations. Developing a dedicated AI search policy to guide the deployment and usage of the tool can help to limit risk without missing out on the massive productivity benefits it offers. This article will help your IT and legal teams do just that.
The governance framework that moderates the flow of information within your company is a key factor in shaping your AI search policy. It influences not only which product you choose but also how data is structured, accessed, and protected across your organization.
Internal AI search tools typically function in either a top-down or bottom-up governance framework.
In the case of top-down implementation models, IT teams—in partnership with an organization’s leadership—decide on permissioning and authentication rules that apply to various departments, teams, and levels, locking and unlocking access as they see fit.The goal of these constraints is to avoid “leakage” of confidential information or other details that may be sensitive or simply not relevant to various individuals. In addition to being very expensive to implement, these tools require a lot of upfront work and upload time that—despite the commitment of the individuals on the project—may ignore or leave out unexpected content that could get disseminated and shared unintentionally.
Read AI takes a bottom up approach. With a bottom-up implementation strategy, companies can set restrictions or a framework for what information can and cannot be stored in the centralized database, but it gives employees control to make decisions on a case-by-case basis.
Similarly to how every knowledge worker is well versed in the benefits and risks of forwarding an email to a colleague or a larger team, and how people today recognize the difference between posting a photo to ‘close friends’ or ‘all followers,’ the same rules apply. It is up to the individual to decide whether to share a Google doc or Meeting Report with another Workspace collaborator, thereby making the information within that item searchable within internal AI search.
Read AI utilizes a bottom-up governance framework because we believe that the top-down strategy introduces a burdensome level of bureaucracy without actually offering better protection. Top-down tools are very expensive and require an enormous amount of upfront work and responsibility from IT teams. Even with the most cautious implementation, a high-level, centralized approach leaves gaps, making it difficult to predict and prevent every instance of unintended data exposure.
Whatever path an IT leader takes, the internal AI search policy should introduce the approach, explain the decision, and outline the risks—ideally on a department, team, and individual level.
As with all great solutions, not all internal AI search tools are created equal. Although some companies, like Read AI, prioritize privacy and security, these standards are not yet uniform.
These questions help determine whether a platform aligns with your organization’s security, privacy, and compliance requirements:
While Read AI addresses all these concerns, not all AI search providers prioritize data protection to the same degree. A rigorous procurement process ensures that only trusted, secure, and compliant tools are integrated into your company’s workflow.
Depending on your industry as well as where your company is based, you will have different legal and regulatory requirements. Local, national, and international laws surrounding copyright, data protection, and misinformation will form the minimum requirements your company will want to meet and will therefore form the foundation of any AI search policy. You’ll want to be sure to review these considerations with your legal team or outside counsel before moving forward.
Even the most advanced AI search tools can fall short without proper user training. Failing to address these pillars in your search policy can introduce unnecessary risks and hinder employees from leveraging the tool to its full potential.
For top-down implementations, training should clearly outline what information is accessible to different roles and departments. It should also address privacy concerns, such as what should happen if an employee stumbles on a piece of information not intended for them.
The benefit of a bottom-up approach is that it follows a standard mental model that most people are familiar with from other apps and services, including email and social media. It puts less onus on the organization to create, schedule, and require training.
When training is desirable, it should address how to navigate the platform and utilize its features effectively. Prompting best practices can help employees get better results, making their searches more efficient. Addressing privacy concerns, such as what should happen if an employee stumbles on a piece of information not intended for them, is likely useful with a top-down approach.
Setting key performance indicators (KPIs) in your internal AI search policy ensures that the AI models you employ are accurate, efficient, and deliver a strong return on investment. Well-defined KPIs can help teams measure the success of its AI search tool and identify areas for improvement.
To determine the most relevant KPIs, consult your overall business strategy and identify the pain points you’re hoping to address with the AI search tool. For instance, if your goal is to improve the efficiency of your customer support team, you might establish a KPI around average resolution time.
Broader KPIs that evaluate enterprise-wide search adoption include:
It is likely the case that teams already have some policy in place to guide the implementation and usage of AI, and that the legal team—as well as employees—understand that these policies will change as investment in AI continues.
Marvel’s Uncle Ben had it right, “With great power comes great responsibility.” Developing an internal AI search policy (or expanding your existing AI governance policy) provides clarity and guardrails for leadership and individual users. It’ll help your company make the most out of this tech while mitigating risk as much as possible.
With its bottom-up governance and an unmatched commitment to privacy and data management, Read AI makes internal search smarter, faster, safer, and more secure.
Read AI utilizes a bottom-up governance framework because we believe that the top-down strategy introduces a burdensome level of bureaucracy without actually offering better protection.
© 2024 Read AI, Inc.
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