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GenAI’s Limitations Can Be Unlocked With Automation

To fully realize GenAI's capabilities, businesses must provide it with context grounding, automation, and transparency.

ITPro Today

May 3, 2024

4 Min Read
wooden letter blocks spelling out generative AI

As enterprises continue to evolve their digital capabilities, Generative AI (GenAI) stands out as a powerful tool that promises to revolutionize operations across industries. GenAI can help employees test ideas, generate content, and quickly retrieve information, just to name a few of the initial applications that are being deployed by organizations today. However, businesses have been slow to move beyond these initial use cases, for good reason.

The truth is that GenAI can only do so much on its own. While GenAI can seem like it’s powered by magic, it’s crucial for organizations to keep in mind that it is only a tool, and all tools have their limitations. In the case of GenAI, the lack of proper context, the inability to act on its own, and insufficient transparency and trust all stand out as deficiencies.

If organizations fail to address these when implementing GenAI, they won’t get the most out of a potentially transformational tool. However, when organizations pair GenAI with automation and provide it with the proper business context and context grounding, GenAI’s limitations can be circumvented so that it can drive tangible business outcomes.

Context Grounding: Tailoring GenAI To Specific Business Needs

GenAI possesses a knowledge base as wide as the ocean—but its understanding is puddle deep, and it lacks true contextual understanding. This is problematic given the need for accuracy and specificity in business settings. GenAI systems need mountains of context to operate like a business needs it to—as a subject-matter expert who knows the organization’s systems, processes, and customers like an employee would. Gathering and providing the tool with this context is time-consuming and resource-intensive, but without it, GenAI is simply outputting generic data. The true challenge of deriving positive outcomes from the tool lies in bridging this gap between the generalist approach of GenAI and the specific requirements of individual businesses.

Related:Artificial General Intelligence: Are We There Yet?

To overcome this limitation, businesses should focus on investing in deep, contextual training for their GenAI tools. This process can be slow, laborious, and expensive because the context needed lives on companies’ intranets, CRMs, filing solutions, cloud data, and other sources. To help, organizations should leverage automation to pull the information, so they can vastly decrease the time and resources needed to provide GenAI tools with the necessary context to make it into something that can substantively impact productivity.

Context Grounding helps businesses improve the accuracy of GenAI models by providing prompts with a foundation of business context through retrieval augmented generation. This system extracts information from company-specific datasets, like a knowledge base or internal policies and procedures, to create more accurate and insightful responses.

Action: Empowering GenAI With Automation

GenAI can analyze, comprehend, and create, but it’s limited in what it can actually do on its own. Automation serves as the missing link that empowers GenAI to translate its insights into actionable outcomes. For example, if a user asks its GenAI model to submit their expenses, they’ll get a rundown of how to do it; the tool won’t do it for them. By contrast, if that GenAI tool was integrated into the system, those expenses could be submitted by an automation. That showcases the fundamental problem with GenAI; it can think, but it can’t act. It has a mind, but no “body.” So, by automating routine tasks and processes, businesses can leverage GenAI to take action—helping to drive efficiency, streamline operations, and enhance productivity.

Trust and Transparency: Building Confidence

Even with the proper context and integration with automation to enable action, GenAI can’t be effectively utilized unless organizations can trust it. While this is broadly true for most enterprise technologies, with GenAI, building that trust is particularly challenging. Models currently in use rely on new data to continuously train and improve, and because of that, businesses rightly fear that their proprietary data might be shared or misused. Because human nature leads people to fear what they don’t understand, this 'black box' of GenAI models, where systems take in data and transform it into something useful through a mysterious process, often leaves businesses questioning the decision-making processes.

Transparency is key to addressing these concerns. Employees using GenAI tools should be using them within the confines of a governance model. Organizations should consider prebuilt GenAI Activities within a governance model that are easy to access and develop with, and leverage high-quality AI predictions in automation workflows that deliver faster time to value. These activities include a collection of GenAI use cases, such as text completion for emails, categorization, image detection, language translation, and the ability to filter out personally identifiable information, enabling enterprises to do more with GenAI.

GenAI is poised to revolutionize business operations, but that revolution can only happen when GenAI is paired with automation and proper transparency. Providing the GenAI ‘mind’ with the ‘body’ it needs through automation minimizes its limitations. By allowing it to act on its insights and meaningfully impact the business, GenAI can realize its full potential.

Luke Palamara is vice president of AI product management at UiPath.

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Generative AI
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