Generative AI in the Enterprise: Start with a Proof of Concept

AI in the Enterprise

For all the complexity and hype around generative AI, there’s one business case that stands above all others: enhancing the customer experience. Brands should be aspiring to transform ordinary experiences into extraordinary ones and to make the impossible, possible. To truly understand the potential of generative AI in the enterprise, you’ll have to dig into your use cases. You’ll need to navigate the misconceptions around it. And you’ll need a proof of concept.   

Navigating Uncertainty

As you look to stand up your own generative AI-powered solutions, you’ll likely face some confusion, misconceptions, and objections. The uncertainty around generative AI can create unrealistic expectations or, on the opposite end of the spectrum, fear of the unknown. Perhaps you’ve heard some of these already:   

  • Overhyped: “Generative AI can do all of that.” Keep in mind that generative AI has its limitations, and organizations must have an appreciation for what it can do and what it can’t.   
  • Underhyped: “Generative AI is just another metaverse.” Ignoring generative AI altogether because it’s a media trend misses the point. How you bottle it, and what you do with it determines its value. 
  • Been there. Done that: “We already have AI.” The large language models behind generative AI have distinct characteristics and applications from other AI, which may already be in use in your organization. It’s important to understand that the skills, requirements, technology, infrastructure, and architecture vary based on the type and flavor of AI employed.  

To see the most success in your own generative AI adoption plan, you’ll need to understand the limitations of generative AI and be able to identify appropriate business use cases. If you don’t already have use cases in mind, collaborating with partners and subject matter experts in a workshop environment can be a shortcut to them. Once you have your use cases, then you can set about testing them in a proof of concept. This approach requires minimal investment, reduces wasted effort, and improves outcomes, bringing you one step closer to innovative solutions. 

Addressing Risks

You understand the fundamentals of generative AI. You have your business use cases in hand. You’re ready to dive into a proof of concept and prove or disprove your hypothesis that Y value can be derived from X amount of investment. But you also need to manage risks along the way, including known risks that are unique to generative AI, such as:  

  • Hallucinations: Because generative AI can fabricate answers, you’ll either need to account for that with human intervention or find use cases where you have a higher risk tolerance. Hallucinations can be avoided by ensuring your data is accurate and adding guardrails around your content.  
  • User abuse: The potential for the misuse of a generative AI-powered solution—it’s only as good as what the user puts in—means you must protect against training the model on bad data and prompts.  

You’ll need governance and a validation process for assessing and verifying the accuracy, reliability, and ethical considerations of AI systems—and through establishing better processes you’ll start getting higher quality responses from your generative AI solution. You wouldn’t just give sharp scissors to small children and walk away. The same goes for generative AI in the enterprise. You can’t just hand it off to users and expect positive results. It needs to be continuously managed.  


Generative AI in the Enterprise, Today

To start a proof of concept, it’s important to define clear objectives and success criteria. This helps in evaluating the effectiveness of the generative AI solution and determining its impact on the business. You’ll need a step-by-step plan that outlines the implementation process, data requirements, and evaluation methods.  

Data plays a crucial role in generative AI applications, and it’s essential to assess the availability and quality of data relevant to the use case. In cases where the required data is insufficient, artificial data generation techniques can be explored to augment the existing dataset. This ensures that the generative AI model has enough information to learn and generate meaningful outputs.  

You’ll want to start small and gradually scale up. This iterative approach allows for testing and refining the generative AI solution before implementing it at a larger scale. By closely monitoring the performance and gathering feedback from users, adjustments can be made to improve the solutions effectiveness and address any challenges—human-created or not.   

Additionally, collaboration with domain experts and stakeholders is crucial throughout the proof of concept. Their input and expertise can provide valuable insights and help align the generative AI solution with the specific needs of the business. Regular communication and feedback loops ensure that the solution is working to fix real-world problems and continuously improves over time.   

The journey of exploring generative AI begins with understanding your unique business problems and opportunities. Simply by testing out generative AI’s potential in a proof of concept, organizations can envision new possibilities, evolve their customer experience, and invent unique solutions that drive business growth. But that won’t happen unless you jump in. To stay a step ahead of the competition that’s already embracing generative AI, you’ll want to start sooner than later.   

Everybody’s talking about generative AI, but few companies are actually doing anything about it. Now’s the time to start delivering generative AI value. Discover how we’re helping brands reimagine CX with generative AI. 

Shawn Ennis

Shawn Ennis

Director, Digital Edge

Contact Concentrix

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