6 Ways To Utilize Generative AI to Improve Productivity

These days, the headlines are full of stories about generative AI (GenAI)—and the news isn't always positive. But is there any real substance behind the GenAI buzz? Can companies successfully integrate AI into their day-to-day workflows and generate positive results? 

At Conga, we have an extensive track record of deploying AI and machine learning into our solutions. To share our experience with AI, Conga experts recently joined the RevOps Co-Op team for a webinar, AI-Powered Revenue Generation: Putting the Buzz of Generative AI to Practice.  

This timely discussion covered important topics like: 

• Top use cases for incorporating GenAI into the revenue lifecycle 
• The benefits and drawbacks of leveraging AI-enabled tools 
• Challenges encountered and lessons learned around GenAI adoption 

Following are some key takeaways from this lively conversation. You can also view the on-demand webinar for more details.  

First things first: what is GenAI? 

At its most basic level, generative AI is defined as follows:  

"Generative AI is artificial intelligence that is capable of generating text, images, or media using generative models. Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics. " 

That's great, but what does it actually mean? The fact is, GenAI is based on just two things: data and math. The math part of the equation comes from the algorithm, which is the "brains" of any GenAI model. This algorithm consumes massive amounts of data to produce its results—but there are important considerations about where that data comes from. (We'll dig into that idea in more detail below.) 

Where Conga has found value in using GenAI 

As the Conga team says, "We're not going to replace people with generative AI. That conversation is not on the table. We're talking about how we make people's jobs easier. How can they be more productive? How can you move faster? How can you make more profitable business decisions and drive value ultimately to your customers?" 

As we all know, there are many processes in the revenue lifecycle that are both repetitive and labor-intensive—which tend to be the best candidates for AI support. Conga started by analyzing all the potential functions where AI can add value. Some of the top use cases for applying AI to improve productivity include: 

• Prospecting and lead qualification: Technology offers a tremendous opportunity to streamline these processes by researching industries, personas, companies, and qualification questions. It is also useful for building customer lists and automating outreach emails. 
• Opportunity management: AI has some potential to support efficient management of sales opportunity, especially when it comes to automating qualification stages or using conversational intelligence to summarize meeting notes and populate CRM fields.  
• Managing customer relationships: Technology can help to streamline the research that takes place before a customer call, and AI-powered chatbots are useful for answering common questions without involving a customer service rep—but AI is no substitute for the human touch in managing customer relationships. 
• Enhancing customer relationships: Some customer service teams are leveraging AI to understand customer satisfaction and churn potential by analyzing various key data points. Technology can also synthesize a variety of articles, whitepapers, and other sources to share relevant, useful information with customers.  
• Case management: Technology is useful for increasing both accuracy and productivity on customer support teams by reducing the need for low-value tasks. It can be used to analyze customer sentiment, summarize interactions, and identify potential resources to solve support issues.  
• Case deflection: AI-powered technology can give customers instant access to curated resources to answer their questions in a way that feels personal. This allows support teams to spend less time answering simple questions and more time on challenging issues where their expertise is highly valuable. 
• Mitigating contract risk: AI can efficiently handle unstructured contract data, aiding negotiations and minimizing risks.Technologies like natural language processing (NLP) and machine learning play vital roles. Additionally, AI finds applications across various revenue lifecycle processes, including prospecting, opportunity management, customer relationship management, case management, and case deflection, enhancing productivity and efficiency. This often plays into the contracting portion of the revenue lifecycle. 

The importance of implementing guidelines around AI use 

It's critical to understand what happens when AI tools ingest your data. There have been some high-profile incidents where software developers have tried to use a public version of ChatGPT for debugging and wound up inadvertently exposing some of their proprietary code and other intellectual property. When you feed data into an AI tool, it becomes part of the algorithm's learning model—regardless of what that data may contain.  

That's why the team at Conga has been extremely conscientious about understanding not only the output of AI tools, but also how data is used and protected. To address these concerns, Conga has developed strict corporate policies around usage of AI tools. We've also built our own API-driven infrastructure for leveraging open AI tools, and our own data lake to train GenAI technology. Rather than feeding data directly into the algorithm, it is housed in private blob storage with Microsoft Azure.   

This disciplined approach is critical to ensuring that proprietary information and private customer data remains secure—which is just as important as the positive business impact of these tools. Conga experts tell us, "Our general counsel and our CISO are very pragmatic about new technology applications. In their position, they have to be very concerned about data and privacy."  

Lessons learned on deploying AI solutions—and minimizing risk 

When it comes to maximizing value from GenAI tools and minimizing associated risk, the Conga team shared the following recommendations:  

• Experiment with different GenAI solutions to find the best match for your needs. These include ChatGPT, Google Bard, and more.  
• Don't try to build a solution from scratch; leverage tools that are already available on the market. 
• Develop a plan with specific goals so you don't want time chasing results that aren't sustainable. 
• Avoid inputting any private or proprietary information about your company, your customers, and your code into GenAI tools. 
• Test everything on internal-facing use cases before you start rolling out AI-powered tools that will impact your customers. 
• Don't implicitly trust AI-generated results; keep comparing answers to known information until you're sure it can be trusted. 
• Treat AI-generated content as a starting point, not a final product. Don't simply copy-paste it into your communications, as it tends to come off as disingenuous and robotic. People are better than you'd think at spotting a fake. 

To summarize, "Don't expect GenAI to solve all your problems. It's not a silver bullet. As you dig in, you'll find that it can solve some things very well, while it struggles with others. And avoid falling into the trap of shiny object syndrome. Everyone thinks they've got a perfect example of what generative AI can do and the value it can provide to a business. But if you chase every one of them, you'll never get anything done. Identify business problems that need a solution and then ask how AI can help—not the other way around."  

For more detail and expert insights from this timely discussion—plus Q&A with our live audience—you can watch the webinar recording on demand.