CTO and co-founder of 6sense, revolutionizing efficient revenue growth through the power of AI and insights to uncover buyers in market.

When ChatGPT hit the scene in November 2022, it put generative AI into the hands of anyone who wanted to try it. Now, it seems everyone has an opinion about using generative AI to simplify various aspects of work and life.

Generative AI has thrust the AI discussion right into the mainstream because its outputs are so relatable. Anyone can play around with it and imagine the ways they might be able to make use of it. That’s certainly true in business settings. Thomson Reuters’ August 2023 report shows that people are readily embracing generative AI to enable productivity and efficiency and empower them to provide a better experience for their customers.

However, while ChatGPT’s big splash may give the impression that AI is new, it’s actually been solving big business problems behind the scenes for years.

As someone who has been fully immersed in the business applications of AI for the past decade, I want to offer a peek under the hood to show how AI can help businesses operate more efficiently and effectively. Understanding these applications can help you derive more benefit from the current AI darling—generative AI—as well.

Keeping The “Intelligence” In Artificial Intelligence

Generative AI has become a must-have tool for businesses, and its outputs can create real efficiency in countless ways. However, the outputs depend on the information that goes into it—whether that’s the prompts it’s given or the data it’s trained on.

Modern organizations can use AI to take the huge volumes of data they have access to and turn that data into intelligence. In B2B marketing and selling, for example, companies use incredible depth and variety of data to reach the right customers with relevant messages at the ideal time in their buying journey.

Having the data is only the first step, though. Without the ability to analyze it and turn it into intelligence, revenue teams operate on guesswork—which contributes to nearly $2 trillion in wasted spend and opportunities each year, according to Boston Consulting Group.

This is where other types of AI come in to analyze the data and turn it into intelligence that businesses can use. Four key types of AI-powered analytics are useful in business, including sales and marketing initiatives—descriptive, diagnostic, predictive and prescriptive. Here’s a simple way to think of them:

• Descriptive analytics pull historical data—what has already happened. It’s best used to present KPIs such as year-over-year sales growth and helps add data to presentations and dashboards.

• Diagnostic analytics looks at why something happened. It helps organizations understand the cause of trends and correlations between variables.

• Predictive analytics help determine what’s most likely to happen next—future outcomes. In sales and marketing, organizations can use predictive analytics to analyze millions of buying signals to predict which accounts are most likely to buy what they’re selling so they can appropriately prioritize time, budget and resources.

• Prescriptive analytics helps determine what to do about the insights gleaned from your data—for instance, how to leverage them to improve revenue.

Best Practices And Considerations For Generative AI In Marketing And Sales

Together, these uses of AI make generative AI tools more powerful. Given the right information, generative AI can improve processes and help build content—even generate revenue by giving users the ability to more effectively identify, reach and engage with prospects.

Some of the ways organizations can effectively leverage generative AI in marketing and sales work include creating more personalized messaging to maximize the effectiveness of email outreach and marketing campaigns, aiding SEO search research by uncovering keywords that align with targets, and maximizing advertising ROI by tailoring ads to buying stage, persona and interest.

It’s important to keep in mind a few considerations when incorporating generative AI into workflows, including:

• Garbage in, garbage out. In other words, quality results require quality data. In addition, it takes time to fine-tune and prompt the tool the right way. Robust data and the AI required to process it into intelligence can create easier processes, more effective workflows and more accurate outputs.

• Set goals. To promote generative AI from novelty to needle-mover, it’s important to set and measure real business goals for the technology. For example, we set a goal of generating 10% of our pipeline from AI-powered conversational emails in one year. That big goal stretched the team to think differently and consider new technology and tech to make it happen. The approach was so effective that we accomplished the goal in less than six months, propelling our team to continue the campaigns and expand into additional use cases.

• Stay flexible. With the generative AI wave emerging so quickly, it’s important to remember that technology is not a one-and-done type of situation. We can expect things to keep changing quickly; the deluge of new tools and applications that are emerging seemingly daily is proof of that. Stay agile and be ready to adapt to what’s next, whether that’s government regulation or new tools and use cases.

Creating An Effective AI Toolkit

Once people know what to expect from technology and have the digital skills needed to work with it, the right tools can empower them to be more effective at their jobs. They’ll be armed with powerful insights while saving time, avoiding stress and eliminating needless busy work.

There’s no doubt that generative AI is a game-changer. It has the potential to create real results for marketers and sellers. When quality data and intelligence are feeding it, it becomes more than a novelty—it’s a tool to truly transform the way we work.

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