How AI Gets Real in 2024: Here’s What Go-to-Market Executives Expect

By now, we’ve all seen how the transformative potential of generative AI can quickly devolve into sensationalism, making it hard to discuss how AI is changing the way we work without resorting to hyperbole.

But that’s precisely what we asked our leaders to do. 

We recently invited several members of ZoomInfo’s executive team to identify how generative AI is impacting their daily work, and to forecast how such technologies will continue to drive change — as we both use AI, and integrate AI technologies into ZoomInfo’s products.

This is the year that AI gets real for business. Here’s what it will mean for go-to-market teams.

Generative AI as the Next Frontier of Customer Trust

The sheer ubiquity of generative AI is accelerating at a pace that has surprised even seasoned industry analysts and raised important questions about responsible regulation and governance.

Given just how widely generative AI is being deployed, businesses and end-users alike are discovering that trust is quickly becoming the most crucial aspect of generative AI. 

“One of the risks with AI is blindly trusting whatever the output of any algorithm or product is,” says Dominik Facher, ZoomInfo’s chief product officer. “The growth of AI and AI products is, fundamentally, a matter of trust — in your data, your customers, and your solutions.”

According to Facher, the increasing use of generative AI means that stakeholders must be supremely confident in the quality and accuracy of the underlying data being used by AI systems.

This, in turn, is changing how our teams work. Any new technology must be rigorously tested, but the speed with which generative AI is improving means it’s vital for businesses to cultivate cultures of ongoing learning and experimentation, as well as robust oversight and governance policies to control how data is being used.

“With AI, we’re putting a lot more control into an algorithm,” Facher says. “Growing AI is a function of continually building trust, making sure all the data and inputs going into an algorithm are as good as it gets, and really figuring out how to test the outputs of AI at scale and make it relevant for our customers.”

Predictive AI Fueled by Accurate, Actionable Data

In addition to generative AI, which creates new, human-like outputs based on supplied and stored data, many companies are attempting to anticipate market movements using predictive AI (also referred to as predictive analytics), systems that analyze previous events to model how the future might unfold. 

According to Colby Greene, ZoomInfo’s VP of solutions, data services, & delivery, data quality is the single most important factor to consider when factoring in the potential impact of predictive AI systems.

“One of the best go-to-market use-cases of AI is predictive analytics,” Greene says.

Predictive AI has been widely used in many industries for some time, such as the financial sector, which relies upon predictive analytics for fraud detection, risk management, and other applications — with billions of dollars and basic trust in financial institutions at stake.

However, in order for predictive analytics systems to make accurate, reliable recommendations, it’s vital those systems are working with the very best data possible.

“Do you have complete and thorough data on your customer base, and do you have complete and thorough data on your entire addressable market?” Greene says. “If you feed the right data into your AI, it can not only tell you the right companies to target, it can also tell you who to target at those companies when certain events occur. So now you’re targeting the right person at the right company, at the exact right time.”

Generative AI Empowers Frontline Salespeople

While managing structural and organizational risk is difficult, effectively future-proofing generative AI’s potential to disrupt traditional labor markets is much harder.

Fears of the true potential extent of automation are warranted, and headlines about industry “disruption” by AI are now commonplace. Many industries, particularly the financial services sector, have felt the impact of recent advances in AI automation sharply, and professionals across entire sectors of the economy are rightly nervous about what the future may hold.

However, while these concerns are legitimate, we’re seeing the most successful companies embracing generative AI technologies as another means to empower their employees, rather than a tool with which to replace them.

“When I think about selling with AI, I don’t worry about a team of AI-driven robots that are going to go out and sell better than your teams or prospect better than your SDRs — I think about a world in which data is the most important thing,” says James Roth, chief revenue officer at ZoomInfo.

According to Roth, generative AI can be a boon for frontline salespeople because it excels at the tedious, routine, but vitally necessary legwork that takes up so much of a salesperson’s time. 

With a strong data foundation, and when integrated properly into a company’s revenue operation, generative AI can have an immense impact on individual salesperson productivity. 

“Have a great data foundation, serving up the right signals — whether that’s people moving, whether that’s funding, whether a company gets acquired — those are all signals served up via data,” Roth explains. “The AI sits in the middle and pulls out those key things — it would take three or four hours to comb a 10K or read an earnings transcript — combined with those signals, to then inform the right message, at the right time, and take the right action.”

Risk Management is Crucial to Generative AI Initiatives

The opportunities offered by generative AI are truly transformative, but so too are the risks of irresponsible use, which is why risk management is so crucial to ZoomInfo’s policies and plans for AI in our products. 

As noted in our 2023 Sustainability Report, last year saw ZoomInfo align itself with the National Institute of Standards and Technology’s AI Risk Management Framework (NIST RMF). This standard, which has been adopted by companies including IBM, Microsoft, and OpenAI, is emerging as the leading standard by which the risks of AI can be effectively managed, and ZoomInfo is proud to join more than 240 other signatory organizations, including many ZoomInfo customers.

As ZoomInfo Chief Compliance Officer Simon McDougall explains, good data is as vital to AI risk management as good governance, which is why it’s important to work closely with AI data vendors.

“Make sure you understand what data your company is getting hold of,” McDougall says. “It may well be they’re using new sources of data to drive their models, and you want to ensure the data has been gotten ahold of appropriately and is being used appropriately.”

Building the Future of GTM, Today

It may be difficult to accurately predict how generative AI and other technologies might advance in the near future. Having seen what’s already possible, and how these technologies are advancing, it’s unlikely to get any easier any time soon.

As evidenced by the diversity of our executive team’s expertise, we’re already seeing an immense impact from using and developing generative AI technologies across every area of our business. 

We’re incredibly excited to continue refining and developing products such as ZoomInfo Copilot, and we can’t wait to see how our customers use it to propel their businesses to new heights of growth.