How enterprise revenue leaders now look to AI as a strategic business decision enabler

Enterprise revenue leaders now trust AI to help make business decisions as the technology has evolved from tackling efficiency increases to a more strategic purpose.

That’s the conclusion of The State of Revenue AI report, from Revenue Intelligence platform provider Gong, which is based onanalysis of 7.1 million sales opportunities across more than 3,600 companies, and a survey of more than 3,000 global revenue leaders.

There’s a shift going on among forward-looking Revenue teams, suggests Amit Bendov, CEO of Gong:

AI is no longer a helpful sidekick, but a strategic partner. Revenue teams that embrace AI aren’t just seeing better revenue outcomes, they’re re-shaping go-to-market (GTM) functions. The data shows the future of sales will not be shaped by humans or by AI, but rather by the power of both working together.

In fact, argues Tarek Nseir, Senior Value Partner and Co-Founder, Valliance, a specialist AI consultancy start-up, AI has been used by organizations as a strategic tool in decision making for a while:

It's been the case for decades, but this is the challenge with AI and the way everyone's describing it. It's just such a different flavor than leveraging LLMs (Large Language Models) to help you produce a Board report or a piece of content faster. The spectrum is so broad that this is part of what's creating all of this lack of lack of clarity.

AI's primary use case, without question, as it was pre-gen AI, as it is post-gen AI is it allows human beings to make better decisions. Does that mean to say it's making the decisions for us? No, of course not. No business leader will have a piece of technology make their most important decisions for them. But every business leader will take as much input as they can. This principle is the same principle as it's been for decades.

Divide

The research shows there is a gap between adoption between the US and UK markets, with 87% of US companies using AI in their revenue teams versus 70% of UK companies. Nseir thinks this isn’t surprising:

The US being ahead of the UK in terms of adoption, whether it's for a sales team or any other team, frankly doesn't come as a surprise at all. It's logical. It's just very much a fact of where the US has always been in terms of these adoption curves.”

Legislation around AI on both sides of the Pond are telling, he reckons. President Trump’s AI Action Plan, which was unveiled in last July, concentrates on removing regulations, building AI infrastructure and the AI tech stack, whereas the EU AI Act concentrates on security, regulatory frameworks and ethics.

Brexit Britain’s approach sits somewhere in the middle, with, as Nseir sees it, little clarity.

I don't think that you could argue clearly that UK policy making is leading to this trailing adoption. I believe that UK policy could and should consider both enablement and acceleration alongside taking a prudent stance, which I don't think it does at the moment. The US has the AI action plan, and the typical Trump-esque Executive Order style approach around removing barriers. Its accelerating adoption clearly has done a good job in many ways, pushing the US economy towards more aggressive exploration of AI, and I think that's fair to say in the UK, the stance is a bit confusing.

However he doesn’t think the UK is currently aligned with EU AI policy either:

You can see the political pressure building in terms of trying to create tougher and more binding rules, and only recently there was a cross party group of MPs who have asked for better regulation of AI systems.

I don't think the UK position on AI is clear. It feels like its viewpoint on being pro-innovation and taking a more principles led approach, is attempting to sit in the middle of the European and the US position.

The UK needs to move faster to close the gap with global peers, suggests Lesley Ronaldson, EMEA GM at Gong;

As economic pressure persists, businesses have to take every opportunity to stay ahead of their customers and remain competitive. AI offers much-needed efficiency gains and the capability to extract valuable insight from data. For businesses, that means being able to predict and act on customer needs, and forecast revenue and growth with much greater accuracy.

Trust issues

Trust in AI still may still have some way to go, but, according to the Gong data, seven in ten enterprise revenue leaders already trust AI to help make business decisions. In 2025 the number of US companies using AI to forecast and measure strategic success increased by 50% year over year. The data shows that organizations using revenue-specific AI solutions were twice as likely to use AI for forecasting and predictive modelling, driving outcomes including a 13% higher revenue growth and 85% greater commercial impact.

Along with trust, Nseir believes organizations should concentrate on solving today’s problems:

The opportunity exists today without having to do big, complex tech programs and huge programs of change. Thinking about how you make a salesperson or your sales teams more productive in a safe way? That's a question for today.”

Where organizations are failing to provide these types of AI tools, they are having to deal with shadow AI, according to Nseir, where employees are just using non-authorized AI in the workplace:

People are using these tools on a non-regulated basis because the organization hasn't found an enforced, enabled way of using these tools properly in the workplace, and those ways exist today.

Nseir concludes:

It's just very early, and all of the work genuinely is still in front of us. I think the US are just much more Cavalier-ish, partly because of that presidential stance of the work, and partly just because of the fundamental cultural differences.

My take

For organizations to get the most out of AI they need to look at the small stuff, and get that right before looking at major system and process overhauls. Although companies are increasingly using AI to help them make business decisions, those outside of the US certainly still remain fairly cautious. Business leaders will take every bit of data they can lay their hands on, whether it's produced by AI or gen AI, if it helps their decision making. Different government approaches will have some impact on the rate of AI adoption, but culturally the US has always had a more gung ho approach to adopting new technologies. I know it doesn’t feel like it but it is still early days for AI.