Head of Sales shares the AI tools that are changing the game in his sales organization

Grant Evans is Head of Sales at Global Payments' Integrated and Platforms partnerships across the UK and EMEA. He's also the creator of The Payments Shed podcast and newsletter.

We caught up with Grant to learn which AI tools are having the biggest impacts on his sales organization. Here's what he said.

The next inflection point: AI in go-to-market strategy

I'm Grant Evans, and I lead new business sales for Global Payments' Integrated and Platforms partnerships across the UK and EMEA. I've spent over 15 years in commercial strategy and partnership growth, mostly focused on making complex topics like embedded payments, open banking, digital commerce, and emerging payment technologies accessible and practical for the businesses I work with. That's also the thread that's run through my recognition as a LinkedIn Top Voice for the past three years running.

Right now, I'm gearing up for the next inflection point: the crossover of AI tools into how commercial teams run their go-to-market strategy. Payments and fintech have always been early proving grounds for new technology. We saw it with open banking; we saw it with embedded finance; and AI adoption in revenue and partnership functions follows a similar curve.

At the same time, I've observed a trend from the other side of the table. Customers are increasingly using AI for contract redlining. Everyone can be a lawyer now, seemingly. That's not a small shift. It means the pace and sophistication of negotiation are changing, and commercial teams need to prepare for counterparts who are more AI-enabled than ever before.

AI is not a future consideration for revenue leadership. It's already happening. So my focus now is understanding how that shift changes the way commercial teams prospect, qualify, negotiate, and scale partnerships — helping teams get ahead of it rather than react to it.

Pressure to offer embedded financial experiences

Global Payments for Platforms, formerly Worldpay for Platforms, focuses on monetizing payments for SaaS platforms through embedded payments. We help software companies turn payments from a cost center into a genuine revenue stream by embedding payment processing and increasingly broader financial services, like lending and card issuing, directly into their platforms.

I manage a team of ten focused on new business across the UK and EMEA, though the wider platforms organization operates globally, including the US, Canada, Australia, and New Zealand. We're currently in a post-acquisition phase, integrating into Global Payments' broader scale and infrastructure. This provides us with a strong platform, no pun intended, to accelerate growth in a market where software providers face significant pressure to offer embedded financial experiences or risk losing merchants to platforms that do.

How Gong and Clay are affecting revenue organizations

Before we adopted Gong, pipeline visibility relied heavily on reps self-reporting call details and manually updating the CRM. As a leader, I often worked with a lagging, incomplete picture of deal health. But now, Gong automatically feeds clean notes into HubSpot, preventing anything from getting lost between the call and the CRM. I can see what's happening in the conversations themselves instead of relying on what reps log after the fact.

This has been a game changer for pipeline efficiency and BD accountability. I can spot where a deal is stalling, coach reps on specific moments in a call rather than giving generic feedback, and hold the team accountable to what happened rather than what was reported. It has also improved the experience for our prospects, who can revisit conversation summaries, adding transparency to the sales process.

I advise CROs to look hard at any workflow in their revenue org that still depends on secondhand reporting as its primary signal, because that's exactly the kind of area where AI can close the gap between what leadership thinks is happening and what's actually happening on the ground.

Alongside that, our strategy team has been using Clay to support TAM build-outs across each of our regions. Market mapping used to be a slow, largely manual research exercise. Clay has since enabled us to build and refine total addressable market data far more efficiently, directly feeding into how we prioritize new business focus by region.

Why AI must stay out of pricing and relationship management

These tools are shaping how we plan and execute go-to-market, but I cannot yet point to hard, validated numbers for improvements in win rate or cycle time. That's partly because pricing and relationship management, the parts of the business that most directly move those numbers, remain human-led — I explicitly keep AI out of them.

Pricing in payments is a genuinely complex area. So many variables and nuances specific to each platform, region, and vertical demand the judgment of an experienced commercial mind rather than an algorithm.

AI will never replace relationship management. Our talent pool's ability to build trust, read a room, and navigate complex partner relationships actually closes and retains business. AI can inform those conversations, but it can't have them.

This is all particularly relevant in financial services, where we face regulatory complexity that many other sectors don't navigate. This means we cannot yet use AI tools and use cases available to teams in less regulated industries — or we can only adopt them in a far more limited or cautious way. Payments, in particular, face close scrutiny, so anything touching customer data, pricing decisions, or compliance-adjacent processes must clear a much higher bar before we bring it into our workflow.

So, this isn't a criticism of the technology; it's simply the reality of building GTM strategy in a heavily regulated space. It's also why I maintain a more cautious, deliberate posture rather than immediately adopting everything available.

Grant Shares

AI will never replace relationship management. Our talent pool’s ability to build trust, read a room, and navigate complex partner relationships actually closes and retains business. AI can inform those conversations, but it can’t have them.

Why AI should augment, not replace, instinct and experience

I've spent 15 years valuing experienced sellers' gut instincts, and I still do, but I had to let go of the assumption that instinct alone tells you the true state of a deal or market. The best outcomes now come from pairing that experience with what the data and call intelligence show, rather than choosing one over the other.

I think that mentality applies just about everywhere. AI isn't a replacement strategy; it's an augmentation strategy. CROs who get this wrong look for AI to replace judgment, whether that's pricing decisions, relationship management, or seller instinct. Those who get it right understand that AI gives their people better visibility, better information, and more time to focus on the parts of the role that genuinely require human judgment.

If you expect AI to do your thinking, you'll be disappointed when it doesn't — or worse, you'll hand over decisions it isn't equipped to make well, especially in a complex, regulated space like payments. But if you treat it as a tool that sharpens your people rather than replacing them, you enable your team to genuinely benefit from it and avoid costly missteps that come from overtrusting a tool with something that still needs a human at the center.

Why Clay still requires human input

Despite the advantages of Clay, there are some limitations. Initially, we expected it to meaningfully compress the time and effort to build a usable TAM list. In practice, however, it can reach too broadly.

It excels at surfacing volume and scale, but it still requires significant human input to consolidate that initial pool into a genuinely actionable TAM list for the team.

So, the impact hasn't been the clean, fully automated efficiency gain we hoped for initially. Instead, Clay has changed the work's starting point, giving us a much bigger initial pool, much faster. But our strategy team still judges which markets and accounts are genuinely worth pursuing.

If I'd known that upfront, we could have avoided some early friction around resourcing and expectations. We'd have planned from day one for that human consolidation layer to be part of the process, rather than treating it as an unexpected extra step once we were already underway. It wouldn't have changed our decision to adopt the tool; Clay still gives us real value. But it would have meant a smoother rollout and a more accurate sense from the outset of where the time and effort in the workflow would actually be spent.

Grant Shares

My advice? Don’t be afraid to experiment…The organizations that get ahead here won’t necessarily adopt the most tools fastest; they’ll find the right tools for them.

How revenue teams must encourage AI experimentation

My advice? Don't be afraid to experiment.

There's a lot of noise right now about which AI tools are best, but what works will look different for every CRO and every team. Your industry, regulatory environment, and team's technology maturity all shape what will move the needle for you.

Rather than waiting for a definitive playbook or copying another organization, I encourage CROs to give their teams room to trial tools, be honest about what's working versus what's just noise, and build their own AI adoption strategy that fits their business.

The organizations that get ahead here won't necessarily adopt the most tools fastest; they'll find the right tools for them.

Follow along

You can follow Grant Evans' work on LinkedIn. And check out The Payments Shed podcast and newsletter.

More expert interviews to come on The CRO Club!