AI Coding Tools Make Engineers More Efficient. Here’s Why That’s a Big Deal for GTM Teams.

By Kyle Lacy

GTM teams were some of the earliest and most enthusiastic adopters of AI tools. While employees use ChatGPT and other genAI tools for everything from content creation to localization to photo editing, GTM leaders embrace AI agents to perform the roles of SDRs, customer success managers, and sales engineers.

We could endlessly debate how GTM teams should use AI and whether the value is worth the investment. However, we might be overlooking another advantage of AI. What does it mean for GTM teams when other business areas invest in AI tools? In particular, what are the knock-on effects for GTM when engineering teams start using AI coding tools?

Over the past year, our Copilot Impact tool tracked the performance of engineers using GitHub Copilot. Our data shows that AI makes engineers more efficient: when engineering teams use Copilot, their engineering capacity increases by as much as 15%. That means they’re resolving more Jira tickets, from new feature work to bug fixes. When you scale that figure up to the company level, AI-enabled engineering teams resolve an additional 48 Jira tickets monthly.

Why should GTM teams care? With the right strategies, increased engineering capacity can pay big dividends to your organization.

What AI Engineering Tools Mean for GTM Teams

1. More products brought to market

The first benefit is pretty obvious: an engineering team resolving more Jira tickets will bring more products and features to market in a given year. For GTM teams, that means you’ll get more bites at the apple when it comes to delivering value to your potential buyers – and expanding relationships with your existing customers.

Increased capacity means that your R&D colleagues will be more receptive to suggestions and can better incorporate feature requests into their roadmap. Suppose you have potential customers clamoring for a specific feature. In that case, you want to talk to the engineering team that can churn out an extra 48 tickets per month – not the team already falling behind on a limited roadmap. 

In our analysis of engineering capacity, we equate the number of Jira tickets with specific deliverables. Small deliverables like bug fixes require 24 tickets or less; medium deliverables like new features or integrations can require between 25 and 40 tickets; while large deliverables like major product updates and new modules take over 40 tickets. Think about what a 15% increase in capacity could mean for your engineering organization – what would you be able to do with an additional large deliverable every single month?

2. Increased predictability

We’ve all lived through the nightmare of trying to sell a product that’s several months late.

For SaaS GTM teams, everything we do follows from an engineering delivery date. Sales and marketing campaigns depend on specific features delivered within a specific window. If the engineering team misses the deadline, your plans go up in smoke.

Increased capacity from AI coding tools means your engineering colleagues can respond to sudden changes without knocking their timeline off course. Engineering is an iterative, sometimes unpredictable process. A team might be pulled away from new feature work to resolve a bug for their most important customer. Without flexibility in the form of spare engineering capacity, supporting work could mean missed deadlines and delayed deliveries. Those delays send shockwaves throughout the rest of the company, making it much harder for GTM teams to develop strong relationships with their customers.

3. Improved collaboration

Let’s be real for a second: increased capacity isn’t a miracle metric. Suppose your engineering team continues to improve capacity month over month. In that case, they’re not going to end up releasing hundreds of products per year – there’s a hard limit to what the organization and the market can bear.

More features and better predictability are great. Above all, that increased capacity means your engineering peers will be freed up to spend less time doing hands-on-keyboard coding and more time working collaboratively with other business areas. 

In an ideal world, increased engineering capacity should allow GTM and R&D to work together on a stronger product roadmap – and a tighter feedback loop between sales conversations and feature development. When GTM and engineering communicate regularly and effectively, the sales team will be able to deliver feedback from their conversations with potential customers, which the engineering team can implement more quickly. The engineering team can keep the GTM teams up-to-date on their short-term and long-term feature pipeline, giving marketing the advanced notice they need to brainstorm and implement the right campaign.

The more your GTM and R&D organizations work together, the more likely it is that engineering work aligns with the business's overall goals. That’s an outcome everyone should be working towards.

How to work together with an AI-enabled engineering team

AI coding tools deliver follow-on benefits to GTM teams. So far, so good. But what should you do with that information? How can you make the most of your AI-enabled peers in engineering?

For one thing, it’s time to get proactive when communicating with the engineering organization. If they’re delivering more features, you should take every opportunity to work collaboratively on that roadmap and ensure they know what your customers are asking for. AI is delivering on its promise of freeing employees to focus on higher-level work and strategic thinking. Sales, marketing, engineering, and product leaders should use that increased capacity to meet and exchange notes on a much more regular basis.

You should also encourage your engineering peers to provide granular updates on how they’re executing against target release dates. The reality is that everyone is working faster due to AI, and you will need more consistent information to market and sell all those new features and updates effectively. Everyone is on the same team working towards the same goals – improving communication benefits everyone in the long run.