Where to Start with AI: A Practical Guide for GTM Teams

Over the past year, I’ve had hundreds of conversations with business leaders about AI. The pattern is always the same. They’re not short on tools or ambition. They’re struggling with where to get started and how to get value. The pressure to adopt AI is real. But pressure without direction leads to experiments that don’t stick, tools that don’t get used, and teams that grow more skeptical. Why? Because AI output didn’t lead to actual outcomes. Here’s what I’ve learned from watching teams that succeed with AI: they don’t start with AI. They start with a problem. A specific, painful, time-consuming part of their work that they want to fix. Then, they find the right AI use case to achieve that goal. As they see results, their confidence grows, and they explore other AI capabilities – again, tied to a clear goal. That’s the approach I want to share. Not an exhaustive list of everything AI can do, but a practical guide to where marketing, sales, and service teams can get started and see real value with AI.

For transparency, we’ve organized use cases by how ready the technology is today. At HubSpot, we are building and improving these capabilities every day. Let’s start with simple definitions:

- **Established**: These are use cases where AI works reliably. Implementation is straightforward. Results are repeatable. If you’re wondering where to start, it’s here!

- **Emerging**: These use cases are available today and improving quickly. They’re delivering value, but still evolving. As AI gets more data and context, they will become more powerful.

- **Early**: These are high-potential use cases that are still taking shape. If you consider yourself an early adopter, this is where you can experiment (with patience).

**Use Cases for Marketing**

Marketing teams have been under pressure to do more with less. More channels, more content, more personalization. All without making trade-offs in quality or customer experience. Here are some AI use cases that can help:

1. **Chatbots**: Established. AI-powered chatbots can handle routine customer inquiries, freeing up human agents for complex issues. They can be integrated into websites, social media platforms, and messaging apps.

2. **Content Optimization**: Emerging. AI can analyze customer data to suggest content ideas, refine messaging, and predict which content will perform best. This helps teams create more effective campaigns with less guesswork.

3. **Predictive Analytics**: Established. AI can analyze historical data to predict which marketing campaigns will succeed. This enables teams to allocate resources more efficiently and avoid wasting time on underperforming initiatives.

**Use Cases for Sales**

Sales teams face intense competition and the need to close deals faster. AI can help by automating tasks, improving customer insights, and enabling personalized outreach. Here are some AI use cases that can make a difference:

1. **Lead Scoring**: Established. AI algorithms can assess potential customers based on their behavior and demographics, helping sales teams prioritize their efforts.

2. **Sales Forecasting**: Emerging. AI can predict sales trends and forecast revenue, enabling teams to adjust strategies proactively and avoid costly surprises.

3. **Personalized Outreach**: Early. AI can analyze customer data to generate personalized email campaigns and sales pitches. While this is still evolving, early adopters can start experimenting with simple personalization techniques.

**Use Cases for Service**

Service teams are tasked with resolving customer issues quickly and effectively. AI can help by automating responses, routing inquiries, and providing proactive support. Here are some AI use cases that can improve efficiency:

1. **Ticket Routing**: Established. AI can analyze customer inquiries and route them to the most appropriate support agent, reducing resolution times and improving customer satisfaction.

2. **Proactive Support**: Emerging. AI can monitor customer behavior and proactively offer assistance or solutions before issues escalate.

3. **Knowledge Base Enhancement**: Early. AI can analyze customer inquiries and update knowledge bases with common issues and solutions, empowering agents to resolve problems more quickly.

In conclusion, the key to successful AI adoption is starting with a clear problem and finding the right use case to address it. By focusing on established solutions first, teams can build confidence and gradually explore more advanced capabilities. The goal is to deliver tangible outcomes that justify further investment in AI, ensuring that the technology remains a priority as it continues to evolve.