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 efficiency. Here are some AI-driven solutions to help marketing teams tackle these challenges:

1. **Chatbots for Lead Generation**: Established. AI-powered chatbots can engage with website visitors in real-time, asking probing questions to qualify leads. This not only saves time but also improves the quality of leads generated.

2. **Content Optimization**: Emerging. AI can analyze past performance data to suggest improvements in content titles, meta descriptions, and images. This helps marketing teams create content that resonates with their audience and performs better.

3. **Predictive Analytics for Campaign Performance**: Established. AI models can predict which campaigns are likely to succeed based on historical data. This helps marketing teams allocate resources more effectively and avoid wasting time on underperforming campaigns.

**Use Cases for Sales**

Sales teams face similar challenges, but their focus is often on closing deals and improving customer relationships. Here are AI-driven solutions to help them succeed:

1. **Sales Automation with AI**: Established. AI can automate routine tasks like follow-ups, data entry, and lead scoring. This frees up sales reps to focus on high-value activities that drive sales.

2. **Personalized Sales Outreach**: Emerging. AI can analyze customer data to identify the right messaging and timing for outreach. This increases the chances of a successful sale and improves customer satisfaction.

3. **Contract Management and Negotiation Support**: Early. AI is still evolving in this area, but it has the potential to automate contract management and provide insights into negotiation strategies.

**Use Cases for Service**

Service teams are tasked with resolving customer issues quickly and efficiently. AI can help them achieve this by:

1. **Automating Customer Support with Chatbots**: Established. AI chatbots can handle routine inquiries, reducing wait times and improving customer satisfaction.

2. **Route Customer Inquiries to the Right Agent**: Emerging. AI can analyze customer inquiries and route them to the most appropriate agent based on expertise and availability.

3. **Proactive Issue Resolution**: Early. AI can predict potential issues before they occur and proactively resolve them, improving customer experience and reducing support tickets.

In conclusion, the key to successful AI adoption lies in starting with a clear problem and finding the right AI use case to solve it. By focusing on established and emerging use cases, teams can see tangible results quickly, building confidence and paving the way for further exploration of AI capabilities. Whether you’re in marketing, sales, or service, there are AI solutions tailored to your team’s biggest challenges. The goal is to use AI not as an end in itself, but as a tool to deliver real value and improve business outcomes.