AI x Product: What It Really Means for Growing B2B SaaS Companies

Okay, let's be honest. "Artificial Intelligence" or "AI" is thrown around a lot these days. It can feel like another buzzword, something far off and maybe even a little intimidating. But for those of us building and growing B2B SaaS products, AI is becoming increasingly relevant, and, frankly, pretty important for staying competitive.

This isn't about futuristic robots taking over or magic (although “Any sufficiently advanced technology is indistinguishable from magic” - Arthur C. Clarke). It's about practical ways AI can help us build better products, understand our users and customers better, and ultimately grow our SaaS businesses. And you don't need a massive team of data scientists to get started.

This is the first post in a series where we'll explore AI specifically for B2B SaaS product growth. We'll cut through the jargon, look at real examples, and offer some practical steps you can take, especially if you're leading product at an early-growth company. No hype, just real talk about how AI might fit into your world.

AI Demystified - Making Sense of the Tech

Let's break down what we mean by AI in a product context. Forget the sci-fi movies. We're really talking about using data to make our products smarter. And a big part of the current AI wave is the rise of Large Language Models (LLMs) and the emerging concept of Agentic AI.

• Machine Learning (ML): Think of this as teaching your product to learn from data. For example, it can learn to spot patterns like which user actions tend to lead to churn, or which features are most loved by your best customers. It's the foundation for a lot of AI applications.
• Deep Learning (DL): A more complex type of ML that uses artificial neural networks with many layers (that's why it's called "deep"). These networks can process vast amounts of data and find incredibly intricate patterns. It excels at tasks like image and speech recognition, which are becoming more relevant to SaaS products.
• Natural Language Processing (NLP): This is a broader field focused on how computers can understand, interpret, and generate human language. It's been around for a while, but it's been supercharged by...
• Large Language Models (LLMs): These are a specific type of AI model trained on massive amounts of text data. LLMs like GPT-4 (the tech behind ChatGPT), BERT, and others, are incredibly good at understanding context, generating human-quality text, translating languages, and even writing different kinds of creative content. They're essentially very powerful NLP engines.
• Agentic AI: This is a newer and evolving concept, but it essentially refers to AI systems that can act more independently and autonomously to achieve goals. Think of it as giving AI agents the ability to not just understand and respond, but to plan, reason, and take actions in a more sophisticated way. While still in its early stages, the idea is that these agents, often powered by LLMs, could work as digital assistants or co-pilots within your product, proactively helping users accomplish tasks.

Why does this matter to your B2B SaaS product? Because these tools, especially LLMs and the potential of Agentic AI, can help your product:

• Make sense of your data: Not just basic metrics, but really understanding user and customer behavior, product performance, and what's happening in your market. LLMs can analyze customer feedback, support tickets, competitor events, and even social media to provide deeper insights into user sentiment and needs.
• Automate the tedious stuff: Free up your team from repetitive tasks like basic customer support, lead qualification or documentation so they can focus on bigger things. LLMs can power chatbots that can handle a wider range of customer inquiries, summarize documents, and even generate first drafts of content.
• Personalize the user experience: Deliver the right content, recommendations, and interactions to each user, making them happier and more engaged. AI and LLMs can help tailor in-app messages, create personalized tutorials, and even adapt the product's interface based on user behavior.
• Get a glimpse into the future: Start to predict things like which customers are at risk of churning or which market trends you should be paying attention to. With AI you can analyze market data and industry news to provide insights that inform your product strategy.
• Create new product features: LLMs open up possibilities for innovative features like intelligent search, automated content creation within your product, and even code generation assistance for developers using your platform.
• Introduce Agentic Workflows: With Agentic AI, imagine features where your product can not only answer user questions but also proactively suggest actions, automate complex workflows, and even learn from user interactions to become more helpful over time. For example, an agent could help a user set up a complex report by guiding them through the process, automatically populating fields, and even suggesting relevant data sources. This could also have use cases for internal teams such as customer success and sales.

Think of it this way: ML provides the general ability to learn from data, DL allows for processing of complex data, NLP provides the ability to work with human language, LLMs are the current cutting-edge tool for doing powerful things with text, and Agentic AI represents the next step, where these technologies are combined to create AI systems that can act with a degree of autonomy to achieve goals, making software tools even more powerful and intuitive.

From Data-Driven to AI-Informed to AI-Native: A Natural Progression

We've all heard about being "data-driven". That's still important, but AI takes it a step further. It's not just about looking at past data; it's about using AI to anticipate what might happen next and be proactive. And now, there is an emerging concept called "AI-native" products.

Here are the steps in the progression:

• Data-Driven: Looking back at what happened. Analyzing historical data to understand past performance and make informed decisions.
• AI-Informed: Using insights from AI models to predict future behavior, identify trends, and proactively address them before they become problems.
• AI-Native: This is about building products with AI at their core from the ground up, not as an afterthought or add-on. The product's fundamental capabilities and user experience are designed around AI.

Here's how this changes things:

• From reactive to proactive: Instead of just analyzing past behavior, you can start predicting future actions and take steps to address them. For example, if an LLM identifies patterns in customer support tickets that suggest a growing frustration with a particular feature, you can proactively address the issue before it leads to churn.
• From manual to automated: Many tasks that used to be done by hand can now be handled by AI, such as identifying valuable leads or segmenting users. LLMs can automate the process of categorizing and summarizing customer feedback, freeing up your team to focus on higher-level tasks.
• From generic to personalized: AI lets you create tailored experiences for each user at scale, increasing engagement and satisfaction. LLMs can help craft personalized messages, recommend relevant content, and even adjust the product's interface based on individual user needs.
• From add-on to built-in: With the AI-native approach, AI isn't just a feature; it's the foundation. The product's core functionality and user experience are designed with the assumption that AI will play a central role. Imagine a project management tool that's not just tracking tasks but using an LLM and potentially agentic workflows to proactively suggest optimal workflows, predict potential roadblocks, and even draft project proposals.

What does it mean to be AI-native?

• AI is a core competency: The product team has deep AI expertise, and AI is considered a first-class citizen in all product decisions.
• Data is treated as a strategic asset: Data collection, processing, and analysis are built into the product's DNA to continuously fuel and improve the AI models.
• Continuous learning and adaptation: AI-native products are designed to constantly learn from user interactions and improve their performance over time. The AI models are regularly updated and refined.
• User experience is centered around AI: The user interface and overall experience are designed to seamlessly integrate AI capabilities, making them feel natural and intuitive.

This shift means we need to think differently about product strategy. It's not just about collecting data or adding AI features; it's about building a plan for how AI can be integrated into your product roadmap to enhance existing features, unlock new possibilities, and ultimately create products that are fundamentally AI-native.

Real-World Examples - AI in B2B SaaS Today

This isn't just theory. Here are some examples of how B2B SaaS companies are already using AI:

• Salesforce's Einstein: Helps sales teams by predicting which leads are most likely to convert and suggesting the best actions to take. It also uses LLMs to summarize meeting notes and generate follow-up emails.
• Zendesk's Answer Bot: Uses NLP, and increasingly LLMs, to answer common customer support questions automatically, improving response times and freeing up human agents for more complex issues.
• HubSpot: Uses AI to personalize content, score leads, and improve email marketing. They're also exploring LLMs to help users generate marketing copy and content ideas.
• Drift: Offers AI-powered chatbots that engage website visitors and qualify leads, speeding up the sales process. 
• Grammarly: Uses AI to improve writing, giving real-time suggestions for grammar, style, and tone. They are further using LLMs to provide more comprehensive writing assistance.
• Notion AI: Leverages LLMs to help with various writing tasks, such as summarizing text, generating ideas, and continuing writing based on user prompts.

These are just a few examples, and the possibilities are constantly expanding, especially with the rapid advancements in LLMs.

Why Now? - It's Time to Pay Attention

For B2B SaaS companies, especially those in the early growth stages, now is the time to seriously consider AI. Here's why:

• We're swimming in data: SaaS products generate tons of data, and that's what fuels AI, especially data-hungry LLMs.
• AI tools are more accessible: You don't need a huge data science team to get started. Many user-friendly tools and platforms are available, including APIs for integrating LLMs into your product.
• Competition is heating up: The B2B SaaS market is crowded. AI can help you stand out by providing a better product and experience. Leveraging LLMs for unique features can be a significant differentiator.
• It's more affordable than you think: Cloud-based AI services have made it more cost-effective to implement these solutions.
• Customers are ready for it: The market is increasingly open to AI solutions that provide real value, and LLMs are starting to power experiences that users find genuinely helpful.

Delaying AI adoption is becoming a risk. Companies that wait too long might find themselves playing catch-up, especially as competitors start to leverage the power of LLMs to create more intelligent and intuitive products.

Getting Started - Practical First Steps

You don't need to do everything at once. Here are a few practical steps to start exploring AI, keeping LLMs in mind:

• Look at your product strategy: Where could AI make the biggest difference? Maybe it's improving onboarding, reducing churn, or making your product more personalized. Are there areas where LLMs could automate content generation, improve search, or enhance user interactions?
• Find the low-hanging fruit: What are some simple AI features you could implement relatively easily that would still provide value? Could an LLM-powered chatbot handle basic customer inquiries? Could you use an LLM to summarize long documents or generate reports within your product?
• Assess your data: Do you have the right data to power AI? If not, start figuring out how to collect and organize it. Consider what kind of text data you have that could be used to train or fine-tune an LLM.
• Check out AI tools: Research AI-powered analytics tools, personalization platforms, and other solutions that might be a good fit. Explore APIs and models like those offered by OpenAI, Hugging Face, or Cohere to see how you might integrate them into your product.
• Think about your team: Do you need to bring in some data science or AI expertise? Or maybe you can upskill your existing team. Consider if you need someone with specific experience working with AI.
• Don't forget ethics: Build trust with your users by taking an ethical approach to implementing AI in your product. Be transparent about how you're using data and ensure fairness and accountability, especially when deploying LLMs.

Conclusion - Taking the First Step

The move towards AI in product is happening now. For B2B SaaS companies, especially those in the early stages of growth, it's a real opportunity to build better products, create happier customers, and grow faster. The emergence of powerful LLMs adds a whole new dimension to what's possible and accessible.

This article is just the beginning. In the rest of this series, we'll dive deeper into specific areas like AI-powered analytics, building teams for AI, creating your first AI features, and the ethical side of AI.

This journey might seem a bit overwhelming, but it doesn't have to be. By taking that first step, you're positioning your company for the future of SaaS.

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Take the first step: Subscribe to the AI x Product newsletter and get a free checklist “Is Your Product Ready for AI?” that helps you to assess your product’s AI readiness, discover your “AI moat”, and take the steps for starting your AI Product journey!

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Take care 👋,

-Toni / Builder of GrowthCues