Generative AI is reshaping how HubSpot organizations operate, promising unprecedented speed and efficiency, but it also introduces complex challenges that could complicate scalability and sustainability if not carefully managed.
For businesses leveraging HubSpot, the rise of generative AI tools has added a new layer of complexity to the conversation. While AI promises to streamline processes and unlock productivity, it could also accelerate the accumulation of technical debt if we’re not careful.
At Inbound24, HubSpot laid out an ambitious vision for an AI-powered future, introducing tools like Breeze AI and agent.ai to revolutionize the platform. These innovations are designed to address inefficiencies, simplify workflows, and unify data. But as we eagerly embrace this new era of automation, we must also confront the reality of the systems we’re building—or risking breaking.
The question is no longer whether generative AI will shape the future—it’s whether it will help us address our existing challenges or simply create new ones.
A Look at Present Challenge: Landscape of Technical Debt in HubSpot Organizations
In HubSpot organizations, technical debt isn’t a result of neglect—it’s the natural outcome of fast-paced decision-making in competitive markets. The very flexibility and ease-of-use that make HubSpot an indispensable tool for marketing, sales, and service teams can also lead to unchecked growth, inefficiencies, and hidden complications.
For many organizations, the drive to meet immediate goals often overrides long-term considerations. Campaign-specific custom properties are created without a clear deprecation plan. Workflows are spun up quickly to address one-off needs, without revisiting whether existing automation could be optimized. Third-party integrations are connected to solve isolated problems, often bypassing governance or strategic alignment.
The result? A system that becomes increasingly cluttered and harder to manage over time. Data quality suffers as properties and records proliferate, workflows grow redundant or contradictory, and teams lose visibility into what’s actively driving value. What initially feels like flexibility starts to feel like fragility, with the platform’s efficiency eroded by the complexity of its own growth.
These challenges aren’t unique to HubSpot, but they’re particularly pronounced given the platform’s rapid adoption and its appeal to diverse teams across organizations. The more accessible and intuitive the tools, the easier it is for well-meaning users to unintentionally create systemic inefficiencies.
Today’s HubSpot organizations face a dual reality. On one hand, they have a powerful, unified platform capable of driving growth. On the other, they’re navigating the risks of systems built for speed rather than sustainability. Addressing this tension is the key to ensuring HubSpot continues to be a growth enabler, not a growth inhibitor.
The AI Dilemma: Progress or Pitfall?
Generative AI has arrived as both a promise and a puzzle for HubSpot organizations. It offers tools to supercharge workflows, enhance customer interactions, and unify GTM efforts across teams. Yet, the same attributes that make AI compelling—speed, scalability, and ease—can also deepen the challenges of managing technical debt if not handled thoughtfully.
Accelerated Complexity
AI tools can rapidly create workflows, content, and even predictive models, enabling teams to achieve in minutes what might have previously taken hours. But this speed can come at a cost. The sheer volume of AI-generated outputs risks overwhelming systems already burdened with duplicative workflows, scattered custom properties, and redundant data. Instead of solving inefficiencies, AI can accelerate them, burying organizations under a mountain of unmanageable automation.
Shallow Understanding
HubSpot's AI features lower the barriers to creating solutions, but they can also distance users from the mechanics of what’s being built. A workflow generated by AI might work perfectly on day one, but if users don’t fully understand how it functions—or how it fits into their larger processes—it becomes a black box. Troubleshooting, iterating, or scaling that solution later becomes far more difficult, adding to the organization’s operational friction.
Unchecked Proliferation
The intuitive nature of AI-driven tools, such as those introduced at INBOUND 2024, could lead to a proliferation of assets—new workflows, segments, or content variations and at many times without the corresponding controls to manage them. What starts as a tool to simplify operations could evolve into a source of chaos, with GTM teams struggling to identify which elements are delivering value and which are creating noise.
Integration and Ecosystem Challenges
As HubSpot integrates AI deeper into its platform, businesses are also exploring how to connect it with third-party tools. This introduces additional layers of complexity. Without proper alignment and oversight, organizations risk creating disjointed systems where AI solves isolated problems but doesn’t contribute to a cohesive strategy. The result? Fragmentation that undermines the promise of a unified platform.
The real dilemma is this: AI doesn’t inherently solve technical debt—it magnifies existing behaviors. If organizations approach AI with the same lack of governance or long-term planning that created their current challenges, it will exacerbate inefficiencies rather than reduce them. Conversely, AI has the potential to be a powerful ally in simplifying and unifying operations, but only if it’s used with a clear strategy and thoughtful oversight.
A Look to the Future
Generative AI in HubSpot presents an undeniable opportunity, but its impact on technical debt will depend on how teams approach it. The future may hold several possibilities:
- A Smarter Approach to Maintenance: AI could evolve to not only create but also maintain systems, proactively identifying inefficiencies and suggesting optimizations. Imagine workflows that self-diagnose redundancies or content libraries that auto-archive outdated assets.
- An AI-Driven Governance Framework: As AI becomes more integrated, businesses may develop stricter frameworks for its use, ensuring that automation aligns with broader strategic goals. This could prevent the unchecked growth of workflows, properties, or integrations.
- Deeper Integration Challenges: On the flip side, as AI tools multiply, the complexity of managing integrations and ensuring data consistency may grow. Teams might find themselves relying on AI to clean up problems created by AI—a recursive cycle that requires careful oversight.
Final (Balanced?) Perspective
The rise of generative AI in HubSpot is both thrilling and daunting. It offers the promise of greater efficiency, faster decision-making, and a more unified GTM approach. But it also brings risks that shouldn’t be ignored.
Technical debt has always been a cost of progress, but generative AI raises the stakes. By accelerating development, it forces us to confront the long-term consequences of our decisions sooner rather than later.
The real challenge isn’t just building systems that work today
It’s ensuring they can evolve tomorrow without breaking.
As we look ahead, one thing is clear: the choices we make now, in the early days of HubSpot’s AI revolution, will shape the future. Whether generative AI becomes a solution to technical debt or its greatest accelerator depends on how thoughtfully we embrace its potential.
In the words of HubSpot’s Dharmesh Shah: “There’s an agent for that.” But maybe, just maybe, the agent we need most is the one reminding us to think before we act.
