Over the past year, I have seen AI tools such as Claude and Chatgpt transform from cool tech news to total game shutters. These technologies are no longer good at having-they are critical of any organization that wants to remain competitive.
Of course, the implementation of new AI-driven tools is often easier said than done. In this article, I will share some common roadblocks, including a few I have encountered as HubSpot’s Senior Director of Global Growth. Then I share some tips and tricks to become AI champion in your marketing team.
At the end of this post, you have the tools you need to drive effective AI -resolution across your organization. Let’s dive in.
Table of contents
Ordinary roadblocks for implementing AI
In my experience, AI Implementation requires buy-in on all levels from leaders who need to log on to tools to the individual contributors who actually use AI tools. So to run adoption, tackle a wide range of concerns. Here’s the most common I’ve heard.
Decision producers are excited about AI, but have not yet unlocked its productivity potential.
Many people can see AI tools as fun new toys, but struggle to recognize their potential as useful productivity tools. As a result, I have seen teams happy to spend AI on smaller projects, but hesitant to invest in a major implementation.
For example, when I first started learning about AI, I appreciated how it could help me with discreet tasks, such as putting together memos, coming up with ideas for experiments and preparing copy. But I didn’t really understand how powerful AI could be until I started using it for data analysis.
Specifically, after a long-standing project optimization project, I specifically used Claude to understand exactly how the change affected our top-of-funnel and down-funnel measurements. Then I asked it to create two summary: one to an audience at the executive level and one for my team and our other stakeholders.
The whole process took less than two hours (including the time it took for me to check Claude’s conclusions, which I always recommend doing). Without AI it would have taken me days as well as at least one (human) analyst.
When people see AI as just a funny news, they are unlikely to invest their time and energy in implementation. So to get people excited about AIS true potential, it is important to highlight the incredible, quantifiable influence it can have on your business. Communicate your winnings in terms of hours and saved dollars.
People don’t understand how AI works.
Despite the rising occurrence of AI across a wide range of applications, many people are still not aware of how technology is the basis of these tools. This can make people understandably nervous about relying on AI-driven tools for important business needs.
I have noticed that reluctance and AI nerves are especially widespread when stakeholders are less technologically knowledgeable. Giving some basic AI education can help soothe this fear.
Stakeholders have been burned before.
In other cases, I have seen teams hesitate to adopt a new AI solution because they have previously been burned by a similar initiative that did not go well. Maybe a product was announced as a high -powered solution, but ended up offering a little to the team. Or maybe a previous solution was just poorly implemented.
Leaders who have seen an AI solution fail before May hesitate to try another. Champions will have to move themselves with extra data and a compelling case as to why the results will be better this time.
Teams have enthusiasm without a strategic perspective.
Of course, resistance to AI is not the only factor that can prevent effective implementation. At the other end of the spectrum, I have run into leaders and leaders who are extremely eager to adopt AI. However, they may lack the strategic perspective to identify and move on with the best approach.
These AI enthusiasts may jump to sign up for the latest, biggest AI tool without stopping to determine if it really fits their organization’s needs. Similarly, they can hurry the implementation phase and skip important planning or communication steps.
While experimenting and moving quickly can lead to AI winner, teams should take the time to build an AI strategy with clearly mapped goals. Are you trying to save time and/or money? Improve quality? Getting aware of the strategy and “why” from the jump can help you choose the right tools, speed up implementation and remain in line with leadership.
Teams have muted data.
Finally, the challenge I have encountered is most often when I implement AI across an organization, silenced, disconnected data. After all, your AI recommendations can only be as smart as the data you feed into the system.
If you do not have access to all the data relevant to the current task, you are struggling to get value out of AI tools. It is also true when you access this data requires hours of manual work across multiple inconsistent systems.
How to run lasting ai -adoption
When it comes to implementing AI, there are no solutions in one size to suit everyone. Organizations will face different challenges and take advantage of different approaches. That said, I have found the strategies under an effective route to overcome the roadblocks.
The end result lasts AI adoption that helps your marketing team grow.
Illustrates a convincing “before” and “after.”
To get buy-in from key decision makers, it is important to move past the theoretical benefits of AI and make the matter your specific project. To tell that story, you need to illustrate a clear, convincing “before” and “after.”
For example, the first BIG AI initiative I turned up to my team was for an AI-driven search classing. The project would use Openais API to tell the prospects how well their brand worked in AI hot meter such as Chatgpt, confusion and Gemini.
To get people on board, I didn’t just claim that the tool would be useful. Instead, I explained how I currently did many hours of manual analysis every week to calculate how often Hubspot appeared – and how – in the AI ​​engine’s answer.
I also explained that our prospects and customers would also face this challenge (if they didn’t already face it!). And it worked: Our management got it right away and they quickly littered the project.
When it comes to AI I have found that describing how the current system works now and how it could Working with the new tool is usually successful. Be sure to outline the ROI and the benefits of the future state clearly.
Educate the most important stakeholders.
AI masters are often teachers. After all, people probably won’t be excited about AI if they don’t know how it works – and even if they do, they’re unlikely to be able to use it successfully without at least a basic understanding of the substantive technology.
With this in mind, when I work with colleagues who are less familiar with AI, I start by explaining how the system will work. In some cases, I will share the basics of these technologies, including what a large language model (LLM) is and the best practice of using it. In addition to these general explanations of the technology, I will also explain how our special implementation will work.
When you act as AI teacher, make sure you provide the necessary information to understand and adopt the idea without drowning people in detail.
Start with a proof-of-concept.
Big ideas can be exciting, but I’ve found that starting with a rough proof-of-concept is often the best way to get buy-in and bring an idea to life. A low risk, minimum viable product (MVP) can help illustrate the benefits of AI without requiring a large investment in the front.
By offering a smaller scale-proof-of-concept, you can help your executive team feel more comfortable with green an AI project. You can also hit your investment as an experiment rather than a long -term commitment.
Reign in excessive enthusiasm.
On the other hand, when stakeholders are so excited about AI that they can hurry implementation or ignore critical problems, I try to rule them in. Instead of directly squeezing their ideas, I ask many questions.
For example, I might ask, “Why are you thinking about the project this way?”, “What are we trying to achieve with AI?” And “Why is AI valuable for this work?” In the end, I usually say, “Now when I better understand what you are trying to do, can I suggest an alternative?”
To approach over-enthusiasm with real curiosity and a willingness to solve for their end goals can help you control them in a better direction while retaining the relationship.
Embrace an ecosystem approach.
I have learned first -hand how important it is for marketers to utilize data from across their platforms to create growth. This means knocking down silos and embracing an ecosystem approach. Making this shift involves engaging internal teams across the organization and external partners.
What does it look like in practice? Let’s start with the internal team. Say your sales and service teams are using gong to track customer calls. GONG gives you access to comprehensive call traps that are rich in prospectus data that provides insight into how to best place your product.
Marketing people can use AI tools to analyze this information and identify potential risks or growth opportunities. However, this is only possible if teams know what data they collect and share this information freely.
Now on the external ecosystem. When working with partners, not all AI-driven innovation should be built by your team. You can work with external partner organizations in your ecosystem that can build solutions for your business.
Let’s use hubspot as an example. Our Solutions Partners provide services that complement HUBSPOT’s platform offers-from implementations to AI-driven analysis to advanced custom integrations. Independent Software Vendor (ISV) Partners Build and Sell apps that improve our software capacity, including those with AI. The average hubspot customer uses 9+ apps – utilization of custom tools to help them better earn users in their industries or vertical.
It’s a win-win. Our partners gain access to hubspot customers, an ecosystem that represents an opportunity for $ 30 billion for app and service partners in 2028. Our platform gets improved capabilities that can better earn and attract customers – all without investment dollar from our internal team.
By building AI capabilities as part of a larger, integrated ecosystem, companies can better earn and grow their customer base. In my experience it speaks to leadership and drives buy-in.
Become your team’s AI Master
At the end of the day AI is a tool like any other. In order for it to have an impact across an organization, it needs a master to get everyone on board and drive lasting growth. By steering away from ordinary roadblocks and using the strategies I described above, you will be on your way to becoming AI master that your marketing team needs.