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Future forward: Top tips for implementing AI into your business

Whether you’re joking about it taking over your job or using ChatGPT for the first time, AI has become quite the buzzword. And with all the curiosity around it, it’s important to understand how it fits into the B2B world. Kavita Singh spoke with Julia Pilkes, EMEA Head of Marketing, HubSpot to round up the top tips for implementing AI.

Know the common pitfalls and avoid them

Before you start experimenting with AI capabilities and tools, it’s important to understand the fundamentals and misconceptions. That means eliminating the notion that your role will be replaced with AI. Instead, leaders should be stressing its potential to the business.

Julia said: “AI technology is designed to assist and support human efforts and not replace them. If you look at the integrations, you’re filled with the aim of automating repetitive tasks and improving efficiency throughout, but it will actually help employees to focus on strategy and other creative tasks as opposed to the repetitive ones. So I think that’s actually a very positive change and therefore also a huge misconception.”

An additional misconception? AI is expensive, when in actuality, there are plenty of affordable (and sometimes even free) tools that are quite accessible for any type of business and for all types of budgets. For more on this, check out this informative session on Propolis which outlines some free AI tools.

Another huge pitfall is using AI, but in the wrong way. Firstly, marketers should be aware of the privacy and data security risks. While managing sensitive information and personal data, it is crucial to be using it responsibly. According to a recent BrightBid study, 71% of respondents expressed concerns over ethics in the use of AI.

Julia said: “Trust is built by showing that AI technology delivers the expected outcomes with consistency, operates ethically, and respects privacy and security considerations. If AI systems are incorrectly implemented, it can lead to violations of privacy laws and regulations, resulting in potential legal consequences and penalties. The other example could be when AI systems are not configured in the right way or lack access controls – this could lead to unauthorized individuals accessing sensitive information and this could then also result in privacy breaches.”

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