My experiments with AI and challenges around enterprise adoption

I've spent the past few weeks diving into the world of generative AI. While it's been interesting so far, I'm just getting started. While much of the good, bad and ugly has been talked about, I want to focus on the top 3 challenges that don't get much attention.

1. There are choices beyond ChatGPT/ Bard and it’s a problem of plenty: There are multiple AI tools, and the choice can be overwhelming. From copywriting, social media, and design, to campaign analysis, the tech can help with all. New tools are popping up all the time, which makes it harder to decide which one to use. Each tool has its unique features, pricing, and other rules of engagement. Most tools are made for small businesses, so it's not easy to integrate with an enterprise set-up or their existing technology frameworks.

2. Data privacy and information integrity: The perils of data privacy have been much talked about, but let's review it from an enterprise perspective. Companies use AI in many ways, and in-house AI teams have long lists of potential use cases. But every time they test a new use case, they risk exposing private data to the world. The young teams responsible, may not be aware of the risks to data privacy and businesses. Without guidelines on masking data or adding anonymity, the potential risk increases, especially while dealing with internal communication or pre-sales activity.

3. Lack of governance approach: Marketers, continue to approach this and similar problems in isolation. The user perspective of b2b marketers is rarely documented and doesn’t reach the development gurus. Hence many tools continue to fall short in functionality and/ or get lost in enterprise compliance. As marketers, we do not have a self-governance mechanism to identify and nip these challenges in the bud.

Can the marketing fraternity rise to this new curve ball and make the best of it so that marketing continues to be an enabler and positively impact the business?