With AI, Vanity Integrations are a Thing of the Past
Customers today are savvier than ever before. They understand the difference between well-thought-out experiences and those that are clearly rushed to market.
You know the story. Someone identifies a potential partner with millions of users, impressive brand recognition, or both. The next steps seem obvious: build an integration, get listed in their marketplace, and watch those lovely leads pour in.
The executive team gets excited. Engineering resources get allocated.
Then reality hits.
The integration gets built quickly, often by engineers who lack the proper context to understand either the partner’s platform or the actual customer use case. It gets shipped to meet a conference deadline or marketing launch. And then... nothing.
Or worse than nothing - a steady stream of one and two-star reviews from frustrated customers who installed an integration that promised much, but delivered little.
I’ve seen this happen time and time again. Sure, the integration served its marketing purpose. But it failed customers, by not creating any meaningful utility value for them. And, in failing customers, it ultimately failed every stakeholder.
These are vanity integrations - integrations driven by perceived distribution opportunities rather than genuine customer needs.
In the age of AI, they’re not just ineffective. They're dangerous.
What makes a “vanity integration”
First, vanity integrations are opportunity-driven rather than problem-driven. They start with someone identifying a potential distribution channel - “If we integrate with Salesforce, we’ll get access to their entire customer base!” - rather than starting with a clearly defined customer benefit in mind.
Second, they’re built with minimal customer research or validation. Because the focus is on the distribution opportunity rather than the customer need, teams often skip the hard work of understanding how customers might actually use it. They release the integration, but don’t test it. The integration is a means to a marketing end.
Third, they’re launched with great fanfare but minimal ongoing investment. Once the integration is live and the marketing milestone is achieved, it often gets relegated to maintenance mode. Bug fixes are slow, feature requests are ignored, and the integration slowly dies as both platforms evolve around it.
Finally, vanity integrations are measured by vanity metrics. Success is defined by the amount of press coverage, or the number of leads generated at launch - not by customer satisfaction, retention, or actual business value created.
This creates an unhelpful incentive structure where teams are rewarded for shipping integrations that look great on paper but don’t actually work well in practice.
Why AI changes everything
The rise of AI assistants and agents fundamentally changes the stakes for integration quality, trust and safety. What was merely ineffective in the traditional SaaS world becomes actively harmful in the AI era.
If a customer tries a poorly built Slack integration for your project management tool, they might leave a bad review and uninstall it. Annoying, but fairly contained.
If an AI assistant tries to use a poorly built integration to access your data, and fails to answer the user’s question or successfully complete a task, the user doesn’t just think poorly of your integration - they potentially lose trust in AI altogether.
When someone asks ChatGPT or Claude to “analyze my sales pipeline and suggest next steps,” they're not thinking about which specific API endpoints the integration might need access to to answer their question. They just expect it to work. If the MCP connector fails or provides incomplete data, the entire AI experience feels broken.
AI assistants use unstructured inputs - prompts - and relevant context to generate answers or artefacts for users. Context is delivered through integrations, but if integrations aren’t built, configured, or tested with a variety of potential prompts and tasks in mind, customers will feel let down by the AI assistant, not the integration.
Even worse, if integrations with AI assistants aren’t scoped properly, or the right data access controls aren’t in place, sensitive data could be exposed to users that might not have the right to access it. Example: a front-line employee might inadvertently access sensitive financial information about the publicly-listed company they work for.
Non-technical users shouldn’t need to learn about MCP connectors, OAuth scopes or data sync delays. They just want questions answered and tasks completed. If your integration can’t help deliver that, it’s not just a poor integration - it’s a broken promise.
Your customers are not morons
David Ogilvy once wrote, “The customer is not a moron, she is your wife.” It's a fantastic reminder that we should never doubt the intelligence of our customers, because our customers are our friends, colleagues, and family - and ourselves.
It’s a reminder that’s never been more relevant than in the AI era.
Customers today are savvier than ever before. They understand the difference between well-thought-out experiences and those that are clearly rushed to market.
They spot vanity integrations a mile away - and they’ll tell you about it in their reviews.
For too long, partnership teams tolerated a culture where integrations were treated as marketing exercises rather than product experiences. Companies would rush to build connections with high-profile partners, ship barely functional integrations to meet conference deadlines, and then abandon them to accumulate negative reviews.
AI changes this dynamic completely. When integration quality directly impacts AI reliability, and AI reliability directly impacts customer trust, there’s no longer any separation between integration quality and business success. Companies that ship poor-quality AI integrations won’t just miss out on distribution opportunities - they could actively damage their brand and relationships with both customers and partners.
This shift forces all of us to take integration quality more seriously.
Partnership teams should invest more time and energy in customer and partner research, and integration testing. Engineering teams should build integrations with the same care and attention they give to core product features. Product teams should measure integration success by customer satisfaction - not press mentions.
But for customers - and for the industry as a whole - this shift represents a massive improvement. Better integrations mean better AI experiences. Better AI experiences mean more customer value. More customer value means faster - and better - AI adoption, and more sustainable business success for everyone involved.
AI is killing vanity integrations, and we should all be excited about that.
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