I see it happening everywhere. Board meetings where someone inevitably asks, “Don’t we need a VP of AI?” I saw a term sheet the other day where a leading VC firm reserved $1m of the round … for hiring a “VP of AI”.
Leadership teams scrambling to post job descriptions for “Head of Artificial Intelligence.” Recruiters cold-calling anyone with “machine learning” on their LinkedIn.
But here’s the uncomfortable truth: hiring a “VP of AI” is often just conceding defeat before the real battle even begins.
The Silo Problem
When you hire a VP of AI, you’re essentially saying: “AI is someone else’s job.” You’re creating a silo in an organization that desperately needs the opposite – pervasive, integrated AI thinking across every function.
Think about it. Would you hire a “VP of Internet” in 2010? A “VP of Mobile” in 2015? These technologies became transformative precisely because they weren’t relegated to a single department. They became woven into the fabric of how every team operated.
The same applies to AI today. Your sales team needs to understand how AI can improve lead scoring and outreach. Your customer success team should be experimenting with AI-powered sentiment analysis and churn prediction.
Most importantly, your product team needs to think about to remake the entire product in the age of AI. Not it being a feature line.
The Real Solution: AI-First Culture
The companies winning with AI aren’t the ones with the fanciest AI executives – they’re the ones where AI thinking permeates every level of the organization.
It starts at the top. When leadership is actively experimenting with AI tools, sharing their learnings, and demonstrating genuine curiosity about AI applications, it creates permission for the entire organization to follow suit. HubSpot’s Dharmesh Shah is a perfect example of this. His public experimentation with AI tools doesn’t just inform his own thinking – it signals to every HubSpot employee that AI exploration is not just allowed, it’s expected.
It cascades down through middle management. When VPs of Sales are asking, “How can we use AI to improve our pipeline?” and VPs of Marketing are experimenting with AI-generated content, individual contributors get the message that AI fluency is part of their job, not someone else’s.
It reaches every individual contributor. The most impactful AI implementations often come from the people closest to the actual work. The customer success manager who discovers a prompt that cuts response time in half. The product manager who finds an AI tool that streamlines user research. The engineer who automates a tedious debugging process.
You can hear our deep dive with Yamini Rangan, CEO of HubSpot, on how she is doing this at HubSpot here:
A Better Approach: Democratize AI Investment
Instead of concentrating AI expertise in one highly-paid executive, consider this alternative approach:
Create AI experiment budgets for every team. Give each department a monthly allowance specifically for AI tool experimentation. Make it clear that not every experiment needs to succeed – in fact, expect most to fail. The goal is learning and cultural change, not immediate ROI.
Implement AI bounty programs. Rather than hiring one VP at $300K+, distribute that budget across problem-solving bounties. Offer $5K for an AI solution that reduces customer churn by 2%. $10K for an AI workflow that cuts onboarding time by 20%. Your existing team members understand your problems better than any external hire ever will.
Make AI fluency a core competency. Include AI experimentation in performance reviews. Not as a requirement to become an AI expert, but as an expectation to stay curious about how AI might improve their specific role. The goal isn’t to turn everyone into a data scientist – it’s to turn everyone into an AI-curious professional.
The Exception: When You Actually Need AI Leadership
This isn’t to say AI leadership roles are never appropriate. If you’re building AI as a core product feature, if you’re processing massive datasets that require specialized ML expertise, or if you’re in a heavily regulated industry where AI governance is crucial – then yes, dedicated AI leadership makes sense.
But for most SaaS companies, AI transformation isn’t about hiring one person to “own” AI. It’s about creating an organization where AI thinking is distributed, experimentation is encouraged, and practical applications emerge from the teams who understand the problems best.
The Bottom Line
The companies that will win with AI won’t be the ones with the most impressive AI org charts. They’ll be the ones where a customer success manager casually mentions trying a new AI tool in standup, where the sales team shares prompts that improve their outreach, and where product managers naturally think about AI applications when designing new features.
You can’t hire your way to AI transformation. You have to build it, experiment by experiment, team by team, individual by individual.
The question isn’t whether you need a VP of AI. The question is whether you’re ready to make AI everyone’s job.
