Venture capitalists (VCs) see AI strategy through a different lens than traditional IT leaders, and their perspective highlights why many enterprise AI projects fail. According to VCs, the problems often stem not from the technology itself but from a lack of clear vision, mismanagement, and under-resourcing. While many companies want to become “AI-first,” they lack the foundational systems, budget, and roadmaps to do so effectively.
First, IT leaders must reframe how they view AI — not just as a technical upgrade, but as a force that can fundamentally reshape business structures and processes. As Sandhya Venkatachalam of Axiom Partners points out, AI is increasingly performing human-like tasks rather than just automating old workflows. This means legacy systems and roles will change, and leaders need to think long-term about how AI can disrupt entire functions, such as customer support.
Second, rather than getting bogged down in the intricacies of AI technology, leaders should focus on outcomes. Julia Moore from Breakout Ventures advises framing projects around business impact, not just algorithms. Brad Harrison of Scout Ventures adds that CIOs should prototype with a big-picture mindset: understand, iterate, and prioritize value over novelty.
Third, VCs recommend that organizations think ahead, not just about immediate needs. Short-term AI wins may feel good, but real transformation comes when leaders prepare for future shifts — even if it means moving away from building proprietary tools like internal LLMs that duplicate what big research labs already offer.
Fourth, speed is essential. Enterprises should partner with “AI-native” startups to move faster, leveraging their agility rather than trying to build everything in-house. VCs also emphasize aligning AI strategy with industry verticals; for instance, AI in defense or biotech demands different approaches and accountability than in software.
Fifth, culture matters. Creating an “AI-first” organization requires more than just tools — leaders must foster a mindset of experimentation, especially among younger digital-native employees. Finally, IT leaders themselves should roll up their sleeves: prototyping, experimenting, and building alongside data scientists builds trust and keeps them grounded in the reality of AI’s fast-evolving landscape.
