Your CTO is Thinking a Lot About AI. It’s Time to Put the Plan to Action.

Chief Technology Officers (CTOs) are typically among the first to champion new innovations that can benefit their businesses, both internally and on the product side. Most have been thinking deeply about AI integration for the past two years, attending conferences, evaluating vendor tools, launching pilots, and shaping company-wide AI strategies. 

Yet many companies are having difficulties with their AI initiatives. Research shows that a large percentage struggle to move beyond the pilot phase, while others fail to generate meaningful ROI. Building, deploying, and scaling AI for both internal operations and customer-facing products is a massive undertaking that requires more than strategic thinking. It demands dedicated leadership with hands-on experience turning AI vision into competitive advantage. 

Why Strategic Thinking Isn’t Enough 

Even though most CTOs understand how their companies could leverage AI strategically, the nature of their role makes meaningful progress difficult. CTOs are constantly managing technical debt, product release schedules, core infrastructure, security, risk, and data challenges while collaborating with other C-suite executives on broader business priorities. Their role is complex, and these leaders know developing and scaling an AI program isn’t a side project; it demands focused attention and expertise. 

But bandwidth isn’t the only barrier. Several factors prevent companies from moving beyond pilots: 

• Resource constraints: AI functionality, along with the computing power and data infrastructure to support it, requires significant capital. Many companies hesitate to commit without clear ROI projections. 
• Lack of a concrete plan: Companies that invest in AI simply for optics or to keep pace with competitors often fail to define clear use cases, which leads to minimal returns. 
• Specialized talent gaps: AI programs need leaders with proven experience, along with teams that have advanced expertise in ML ops, data engineering, model deployment, and AI security. 
• Cross-functional misalignment: Integrating AI into both operations and products requires tight collaboration between engineering, product, marketing, legal, and sales. Many organizations lack the processes and culture to support this level of coordination.  

Choosing the Right AI Leader 

A major challenge for companies looking for a designated AI leader to spearhead their initiatives is finding someone with direct experience. The reality is that there’s an extremely small talent pool of true AI specialists, most of whom are working for major tech giants and are compensated at levels beyond the limit for smaller businesses. 

But even if companies can’t capture one of those rare professionals, that doesn’t mean there aren’t viable candidates out there. Many tech and product-focused executives and senior leaders possess the foundational skills and experience with sophisticated systems at scale that AI initiatives require. 

Looking at professionals currently leading AI initiatives, we’ve identified two backgrounds that consistently translate well:

Technology leaders with data engineering expertise: CTOs and VPs of Engineering who’ve managed ML and/or data teams and leveraged contemporary AI toolsets (maybe even started to build their own LLMs), heads of Data Engineering who’ve built enterprise-wide data pipelines, or Chief Data Officers who’ve led large-scale data transformations. These executives understand how to architect systems that scale, ensure data quality and governance, and move intricate technical initiatives from concept to production. 

Product leaders with algorithmic experience: Chief Product Officers, VPs of Product, or Heads of Product Analytics who’ve built recommendation engines, personalization systems, real-time ad serving and/or bidding platforms, search algorithms, fraud detection tools, or dynamic pricing platforms. These leaders know how to translate customer needs into technical capabilities and features that users readily adopt because the resulting solutions deliver measurable business value. 

The key is evaluating candidates with hands-on experience building intelligent systems at scale, not specific job titles. Focus on what candidates have built and deployed, not just whether “AI” appears in their previous roles. 

Making the Move  

Here’s the reality: your CTO can’t do this alone. They’re already stretched across infrastructure, security, product roadmaps, and a dozen other priorities. AI transformation isn’t something that happens in the margins; it needs someone wholly focused on execution. 

The good news is you don’t need to compete with tech giants for a unicorn AI executive. The right leader is often someone who’s already built intelligent systems at scale, whether that’s a VP of Engineering who’s deployed ML models in production or a Chief Product Officer who’s created recommendation engines that actually work. What matters is proven experience turning sophisticated technology into business results. 

Your CTO is right to push for AI investment, but strategy only gets you so far. The companies seeing real returns are the ones that stopped treating AI as a side initiative and brought in dedicated leadership to drive it forward. 

At JM Search, we work with PE-backed and growth companies navigating exactly this challenge: finding the technical and product leaders who can take AI from pilots to production. Learn how we help companies find the leaders who turn AI ambition into competitive advantage.