The AI Capability Gap: Why Most Consumer Brands Are Building the Wrong Skills

When consumer brand leaders talk about their AI investment priorities, the conversation almost inevitably turns to talent. There’s a common anxiety about ensuring they have the right technical expertise in a competitive market – data scientists, machine learning and prompt engineers. The focus is overwhelmingly on finding the right technical skills, often at premium compensation levels. Ironically, the brands making the most significant progress with AI are rarely those with the deepest technical benches. A recent IBM study revealed that organizations reporting the highest ROI from AI weren't necessarily those with the most advanced technical capabilities but those that excelled at integrating (or as we said last week reimagining) AI in their business processes. This capability paradox gets to the heart of why so many consumer brands are struggling with AI implementation despite substantial investment. They're building the wrong skills.

The Underestimated Talent Transformation

The scale of talent transformation required for effective AI implementation is routinely underestimated. Most organizations approach AI capability building as a technical hiring challenge rather than a comprehensive organizational transformation. According to research from MIT's Center for Information Systems Research, companies successful in AI adoption typically retrain 80-90% of their existing workforce rather than focusing primarily on new technical hires. 

The reason for this disconnect is obvious in retrospect. The value of AI doesn't come from the algorithms themselves but from how they're applied to business challenges. Organizations that prioritize broad-based AI literacy over specialized expertise are 3.5x more likely to see returns from their AI investments. This pattern appears consistently across consumer categories. Companies that take a "bolt-on" approach to AI capabilities – creating isolated technical teams without transforming their core business capabilities – consistently underperform those that integrate AI literacy into existing functions. This isn’t a new phenomenon – take design, brand marketing, consumer insights, or data analytics as core capabilities. When left in isolation they can make modest impact, but when implemented systemically they can have profound transformative power on a brand’s ability to generate value.

The Missing Middle

The most acute talent shortage isn't for technical AI specialists but for what we call "translators" – leaders who can bridge technical capabilities and business value. McKinsey's research identifies this "missing middle" as the most significant barrier to AI adoption. These translator roles are challenging because they require a rare combination of technical fluency, business acumen, and change leadership skills. They need enough technical understanding to know what's possible, enough business knowledge to identify high-value applications, and enough leadership capability to galvanize executives and drive organizational adoption. In consumer products, this translator gap is particularly pronounced, and it explains much of the frustration executives express about AI investments not delivering expected returns. This translator gap has significant implications for how to structure your AI capability building. Rather than competing for scarce technical talent, the higher priority is often to identify and develop business leaders who can be upskilled with sufficient AI literacy to play this translator role.

The Capability Paradox

Perhaps the most counterintuitive finding from our work is that companies with the most advanced AI tools and technical talent often see the least business impact. A Stanford University study of AI implementations across industries found that technical sophistication explained only about 20% of the variance in business outcomes from AI initiatives. The bulk was determined by the organization's ability to integrate AI into existing processes, adapt operating models, and build broad-based literacy.

This pattern is particularly evident in consumer brands, where business context and consumer understanding are often more important than algorithmic sophistication. The lesson is clear – don't confuse technical sophistication with business value. The ability to integrate AI into your operations and decision-making processes is far more important than the underlying technical complexity.

Staged Capability Development

Given these insights, how should consumer brands approach capability building for AI? The most successful organizations take a staged approach that matches capability development to their AI maturity journey. Like any injection of technology think of it across roughly 4 phases; experimentation, expertise, application, operation. The priority shifts from learning, awarenss and literacy about what is possible to toward specialized skills for specific deployment and ongoing (measured) operations. Aligning the organization’s capabilities with the maturity of these stages of AI adoption prevents the technology from leading the business objectives. The most effective approach we've seen combines systematic upskilling of existing teams with selective hiring (or contracting) for critical roles.

A tiered literacy model works well in conjuction, too. Create a baseline AI literacy program for all managers, deeper application fluency capability for key functions, and specialized expertise concentrated ONLY where it can create the most value. Creating the right culture of AI adoption is foundational as well; cultural readiness is more predictive of successful outcomes than technical sophistication. Organizations that invested in change management, established clear governance models, and built psychological safety around AI adoption have seen significantly better results.

No AI SWAT teams

Just like most other “advanced initiative”, the most common mistake we see is creating an isolated "AI team" that operates as a separate function disconnected from core business operations. Centralized AI teams are effective for initial capability building but frequently became bottlenecks as organizations scaled their efforts. Remember the objective is a culture transformation – which means this exploration work needs to be insulated for focus but integrated into the business to have meaningful impact. The most successful long-term model is likely a hybrid approach that combined a small central team responsible for standards, platforms and governance with embedded AI capabilities within business functions. 

The evolution of this structure should follow your AI maturity journey. In early stages, a small central team can drive initial use cases and capability building. As applications multiply, embedding capabilities within business functions becomes increasingly important. The central team's role evolves from building and deploying solutions to enabling business teams and establishing governance.

Realistic Timelines

Perhaps the most pervasive misconception about AI capability building is the timeline required. Most executives dramatically underestimate how long it takes to build mature AI capabilities that deliver substantial business value. Depending on how you define AI investment initiatives, the average time from initial AI investment to significant business impact can be upward of 18-24 months. The critical phases – foundational data work, initial use cases, capability building, and scaled deployment – simply cannot be compressed beyond a certain point. This timeline reality has important implications for how you approach AI capability building. Rather than attempting to build all capabilities simultaneously, a staged approach that delivers consistent incremental value while building toward more sophisticated applications is almost always more effective.

Moving Forward

Remember, the gap isn't primarily technical – it's organizational, cultural, and strategic.  As you assess your AI capability building approach, I'd suggest three key priorities.: 

1. Rebalance your focus with a bias for translators. The most important roles are those who can bridge technical possibilities and business value.

2. Prioritize broad-based AI literacy over isolated pockets of expertise. Simple, global use is much more adaptable and will far outweigh deep and specialized applications in the long run.

3. Match your maturity. Align your capability building approach with your maturity stage rather than attempting to build all capabilities simultaneously.

For most sectors, the organizations with the most advanced technologies and specialized talent aren’t guaranteed an outsized advantage. It’s the ones that have built business-focused AI capabilities, integrated them effectively into their operations, and approached the capability journey with patience and discipline that are likely to reap the reward.