Beyond the Management Consulting Mindset
In the rush to adopt AI, many organizations are turning to traditional management consultants and “strategists” to chart their course. While these professionals bring valuable skills in structured thinking and business analysis, there's a critical element missing from this approach: hands-on experience with AI technology itself.
The Unique Nature of AI Strategy
AI isn't just another technology implementation like cloud computing or ERP systems. Its probabilistic nature and emergent behaviors create a fundamentally different strategic challenge. Unlike traditional technology where limitations and exceptions can be documented in operations manuals, AI's "gotchas" and edge cases often only become apparent through direct experience.
Consider these critical aspects of AI that can't be fully understood through case studies alone:
- The same prompt can yield wildly different outputs
- Models can be confidently wrong
- Hallucinations occur in unexpected ways
- Performance degrades in subtle edge cases
- Different model architectures handle identical tasks differently
- Data quality impacts results non-linearly
- Fine-tuning and RAG implementations have practical limitations that aren't obvious on paper
The Democratization of AI Experience
What makes this situation unique is that AI tools have become increasingly accessible to non-technical users. Unlike previous technological waves where "getting your hands dirty" meant learning to code or managing complex systems, today's AI tools often require minimal technical expertise to experiment with and understand.
This democratization means there's no excuse for AI strategists not to have direct experience with:
- Testing different LLMs through simple interfaces
- Building quick prototypes with no-code tools
- Experimenting with prompt engineering
- Trying different RAG implementations through platforms
- Testing model fine-tuning through user-friendly interfaces
- Experiencing hallucinations and limitations firsthand
The Risk of the Pure Consulting Approach
Organizations that prioritize traditional consulting backgrounds over hands-on AI experience risk:
- Receiving recommendations that look good in PowerPoint but fall apart in practice
- Missing critical implementation challenges that aren't visible from case studies
- Being unable to effectively evaluate vendor claims and capabilities
- Underestimating organizational readiness requirements
- Missing the subtle complexities of AI integration with existing systems
Building Better Mental Models
The key to effective AI strategy lies in building accurate mental models of how the technology behaves in practice. This can only come from direct experimentation and implementation experience, even if just with sample projects. It's the difference between reading about swimming and actually getting in the water – you can study fluid dynamics all day, but until you've experienced how water behaves around your body, you don't truly understand swimming.
The Path Forward
As organizations build their AI strategy teams, they should look for professionals who combine:
- Strong business acumen and strategic thinking
- Direct experience building and implementing AI solutions
- Understanding of organizational change management
- Ability to translate between technical and business stakeholders
- Hands-on experience with current AI tools and platforms
While traditional management consulting skills remain valuable, they should be seen as necessary but not sufficient for AI strategy roles. The most effective AI strategists will be those who have rolled up their sleeves and experienced both the possibilities and limitations of AI technology firsthand.
Conclusion
The low barrier to entry for AI experimentation today means that hands-on experience should be table stakes for anyone making strategic AI decisions. Organizations that recognize this will be better positioned to develop realistic, implementable AI strategies that deliver real value. Those that stick to traditional consulting approaches risk developing strategies that look good on paper but fail to account for the unique challenges and opportunities that only become apparent through direct experience with AI technology.