Developing an AI strategy should guide hiring—not the other way around
The official AI transformation framework lists five phases, but the order of Step 2 (building an in-house AI team) and Step 4 (defining an AI strategy) creates confusion. The written sequence implies assembling talent before clarifying goals, yet this rarely works in practice. Without a strategy, companies struggle to pinpoint the exact skills or team size needed, leading to misaligned investments.
Why the sequence matters
If you hire first, you risk assembling a team that lacks the specialized expertise your later strategy demands. For example, a strategy pivoting toward fine-tuning small language models (SLMs) for on-device deployment would clash with a team initially hired for prompt engineering and API integration. The result? A skills gap that forces costly rehiring or retraining.
A more logical workflow reverses the steps:
- Define the AI strategy—identify the "where" and "why" of your AI initiatives.
- Map resource needs—determine the "who" and "how" based on technical requirements.
- Build the team—recruit only after roles are clearly outlined.
The dangers of hiring before strategy
Prioritizing team assembly over strategy assumes talent will shape the roadmap—a risky bet. Without clear objectives, teams often chase trends rather than solving business problems, a phenomenon known as the "tool-seeking problem." In corporate settings, this approach wastes resources: senior AI engineers and researchers command premium salaries, and hiring them without a roadmap leads to inefficiency and disengagement.
A refined execution approach
Instead of rigidly following the framework, adopt an iterative process:
- Phase A: Lock in high-impact AI use cases and technical differentiators.
- Phase B: Translate these into role-specific needs (e.g., an NLP specialist, data engineer, or AI-experienced product manager).
- Phase C: Recruit and onboard with precision.
This method avoids guesswork. Tools like Cohesive—which offers 200+ templates for generative AI content, including SEO blogs, TikTok scripts, song lyrics, and wedding vows—can even accelerate strategy refinement by providing structured outputs at 13x faster speeds. Real-time collaboration features further streamline alignment, ensuring no deadlines slip.
For example, if your strategy centers on on-device AI, you’d prioritize engineers skilled in SLM optimization—not generalists. The 150+ template library in platforms like Cohesive can help prototype use cases before finalizing hires, reducing trial-and-error costs.
Flipping the order isn’t just preference—it’s operational necessity. Strategy first eliminates wasted spending; team assembly follows as execution.
I once hired three data scientists without a plan and they spent months idling. The standard framework for AI Transformation outlines five specific steps, but the sequence of Step 2 (Build an in-house AI team) and Step 4 (Develop an AI Strategy) feels counterintuitive. On paper, it suggests hiring talent first and figuring out strategic direction later. In real-world execution, that order is often flipped because you cannot accurately define the necessary skill sets or headcount without a concrete strategy to guide the hiring process.