AI Is Infrastructure, Not Strategy. Here's Why That Matters for Your Organization
Josh Skorupa, head of digital transformation at GovTech Singapore, has a straightforward message for leaders preparing their organizations for AI: stop treating it as a strategy and start treating it as infrastructure.
The distinction matters because it changes how you invest, what you measure, and whether your AI work actually delivers results. Skorupa has spent years moving from advising on transformation to running it directly - a shift that exposed a pattern he sees repeatedly across organizations.
The Pattern That Derails AI Projects
Organizations typically approach AI the same way they approach any major initiative: as a strategic priority requiring significant investment and organizational restructuring. This creates problems.
AI has a low barrier to entry and amplifies everything - good and bad. When companies deploy systems without clarity on business outcomes, data quality, or how work actually happens, AI accelerates those existing problems. You end up with faster failures instead of faster wins.
Skorupa said the familiar mistake is treating AI as the strategy itself. Instead, he advises leaders to invest in simplifying and automating how the business operates, then use AI as a capability to support that work.
What This Means Practically
Start by removing ambiguity. If you are not clear, neither is the system. Unclear prompts, undefined outcomes, and fuzzy business cases produce unreliable results.
Second, measure against actual outcomes. If work fails to deliver impact, pivot or stop it. Skorupa said transformation happens in a live environment with competing priorities and trade-offs - not in a controlled setting where everything aligns perfectly.
Third, expect roles to evolve. Rather than forcing people into fixed structures, let roles develop around what people can actually do and what the team needs. Change embeds when people start driving improvements themselves.
Why This Matters for HR Leaders
The shift from strategy to infrastructure has direct implications for how you develop capability and manage workforce change. When AI is infrastructure, you're not training people to use a new platform - you're preparing them to work differently in a system where intelligence is embedded.
That requires clarity on outcomes, honest conversations about which work changes, and leadership that can absorb disruption without becoming disrupted. It also means hiring and developing people who can navigate ambiguity and drive change from within teams, not just from the top.
For more on preparing your organization for AI adoption, see resources on AI for Human Resources and the AI Learning Path for CHROs.
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