Hyperstandardization: The Missing Step in AI Readiness
AI Adoption Is Backwards: Why Foundations Matter
Organizations are rushing into AI, but many are approaching it the wrong way. Instead of addressing broken systems and redundant processes, they throw “band-aid” solutions at the problem, screen-scraping tools, quick connectors, or AI pilots that patch over old interfaces. These create short-term wins, but they don’t address the real inefficiencies.
From my point of view, companies should go through a pre-work phase (What I define as PrevAI) before attempting AI adoption. It’s the time to get the house clean. You cannot put luxury furniture on an uneven surface without it wobbling, tipping, or even falling, and eventually getting damaged, and the same logic applies to AI. Without clean processes and standardized systems, advanced AI initiatives will only create instability and extra costs later. The first question leaders should ask isn’t “What AI tool should we try?” but rather “What systems, data, and processes aren’t aligned?”
Stop Patching, Start Standardizing
A good example is invoices. Companies spend on AI that “reads” inconsistent formats when the smarter move is to standardize and digitize them. That way, AI multiplies value instead of compensating for legacy chaos.
In many organizations, AI projects are sometimes pursued as much for visibility and leadership growth as for business transformation. That’s understandable, but to unlock real value, adoption needs to balance both, advancing leaders and the organization together. When AI is applied over an unstructured foundation, most initiatives become pilots rather than true adoption. These projects often deliver short-term benefits but fail to create lasting transformation. This is the essence of Hyperstandardization, aligning processes, removing redundancies, and setting the foundation for scalable AI adoption. Without this groundwork, pilots will always look more like experiments than real progress.
Data Over Band-Aids: Building the Real Basis for AI Success
The future isn’t about patching gaps, it’s about mastering data. Accessibility, integration, and usability of data will define which companies thrive. The older approach of automating user interfaces and flows is already becoming obsolete as technology shifts toward deeper, data-driven intelligence.
Today, tools like Microsoft Copilot and GitHub Copilot are reshaping productivity by embedding intelligence directly into workflows. Platforms such as Databricks are redefining how enterprises manage and unify their data for advanced analytics. Machine learning models, LLMs (Large Language Models), and emerging agent-based systems are proving that structured and semi-structured data can be transformed into knowledge, insights, and even autonomous actions. These are not just incremental improvements, they represent the real direction of digital transformation. Companies that evolve from patching processes to unlocking the full value of data will set themselves apart.
AI Isn’t the Starting Point, It’s the Multiplier
AI should not be seen as the starting line of transformation, it’s the multiplier that accelerates once the groundwork is in place. Pilots and experiments can help organizations learn, but without strong foundations they risk becoming showcases for vendors rather than solutions for businesses. Hyperstandardization ensures that when AI is deployed, it can scale, stick, and deliver measurable impact.
And here’s the critical question leaders should ask: Have you imagined how your company would stand out if, instead of chasing ROI projections and “workforce hours saved per year,” you had invested in aligning tools, standardizing processes, and preparing data for what’s now coming? That is the real challenge, and the true opportunity.