This article was originally published on LinkedIn by Christine Morris.
AI Doesn’t Fix Broken Processes. It Magnifies Them.
After a decade working across the ServiceNow ecosystem, I’ve seen every major platform shift firsthand. There is one misconception I continue to hear from organizations of every size and maturity: the belief that AI can simply be “turned on” and instantly deliver value.
If only it were that easy.
Even with the speed of innovation, the fundamentals still matter. AI does not magically solve operational problems. AI doesn’t fix broken processes. It magnifies them.
AI Magnifies Bad Processes
If an organization struggles with inconsistent categorization, incomplete ticket descriptions or missing documentation, AI will surface those gaps immediately. If knowledge lives in ten different places and none of it is current, the model cannot learn from it. If teams close tickets without explanation, AI cannot identify patterns or make accurate recommendations.
The outcomes you get from AI are a reflection of the processes and discipline already in place. When the foundation is weak, the insights will be too.
The Hidden Cost of Data Debt
Most organizations underestimate the impact of data debt on AI readiness.
When knowledge is scattered across SharePoint, group drives and legacy systems, AI cannot meaningfully connect the dots. When the CMDB is incomplete or customized beyond recognition, models cannot understand relationships or dependencies. When processes vary by team or region, AI receives conflicting signals.
These issues do not just slow adoption. They undermine trust. Leaders invest in AI and then wonder why the platform is not delivering value. But the truth is simple. AI cannot overcome the absence of process, structure and clean data.
What AI-ready Organizations Do Differently
The organizations realizing value from AI share several common traits:
- They standardize processes. There is agreement across teams on how workflows, how it is documented and how success is measured.
- They invest in product ownership. Strong, experienced product owners serve as the bridge between the business and the platform, ensuring that decisions align to outcomes rather than quick fixes.
- They embrace governance. AI-ready organizations operate with a mature governance model. They make decisions as an enterprise, not as independent silos competing for attention.
- They measure service health continuously. Mature teams use metrics and KPIs as the heartbeat of their operations. These insights create a clean runway for AI to work effectively.
My Advice to Leaders Looking to Adopt AI
- Start with an assessment. Understand your current processes, data quality and knowledge landscape. Identify the gaps that will limit AI’s effectiveness.
- Adopt AI in small increments. Let your teams learn, adjust and improve with each step. You do not need to implement everything at once.
- Invest early in structure. Strong product ownership, governance and enterprise knowledge will do more for your AI strategy than any single feature release.
When these fundamentals are in place, AI does exactly what it is meant to do. It reduces effort, accelerates insights and unlocks new value across the enterprise.
Slow Down Before You Speed Up
The organizations achieving real impact with AI are not moving the fastest. They are moving the smartest.
AI value compounds when the groundwork is solid. When processes are consistent, data is reliable and governance is strong, AI becomes a powerful engine for transformation. If you want AI to deliver value, start with the foundation. The returns will follow.
