Feature
Artificial intelligence is rapidly transforming industries, with leading companies deploying AI to automate repetitive tasks, streamline workflows and unlock new sources of value. Organizations adopting generative AI (GenAI) tools report average performance improvements of 66%, with even higher gains for complex tasks.
Yet as organizations rush to implement generative AI, a troubling pattern has emerged: Companies are using the technology to mask fundamental operational problems rather than solving them. Industry leaders across consulting, workforce management, data science and software development warn that GenAI often acts as expensive window dressing on broken processes, amplifying dysfunction rather than fixing it.
Table of Contents
- The Hidden Cost of Poor Documentation in AI Deployments
- The AI Productivity Visibility Gap
- AI as a Shortcut? Here’s Why That Strategy Backfires
- Incorporating Workflow Knowledge into AI
- GenAI Won’t Work Until Your Operations Do
The Hidden Cost of Poor Documentation in AI Deployments
The most critical issue facing GenAI implementation centers on the quality of underlying data and documentation. ”The biggest flaw is broken upstream data or data quality issues,” said Melissa Copeland, founder of Blue Orbit. “When data feeding GenAI applications is contradictory or inaccurate, the system draws incorrect conclusions—a problem compounded by the frequent absence of human oversight.”
Copeland framed successful GenAI deployment around two components: clean, high-quality data with appropriate controls, and well-documented work processes that allow steps to be traced and issues isolated when necessary. Without these elements, solutions simply speed wrong and frustrating information.
Most companies lack a clear picture of how their workflows actually operate, said, Ashish Patel, founder of Simpat Tech. Different teams use different tools, each maintaining their own versions of the process that doesn't match reality. When GenAI is dropped into a workflow with inconsistent data and mismatched steps, it amplifies the disconnection, fueling the chaos.
Well-documented workflows require more than flowcharts. They must include specific objectives, clear inputs and outputs, ordered steps with designated responsibility, decision points with associated logic and fallback approaches, identified tools and systems and applicable compliance requirements.
Patel offered a practical test for documentation quality: if people say the information is in the documentation but then still have to walk you through it live, that process isn't truly documented. Another red flag: documentation that's out of date the moment you open it, or when different teams maintain their own private versions.
The AI Productivity Visibility Gap
There's a disconnect in how organizations understand AI use, Jared Brown, co-founder and CEO of Hubstaff told Reworked. While 85% of professionals say they use AI, only 4% of their tracked work hours involve AI tools. This gap reveals how little visibility most companies have into how work gets done.
Leaders need to track the time and steps required to complete a task without AI, then measure improvements when AI is implemented, Brown said.However, hours alone shouldn't be the only measure.
Instead, leaders should measure how work flows through their systems and quantify how AI improves those processes. According to anonymized data from Hubstaff's tracking software, teams using AI recorded 30 minutes less unproductive time per day.
The principle Brown advocates is clarity first, AI second. Only when leaders understand their present state can they see where GenAI can take their future.
"AI is not a quick-fix solution but a tool that needs time to adapt and integrate into existing business processes,” said Juan José López Murphy, head of data science and AI at Globant. Understanding the stage of a project and why performance aligns with that stage becomes important.
During proof-of-concept stages where many projects are abandoned, teams may be exploring the technology or trying to understand use case factors that lead to adoption. Without clarity on these objectives, teams won't see value in continuing.
Some projects don't have clear metrics directly related to value but instead affect adaptability or the ability to use technologies in the future. Pricing these outcomes is complex, causing them to fall out of ROI calculations, ultimately undervaluing the project.
AI as a Shortcut? Here’s Why That Strategy Backfires
"When the first question is 'how do we plug AI into this' instead of 'why is this process slow or painful in the first place,' you know they are looking for a shortcut,” Patel said. Another indicator: When people want AI to summarize chaos so they don't have to clean up data, systems or ownership. If nobody can answer who owns the process, they're not ready for AI.
The consequences become visible in customer-facing applications. Copeland described situations where patients or customers used GenAI applications and got stuck in loops. When instructed to schedule an appointment but data in the system is faulty, the system errors out and tells the user to call a number. This wasted time, irritated users and still required a phone call, leaving organizations worse off than before.
When processes already produce consistent, high-quality outcomes and the issue is volume or speed, GenAI helps, Patel said. However, if the process is full of exceptions and manual fixes, AI will mostly speed confusion. His test: Describe the current process in a few steps with known inputs and outputs. If you can’t, AI isn't your first move.
AI doesn't hide operational problems — it amplifies them. If data and inputs are messy, the AI outputs expose the inconsistency of underlying systems and processes.
Incorporating Workflow Knowledge into AI
The challenge of tacit knowledge blocks effective GenAI implementation. Copeland's straightforward approach to handling workflow knowledge that is only in people's heads: document, document, document. If you don't have appropriate documentation, start with a map of the most important, most frequently used processes.
You cannot build serious AI or automation on top of tribal knowledge, Patel said. The first step involves pulling that knowledge out through structured interviews, shadowing and process maps, then validating it with the people who do the work. Only after capturing and standardizing that knowledge does it make sense to incorporate it in AI-powered tools.
Governance gaps are the biggest risk in GenAI deployment. Copeland identified "a lack of a product owner or assigned owner for the GenAI capability" as the most common governance gap when companies assume GenAI self-corrects. Organizations need someone who owns the inputs and outputs and is accountable for accuracy, efficiency and overall results.
Patel pointed to a bigger misconception: the belief that AI somehow learns its way out of bad inputs and fuzzy rules. In reality, organizations still need clear policies on what data the model uses, who reviews outputs, how errors are handled and what gets logged.When those basics are missing, AI decisions become a black box, and you only notice the problems when a customer, a regulator or a court points them out.
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GenAI often creates bottlenecks rather than removing them, particularly around review and approval processes. AI generates content quickly, but then everything gets bunched up in human review because there's no clear standard around what is acceptable.
Another common choke point occurs in data access: if AI has to pull from 10 fragmented systems, the new bottleneck becomes the integration and governance layer.
GenAI Won’t Work Until Your Operations Do
The point is that GenAI is powerful, but only when applied to solid operational foundations. Organizations rushing to implement AI without first addressing data quality, process documentation, measurement systems and governance structures are setting themselves up for amplified dysfunction rather than improvements.
Technology won't hide broken workflows. It exposes them, often at the worst time.
Success with GenAI means organizations need to resist the temptation of shortcuts.
- Document your processes in enough detail that they match what people do.
- Extract knowledge in your employees' heads and standardize it.
- Establish clear ownership and accountability for AI systems.
- Measure not just whether people use AI, but whether it improves how work flows through your organization.
These unglamorous tasks don't generate headlines or conference presentations. But they represent the only reliable path that delivers on GenAI’s promise rather than becoming another expensive layer on top of operational chaos.
The choice is clear: Fix the foundation or amplify the cracks.
Related Articles:
- Why Businesses Overestimate Generative AI ROI — Experts warn that as enterprises rush to adopt GenAI, hype-driven ROI claims ignore the governance, data discipline and structural change real value requires.
- Modeling Business With AI — Modeling work with AI isn't about trying to replace all the humans. It is about understanding how work is done so that you can optimize it.
- Why AI Readiness Relies on Data Architecture, Fabric and Knowledge Graphs Working Together — AI can’t deliver value without the right data stack. Learn how architecture, data fabric and knowledge graphs create the foundation for reliable, scalable AI.