Domz D

I support View B: organisations should wait for understanding before acting on AI predictions. While AI can provide accurate early warnings, sustainable improvement requires understanding the root cause of failures rather than reacting blindly to predictions. In the call center industry, AI models may predict a potential increase in call abandonment rates or a drop in customer satisfaction. If management acts immediately on a black-box prediction without understanding the reason, they might simply add more agents or tighten monitoring that will require additional operational cost. While this may temporarily reduce the problem, it does not address the underlying process issue. This is where a Master Black Belt can play a critical role. A Master Black Belt, using Lean Six Sigma methodologies, can analyze the situation systematically. Instead of relying solely on the AI prediction, they can lead root cause analysis using tools such as process mapping, data analysis, and the DMAIC framework to identify what is truly causing the predicted failure. By combining AI insights with structured problem-solving methods, the organisation can gain real process knowledge and implement long-term improvements. Without this understanding, companies risk becoming dependent on black-box systems and losing the ability to manage and improve their own processes. Therefore, waiting for understanding, supported by expert analysis from roles such as a Master Black Belt, ensures that organization's move from reactive fixes to sustainable operational excellence.

In most companies, work is often divided between the Continuous Improvement (CI) team and the Automation team. The CI team focuses on analyzing processes, identifying inefficiencies, and leading Lean or Six Sigma projects to improve accuracy and cycle time. The Automation team designs and implements tools such as RPA bots or digital applications to execute those improvements. Currently, the process operates as a handoff: CI identifies which process steps to improve, and Automation builds the solutions. If AI becomes fully integrated across organizational processes, the traditional divide between CI and Automation would begin to fade. Merged roles: New hybrid position such as AI-enabled Process Designer could emerge, combining process analysis and automation expertise. These professionals would leverage AI insights to refine and implement improvements directly. Shifted responsibilities: The CI function would transition from finding and fixing inefficiencies to overseeing AI systems that autonomously optimize process steps. Automation would focus more on AI governance and integration, ensuring models operate safely and effectively. Ultimately, AI would merge the “thinking” and “doing” aspects of process improvement. The distinction between Continuous Improvement and Automation would evolve into a single, adaptive framework centered on continuous, AI-driven process optimization.

Let’s talk about an AHT improvement project in any industry. Define Phase • AI can quickly process historical data to highlight key drivers, outliers, and bottlenecks, helping to build a stronger business case. • Human judgment is necessary to select appropriate team members and to validate whether the AI-generated data aligns with business needs. Measure Phase • AI can extract and generate AHT data sets (monthly, quarterly, team data, etc.) much faster than humans. AI can also assist in creating process diagrams. • Humans are needed to ensure and validate that the data provided by AI is correct and aligns with operational standards. Analyze • AI can recommend and produce relevant analyses, such as correlations, Pareto charts, box plots, etc., with less effort. • Human involvement is essential to validate AI findings, interpret root causes, and determine their impact. Improve • Teams can implement AI-powered solutions such as chatbots, voice bots, or automate manual processing tasks. AI can also assist with macro programming. • Humans play a critical role during user acceptance testing (UAT), ensuring that current outputs and processes remain compliant. Control • Teams can leverage AI to create real-time performance dashboards and automated reports. • Human oversight is still needed to monitor results and investigate the root causes of new patterns. In summary: Humans should utilize AI to improve certain tasks and support solution implementation.