{"id":926,"date":"2026-06-14T14:13:45","date_gmt":"2026-06-14T14:13:45","guid":{"rendered":"https:\/\/pragyconsulting.com\/?p=926"},"modified":"2026-06-14T14:13:49","modified_gmt":"2026-06-14T14:13:49","slug":"algorithmic-babysitting-when-operational-excellence-means-managing-the-ai","status":"publish","type":"post","link":"https:\/\/pragyconsulting.com\/index.php\/2026\/06\/14\/algorithmic-babysitting-when-operational-excellence-means-managing-the-ai\/","title":{"rendered":"Algorithmic Babysitting: When Operational Excellence Means Managing the AI"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Executives may be writing serious strategy papers about hyper-automation, autonomous workflows, and agentic orchestration. On the ground, many operational excellence teams are dealing with something much more practical: algorithmic babysitting.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is the daily work of watching, questioning, correcting, and occasionally calming down automated systems that are supposed to make operations easier.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The phrase is funny because it feels painfully familiar. A workflow automation tool closes the wrong ticket. A procurement agent misreads demand. A planning assistant recommends a schedule that looks efficient on paper but creates problems on the production floor. A reporting agent summarizes performance without understanding the operational context behind the numbers.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI agents can absolutely support better operations. But they do not remove the need for process thinking. In many cases, they increase the need for clear process ownership, reliable data, practical dashboards, and disciplined review routines.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The \u201c5 Whys\u201d Has Gotten Very Weird<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Root cause analysis used to focus on broken equipment, unclear forms, poor handoffs, missing training, or outdated standard operating procedures. Those problems still exist. The difference now is that the process failure may also involve an AI agent making a decision based on weak signals, incomplete data, or misunderstood context.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A modern \u201c5 Whys\u201d discussion might sound something like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Why did procurement order 40,000 staplers?<\/strong> Because the procurement agent detected a demand spike.<\/li>\n\n\n\n<li><strong>Why did it detect a demand spike?<\/strong> Because the forecasting model interpreted internal chatter as a supply risk.<\/li>\n\n\n\n<li><strong>Why did it interpret chatter as a supply risk?<\/strong> Because it was ingesting unstructured comments from a communication channel.<\/li>\n\n\n\n<li><strong>Why was it allowed to influence purchasing decisions?<\/strong> Because no approval threshold was built into the workflow.<\/li>\n\n\n\n<li><strong>Why was no threshold in place?<\/strong> Because the automation was deployed faster than the control plan was designed.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The joke is about staplers. The real issue is control.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Operational excellence teams are now being asked to understand not only the process but also the logic, data sources, permissions, and decision boundaries behind the automation. That does not mean every OpEx professional needs to become a data scientist. It means process improvement work must include better questions about data quality, governance, and escalation rules.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI Agents Do Not Remove Process Waste Automatically<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A common mistake is assuming that automation eliminates waste by default. It does not. Automation can also accelerate waste.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If a process has unclear ownership, poor master data, duplicate approvals, weak KPI definitions, or inconsistent business rules, an AI agent may simply move the problem faster. It may create more exceptions, more rework, and more meetings to explain why the system did what it did.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, in a manufacturing environment, an AI scheduling assistant may recommend a production sequence that improves machine utilization but ignores changeover complexity, material release timing, quality hold status, or operator availability. In a finance process, an automated reporting agent may produce a clean summary but miss the fact that two regions are using different definitions for the same KPI. In a supply chain process, an agent may recommend expediting materials without considering minimum order quantities, supplier constraints, or inventory already reserved for another product line.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The process still needs Lean thinking. The dashboard still needs the right measures. The workflow still needs governance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Algorithmic Babysitting Is Really Process Governance<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The humorous label hides a serious operational requirement. Algorithmic babysitting is not about mistrusting technology. It is about making sure automated decisions remain aligned with business reality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Good AI-enabled operations need practical governance at several levels:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data governance:<\/strong> Which data sources can the agent use, and are they reliable?<\/li>\n\n\n\n<li><strong>Process governance:<\/strong> What actions can the agent recommend or execute?<\/li>\n\n\n\n<li><strong>Decision governance:<\/strong> Which decisions need human approval?<\/li>\n\n\n\n<li><strong>KPI governance:<\/strong> How will leaders know whether automation is improving performance?<\/li>\n\n\n\n<li><strong>Exception governance:<\/strong> What happens when the agent behaves unexpectedly?<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is where operational excellence and business intelligence should work together. A Lean team may define the process controls. A Power BI dashboard may monitor the exceptions. A business intelligence model may show whether the AI-assisted workflow is improving cycle time, service level, schedule adherence, forecast accuracy, or rework.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Without that structure, teams end up manually reviewing every odd decision. That is not operational excellence. That is babysitting without a control plan.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The New Role of Power BI Dashboards<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Power BI dashboards are no longer just for monthly performance reviews. In AI-supported workflows, dashboards can become an operational control layer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of only showing lagging indicators, a useful dashboard can help leaders monitor whether automated workflows are behaving as expected. Examples include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Number of AI-generated recommendations accepted, rejected, or overridden<\/li>\n\n\n\n<li>Exception rates by process, site, product family, supplier, or customer group<\/li>\n\n\n\n<li>Cycle time before and after automation<\/li>\n\n\n\n<li>Manual rework caused by incorrect automated actions<\/li>\n\n\n\n<li>Approval delays for AI-recommended actions<\/li>\n\n\n\n<li>Frequency of unusual recommendations outside normal process limits<\/li>\n\n\n\n<li>Data freshness and missing data indicators<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These measures help teams move from opinion-based debates to fact-based improvement. Instead of asking, \u201cDo we trust the AI?\u201d leaders can ask, \u201cWhere is the AI creating value, where is it creating exceptions, and where do we need stronger controls?\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Lean Six Sigma Still Matters<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI agents do not make Lean Six Sigma obsolete. They make disciplined problem solving more important.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">DMAIC still applies:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Define:<\/strong> Clarify the business problem before automating anything.<\/li>\n\n\n\n<li><strong>Measure:<\/strong> Establish baseline process performance and data quality.<\/li>\n\n\n\n<li><strong>Analyze:<\/strong> Identify root causes, including system logic and data inputs.<\/li>\n\n\n\n<li><strong>Improve:<\/strong> Redesign the workflow with the right human and AI responsibilities.<\/li>\n\n\n\n<li><strong>Control:<\/strong> Monitor outcomes through dashboards, alerts, audits, and review routines.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The control phase becomes especially important. If an AI agent can recommend, route, approve, escalate, summarize, or trigger actions, then the process needs visible controls. Leaders should be able to see when the system is working, when it is drifting, and when people are overriding it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Practical Examples Across Operations<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Manufacturing<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An AI assistant recommends production priorities based on demand and capacity. A strong control dashboard should also show material availability, quality release status, changeover impact, and schedule adherence. Otherwise, the recommendation may look efficient but create shop-floor instability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Pharma Operations<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An AI workflow tool flags batch documentation issues for review. The process must maintain clear human accountability, audit trails, and defined escalation paths. In regulated environments, speed is useful only when controls remain clear and traceable.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Supply Chain<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A planning agent suggests safety stock changes based on forecast movement. Leaders should monitor forecast accuracy, supplier reliability, inventory exposure, and exception approvals before allowing broad changes to planning parameters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Finance<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An automated reporting agent generates variance commentary. Finance leaders still need consistent KPI definitions, source validation, and review logic so that commentary does not explain the wrong issue with confidence.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How This Helps Business Leaders<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For business leaders, the key message is simple: AI-enabled operations need operating discipline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Algorithmic babysitting becomes a risk when automation is added without process clarity. It becomes a capability when leaders put the right governance, dashboards, and review routines in place.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This helps leaders:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reduce unnecessary manual checking by defining clear control points<\/li>\n\n\n\n<li>Identify where AI recommendations are improving performance<\/li>\n\n\n\n<li>Spot process drift before it becomes a larger operational issue<\/li>\n\n\n\n<li>Improve trust in automation through transparent metrics<\/li>\n\n\n\n<li>Keep human accountability clear in important business decisions<\/li>\n\n\n\n<li>Align AI initiatives with practical KPIs, not just technology adoption<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The goal is not to slow innovation. The goal is to prevent uncontrolled automation from creating new forms of waste.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Good Looks Like<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A mature AI-supported operational process should have a few basic features:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A clearly documented workflow<\/li>\n\n\n\n<li>Defined roles for humans and AI agents<\/li>\n\n\n\n<li>Decision thresholds for approval or escalation<\/li>\n\n\n\n<li>Reliable source data and ownership<\/li>\n\n\n\n<li>Power BI dashboards that track performance and exceptions<\/li>\n\n\n\n<li>Regular review meetings focused on facts and root causes<\/li>\n\n\n\n<li>A control plan for changes, overrides, and unusual decisions<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">When these elements are missing, teams spend time explaining the automation. When they are present, automation becomes easier to manage and improve.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How Pragy Consulting Can Help<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Pragy Business Process Consulting Services helps organizations connect operational excellence, Power BI dashboards, KPI reporting, and process improvement into practical business systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For companies adopting AI-assisted workflows, Pragy Consulting can support:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Mapping current processes before automation<\/li>\n\n\n\n<li>Identifying control points and approval thresholds<\/li>\n\n\n\n<li>Designing KPI dashboards to monitor AI-supported workflows<\/li>\n\n\n\n<li>Improving data quality for reporting and decision support<\/li>\n\n\n\n<li>Building Power BI dashboards for operations, supply chain, finance, quality, and leadership teams<\/li>\n\n\n\n<li>Applying Lean Six Sigma thinking to reduce rework, exceptions, and process variation<\/li>\n\n\n\n<li>Creating practical review routines so leaders can act on dashboard insights<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">AI may change how work gets done, but the basics still matter: clear processes, reliable data, useful KPIs, disciplined reviews, and accountable decisions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If your organization is introducing automation, AI agents, or more advanced reporting workflows, Pragy Consulting can help you build the dashboards, controls, and process discipline needed to make those systems useful in daily operations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI agents are entering Lean workflows, but operational teams are learning that automation still needs supervision, validation, and process discipline to avoid costly decisions.<\/p>\n","protected":false},"author":1,"featured_media":927,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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