Custom AI visualization and analytics dashboards to leading pharma consulting company.
The Hidden Cost of Decision Latency
When Attrition Goes Unseen: How a US Manufacturing Major Used Turinton’s Insights AI to Prevent Hidden Downtime
AI in US Manufacturing: Ambition vs. Reality
AI in US Manufacturing: Ambition vs. Reality Across the US, manufacturers are investing heavily in AI to improve efficiency, quality, and profitability. But while the ambition is strong, the reality often lags behind. Many firms test AI but can’t scale it. Others face poor data foundations, legacy systems that don’t integrate well, or lack the […]
The OEE Mirage: Why Good Numbers Hide Bad Performance
When the numbers look good, but the business still bleeds Manufacturers have lived and died by OEE (Overall Equipment Effectiveness) for decades. It’s the single number that plant managers, operations leaders, and executives alike use to measure availability, performance, and quality. Hit 85%, and you’re “world class.” Dashboards glow green. Quarterly reports point to progress. […]
The Changeover Cost Spiral: How AI Can Turn Setup Time into Competitive Advantage
When minutes cost millions In manufacturing, minutes aren’t trivial—they compound into millions. A line that sits idle during setup isn’t producing, and each delay ripples across orders, labor, and customer promises. Changeovers—switching machines from one product to another—are unavoidable, but the way most plants manage them creates a changeover cost spiral: downtime cuts output, lost […]
The Quality Debt Crisis: How Slow Drift Costs More Than Scrap
Executive summary Most manufacturers obsess over visible scrap and rework. What often gets missed is the slow drift — those small deviations in process, workforce performance, or supplier quality that accumulate quietly until they create large financial and operational debt. Scrap is immediate; drift is invisible. And as we see across plants, drift often costs […]
AI Agent Ecosystems: The Future of Continuous, Contextual Enterprise Intelligence
Introduction: Beyond Dashboards and Models Enterprises don’t struggle because they lack data. They struggle because the data they have doesn’t turn into intelligence quickly enough. Multiple studies show roughly half of enterprise data never informs a decision, and in many large firms the unused share is even higher. Even when data does move, it often “supports” […]
Why the Next AI Wave Is Platform-Led, Not Model-Led
The hype cycle is shifting The AI conversation has been dominated by models—bigger benchmarks, higher accuracy, faster inference. But here’s the problem: the half-life of a “state-of-the-art” model is shrinking. Industry data shows the frontier lifespan of leading AI models is now measured in a few years—often two to four—before something new pushes them aside. […]
The Economics of AI Waste: Quantifying the Cost of Unused Models
The Unseen Cost of AI Waste For every AI project that becomes a business success story, there are others quietly shelved in shared drives and Git repositories—never deployed, never delivering value. CFOs know this pattern well in other investments, but AI waste has its own flavor: large upfront costs, high expectations, and almost no residual […]