Enterprise AI budgets are growing about 50% a year, but the share of companies reporting any earnings impact has barely moved. In Part 1 of our six-part series, MILL5 explains where AI value leaks between the model and the P&L, and what leading companies do to close the gap.Read More

Even efficient manufacturing plants have room to improve. Here’s how manufacturing leaders can decide which AI use cases deserve investment and turn early results into improvements that last.Read More

A medtech AI model can achieve high test accuracy by recognizing a scanner’s image characteristics instead of the anatomy or abnormality it was trained to identify. When images come from a different scanner, accuracy can drop because the model relied on equipment-specific patterns that its test data did not challenge. Read More

Millie Summary:  AI slop is a warning sign that AI is scaling faster than an organization can manage it, allowing unreliable information to spread across systems and decisions.  Changes to prompts, data, retrieval, and business conditions can gradually reduce performance after deployment.  Keeping AI reliable requires trusted data, continuous evaluation, clear monitoring, and people who...Read More

Millie Summary: AI is reshaping the economics of pharmaceutical R&D by improving scientific decision-making, helping researchers evaluate more possibilities, eliminate weaker candidates earlier, and concentrate expensive laboratory and clinical resources on the opportunities supported by the strongest evidence. For medical-aesthetics manufacturers, the larger opportunity is increasing scientific throughput across the entire product lifecycle, from discovery...Read More

Millie Summary: Quantum readiness is a data problem before it’s a hardware problem, which means trusted enterprise data and interoperable architectures have to coalesce before the qubits do. AI is becoming the orchestration layer for enterprise computing, with quantum serving as a specialized accelerator for the combinatorial optimization and simulation work that classical architectures handle...Read More

Millie Summary: AI is entering a new economic phase where the key question is no longer ‘What can AI do?’, but ‘What does AI cost per business outcome?’. The rise of agentic AI creates a cost paradox: smarter, multi-step AI workflows can become more expensive even as token prices fall. The organizations that win will...Read More

Millie Summary: Healthcare organizations are moving AI from pilots into enterprise use, but roughly 80% of these AI initiatives stall before production due to a lack of governance, security, and the data foundations needed to scale. Strong data governance is what lets healthcare teams move quickly, since clear policies, lineage, and access controls allow them...Read More

Millie Summary Strategy: AI has made idea generation abundant; the real advantage is identifying and framing the right business problem before investing in solutions.   Build: Once the right problem is defined, value comes from engineering secure, scalable, production-ready AI, data, cloud, and application solutions that fit real workflows.   Operate: Long-term AI impact depends on monitoring performance, adoption, cost, risk, and...Read More

Why The Next Wave Of Enterprise AI Will Be Won Below The Surface — In Data, Cloud, Security, Integration, And Operating Discipline For much of the generative AI cycle, the executive conversation was framed around applications. Which copilots should we deploy? Which model should we use? Which teams can automate content, code, support, analysis, or...Read More