According to a recent LinkedIn post from Method, the company is drawing attention to the operational gap between machine-learning experimentation and deployment, citing an estimate that 80% of ML models built in notebooks never reach production. The post attributes this not primarily to technology, but to issues such as unclear ownership, underinvestment in data engineering, and weak data foundations.
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The post highlights a new episode of Method’s “Build What’s Next” series, featuring Theo Munoz, Miguel Ribeiro, and Natan Szczepaniak discussing how to scale AI safely as adoption accelerates across industries. For investors, this emphasis suggests Method is positioning itself around governance, infrastructure, and process maturity in AI deployment, areas likely to command sustained enterprise spending as companies seek to translate AI proofs of concept into production-grade systems.
If Method’s offerings or advisory work align with solving these bottlenecks, the focus on data engineering and safe scale-up could support demand from larger, more complex customers with recurring needs. The thought-leadership format also signals an effort to build brand authority in AI operations and risk management, which may enhance the firm’s competitive profile and support longer-term growth prospects in the enterprise AI implementation market.