AI & ML Strategy
Enterprise AI adoption, LLM deployment, and machine learning strategy.
13 articles
The data flywheel: how compounding data advantage actually works
The data flywheel is one of the most cited concepts in AI strategy and one of the least understood in practice. This article breaks down the actual mechanics so that CDOs can assess whether their organization is genuinely building one or just accumulating data.
Aug 8, 2026Feature stores and the ML data supply chain: what CDOs actually need to understand
Feature stores solve a problem that most organisations discover too late: the painful gap between raw data and production-ready ML inputs. Understanding the mechanics and honest tradeoffs is essential before committing to one.
Aug 5, 2026Model monitoring, drift detection, and retraining triggers: a CDO playbook
Production ML models degrade silently, and most organizations only notice when business outcomes have already suffered. This playbook gives CDOs a concrete sequence for detecting drift early, deciding when to retrain, and building the governance structure that makes both systematic.
Aug 2, 2026Feature stores and the ML data supply chain: what CDOs actually need to understand
Feature stores solve one of the most expensive and least visible problems in enterprise ML: the repeated, inconsistent transformation of raw data into model-ready inputs. Understanding how they work, and when they are worth the investment, is now core CDO territory.
Jul 31, 2026How Uber built its ML data supply chain: lessons from the Michelangelo feature store
Uber's Michelangelo platform forced the company to confront a problem most ML teams hit eventually: the same features being rebuilt repeatedly by different teams, with no shared infrastructure underneath. The decisions Uber made in 2017 and 2018 still define how serious organisations think about feature stores today.
Jul 29, 2026Feature stores and the ML data supply chain: a CDO's execution playbook
Most ML projects stall not because of model quality but because data preparation is reinvented from scratch every time. This playbook gives CDOs a concrete sequence for building a feature store and treating ML data as a managed supply chain.
Jul 23, 2026Feature stores: the missing infrastructure layer in your ML data supply chain
Most ML failures are not model failures. They are data supply chain failures, and feature stores are the architectural response that serious ML organizations have adopted to fix them.
Jul 21, 2026When the AI model is wrong: what CDOs must own in 2026
Most AI failures in production are not model failures. They are governance failures, and CDOs who treat the two as interchangeable are building on unstable ground.
Jul 14, 2026When AI strategy becomes infrastructure: what CDOs must own in 2026
Most organizations have moved past the question of whether to invest in AI. The real pressure on CDOs now is deciding which decisions to own, which to delegate, and how to build the underlying data infrastructure that makes AI something other than a series of expensive experiments.
Jul 7, 2026When your AI strategy outpaces your data infrastructure: what CDOs must fix first
Many organizations are deploying AI models on top of data foundations that were never designed to support them. The performance gap this creates is not a technical footnote, it shapes whether enterprise AI delivers any measurable return at all.
Jun 30, 2026When AI agents go rogue: what CDOs must do now to maintain control
AI agents are no longer a future concept, they are making decisions inside enterprise systems today, often faster than any governance framework can track. CDOs who fail to architect control mechanisms before deployment will find themselves managing consequences, not outcomes.
Jun 23, 2026Why most AI strategies fail before they start: the CDO's structural blind spot
Most organizations invest heavily in AI tooling while systematically underinvesting in the data foundations that make those tools work. For CDOs, closing this gap is not a technical problem, it is a governance and organizational design challenge that demands a fundamentally different approach.
Jun 16, 2026Why most AI strategies fail before they start: the data foundation problem CDOs can't ignore
Organizations are pouring billions into AI and machine learning initiatives, yet Gartner estimates that 85% of AI projects never make it to production. The root cause is rarely the algorithm, it's the data strategy underneath it.