AI Strategy and Governance
Generative AI: Cultural Fluency, Diversity & Ethics
By Nacho De Marco7 min readAI and Machine Learning
AI Strategy and Governance
Practical guidance on AI governance for engineering leaders who need to scale AI adoption without losing control of risk, cost or accountability.
All 64 articles
Newest first · Page 5 of 8
By Nacho De Marco7 min read
AI Strategy and Governance
AI Energy Consumption: Concerns and Solutions
By Agustín Achille9 min read
AI Strategy and Governance
Adversarial AI: Challenges and Solutions
By Monica Dodla12 min read
AI Strategy and Governance
Yes, You Can Trust AI. Here Is Why
By Dustin Dolatowski15 min read
AI Strategy and Governance
7 Types of AI: A Comprehensive Overview
By Federico Schwarzbock9 min read
AI Strategy and Governance
Not Adopting AI? You Might Be Hindering Your Productivity
By Justice Erolin15 min read
AI Strategy and Governance
9 Critical Skills Tech Workers Need in the Emerging AI Era
By Natalia Rodriguez13 min read
AI Strategy and Governance
Is AGI Even Possible? What Sci-Fi Tells Us About AI
By Pablo Chamorro15 min read
AI Strategy and Governance
AI Developments to Watch for in the Next 10 Years
By Dustin Dolatowski13 min readStart here
Scaling AI in the Enterprise: A Five-Dimension Operating Model
The Specification Architect: Translating Organizational Judgment Into AI Systems
The Autonomy Gap: Why AI Stalls Between Pilot and Production
What AI is not Replacing: Durable Competencies and Technical Training in the Era of AI-Assisted Development
4 in 5 Tech Leaders Feel Pressure to Overstate AI Progress
AI Oversight: 4 Failure Modes That Are Quietly Undermining Your AI SystemsAbout this topic
Most organisations rolling out AI are learning governance on the job, often after a model has already shipped. This hub collects the thinking behind that work: how to weigh everyday benefits of AI against the risks that keep boards awake at night, and where ethical questions in AI development actually bite during a build. We also track the practical anxieties engineering teams raise directly, including workforce impact of automation and what it means for hiring and role design over the next few years.
None of this is abstract. Every model in production needs an owner, an audit trail and a policy for what happens when it fails. That is the core of ai governance: turning good intentions into decisions someone can defend. It also connects directly to digital transformation strategy, since governance choices made early tend to determine how fast an organisation can adopt new AI capability later without rework or exposure. Teams that treat governance as an afterthought usually pay for it in delayed rollouts or retrofitted controls.
If your organisation is past the reading stage and into planning execution, our AI transformation services team works with engineering leaders to build governance into the delivery pipeline rather than bolting it on afterward. For teams further along, we also run enterprise AI adoption services engagements that pair governance frameworks with hands-on delivery. For related technical ground, see how AI gets applied by function in AI Applications by Function, how generative models are governed differently in Generative AI and LLMs, and the modelling foundations in Machine Learning and Data Science.