The pace of AI capability development is outrunning the governance frameworks that enterprise boards have in place to manage technology risk. This is the governance framework that can keep pace.
Enterprise IT organisations that invest only in AI technology without redesigning the processes and governance frameworks around AI-augmented work will find that the technology delivers individual productivity gains that never aggregate into organisational improvement.
Two forces are converging on enterprise technology strategy heading into 2023: the post-ChatGPT AI acceleration and the cloud cost and complexity reckoning. The intersection of these forces defines the strategic decisions that matter most.
Security investment decisions are made in a measurement vacuum in most enterprises. The cost of a breach is estimated, the cost of prevention is known, but the relationship between specific security investments and specific risk reductions is rarely quantified with enough rigour to drive board-level decisions.
ChatGPT’s launch has made the capabilities and limitations of large language models viscerally real to executive leaders. The question is no longer whether AI will change enterprise operations. It’s how quickly and on what terms.
The relationship between security and development teams is a structural trust problem with a long history. Most DevSecOps programmes fail to resolve it because they focus on tooling integration rather than relationship redesign.
Enterprise cloud security programmes have accumulated point tools at a rate that now creates more risk than it reduces. The case for consolidation is not cost reduction. It is security effectiveness.
Log4Shell made software supply chain security a board-level topic. A year later, most enterprise DevSecOps programmes have added tooling to their pipeline but haven’t addressed the deeper process and governance gaps that make supply chain security genuinely effective.
Operating distributed systems at enterprise scale requires a fundamentally different approach to application health than traditional infrastructure monitoring provides. Kubernetes changes the operational model in ways that expose the gaps in most enterprise observability programmes.
Cloud complexity has a cost that rarely appears on the cloud bill: the engineering time consumed managing complexity rather than delivering features, the security incidents caused by configuration gaps, and the operational overhead accumulating in every platform team.