Newsletter · JAVAS
This article matters because AI agents can shift work rather than remove it. IT leaders should evaluate task-level fit, review overhead, and governance handoffs before scaling AI in delivery teams, or risk higher cycle times, more false positives, and unmeasured rework.
As frontier AI research moves behind corporate walls, IT leaders face a growing dependency problem: less independent scrutiny, more vendor opacity, and a risk of overinvesting in general-purpose LLMs while overlooking specialized AI with clearer enterprise value.
GlobalFoundries’ quantum plan matters because it turns a speculative technology into a manufacturing and governance issue. For CIOs and CTOs, the key question is not just scientific progress, but when industrial reliability, vendor readiness and portfolio discipline will justify enterprise investment.
AI for scientific research matters because it can shorten discovery cycles, but only if leaders put governance, reproducibility and portfolio discipline around it. The decision is less about adopting another model and more about how much autonomy, assurance and vendor dependence the organisation is willing to accept.
As companies emerge from the COVID-19 pandemic, they are realizing that talent management has become even more urgent. Given the disruptions to so many business models, organizations are grappling with how to make hybrid teams function better , which new skills are needed, and what the workforce of the future will look like more broadly.
AI-generated living documentation matters because it can turn unknown technical estates into managed assets, while also building practical AI fluency in delivery teams. For IT leaders, the real issue is governance: how to make the outputs trustworthy, useful and tied to portfolio decisions.
Enterprise investment in AI is shifting from experimentation to implementation. Understanding the total cost of ownership (TCO) for AI projects is crucial for CTOs, highlighting the significant hidden costs associated with maintenance and compliance. This report analyzes AI spending trends, cost models, and the impact of disciplined engineering practices on budgeting for successful AI adoption and integration.
External innovation only creates value when firms can govern it across divisions. This piece matters because it shows why cross-functional decision rights, incentives and adoption capability can determine whether start-up knowledge becomes a scalable product or another stalled idea.
Since ChatGPT’s launch by OpenAI a year ago, artificial intelligence (AI) has ignited investor interest. The tool, which uses generative AI, has driven a 200% surge in Nvidia NVDA’s stock prices, while the Morningstar Global Next Generation Artificial Intelligence Index rose by roughly 68% in 2023. Both AI data-related infrastructure companies and diverse industries stand to benefit from AI adoption.
Far too often teams are getting pressure from upper management, colleagues, or external teams to just get started with their AI efforts, and projects move forward without a clear answer to the problem they are
In this podcast, LeadingAgile CEO Mike Cottmeyer and host Dave Prior candidly explore and discuss the details of the LeadingAgile Systems-first approach, including how it started, why it has become a proven and
With clear acceptance criteria and definition of done, the development teams know precisely what work must be finished by the end of the designated time period or sprint. The process of getting to a definition of
We are placing project managers into a job with no clearly defined career path and holding them accountable to get the product out the door the way that we always have, meanwhile expecting that we achieve the Agile