What Ran Aroussi talks about

AI at the Organisational Level: How AI can move work forward across a business by aggregating proposals, progressing tasks, nudging people and drafting without constant prompting.

Moves the conversation beyond ChatGPT and individual productivity toward AI operating at the organisational level. Ran can explain his vision of businesses where work naturally moves forward…

AgentOps and the Four Platform Shifts Before It: what operating systems, the web, cloud and DevOps predict about how AI agents get run

Ran Aroussi has watched the same shift land four times in 30 years, from ad engines serving 3 billion ads a day to yfinance, now installed more than 30 million times a month. Operating systems, the…

Shaping AI Around the Organisation: Why AI initiatives in mid-market businesses fail when they ignore how people work, and why the technology should adapt to the organisation.

Challenges the conventional approach to AI transformation: buying new tools, rolling them out and expecting employees to adapt. Ran argues the opposite - AI should be moulded to the organisation.…

Where the Value Lands in the AI Agent Stack: how memory, orchestration, and observability decide who captures the returns from agent adoption.

Ran Aroussi has spent 30 years building the parts of software nobody sees, from ad engines serving 3 billion ads a day to yfinance, downloaded more than 30 million times a month. Most of the…

What Agent Infrastructure Has to Do Before Production: Memory, orchestration, audit trails, and the failure handling that decides whether agents hold up in real use

Ran Aroussi has built the layers other people's software runs on for 30 years, from ad engines serving 3 billion ads a day to yfinance, now installed more than 30 million times a month. Most teams…

Organisational Memory for AI: Building AI that understands a company's knowledge, context, decisions and ways of working as institutional memory.

This introduces Ran’s work on organisational-level memory and the idea that AI needs to understand a company, not simply remember individual conversations. Ran can explore the breakthrough behind…

Background AI That Fits How People Already Work: The concept of resident AI that moves tasks forward, drafts documents and connects information without new tools to learn.

Explores Ran’s concept of “resident AI” and why the most effective AI may be the AI employees barely notice. Rather than asking teams to learn another tool, change their workflows or constantly…

Multi-Agent Coordination in Production: shared memory, task ownership and audit trails that stop agents duplicating each other's work

Ran Aroussi builds the coordination layer that decides whether two agents split a job or do it twice. Thirty years of systems work sits behind that view, including ad engines serving 3 billion ads a…