For companies that want to spread AI-agent adoption beyond a handful of willing volunteers, this article explains how to build an internal AI certification scheme. It works through role definitions, curriculum, practical examinations, incentives and the update cycle.
When an AI agent behaves unexpectedly, where should a human step in, and how do you carry that through to root-cause analysis, correction, and prevention of recurrence? This piece sets out a common way of thinking, spanning fallback, log analysis, reproduction testing, and the post-mortem.
For companies pressing ahead with AI agents while their work remains person-dependent, this article explains how to design standard operating procedures, exception handling, branching and escalation.
Aimed at HR leaders and executives, this piece sets out the skills employees are asked for in the age of AI agents. It marshals the thinking behind reskilling that develops AI-capable people — business-design skill, supervisory skill, critical thinking, dialogue design and more.
For CS leaders and supervisors looking to drive customer support reform, this article explains first responses, channel design, knowledge management, quality control and escalation design built on the assumption of CS AI.
For DX leads and cross-functional managers who struggle to spread AI success stories beyond a single team, this piece sets out case-study formats, internal study sessions, sharing communities and a Kanata-based knowledge-sharing system.
A practical look at how to run prompt operations, knowledge improvement and workflow improvement as a proper AI operating model rather than leaving them to individuals. It sets out roles, review meetings, improvement KPIs, guidelines and how to think about tool selection.
For DX leads and IT teams who are collecting AI usage logs but not yet putting them to work for improvement, this article explains how to analyse prompt logs and user behaviour and translate them into concrete improvement measures.
For teams where reviews of AI-generated text and deliverables vary from person to person, this article explains how to build AI review criteria, an evaluation rubric, and a hallucination check.
We set out why AI PoCs stall at the trial stage and lay out how to run an implementation project that carries through to production, operational design, governance and building AI capability in-house.
For companies running several SaaS tools internally, this article explains the fundamental design of business automation through AI SaaS integration. It works through iPaaS, API integration, webhooks, OAuth, access control and audit logs.
An explanation, for HR leaders and executives, of the skills employees need in the age of AI agents. It sets out the thinking behind reskilling that develops capable AI talent—business design, supervision, critical thinking, dialogue design and more.
