A walk-through of the design principles for connecting work-executing AI agents to your CRM, SFA and MA. We set out the shared groundwork: leads, deals, email campaigns, tidy data, sync errors and permission design.
For anyone wrestling with where to draw the line between an AI agent's automatic execution and human approval, this article explains how to design an approval flow using risk grades, thresholds, approver roles and guardrails.
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 corporate planning teams, DX leaders and departmental managers, this piece sets out how to design a monthly AI review meeting that turns departmental experiments into company-wide investment decisions — the agenda, dashboard fields, participant roles, risk register, how to present operational AI in management reports, and how to stop the meeting becoming a formality, with templates you can reuse.
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.
