Resource index
All resources.
Search the full chronological index, then narrow the list by topic or category.
Resource index
Search the full chronological index, then narrow the list by topic or category.
Methodology
Why professional firms need repeated review, testing, and practice change more than one-off AI rollouts.
Concepts
Why professional context is not just stored data, and why AI needs more than searchable records to support serious work.
Methodology
Why AI risk in professional work often comes from access, permissions, memory, tools, and review paths more than from the model alone.
Concepts
Why professional trust depends on whether a firm can remember what it said it would do.
Concepts
Why buying AI tools is not the same as changing how a firm captures, checks, hands over, and remembers work.
Methodology
A practical argument for designing AI and professional work interfaces as operating boundaries and worldviews: connected enough to share context, separate enough to preserve role, authority, focus, and review.
Concepts
Why review packets are one of the most useful AI artifacts for professional teams working across scattered sources, owners, and decisions.
Applications
How AI can compress the messy first pass of professional research while keeping verification, judgment, and final decisions with people.
Methodology
A practical scoring guide for professional teams choosing the first AI workflow to pilot safely and usefully.
Methodology
A practical way to measure AI value in professional work through faster preparation, better review, clearer evidence, and fewer dropped commitments.
Methodology
What a practical Proximity deployment looks like in the first month: workflow mapping, source review, first review packet, feedback, and operating rhythm.
Methodology
Why teams should build AI trust by using agents for internal preparation before delegating forward-facing work.
Methodology
Why serious AI adoption starts by making work visible, reviewable, and accountable before asking agents to act.
Methodology
A practical guide to AI handoffs that preserve sources, assumptions, missing context, ownership, next steps, and approval boundaries.
Concepts
Why professional AI should improve review quality by preparing evidence, options, and gaps instead of hiding judgment behind automated conclusions.
Concepts
Why chat-based AI is useful for individual tasks, but professional workflows also need live work state, source grounding, permissions, ownership, and review paths.
Concepts
Why professional services AI is hard: expert work depends on context, evidence, exceptions, relationships, risk, and accountability, not only repeatable tasks.
Applications
An example Proximity system for a 12-person disputes team that prepares weekly matter reviews without automating legal judgment or advice.
Applications
An example Proximity system for a 15-person finance and procurement team preparing vendor renewal reviews without automating spend judgment.
Concepts
Why grounded AI and RAG matter for professional work: source grounding shows evidence, freshness, missing context, provenance, and trust boundaries.
Concepts
A practical framework for useful AI in professional services: better judgment, preparation, coordination, source review, and follow-through.
Concepts
A practical definition of organisational context for AI: roles, history, priorities, obligations, evidence, relationships, standards, and timing.
Concepts
A practical definition of operating intelligence, operational context, digital twins for business, and why agentic AI needs legible organisations.
Concepts
A plain-English guide to human-in-the-loop AI, human oversight, review boundaries, and how accountable teams keep judgment over risky decisions.
Methodology
Why AI transformation is mostly operating work: mapping workflows, sources, permissions, ownership, review points, and automation boundaries.
Methodology
A practical critique of the belief that AI should automate everything, and a decision rule for building governed systems that know what to automate, prepare, escalate, or leave human.
Methodology
Automation vs delegation in AI systems: why fixed workflow execution differs from giving agents responsibility for outcomes.
Methodology
A practical explanation of how Saas, MSPs, agencies, consultants, platforms, and forward-deployed engineering differ when companies modernise or enable the organisation with AI.
Methodology
An AI readiness checklist for deciding when agents or automation can act: context, source coverage, authority, confidence, reversibility, and review.
Industries
An example Proximity system for a consulting team onboarding analysts onto live client projects without automating professional judgment.
Industries
An example Proximity system for a specialist advisory team preparing sensitive client follow-up without automating care, advice, or professional responsibility.
Industries
An example Proximity system for a 20-person architecture and project team coordinating drawings, RFIs, site notes, and client decisions without automating professional judgment.
Concepts
An example Proximity system for a partner-led advisory practice tracking commitments, context, and follow-up without automating relationship judgment.
Concepts
A practical guide to agentic behaviour: how systems progress from chatbots to workflows, tool use, and goal-directed systems that need context, feedback, boundaries, and review.