Methodology
AI adoption fails when it becomes a launch event
Why professional firms need repeated review, testing, and practice change more than one-off AI rollouts.
Resources for building operating intelligence.
Practical notes on orchestration, ontology, review cadence, spend context, and practice-specific operating systems.
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Concepts
Context is not hiding in your database
Why professional context is not just stored data, and why AI needs more than searchable records to support serious work.
Methodology
The model is not the risky part
Why AI risk in professional work often comes from access, permissions, memory, tools, and review paths more than from the model alone.
Concepts
Most organisations forget their own promises
Why professional trust depends on whether a firm can remember what it said it would do.
Concepts
A tool helps you work. A system changes how work happens.
Why buying AI tools is not the same as changing how a firm captures, checks, hands over, and remembers work.
Methodology
Why Interfaces Need Operating Boundaries
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.
AI readiness and governance
How teams decide whether AI can answer, prepare, recommend, escalate, or act with accountability.
Methodology
The model is not the risky part
Why AI risk in professional work often comes from access, permissions, memory, tools, and review paths more than from the model alone.
Methodology
The First Draft
Why teams should build AI trust by using agents for internal preparation before delegating forward-facing work.
Methodology
What a good AI handoff looks like
A practical guide to AI handoffs that preserve sources, assumptions, missing context, ownership, next steps, and approval boundaries.
Concepts
Why AI should prepare the review, not replace it
Why professional AI should improve review quality by preparing evidence, options, and gaps instead of hiding judgment behind automated conclusions.
Concepts
Why ChatGPT is useful but not enough for professional work
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 work is hard to automate
Why professional services AI is hard: expert work depends on context, evidence, exceptions, relationships, risk, and accountability, not only repeatable tasks.
Operating intelligence and organisational context
The operating model behind useful AI: context, digital twins, shared memory, sources, cadence, and tools.
Methodology
Why Interfaces Need Operating Boundaries
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
Context is not hiding in your database
Why professional context is not just stored data, and why AI needs more than searchable records to support serious work.
Concepts
A tool helps you work. A system changes how work happens.
Why buying AI tools is not the same as changing how a firm captures, checks, hands over, and remembers work.
Concepts
Most organisations forget their own promises
Why professional trust depends on whether a firm can remember what it said it would do.
Concepts
From scattered context to a review packet
Why review packets are one of the most useful AI artifacts for professional teams working across scattered sources, owners, and decisions.
Methodology
The first 30 days of a Proximity deployment
What a practical Proximity deployment looks like in the first month: workflow mapping, source review, first review packet, feedback, and operating rhythm.
Automation vs human judgment
Where AI should automate, prepare, delegate, escalate, or leave responsibility with people.
Methodology
AI adoption fails when it becomes a launch event
Why professional firms need repeated review, testing, and practice change more than one-off AI rollouts.
Methodology
How to choose the first AI workflow
A practical scoring guide for professional teams choosing the first AI workflow to pilot safely and usefully.
Concepts
Why AI should prepare the review, not replace it
Why professional AI should improve review quality by preparing evidence, options, and gaps instead of hiding judgment behind automated conclusions.
Concepts
Why ChatGPT is useful but not enough for professional work
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 work is hard to automate
Why professional services AI is hard: expert work depends on context, evidence, exceptions, relationships, risk, and accountability, not only repeatable tasks.
Concepts
What makes AI useful in professional work
A practical framework for useful AI in professional services: better judgment, preparation, coordination, source review, and follow-through.
Industry operating context
Example walkthroughs of Proximity systems for specific professional workflows, team sizes, and project contexts.
Applications
Getting to 80 percent: how AI changes research work
How AI can compress the messy first pass of professional research while keeping verification, judgment, and final decisions with people.
Methodology
How to measure AI value without pretending it replaces people
A practical way to measure AI value in professional work through faster preparation, better review, clearer evidence, and fewer dropped commitments.
Applications
A matter review system for a disputes team
An example Proximity system for a 12-person disputes team that prepares weekly matter reviews without automating legal judgment or advice.
Applications
A renewal control system for finance and procurement
An example Proximity system for a 15-person finance and procurement team preparing vendor renewal reviews without automating spend judgment.
Concepts
Why AI needs source grounding
Why grounded AI and RAG matter for professional work: source grounding shows evidence, freshness, missing context, provenance, and trust boundaries.
Concepts
What is organisational context?
A practical definition of organisational context for AI: roles, history, priorities, obligations, evidence, relationships, standards, and timing.
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