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Explore the ideas, decisions and engineering behind a more capable business.
Strategy
AI changes what’s worth building
Build what differentiates. Buy what earns its place. Connect what works.
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From useful work to lasting capability
Turn a successful first solution into something your team can rely on and improve.
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When useful tools need to work together
Recognize when separate AI solutions need shared context, coordination and control.
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Trace where your AI workflow processes and stores information
Map an AI workflow’s sources, processing routes, saved results and operational copies. Check the actual configuration against agreed handling requirements.
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Choose a first AI workflow worth testing
Compare specific business tasks, observe the current work and define a test with a clear result, review effort and operating owner.
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Hand work between agents with an explicit contract
Define agent handoff tasks, sources, permissions and completion evidence. Enforce authority, handle uncertain outcomes, and test failures before expanding.
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Start with a common business vocabulary
Agree on business definitions, owners, source mappings and change rules. Add formal relationships when a use case needs them, and test how the terms are used.
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Make an executive briefing useful for a decision
Write an executive briefing around a specific choice. Compare evidence, costs, options and unresolved questions, then record the decision and follow-up.
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Build a small evaluation set before changing models
Compare models against real business tasks. Define expected results, check failures, record uncertainty, and plan a limited release before switching.
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Evaluate an AI assistant for security investigations
Evaluate an AI investigation assistant with source-linked evidence, incomplete cases and analyst review. Check access, correction effort and operational fit.
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Evaluate an AI platform before buying it
Test an AI platform against a business task before buying it. Compare usable results, integration, permissions, review effort and total operating cost.
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Plan recovery for AI workflows across business systems
Plan AI workflow recovery around saved progress, uncertain results, delayed approvals and completed actions. Test interruptions before expanding use.
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Design an audit trail for AI agent actions
Design AI agent audit records around requests, authorization, execution attempts and verified outcomes. Protect the records and test an investigation.
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Decide what an AI agent may read and change
Define an AI workflow’s sources, users and permitted actions. Check access before retrieval, trace later copies and test the cases that challenge the boundary.
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How to evaluate AI vendor dependence
Understand what an AI workflow depends on, test a realistic provider change and compare the technical, commercial and support work involved.
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Turn reusable scripts into reliable operational tools
Make reusable scripts callable with clear contracts, resource permissions, retry rules and recovery. Test uncertain outcomes, stale records and partial ...
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When a shared foundation for business AI is worth the work
Compare shared AI services with focused integrations. Examine repeated work, operating ownership, cost, failure handling and the ability to change direction.
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Harness engineering: how to build dependable AI agents
Learn what an AI agent harness controls, how tools and permissions fit together, and how to test a practical support assistant before expanding access.
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How to choose AI training for business leaders
Choose AI training around leadership decisions: investment, suppliers, pilot scope, operating responsibility and evidence of results.
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AI change management for business
Plan AI change around the work people do. Define responsibilities, train on real tasks, test a focused rollout and measure results before expanding.
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When to use AI agents
Choose between rules, fixed AI workflows and agents by comparing the work, cost, data access and review required.
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Turn the first useful workflow into lasting capability
Reuse connections, evaluation cases and operating controls to turn a first AI workflow into dependable business capability.
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When useful AI tools need to work together
Connect CRM, delivery and finance with the context, permissions and oversight needed for cross-system work.
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AI changes what is worth building
Compare build, buy and extend options using workflow fit, operating costs, permissions and long-term ownership.
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Forward-deployed engineering: turn a business problem into working software
How an engineer works alongside your team to turn a business problem into software you can operate.
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