Turn AI from experimentation into operating leverage.
North Axiom AI redesigns work around data, models, agents and human decision rights, so AI moves from isolated tools into measurable operating systems.
Adoption is not value
AI adoption is now close to universal, but enterprise-level financial impact is rare. Our headline measure is the share of organisations attributing more than 5% of EBIT to AI. The other figures below explain why that share is so small.
Of organisations use AI in at least one function, yet only around 6% attribute more than 5% of EBIT to it.
McKinsey, The State of AI in 2025Of adopters have fundamentally redesigned any workflow, the factor most strongly linked to financial impact.
McKinsey, The State of AI in 2025Of firms report an AI-related incident, which is why control is designed in rather than added later.
McKinsey, The State of AI in 2025The objective is not more AI. It is better economics, faster decisions, lower friction, stronger control and appropriately governed autonomy.
How we approach AI
Eight principles, from economics to measurement.
Adoption is not value
AI is spreading faster than organisations are redesigning work around it.
Start with the economics of the workflow
Identify the cost, time, quality, revenue and risk drivers before selecting technology.
Build the enterprise AI layer
An AI operating model built on models, enterprise knowledge, tools, workflows, APIs, identity, observability and controls.
Give agents bounded authority
Agentic AI in the enterprise needs explicit tool access, approvals, exception paths, auditability and escalation.
Redesign work, not just tasks
Rebuild end-to-end processes rather than attaching copilots to old workflows.
Control is an architectural requirement
Evaluation, access, human review, logging, model risk and change control.
The human system determines the result
Incentives, capability building, role design and management routines.
Measure value at the business outcome
AI value realisation is measured in cost per transaction, cycle time, conversion, quality, risk, cash and EBITDA. Not prompts or token volume.
Where AI has shown results
productivity improvement in customer support, in the settings studied.
Stanford HAI, AI Index 2026in software development.
Stanford HAI, AI Index 2026in marketing output.
Stanford HAI, AI Index 2026Effects vary by task. These are research findings in specific settings, not a promise of results.
Fill in: check each figure against its source before launch. These came from the ChatGPT research document and have not been verified
Rising investment, rising risk
increase in private AI investment in 2025, as global corporate AI investment more than doubled.
Stanford HAI, AI Index 2026of organisations reported at least one negative consequence from generative AI.
McKinsey, The state of AIThis is why control is designed in from the start, not added later.
Fill in: check each figure against its source before launch. These came from the ChatGPT research document and have not been verified
From adoption to value
Where AI value is created, and where it is lost.
More planned visuals
Plots value potential, feasibility, control complexity and effort.
Shows tool access, approvals and escalation for each agent.
Links each AI use case to the business measure it moves.
From AI ambition to operating value
Begin with a single workflow and a measurable outcome.
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AI-generated and AI-assisted outputs require controls and appropriate human review. Accuracy, completeness and suitability are not guaranteed.