The evidence: adoption is ahead of scaling
McKinsey’s 2025 global AI survey reported that nearly two-thirds of respondents had not yet begun scaling AI across the enterprise, even though almost all reported AI use and 62% were at least experimenting with AI agents. The same survey found enterprise-level EBIT impact remained much less common than use-case-level benefits (McKinsey, 2025).
The World Economic Forum’s 2026 financial-services playbook reaches a similar operational conclusion: the challenge is no longer simple access to AI, but integrating strategy, governance, workforce, data and technology so deployment can scale with discipline (WEF, 2026).
Why the bottleneck moves outside the model
As model capability rises, the differentiator shifts toward the system around the model: architecture, data access, evaluation, governance, ownership, monitoring and the ability to redesign workflows. A technically impressive prototype can still fail production criteria if those capabilities are absent.
For Arvec, this is the central industrialisation distinction: a use case proves that something can work; enterprise capability proves the organisation can make it work again under real controls, real data and real accountability.
- 01Pilot
- 02Validated use case
- 03Reusable capability
- 04Production system
- 05Measured enterprise value
Arvec interpretation of the industrialisation path.
What leaders should measure instead of pilot count
Pilot volume is an activity metric. Better industrialisation measures include time from validated concept to production, percentage of components reused across initiatives, quality of evaluation evidence, operational ownership, control coverage, adoption and whether business outcomes can be measured.
These measures change investment behaviour. Funding can move from repeated one-off implementation toward common capabilities that lower the marginal cost and risk of subsequent AI use cases.
Arvec view: each project should leave capability behind
A useful test is to ask what remains after the first use case goes live. Reusable retrieval patterns, evaluation harnesses, access-control models, knowledge structures, monitoring and decision rights are assets. A bespoke workflow that cannot be reused is mostly project expenditure.
AIF7™ is designed around this logic: begin with the enterprise objective, determine the capability required, compare it with the current state, identify the constraint and invest in the few interventions that make repeatable production possible.
Key implications
- 01
Measure AI progress through production capability and value, not the number of experiments.
- 02
Require architecture, governance and operating ownership to be part of the investment case.
- 03
Prefer interventions that reduce the cost and risk of the next AI use case.
- 04
Treat scaling as an enterprise-system problem rather than a model-selection problem.
Related AIF7™ capabilities
AI Strategy & Vision
Connects AI activity to a defined enterprise objective.
AI Value Realisation
Requires outcome measures beyond technical deployment.
AI Organisation & Operating Model
Creates ownership and repeatable delivery mechanisms.
AI Engineering & Delivery
Turns validated prototypes into reusable production patterns.
Sources & further reading
01 · McKinsey 2025
The state of AI in 2025: Agents, innovation, and transformationMcKinsey & Company · 5 November 2025
02 · WEF 2026
The AI Playbook for Financial ServicesWorld Economic Forum · 24 June 2026