How do you move from individual implementations to a repeatable organizational capability?
The first AI solutions may already be running in production, yet every new initiative still starts from scratch. Teams repeatedly redefine roles, technologies, data principles, risk controls, and how value will be measured.
The program structures how the AI portfolio is managed. We assess current implementations and the organization’s ability to scale, then design the target operating model, shared standards, and a governance model for subsequent waves of initiatives.
The goal is not another project, but a lasting organizational capability to launch subsequent AI solutions faster, more cost-effectively, and more predictably.
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Who is AI Scale for?
For organizations with their first production AI implementations, several initiatives running in parallel, multiple platforms or vendors, or plans to build an AI Center of Excellence or AI Factory. -
When should you structure AI scaling across the organization?
When every new initiative begins by redefining roles, technologies, data and risk principles, while pilots move into production too slowly. -
What does AI Scale deliver?
The program creates a repeatable model for launching initiatives, shared standards, an approach to portfolio management, and a value measurement system.
How can we help?
We build a model in which subsequent AI initiatives use shared principles, components, decision frameworks, and methods for measuring value.
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Assessment of current implementations and scaling capability
We identify barriers related to initiative delivery, roles, data, technology, risk, and capabilities.
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Target AI operating model
We design roles, accountability, decision rights, and collaboration between business and technology teams
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Shared standards
We establish data, technology, and risk principles that can be applied across multiple initiatives.
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Portfolio and wave management
We create a model for prioritization, funding, and investment decisions.
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Adoption and capabilities
We develop the ways of working and skills needed to implement solutions repeatedly
How the program works
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Assess
We assess current implementations and barriers to scaling.
Decision question: What is limiting the pace of subsequent initiatives?
Stage outcome: Scaling capability assessment.
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Operating model
We design roles and decision rights.
Decision question: Who is accountable for decisions and value?
Stage outcome: Target operating model.
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Standards
We establish principles for data, technology, and risk.
Decision question: Which principles should be shared?
Stage outcome: Shared standards.
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Portfolio management, adoption and capabilities
We create a prioritization and funding model.
Decision question: How should subsequent initiatives be launched?
Stage outcome: Portfolio model.
We develop the teams’ ways of working.
Decision question: How should teams be prepared?
Stage outcome: Capability model.
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Value measurement and roadmap
We introduce outcome metrics.
Decision question: How should portfolio value be measured?
Stage outcome: Measurement system.
We create a plan for subsequent waves.
Decision question: How should the portfolio be developed in a controlled sequence
Stage outcome: Roadmap.
We assess current implementations and barriers to scaling.
Decision question: What is limiting the pace of subsequent initiatives?
Stage outcome: Scaling capability assessment.
The deliverables provide a foundation for repeatedly delivering subsequent AI initiatives and managing the value of the entire portfolio.
What does the client receive?
- Scaling capability assessment: A diagnosis of the barriers limiting the pace of subsequent initiatives
- Standards and portfolio model: Shared principles for data, technology, and risk.
- Target AI operating model: Roles, accountability, decision-making, and the approach to delivering initiatives.
- Measurement system and roadmap: Business value metrics and a plan for subsequent waves.
From multiple projects to one organizational capability
Scaling AI is not simply about increasing the number of pilots. Subsequent initiatives should use a shared approach to decision-making, delivery, data and technology management, risk control, and business outcome measurement.
Why Grant Thornton?
Building a repeatable capability to scale AI requires combining value and portfolio management with the design of the operating model, standards, architecture, and team ways of working.
Grant Thornton
The Grant Thornton team supports the design of the portfolio management model, accountability, decision rights, investment criteria, and value measurement system.
Digital team | EDISONDA
Grant Thornton’s Digital experts translate the scaling model into shared principles for delivering initiatives, addressing user needs, and managing processes, data, technology, and capabilities.
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30 years+
operating in Poland
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1200+
employees across 7 offices
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2400+
satisfied Grant Thornton clients each year
