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AI at Scale™
Moving from Successful Experiments to Sustainable Enterprise Adoption
Download Course BrochureFlexible delivery options to suit your leadership team and organisational needs.
Duration
5 Days
Certificate
LOCEPX
Level
Executive
Format
In-Person
Delivery Mode
The course combines executive facilitation, AI scaling frameworks, contemporary business cases, structured discussion and practical organisational application. Participants will examine the strategic and organisational barriers that emerge as AI moves beyond experimentation. Cross-functional perspectives will be used to explore dependencies across people, process, data, technology and governance. The methodology remains non-technical and focuses on the leadership decisions required to scale AI responsibly and successfully.
Online
Date: 16th – 20th Nov 2026
Location: Virtual
Fee: £3,495
Online
Date: 23rd – 27th Nov 2026
Location: Virtual
Fee: £3,495
Online
Date: 30th – 4th Nov 2026
Location: Virtual
Fee: £3,495
In Person
Date: 30th – 4th Nov 2026
Location: London
Fee: £4,495
In Person
Date: 2nd – 6th Nov 2026
Location: London
Fee: £4,495
In Person
Date: 9th – 13th Nov 2026
Location: Paris
Fee: £4,495
In Person
Date: 16th – 20th Nov 2026
Location: Dubai
Fee: £4,495
Online
Date: 14th – 18th Dec 2026
Location: Virtual
Fee: £3,495
Online
Date: 21st – 25th Dec 2026
Location: Virtual
Fee: £3,495
Online
Date: 28th – 1st Dec 2026
Location: Virtual
Fee: £3,495
Introduction
This LOCEPX Training course equips leaders to move artificial intelligence beyond isolated pilots and experiments into scalable, sustainable enterprise capability. This LOCEPX Training course enables participants to understand why promising AI initiatives often struggle to progress beyond proof of concept and how leadership, governance, operating models, workforce capability and value discipline must come together to achieve successful scale.
Piloting AI and scaling AI are fundamentally different organisational challenges. An initiative that succeeds within one team or controlled environment may encounter significant barriers when expanded across functions, geographies, processes or larger employee populations. AI at Scale™ provides leaders with a structured approach for determining what is genuinely ready to scale, identifying organisational dependencies and creating the conditions required for wider adoption. Participants will examine scalability through strategy, value, process, data, technology, people, governance and change perspectives. The programme also addresses portfolio decisions, adoption, performance measurement and the leadership discipline required to stop initiatives that cannot demonstrate sufficient value. Participants leave equipped to build a practical pathway from experimentation to responsible enterprise AI adoption.
Strategy and transformation professionals
Functional and business unit leaders
Digital, data and technology leaders
Programme and portfolio leaders
Senior professionals responsible for enterprise AI adoption
Participants will leave the Programme with stronger strategic capability and practical approaches that can be immediately applied within their organisations.
An enterprise AI scaling framework
Practical AI scale-readiness criteria
Stronger pilot evaluation capability
Greater understanding of scaling dependencies
Improved enterprise adoption approaches
Stronger governance for AI at scale
Practical approaches to value measurement
An enterprise AI scaling roadmap
Evaluate AI initiatives for scalability
Identify enterprise scaling requirements
Design effective AI scaling pathways
Manage adoption and governance at scale
Measure sustainable enterprise AI value
Strategic
Thinking
Enterprise
Leadership
Decision
Making
Core Areas
Understanding the AI pilot-to-scale challenge
Distinguishing experimentation, deployment and enterprise scale
Why successful pilots can fail during expansion
Connecting AI scaling with organisational strategy
Understanding enterprise scale and complexity
Identifying the characteristics of scalable AI initiatives
Recognising common organisational scaling barriers
Establishing executive expectations for AI at scale
Evaluating the strategic relevance of AI pilots
Assessing demonstrated business value
Understanding technical and operational feasibility
Evaluating process and workflow readiness
Assessing data and technology dependencies
Understanding workforce and adoption requirements
Evaluating risk and governance implications
Making scale, adapt, pause or stop decisions
Moving from local solutions to enterprise capability
Understanding operating-model requirements for AI scale
Standardising processes while allowing appropriate flexibility
Building data and technology foundations
Establishing cross-functional ownership and accountability
Developing workforce capability for wider adoption
Understanding third-party and ecosystem dependencies
Creating organisational infrastructure for sustainable scale
Designing proportionate governance for scaled AI
Clarifying enterprise decision rights and accountability
Maintaining responsible AI across wider adoption
Managing human oversight at scale
Building employee trust and organisational engagement
Managing resistance and adoption challenges
Aligning leaders and managers around AI-enabled change
Maintaining consistency across functions and business units
Defining success beyond technical deployment
Measuring adoption, performance and business outcomes
Establishing ownership for value realisation
Monitoring AI performance after scaling
Managing changing risks and organisational dependencies
Continuous improvement of enterprise AI capability
Balancing further expansion with value discipline
Developing an enterprise AI scaling roadmap
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