Across industries, AI is no longer a futuristic concept, it’s a competitive necessity.
- Organisations are adopting AI to unlock new revenue streams, enhance customer experience, automate processes, and drive data-driven decision-making with the clear intention to improve efficiency & accuracy by reducing human costs and errors.
- Organisations in the top maturity tier report 15–30% improvements in productivity, retention, and customer experience—and companies that truly scale AI see ~3× higher revenue impact and ~30% EBIT uplift compared to pilot‑only firms.
- Despite growing urgency, less than 15% of enterprise AI initiatives reach sustainable production, and most firms cite a mix of technical, organisational, and cultural barriers.
Many companies pilot AI, few achieve scale. AI at scale isn’t just about technology, it’s about aligning strategy, data, people, and change.
We help you bridge the gap between innovation & enterprise scalability using our 8-step AI adoption framework, which directly targets the most common and costly blockers.
Step 1: Enterprise AI Adoption Strategy
Aligns AI initiatives with business strategy to drive measurable outcomes across the enterprise. Defines a comprehensive AI vision and roadmap aligned with the enterprise’s business strategy.
Step 2: AI Maturity Assessment & Ladder
Evaluates current capabilities and sets a clear roadmap to scale AI effectively. Assesses the organisation’s current AI maturity and outlines a path for adoption.
Step 3: AI Technology & Data Foundations
Builds scalable, secure data and infrastructure foundations to support enterprise-grade AI. Establishes a robust data and technology infrastructure.
Step 4: AI Governance Framework
Establishes ethical, risk-aware, and compliant guardrails for responsible AI adoption. Provides oversight mechanisms to manage risks associated with AI adoption.
Step 5: AI Prototype Driven Use Cases
Delivers quick, value-focused AI pilots using real data to prove business impact. Focuses on practical experimentation: testing AI on a small scale to validate feasibility.
Step 6: Embedding Meaningful AI
Integrates successful AI solutions into core operations and enterprise systems. Takes the successful prototypes and embeds them into core business processes at scale.
Step 7: AI – Human Factors
Equips teams with skills, tools, and change management to ensure AI adoption sticks. Addresses the people side of AI adoption by preparing and enabling the workforce to effectively work with and alongside AI.
Step 8: AI Continuous Innovation
Creates a sustainable AI engine through MLOps, CoE, and a pipeline of new use cases. Establishes mechanisms for continuous innovation & improvement in AI within the enterprise.
We offer three onboarding approaches to kick start your AI journey. Tailored to your organisations’ AI ambitions and current state of adoption readiness.
A) Discovery
PoC | 4 weeks
Self-contained environment with synthetic data to provide a fast and safe means to prove suitability of AI in a real-world use case. This will answer the fundamental question – Can AI help solve real problems in my organisation?
B) Lite
Pilot | 10 – 12 weeks
End-to-end AI use case(s) live in production with all guardrails and organisational readiness in place. MVP approach on a thin slice using your data in your environment.
C) Enterprise
Repeat & Scale | 20 – 24 weeks
Organisational readiness, AI CoE, organisational training and processes established allowing repeatability and scalability throughout the enterprise.