How To Scale AI Successfully

How Woolworths New Zealand is charting their course

Brochure mockup of Scaling AI Successfully whitepaper

Insights in this whitepaper

Scaling AI requires responsible practices, strong business foundations, unified technical teams, and a well-appointed machine learning platform. See how Mantel Group and Woolworths New Zealand came together to tackle these challenges to deliver a modern approach to AI that has unlocked transformative business capabilities.

Establish stable foundations – people, processes, and governance – to ensure success.
Build a holistic approach to leverage AI effectively and foster innovation while also keeping pace with the changing AI landscape.
Create comprehensive AI governing principles and guidance that everyone in your business can understand.
Ensure there is a seamless integration of AI in existing business operations and strategy.

Laying the foundations

Identifying AI use-cases should be a venture shared by technical and business leaders. This venture meets somewhere between the top-down strategic view of financial year planning and the bottom-up lens of what is feasible with data available and in a ‘fit state’.

Engaging business end-users consistently throughout the development process aligns AI solutions with real-world business needs, facilitating seamless integration into existing processes. This inclusive approach ensures that AI solutions not only excel technically but bring value that aligns with strategic objectives with an output that is being harnessed and utilised effectively.

Scalable AI is the linchpin of future-proofing data-driven success. Ensuring that the models and solutions created today can meet the demands of tomorrow.

50%

of AI projects are aligned to business strategy

37%

expect ROI from AI projects within 1 year

10%

have wide-scale adoption of AI and machine learning

*Based on interviews with data leaders at large Australian organisations engaged in AI projects.

6 steps to achieving AI at scale:

Overcome mistrust in AI

Building transparency, accountability, and fairness into AI systems – and educating stakeholders about how AI works and its potential to enhance human capabilities rather than replace them – can overcome anxieties about AI’s decision-making processes and biases.

Achieve buy-in to drive wider adoption

Stakeholder mapping allows you to weigh the significance and importance of the input shared and enable a more streamlined process to absorb information without losing project momentum.

Scale AI responsibly

Adopting efficiently coded, repeatable modularised coding practices which are well documented allows organisations to scale more effectively with the least possible increase in computing power, data centre usage, and energy consumption. In addition to limiting environmental impacts, this creates an interpretable environment that is not reliant on individuals.

Implement robust governance

Scalable AI requires the democratisation of end-to-end knowledge. This de-risks the AI model so the organisation’s ability to scale is not impacted, even as the business evolves and the team members change.

Encourage knowledge and insight exchange

Collaborative learning across various AI projects fosters a culture of continuous improvement. It promotes standardisation of practices as well as enhancing the efficiency and effectiveness of AI teams.

Adopt the right technology infrastructure

Cloud-based infrastructure plays an important role in achieving scalability, providing the flexibility needed for resource allocation. Cutting-edge AI development frameworks and cloud-based platforms, complete with pre-built components, accelerate the development process, using reliable, easily repeatable AI solutions which can be monitored during deployment.

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Follow us as we explore how agentic AI is relevant for business, best practices for development, guidelines for identifying use cases – and how to build AI that works for you.