AI Jump Start | Starting the AI Journey: A strategic guide for adopting AI the right way

Artificial Intelligence (AI) has immense potential to transform digitally-enabled organisations, unlocking new automation efficiencies, and enabling smarter decision-making. For organisations ready to embark on their AI journey, success depends on thinking strategically about the AI opportunities that will deliver measurable value and are within the innovation capabilities of the organisation.

A common challenge we hear from clients is that they’re unsure of where to start, and how to make sure they’re not introducing new risks as they explore the use of AI. We created an ‘AI Jump Start’ framework to help establish the required AI governance and give clients a repeatable process for testing and deploying AI use cases that actually align with their strategic goals.

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Over the next few weeks, we’ll unpack the steps in the framework through a series of articles, with this first one focusing on the Vision and User Stories steps.

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1. Formulate the Vision for AI in the Organisation

The first step in any AI journey is to define a clear and compelling vision. This involves aligning AI initiatives with the organisation’s strategic goals, whether that’s improving operational efficiency, enhancing customer experiences, or enabling data-driven decision-making. Enterprise technology strategy artefacts may also need to be updated to accommodate AI requirements.

A well-articulated vision should also reflect the organisation’s risk appetite. AI, particularly emerging technologies like generative models, can introduce risks related to process failures, discrimination and bias, cyber security, privacy, and reputational impact. Leaders must determine how much risk they are willing to accept and what safeguards are necessary to mitigate it.

Equally important is choosing the right strategy for acquiring AI capabilities. Organisations may opt to:

• Build AI systems in-house using open-source tools and customisable models, which offers flexibility and control but requires significant technical expertise.

• Procure off-the-shelf AI solutions, which can accelerate deployment but may limit customisation and transparency.

• Partner with AI vendors or platforms, leveraging external expertise while maintaining strategic oversight.

The chosen approach should reflect the organisation’s technical maturity, resource availability, and long-term innovation goals.

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2. Prioritise AI Use Cases

Once the vision is set, the next step is to identify and prioritise AI use cases. This begins with building a backlog of potential applications across departments, ranging from automating routine tasks, converting unstructured or semi-structured information sources into structured and quantifiable data, improving employee experience with informational chatbots to navigate internal policy and process, to generating synthetic data sets and enhancing predictive analytics.

Each use case should be evaluated based on several criteria:

• Effort to implement a Minimum Viable Product (MVP) - consider the complexity of development and integration.

• Availability and quality of data - AI systems rely on robust datasets for training and validation.

• Value delivered - estimate potential gains, such as reduced manual effort, improved accuracy, or faster turnaround times.

• Workforce readiness - assess whether employees have the skills and digital literacy to adopt and benefit from AI tools.

• Infrastructure needs - determine if additional technologies or platforms are required to support the MVP.

• Feasibility of transitioning to production – ensuring light weight MVP implementations can be transitioned to enterprise-grade solutions with stable infrastructure, integrations, monitoring, resources for ongoing support and maintenance, and are able to consume timely and high quality production data.

When down-selecting use cases for early implementation, scalability is key. MVPs should be designed with a clear path to production-grade deployment. The goal is to maximise automation value while minimising cost and effort.

Risk appetite also plays a critical role in prioritisation. Internal use cases, where AI tools are used by employees rather than customers, typically pose lower reputational and customer experience risks. Early proof-of-value projects should avoid the potential for high-risk outcomes, such as decisions affecting health, safety, or individuals’ rights. This ensures that the organisation can learn and iterate safely within simpler use cases, before scaling AI solutions more broadly.

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3. Consider Enabling Technologies

Delivering AI solutions requires a robust technological foundation. Organisations must assess and invest in enabling technologies that support development, testing, and deployment.

Key components include:

• Secure sandbox environments that allow teams to experiment with AI models in a controlled setting, protecting sensitive data and systems.

• Model Operations (ModelOps) pipelines for automating the lifecycle of AI models, from training and testing to deployment and monitoring, ensuring consistency and reliability.

• Cloud-based AI development platforms can accelerate development, provide scalable compute resources, and offer pre-built tools for data processing and model training.

These technologies not only support proof-of-value projects but also lay the groundwork for sustainable AI adoption across the organisation.

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4. Establish Governance Infrastructure

AI governance is essential to ensure responsible, ethical, and compliant use of AI systems. Organisations must embed governance structures that provide oversight throughout the AI lifecycle.

This includes forming a cross-disciplinary AI oversight committee or Steering Committee (SteerCo) with representation from:

• Data science and engineering

• Legal, compliance and privacy

• Cyber security and risk management

• Business and operational leadership

The committee should be responsible for:

• Approving new AI initiatives

• Reviewing and managing risks

• Authorising AI system transitions to production (Go-Live)

• Monitoring AI systems in production for safety, fairness, and performance

Governance frameworks should also include policies for acceptable use of AI tools, data usage, model transparency, bias mitigation, human-in-the-loop monitoring requirements and incident response. As AI systems evolve, ongoing monitoring and periodic audits are critical to maintaining trust and accountability. Resilience practices such as business continuity planning and IT disaster recovery should also be refreshed to consider the implications of sustaining an AI technology footprint.

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Next week

By formulating a clear vision, prioritising high-value use cases, investing in enabling technologies, and embedding strong governance, organisations can unlock the full potential of AI while managing risks responsibly.  With the right foundations in place, organisations can move forward confidently, delivering meaningful impact for their people, customers, and stakeholders.

Next, we’ll look at the overall risk assessment process for a new AI use case before making a go/no-go decision.

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‍Authors:  
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James Calder Managing Director, National Cyber Lead
‍james.calder@scyne.com.au | LinkedIn
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Victoria Young Managing Director
‍victoria.young@scyne.com.au | LinkedIn
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Didier Desperles Managing Director
‍didier.desperles@scyne.com.au | LinkedIn