Enabling Value Through AI: A Pragmatic Approach to Prioritisation and Realisation

Enabling Value Through AI: A Pragmatic Approach to Prioritisation and Realisation

By Dave Luttrell (Principal AI Consultant) and James Power (Business Analyst, Advisory Services)

As organisations increasingly turn to artificial intelligence (AI) to revolutionise their operations, the true challenge often lies not just in generating innovative ideas but in translating these ideas into tangible outcomes. While the excitement surrounding AI sparks a wave of creativity and insight from stakeholders, it’s easy to get lost in a sea of concepts without a clear path to value. The key to success is not only generating ideas, but also structuring these ideas into actionable, value-driven strategies.

This article offers practical guidance for organisations looking to unlock value through AI, covering critical areas such as governance, defining success, assessing feasibility, managing risk, and creating a roadmap for execution. By taking these strategic steps, businesses can convert AI’s transformative potential into measurable success.

Establishing the Foundations: Governance and Operating Models

A foundational step in any AI initiative is to lay down a solid governance framework and operating model. This ensures that diverse expertise within the organisation is aligned and that AI initiatives are guided by clear policies, regulations, and ethical standards.

This process can be broken down into several Key Actions, inspired by frameworks like the Australian Government’s Voluntary AI Safety Standard and the 10 guardrails:

Developing AI Policies & Guidance: Formulate guidelines that set the tone for the responsible and ethical use of AI. This can be established through curating and socialising a clear guidance note around the intention and usage of AI within an organisational context. These can be used to formulate policy to address crucial issues like data privacy, security ensuring that AI operates within legal and ethical boundaries.

Establishing a Governance Structure: Create a governance board or similar structure tasked with overseeing AI initiatives, aligning them with the organisation’s overarching goals, and ensuring adherence to ethical standards. This board will be central in fostering transparency and accountability.

Encouraging Cross-Functional Collaboration: Assemble a team of stakeholders from diverse business areas, such as technology, operations, and legal, to ensure a holistic approach to AI adoption. Collaborative efforts across departments help mitigate blind spots and encourage responsible implementation.

With a robust governance structure in place, organisations can embark on their AI projects with confidence, knowing they have the necessary mechanisms to manage risk and maximise value.

Defining Value: Clarifying What Success Looks Like

To realise the full potential of AI, it is essential for organisations to first define what “value” means within their unique context. AI’s value can take many forms – financial returns, efficiency gains, or broader social impact – and a clear definition helps align stakeholders and set realistic expectations.

When prioritising AI use cases, organisations should consider three primary Dimensions of Value:

Monetary Value: This encompasses cost reductions, return on investment (ROI), and the creation of new revenue streams. AI has the potential to optimise operations, streamline processes, and uncover novel business opportunities.

Efficiency Gains: AI can dramatically improve decision-making speed, reduce manual effort, and increase overall productivity. By automating workflows, organisations can save time and resources, achieving more with less.

Societal and Human Value: Beyond financial metrics, AI should be evaluated for its wider impact. Can it enhance customer satisfaction, improve employee well-being, or contribute to fostering sustainable business practices?

By quantifying these benefits and measuring them against industry benchmarks, organisations can establish a clear vision of success, enabling them to set measurable and achievable goals for their AI initiatives.

Assessing Feasibility and Maturity

Turning AI concepts into reality requires not only innovative ideas but also a thorough assessment of feasibility and organisational readiness. To achieve success, businesses must evaluate their people, processes, and technology to ensure they are prepared for AI implementation.

Feasibility and maturity can be assessed using three Key Considerations:

People: Does your organisation possess the skills and expertise required to successfully implement and manage AI technologies? This may involve training staff or fostering a culture of AI literacy to ensure all team members can support AI initiatives effectively.

Processes: Are your existing workflows optimised for AI integration? It’s crucial to identify and refine operational processes that align with your organisation’s AI goals, facilitating smooth adoption.

Technology: Evaluate your organisation’s infrastructure and data quality. Does your current technology stack support AI? Identifying gaps in your technical capabilities and investing in scalable technologies will facilitate AI adoption.

By mapping the current state to a desired future state, businesses can create a clear pathway for AI implementation, ensuring that projects don’t get stalled in the proof-of-concept phase but move forward with a clear roadmap for success.

Addressing Risk and Ethical Considerations

As AI technologies become more pervasive, organisations must proactively manage associated risks and ethical concerns. Addressing potential issues such as bias and data privacy is crucial to maintaining trust and securing stakeholder buy-in.

Practical Steps for managing risk and ethics in AI include:
Conducting Risk Assessments: Evaluate potential risks, such as bias in algorithms or unintended consequences of AI deployment and put mitigation strategies in place.

Establishing Ethical Guidelines & Guardrails: Develop a set of principles to guide AI usage responsibly, backed by an ethics advisory board or a similar structure that ensures ethical oversight. Ensure that these are adhered to by institution of ethical walls and guardrails to prevent against the misuse or risk considerations driven by the use of AI.

Monitoring Compliance: Implement governance structures that allow for continuous monitoring of AI projects, ensuring they remain aligned with organisational standards and ethical expectations.
Striking the right balance between innovation and responsibility is essential to building trust with both internal and external stakeholders.

Prioritising AI Use Cases: From Concept to Execution

With a clear understanding of value, feasibility, and risk, the next critical step is to prioritise AI use cases and create a roadmap for implementation. This ensures that resources are allocated effectively, focusing on initiatives that deliver the highest return.

Effective Tools for Prioritisation include:

AI Impact Navigator: The Australian Government’s AI Impact Navigator offers a framework to evaluate initiatives across four dimensions: social licence and corporate transparency, workforce and productivity, AI effectiveness and community impact, and customer experience and consumer rights.

Prioritisation Matrices: Use a matrix to rank AI use cases based on key factors such as ROI, complexity, and time-to-value.

Roadmapping: Develop a phased implementation plan that starts with high-impact, low-complexity initiatives to build momentum.

By turning these assessments into actionable plans, organisations can seamlessly move from ideation to execution, ensuring that AI initiatives deliver measurable outcomes.

Realising AI’s Full Potential

AI offers immense potential to drive business transformation, but realising this potential requires a structured approach. By setting up robust governance, defining clear value metrics, assessing feasibility, addressing risks, and prioritising use cases, organisations can transform abstract possibilities into concrete outcomes.

While the path to AI value realisation is complex, it offers tremendous rewards for those who approach it thoughtfully. For further insights on AI governance and use case prioritisation, reach out to our AI Advisory team. Together, we can chart a course towards meaningful AI-driven transformation.

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To learn more about AI Governance & AI Use Case Prioritisation frameworks, please reach out for a conversation with our AI Advisory team.  Start your AI Transformation today!