Critical success factors for AI adoption

From practical applications to supporting bold visions, AI tools offer end users opportunities to explore territories where human ingenuity and AI capabilities converge. Innovators who prepare their people, processes and platforms for AI are leading the example for their peers in transforming their culture to be AI-ready. Establishing a level of readiness involves a comprehensive evaluation of your data management practices, current IT infrastructure, personnel and organisational culture.

Understanding your organisation’s ‘why’ helps provide clarity to AI implementation. Identify an area where AI models and tools can add genuine value, be it to enhance productivity, boost competitive advantage, open new revenue lines, offer new services, manage risks or understand regulatory compliance. View AI as a fit-for-purpose solution to derive maximum benefits.

You’re probably already sitting on a gold mine of data. To put things into perspective, ChatGPT was trained on one petabyte of data; enterprises own around 150 petabytes of data in their systems. Having data at your fingertips alone, however, is not an advantage. Data strategy, data platforms and data management and governance are make-or-break factors that will define the trajectory of your AI journey.

It’s challenging to set best practice universal standards for a phenomenon that society is only beginning to grasp. Across the globe, there are custom AI frameworks or different interpretations of what good implementation looks like, based on individual experience. The good news is that the development of AI standards is underway to help organisations effectively leverage AI and mitigate risks. The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) have worked together to create international standards for AI.

The Australian government, through the National Artificial Intelligence Centre (NAIC), has also launched voluntary AI standards, including 10 guardrails, to guide organisations in developing and deploying AI safely and responsibly. These guardrails align with internationally recognised best practice advice such as the EU AI Act, the National Institute of Standards and Technology (NIST) guidelines and ISO 42001.

There is a long way to go to achieve widespread AI adoption, but the voluntary AI safety standards provide pathways for organisations to achieve business outcomes with effective and responsible AI use.

AI requires significant upfront and continued investment. It’s essential to dig into practicalities and map out potential ROI along with the projected short- and long-term costs for your chosen technology stack.

Mainstream AI applications can be broad, and end users need depth to derive real benefit from it. Organisations should focus on challenges that matter most to them. Based on the business problem, identify potential pilot projects and specific use cases that can bring the biggest impact. By embedding purpose into your prototypes or proofs of concept, which will get better or more accurate as experimentation progresses, you’ll have greater confidence to scale.

It's all about openness to learn. Gaining access to AI tools can sometimes feel like being handed the keys to a Formula 1 car that is built for speed. The car can only achieve the best performance with the right people and training; without these, its power and competitive advantage are wasted. AI needs great leadership and talent to translate strategies into wins, and it needs diligent teams to avoid potential dangers. If you don’t drive the car, your competitors will, and you’ll risk being left behind. If you don’t have a pit crew, you won’t get far.

AI offers infinite opportunities, and it’s important to get started with the right foundations: a clear strategy, robust framework, innovation mindset and skilled talent pool. These are key recommendations to guide your organisation’s AI implementation:

• Adopt a ‘technology and human’ instead of ‘technology versus human’ perspective – Consider AI as an ally that augments or complements human intelligence and capabilities instead of an adversary.
• Run an operational audit to uncover areas where AI can bring the biggest impact – Evaluate internal systems and processes to determine which aspects AI can add the most value and solve the most complex challenges – and where you are already using it. This will bring focus on use cases and help establish value propositions.
• Consult international and national standards for effective and responsible AI use – Refer to established guidelines set by the ISO or a country’s specific regulatory bodies to inform decisions or develop quality and safe practices.
• Evaluate data quality and accessibility – Not all data is equal. Know the provenance of your data and check that the quality is sufficient for intended purposes. AI applications rely on updated and quality data to produce accurate and dependable outputs.
• Involve the entire organisation in the journey – Foster AI fluency at every level of the business, from the C-suite to the most junior members of the workforce. An enabling culture, equipped with robust systems and processes, empowers people to get on the right track.
• Plan for short- and long-term investments – Consider the upfront and continuing investment to support your AI journey from concept and experimentation to operation and scalable innovation.
  
  

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