For any competitive business today, generative AI is a board-level priority for the year ahead – and it’s easy to see why. When implemented strategically and thoughtfully, generative AI enables many benefits: higher velocity of innovation, cost reductions, elevated workforce productivity, and enhanced customer experiences – to name a few. These are key advantages that will keep a business competitive and resilient in today’s economic landscape.
While the promise of AI is clear, achieving measurable returns remains a challenge. While 75% of Australian businesses are investing in traditional AI and machine learning, only 38% are seeing a positive return on investment. The biggest barriers to AI adoption are uncertain ROI (28%), integration difficulty (27%) and compliance challenges (26%). These figures underscore a crucial point: AI’s success is not guaranteed – it requires a deliberate, well-structured approach.
That’s why strategic implementation is key. Rather than rushing headlong into implementation, businesses should assess where generative AI can have the most impact and develop a clear, well-measured adoption plan. In other words, they need a generative AI roadmap – one that outlines how AI supports or drives efforts to achieve business aspirations and objectives.
More critically, that roadmap must also account for and address several implementation gaps that will impact and potentially delay adoption early on. Some of these gaps will be new – and unexpected – for businesses implementing AI at scale, for the first time. Overcoming them will be crucial to unlock generative AI’s full potential and benefits of all in the organisation.
The need for a strong data foundation
No matter how advanced AI becomes, one fundamental truth remains: its output is only as good as its input. High-quality, well-structured data leads to accurate, relevant AI insights that drive business value. In contrast, disorganised or irrelevant data results in unreliable – even harmful – outcomes that could impact organisation-wide trust in generative AI.
In other words, for businesses to harness generative AI effectively, a strong data foundation is non-negotiable. Include the creation of a strong data foundation as part of your roadmap. Think of this foundation as a purposefully curated data ecosystem, built on robust and secure data architecture and led by data governance that emphasises transparency in data use, security, and regulatory compliance.
A key characteristic of a strong data foundation is the mechanisms in place to ensure the quality of data. Rigorous data cleaning and validation processes do the work of filtering, classifying, and preparing the right incoming data for AI use. By tapping into specified and relevant external data sources, Retrieval-Augmented Generation (RAG) techniques can further enhance the quality of AI outputs – but care needs to be performed to ensure the continued reliability of all data sources.
The importance of an AI champion
To successfully implement and scale generative AI, businesses need a dedicated leader – a Chief Artificial Intelligence Officer (CAIO) – to drive strategy and execution. The CAIO plays a crucial role in defining a cohesive AI roadmap, ensuring initiatives align with core business objectives to maximise value and return on investment. Their presence in the C-suite also integrates AI into high-level decision-making, keeping efforts focused on the most strategic priorities.
Beyond strategy, the CAIO is responsible for establishing governance and data security frameworks that promote responsible, ethical, and compliant AI use. A key focus should be on ensuring transparency in how AI models process data, minimising bias, and enforcing security-by-design principles. These measures build internal trust in AI initiatives while mitigating costly risks such as data breaches and regulatory non-compliance.
The CAIO’s office also serves as a bridge between AI-focused teams and the broader organisation. By fostering collaboration between data scientists, IT, and business units, the CAIO helps clarify use cases and implementation needs. Partnering with other C-suite leaders – such as the CSO and CIO – they develop top-down AI policies that align with the organisation’s IT, security, and privacy requirements.
The role of continuous AI education
To fully capitalise on generative AI investments, businesses must prioritise ongoing learning and development for employees. In fact, this part of the roadmap never ends. AI literacy isn’t a one-time effort – it’s an evolving and ongoing necessity as AI technology advances, new skill gaps emerge, and potential use cases arise.
What should a generative AI education syllabus cover? A strong AI education program should focus on key competencies, including prompt engineering and data literacy, to help employees maximise AI tools. It should also cover AI and data governance, outlining which data types are permitted for generative AI, ethical AI usage, and security and privacy best practices.
The office of the CAIO, in collaboration with department leads, should continuously assess and refine these learning programs. This includes evaluating new AI models and emerging skill requirements to ensure employees remain equipped to leverage AI effectively.
Closing the gaps in AI adoption
Every organisation’s generative AI journey is unique, shaped by factors like business readiness and long-term goals. However, common implementation challenges like the ones above will likely persist, regardless of organisation size or industry. Addressing these gaps – from leadership and governance to education and security – across a cohesive generative AI roadmap is key to building a sustainable, responsible, and secure AI-powered future.
- About Justin Ciabotti
- About Kyndryl
Justin Ciabotti is the Practice Leader for Applications, Data and AI for Kyndryl Australia and New Zealand. In his current role, Justin is focused on driving customers to modernise their complex technology environments and implement AI at scale. Justin has a wealth of technical experience, and held roles at Red Hat, Dell Technologies and SAP prior to joining Kyndryl.
Kyndryl is the world’s largest IT infrastructure services provider, serving thousands of enterprise customers in more than 60 countries. The Company designs, builds, manages and modernizes the complex, mission-critical information systems that the world depends on every day. For more information, visit www.kyndryl.com.
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