Without a doubt, Artificial Intelligence (AI) is revolutionizing businesses, with Australia’s AI spending expected to hit $6.4 billion by 2026. However, according to The State of Enterprise AI and Modern Data Architecture report, while 88% of enterprises adopt AI, many still lack the data infrastructure and team skilling to fully reap its benefits. In fact, over 25% of respondents stated they don’t have the data infrastructure required to effectively power AI. We also found that over 39% of respondents said that almost none of their employees are currently using AI.
Interestingly, Gartner has predicted that at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025. With that in mind, the question then becomes: How will you embrace technologies and projects when you can’t see the time to value that AI will bring to the organization?
Translating AI’s Potential into Measurable Business Impact
It can’t be denied that a mature enterprise data strategy generates better business outcomes in the form of revenue growth and cost savings. Organizations also see improvements in customer experience, operational efficiency, and supply chain optimization.
However, to fully realize the benefits of AI and its perceived value, organizations must measure their AI objectives against key business metrics used internally. This alignment is crucial for the progression of these projects. It also becomes the basis for communicating to internal stakeholders to secure sustained funding and financial investment. Adopting common business metrics also enhances the likelihood of successful implementation and value realization from these investments.
OCBC Bank’s adoption of AI has effectively impacted revenue generation and better risk management. In addition, it has improved developers’ efficiency by 20%.
Ensuring AI’s Trust with Intent
AI projects cannot begin without trust. Trusting AI equates to trusting the data it uses, meaning it must be accurate, consistent, and unbiased. Ethical AI depends on trustworthy data, guaranteeing equitable outcomes that reflect the company’s principles.
This means access to data completeness is critical. Yet, it’s a challenge for 55% of organizations that suggest accessing all of their company’s data is more daunting than a root canal.
Ensuring AI trust involves understanding your data and scrutinizing data sources, quality, access, and storage within your organization. Consider the intent, potential biases, and implications of AI decisions. Empathize with customers’ perspectives on data usage to guide ethical practices. If you wouldn’t approve of how the data would be used, it’s a sign to reassess your approach.
Kick-starting Your AI Journey
So, how do you transition an AI project from concept to full production and reap its benefits? Here are some tips for organizations starting on their ethical AI journey:
- Formulate a data strategy. This starts and ends with business value. Look at the organization’s mission, vision, and key objectives, and develop a holistic approach that involves people, processes, and technology to leverage your data assets and develop capabilities and use cases to support business objectives.
- Know Your Data, Know Your Intent. Ask yourself: is the data integrated into your systems reliable, and can you trust your organization’s intentions for using that data? A deliberate and thoughtful design of AI systems is crucial to ensure the outcomes are fair and unbiased, reflecting the organization’s ethos and principles. Organizations must have a clear vision of what they aim to achieve with AI to avoid missing out on its benefits or, worse, damaging their reputation and customer trust.
- Utilize a modern data platform that unifies the data lifecycle. Your data platform should facilitate the implementation of modern data architectures – data mesh, fabric, or open data lakehouse – with security and governance as the foundation. This platform should enable your organization to handle the complex data challenges that arise daily across different functions, enabling seamless deployment of workloads between on-premise and cloud (or multi-cloud) without workload refactoring. Most importantly, it should maintain data traceability and uphold stringent security policies and access controls from one environment to another.
AI Assistants – Democratize AI For Users
What’s in trend today may not be tomorrow, and it’s possible that public LLMs will soon become a thing of the past before the next disruptive technology comes along. Perhaps you find accessing your data challenging or you lack the technical skills in-house to build and deploy GenAI capabilities.
Fortunately, modern data platforms with AI Assistants can facilitate AI adoption across the organization, giving Data Analysts access to ‘conversational AI’ capabilities and all everyday users faster access to their data-driven insights.
Learn more about how Cloudera can help accelerate your enterprise AI adoption.
