From theory to practice: The challenges of AI implementation

Many discussions around AI have focused on technological advancements and benefits, such as improved customer service, fewer manual tasks, better predictive capabilities, and so on. AI adoption sounds like an excellent move in theory (and it is), but it is equally important to focus on the practical challenges that arise during implementation. Export Finance Australia says most businesses engage at least four AI service providers and report talent and skills shortages as major obstacles to adopting AI. Here are five challenges of AI implementation and the solutions needed to achieve successful outcomes.

Integrating AI with existing systems

Integrating AI into existing IT infrastructure becomes challenging because legacy systems often cannot support AI’s data-intensive and computational demands. Integration processes can become complex and time-consuming, requiring modifications to support compatibility and efficiency. Updating or replacing outdated systems often compounds the complexity of AI implementation.

Solution: Addressing these issues requires businesses to thoroughly assess their current systems and identify specific areas in which to integrate AI. Collaborating with AI specialists and investing in modern infrastructure will support success. Additionally, starting with small-scale AI projects can help organisations gradually build the necessary capabilities for more extensive integrations.

Building data privacy and security

Data privacy and security remain top concerns when adopting AI. AI relies on large data sets, raising risks of breaches and misuse. In a survey by the University of Queensland, 75% of respondents reported feeling worried about potential AI risks, including cyber security and privacy breaches.

Solution: Organisations adopting AI will need to enforce policies for data handling and ensure that AI does not retain sensitive information from which to learn. Maintaining transparency over how the company collects, processes and stores data can mitigate privacy concerns and foster a culture of trust among users and customers.

Enforcing governance policies

Any AI implementation plan should cover governance policies that guide secure and responsible use. According to research by Datacom, 52% of Australian businesses have policies for AI usage, but only 40% have legal guidelines, and 39% have audit assurance frameworks. Lack of governance can create issues such as bias, breaches of compliance requirements, data security risks and eroded trust.

Solution: Governance policies should specify how team members use AI and data. AI used by the organisation should adhere to ethical standards and regulatory requirements. Continuous monitoring and compliance checks support ongoing adherence to relevant standards.

Managing biases

AI can perpetuate or exacerbate biases present in training data. Biases in AI can arise from various sources, including the data used for training and the design of algorithms. If not addressed, these biases can impact outputs viewed by team members, customers, or business leadership.

Solution: Regularly review and update AI models to reflect more accurate and equitable outputs. Organisations should establish protocols for monitoring and evaluating AI systems to ensure they remain fair and unbiased. Training users to monitor AI outputs for biases can reduce customer and business impacts.

Addressing skill gaps in users

Skill gaps can become a significant barrier when implementing AI, particularly for smaller enterprises that may not have the resources to hire AI talent in-house. The organisation will also struggle to see the full benefits of AI if the team does not have the skills to leverage its full potential. Many organisations struggle with a skills gap, lacking the necessary knowledge to implement and use AI effectively.

Solution: Learning and development programs can bridge the skills gap. Businesses should prioritise upskilling existing employees through targeted AI training courses and workshops. Organisations should also consider hiring AI specialists or collaborating with external experts to support their AI initiatives. These experts can provide the necessary knowledge and experience to guide AI projects and ensure their success.

Conclusion

Taking AI initiatives from theory to practice comes with many challenges. With the right strategy, your organisation can overcome them. Your company can unlock AI’s full potential by addressing integration issues, data privacy, governance, biases and bridging skill gaps. As Australian organisations continue to embrace AI, addressing key challenges will ensure that businesses can see success from AI initiatives.

Why choose MakeSense as your AI implementation partner?

Our team of SenseMakers can help your organisation discover the transformative power of AI and data. As automation, deep learning, and data engineering specialists, we provide the insights you need to make strategic decisions that will shape your company’s future. Our local experts leverage world-class technology platforms and partnerships to guide you through every step of the journey.

Visit our AI and Data Services page for more information on how we can support you.