Frontline lessons for organisational AI preparation

Laying the groundwork for AI adoption 

The first step in preparing for AI involves aligning organisational processes with the demands of this selected AI technology. Dennis described to the audience the initial efforts his team undertook, including building and adopting specific onboarding processes. These initial steps were essential to ensure that data, tools, and stakeholders were adequately prepared to work in AI-driven environments and were a core foundation for the rest of the journey.  

BigID emphasises that AI preparation is not just about technology; it is about establishing processes and frameworks that set the stage for long-term success. 

DataIQ members have spoken at length about the challenges explaining to non-data professionals about the importance of why these steps are so essential. For a successful integration of new AI tools that can extract everything possible, there must be buy-in from organisational decision makers to get the correct frameworks and operations in place before attempting a data-driven journey.  

Leverage data catalogues to reduce risk 

Data catalogues play a major role in reducing risks associated with AI. Dennis demonstrated how effective cataloguing can streamline data management, ensure compliance, and mitigate operational risks, emphasising the need for structured, accessible, and well-maintained data systems. A robust data catalogue not only simplifies data access but also serves as a critical tool for risk management. 

Risk is a natural part of business, particularly when it comes to investing in new technologies such as AI, but it can be mitigated. Data leaders need to ensure a level of data literacy where those using the tools understand how and why risks can happen, but also how they can be addressed and pre-emptively stopped.  

Security and architecture 

Implementing AI-driven changes requires robust security measures. Dennis highlighted the challenges his organisation faced in ensuring data protection while adapting to new technologies. These measures were not only necessary for compliance but also had a significant impact on the organisation’s overall architecture. 

Security must be a foundational element of any AI strategy, influencing how systems are designed and managed. Without security, business will open themselves up to a whole host of issues and regulatory problems. It does not matter where on the AI or maturity journey an organisation is, a solid approach to security will drastically heighten capabilities and culture.  

Proactive actions 

To remain competitive in the fast-evolving AI landscape, data teams must ensure they are running a proactive approach to data problems, not a reactive one. Dennis highlighted that his team had developed proactive approached that included anticipating future needs, addressing potential challenges early, and continuously optimising processes and tools. By staying ahead through proactive actions, teams can adapt to changes and weather turbulent trading cycles more effectively to maintain a competitive edge. 

Common AI concerns 

The session concluded with a lively Q&A segment where Dennis addressed pressing questions from the audience.  

• How does data cataloguing help with AI changes? 
  • Cataloguing provides structure and visibility, making it easier to adapt data systems to the evolving demands of AI. There is a distinct need to futureproof any new AI changes as this is a rapidly evolving area, and cataloguing is a core part of that process.  
• How much manual effort is involved versus automation? 
  • While some organisations still rely on manual processes, automation is increasingly becoming a necessity to handle the scale and complexity of AI. The benefits of automation include a reduction in the chance of human-led errors, speed of completion, and the removal of an additional task for humans to have to handle.  
• Can we use spreadsheets to achieve success? 
  • While spreadsheets may work temporarily for small-scale efforts, tools that are designed for the task offer scalability, automation, and advanced features that are indispensable for larger, AI-driven initiatives. The use of spreadsheets also heightens the issue of shadow and siloed data which negatively impacts the overall process.  
• How can tools support AI adoption? 
  • Tools provide the foundation for effective data management, enabling organisations to stay fuelled with the data and insights needed for AI to thrive. There must be a continuous investment in tools to ensure they are running effectively and upgraded where appropriate to achieve the organisation’s objectives.  

Preparing for AI is a complex journey that requires thoughtful planning, robust tools, and a proactive mindset. These insights underscore the importance of laying a strong foundation with structured processes, leveraging tools like data catalogues, prioritising security, and staying ahead of emerging challenges. 

By addressing these elements, organisations can not only prepare for AI but also position themselves to harness its full potential. 

Contact BigID at info@bigid.com to assist you in your AI journey. 

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