This article focuses on the changing landscape that business systems exist within. With AI popping up everywhere, tomorrow’s leaders are AI enabled. With that observation made, and yes it may sound obvious, there are important factors that inhibit the use of AI for many organisations, particularly regulated businesses. Let's look at these in this article and see where AI sits in this context and understand why preparing now is critical to future success.
Artificial Intelligence brings enticing visions of employees achieving 100x productivity, customer service averaging at 99% satisfaction and more. There sure is a lot of hype. But the promise is real and the need is evident. We are producing and consuming more data points and types than ever which when mined and correlated provide new levels of insight, innovation and productivity.
AI is only as good as the data it is fed, and this is why the most important qualification for an organisation to enter this AI enabled space is the quality of its data and the reliability of the systems that produce it. Immediately this points the finger at all legacy tech stacks (well almost all). Here's why:
Data Silos and Fragmentation: Many organizations, especially those that have grown through mergers and acquisitions, find their data scattered across various systems and departments. This fragmentation makes it challenging to create a unified, comprehensive dataset for AI training and deployment.
Data Inconsistency and Inaccuracy: Years of manual data entry, system migrations, and evolving business processes often result in inconsistent or inaccurate data. AI models trained on such data are likely to produce unreliable or biased results.
Lack of Data Governance: Without robust data governance frameworks, organizations struggle to maintain data quality over time. This is particularly crucial in regulated industries where data integrity is not just a business imperative but a legal requirement.
Insufficient Metadata: High-quality metadata is essential for AI systems to understand the context and relevance of data. Many organisations lack comprehensive metadata strategies, limiting the effectiveness of their AI initiatives.
Privacy and Security Concerns: In an era of stringent data protection regulations like GDPR and CCPA, organisations must ensure that their data practices comply with legal requirements. This often requires careful data anonymization and access control measures, which can be complex to implement.
For many organisations the route they take will involve some pain. The AI trend will force many companies to address systems and data that they have never been forced to before. The fact is, eventually all well run organisations will have to address this issue, but there will be resistance because of the friction and expense it involves.
Those that embrace the challenge will leap forward and the pain for those that remain will increase as they will not be able to match the performance of their peers.
Conducting a thorough data audit is the first step. Understanding what and how data is moving around your organisation and supply chains is your baseline. This will reveal silos and points of failure.
From here you may begin to build a framework for quality and management. Removing silos and building a good home for all your data with the right structure and catalogues will then set you up for a healthy cloud based analytics product. Invest in your people so they can get the most out of your data. Once you have done this and put some solid governance, security and delivery around it you are qualified to enter the AI stage. Congratulations.
We are at the precipice to a new era. What has worked up until now is on notice. Not only your own data property, but the systems by which suppliers operate may also need inspection. AI enabled business requires immediate integration points with excellent infrastructure and capability. For many organisations there will still be technologies and approaches that cannot meet these requirements. As AI becomes ubiquitous we can expect that these industries will change and introduce more technology patterns. But for now, getting on top of your own data and systems will benefit your operations immediately and prepare you for what comes next.
