The unprecedented rise of artificial intelligence (AI) and the vast array of features and capabilities it offers are at our fingertips. This wealth of knowledge can significantly enhance our organization's productivity and could also improve our profitability. The question that arises is whether today's organizations are fully leveraging this potential correctly.
What I observe at most conferences I attend is that AI for organizations is often 80 to 90 percent inaccurate and is either being scrapped or not being utilized. There is an underlying problem that is not being discussed, and it is a common issue. If not addressed, it will cause AI integrations to consistently experience problems, resulting in a failure to achieve the expected return on investment. This can lead to wasted resources, lost opportunities, and a competitive disadvantage in the market.
What are some common problems associated with integrating AI into your organization?
- One of the key issues is the lack of understanding of your organization's business and its revenue generation model. Each business is unique and operates distinctly. A one-size-fits-all approach to technology is ineffective and does not align with the business's practices or operations. This lack of alignment can disrupt operations and negatively impact customers who rely on the product or service.
- Another common problem is a lack of clarity regarding the location and structure of critical data throughout your organization. Failure to tag and categorize your data can lead to AI integration failure, potentially resulting in incorrect answers provided to the organization. Sensitive data lacking controls could be imported into large language models, potentially causing compliance issues for the organization. This is often referred to as data sprawl, which affects all organizations. Data sprawl can happen in several
ways, including rapid growth of data, legacy data in older systems and applications, and duplicate data throughout the organization. Over time, if left unchecked, data can become dispersed throughout the organization, potentially exposing critical or regulatory data leaks.
- Lack of good data governance across the organization can lead to the disclosure, unauthorized use, and unavailability of company data when needed within the organization. With a combination of data spirals, as stated above, and a lack of understanding of your regulations, critical data could lead to massive data breaches with serious legal ramifications. Additionally, having customers' or employees' data, or both, can lead to serious complications when breached. This will lead to a lack of trust and confidence in the organization you are working for, and could take years to repair the damage to the data.
Making AI Integration Successful and More Accurate
- Understanding how your organization operates is key to successful technology integration. These tools help boost your organization's productivity and effectively sell your products or services to your clientele. If the technology you're integrating into the organization has a negative impact, then it's wrong. The technology must align with the business and its processes. This can be achieved by discussing with the executive team and other directors who help run the organization, and understanding how it generates revenue and the pain points within it as well. Only then can you help create the right controls or integration that the organization needs.
- Knowing where your data is is key. Making sure your data is correctly categorized and tagged within your organization. This includes all your data, structured and unstructured. Make sure you know the regulations that apply to your organization. Additionally, to ensure the integration is secure by default and secure by design, thereby protecting the organization and its customers.
- As you know, your personal information without putting any thought into it, which is how you should know where your data is within the organization. Effective data governance is crucial for achieving success within the organization. This ensures that regulatory and critical locations are identified to minimize data sprawl within the organization. This will also help you determine when to purge data that is no longer needed, as well as archive data that is still required but not currently in use. This is to ensure that you implement good housekeeping practices to ensure that when you need the data, it can be accessed and is accurate, and that data that is no longer needed can be removed from the organization. By doing this, you can reduce the attack surface and also save on disk storage costs.
- Validate your data to ensure the correct answer is displayed every time. Additionally, ensuring that critical data is not being used within large language models (LLMs). Working with the legal team ensures that we are not violating any regulations that affect the organization, and also addresses how discovery in future lawsuits will be handled. This is one of the critical points that still involves human interaction with the data, allowing them to verify that it has not been altered and is accurate. Suppose you let the data go unchecked throughout the years. In that case, it will become a significant problem that will eventually prevent you from ensuring the validity of the data.
In summary, to ensure a successful integration, time and effort will be required. This also needsto be well-planned out in terms of design and integration with various systems. This is not going to take overnight, but with diligence and consistency, and constant tweaking to ensure it is correct every time. This will also change as your organization grows and evolves over the years; it's not just a one-time set-it-and-forget-it commitment, but a lifelong commitment to ongoing adjustments throughout the organization's lifespan.
- Knowing your organization's business.
- Knowing your data throughout the organization, structured and unstructured.
- Classifying your data and tagging correctly.
- Knowing the regulations your organization faces.
- Putting controls in place to make sure critical data does not reach the LLMs
- Validating your data results.
