This article is a Forbes Technology Council Post originally published on Forbes.com, read the article on Forbes.
The rise of generative artificial intelligence is transforming the business landscape. Companies across industries are exploring ways to leverage AI to enhance their operations, increase productivity and gain a competitive edge. However, not every company is ready to take advantage of this technology, even though they might think they are.
AI readiness requires a combination of factors, each as important as the next. Here, I’ll review how to determine if your business is ready to take the next step.
First, Ensure Data Integrity And Accuracy
As engineers and CTOs know, data is the fuel that powers AI. It’s essential to ensure that data sets are accurate, complete and reliable. Poor data quality can lead to inaccurate AI models, which can have serious implications for your business. To help ensure data integrity, establish a data governance framework that includes data quality checks, data lineage tracking and data access controls. The most important piece? Invest in data cleansing and normalization tools to ensure that your data is consistent and error-free.
As Harvard Business Review perfectly captures, you’re not ready for AI unless your data is all buttoned up. “Promises of AI vendors don’t pay off unless a company’s data systems are properly prepared for AI. Data is locked in silos, inaccessible, poorly structured, and most importantly, not organized in such a way as to be used as the fuel that makes AI work.”
Identifying And Implementing The Right Tools
Obviously, AI requires specialized tools and infrastructure to deliver value. A great use case is human-centered, AI-powered customer service. If you can find the right tool, it will do more than just power chatbots, it can resolve common cases instantly, predict and prioritize tickets, and help support agents with easy-to-find knowledge.
Another good example is natural language processing tools. Implementing an NLP framework into your own tools or platforms can be tricky for many reasons. For example, using NLP tools in the healthcare industry. The AI tool needs to be compatible not only with the healthcare organization's technology stack but also with the various medical terminologies and language used by healthcare professionals.
As you evaluate AI-related tooling, make sure the tools you choose are compatible with your existing technology stack and can integrate seamlessly with your other systems.
How Healthy Is Your Cloud Ecosystem?
AI models require significant computing power, which is clearly why many companies choose to leverage cloud infrastructure for their AI needs. But there are more AI-centric reasons why a company’s cloud solution needs to be solid. For example, scalability. As mentioned, almost all AI applications require large amounts of computing resources, particularly when training on big data sets. An AI-ready, healthy cloud ecosystem would provide scalable infrastructure that can easily be provisioned and de-provisioned as needed, letting it scale up or down depending on workload.
It seems obvious, but having a healthy cloud infrastructure hosting your AI tools will ultimately save your company money. Given the exorbitant amount of storage and compute needed for AI tools and applications, it makes sense to move to the cloud versus maintaining an on-premises infrastructure as you are beginning to scale.
Risk, Liability And Guardrails For Extreme Situations
AI has the potential to transform your business, but it also introduces new risks and liabilities worth reviewing. For example, AI models can introduce biases or errors that may have legal or regulatory implications. Therefore, it’s crucial to establish risk management and compliance protocols that address the unique risks of AI. This could mean conducting regular audits of your AI models and establishing transparent decision-making processes.
In some situations, AI models can have unintended consequences that may have far-reaching implications. For example, a recruiting tool could show unintentional bias or a self-driving car could choose to crash into a human to avoid a more significant accident. Therefore, it’s essential to establish guardrails or fail-safes that can prevent catastrophic events from occurring. This might mean introducing human oversight, establishing performance thresholds or implementing an emergency shut-off mechanism.
Building Your AI Stack
Building a solid AI stack requires careful planning and attention to detail. Make sure you clearly define your goals and use cases; this will help you identify the technologies and data you need to build successfully. Without a clear goal, you’re building a bit aimlessly, which results in lost time and wasted resources.
As I state above, collect and preprocess high-quality data. Data is the absolute foundation of any AI use case. Experiment with data augmentation techniques to increase the diversity of your dataset and improve model performance. This brings me to an important point: Build robust models. Train your AI models using best practices such as regularization, hyperparameter tuning and cross-validation.
Then, deploy and monitor your models. Monitor for accuracy, speed and scalability, and retrain them regularly to ensure they remain accurate over time. And it’s important to stay on top of the latest tools and techniques.
So, Is Your Business Ready?
Take the time to work with your executive team and your data team to ensure your company is ready to leverage AI technology. Make sure your data is reliable and accurate, double and triple check your cloud system health, and spend the time and resources to build your AI stack carefully and with close attention to detail. Once you’ve cross-functionally investigated and prepared, it could be time to get started with AI in earnest.