If you were to pay attention to the headlines, you'd think that we're in a moment in time where artificial intelligence is autonomously running companies all over the world. Optimization and growth algorithms that touch every facet of a company. They create new ways of working, discover new products and materials, and propel an organization toward a future that only machines can understand. But that's far from the truth. In fact, we're barely getting started. We're collectively all still in the enablement era with AI. 

So what needs to be enabled for AI to be real? Well, the first is data and availability of data. Embeddings and the Transformer technology have opened up a new way to represent information in context with one another. But vectorizing information is not necessarily a simple function of ETL. Putting information into context is not just about copying and pasting into new tables. Data availability, especially in real time, is a huge enabler of AI in a company. 

The second area of enablement that people often do not think about is governance and security. The transformation of data into a format that generative AI platforms can engage with has already been subject to numerous security risks both inside and outside an organization. Prompt injection, permission elevation, and other cyber threats become much more real with the deployment of AI, especially when you are trying to map existing permissions and security protocols into brand-new systems. Understanding and respecting role-based access controls and data governance is a huge enabling factor in making AI real for companies. In addition, data governance is crucial for any kind of AI deployment. This can range from simple, shared definitions of metrics to adherence to GDPR, HIPAA, or other data governance legislation. Without proper data governance enablement, no AI implementation will work in enterprise. 

The final area of enablement that doesn't get enough attention is the real-time nature of running a business. Most enterprise AI deployments today focus on lifting and shifting data from one system to another, then batch-processing it to make it available for deep learning. That approach feels like a step backward for how modern organizations run. With the technologies we’re seeing, the importance of getting time to decision, especially with data, has only grown. Being first, being fast, and delivering value quickly are now critical metrics for companies that want to run effectively. Going through massive data-porting exercises just to make AI work does not suit a modern enterprise. Enabling real-time access is a huge factor in making AI real for companies. 

As we move forward with embracing artificial intelligence in the enterprise, it is essential that we recognize the critical role enablement plays in unlocking high-value AI use in the future. Sprinting ahead to adopt new technologies without proper enablement only leads to wasted money, time, and effort. Helping your organization use AI systems effectively is a key ingredient to the long-sought ROI that has eluded many AI applications. At Adaly, we see this clearly and have built our technology accordingly.