Becoming AI-first: How to get there

When embarking on a transformational journey for the company, you must consider how that decision will affect the culture. People  tend to resist change, especially if they don’t understand the reason for it or see clear benefits. 

An AI-first strategy, therefore, starts with support from the top. Management has to see the value in the transformation to support it, and then work to get buy-in from all stakeholders. When implementing AI, the organization as a whole has to trust the technology and its potential to bring positive change. In other words, people need to believe AI will make their lives better.

It’s critical that employees are informed of the changes AI will bring and how it will affect their jobs. That way, when systems are fully operational, employees will be better prepared for what is coming.

Organizations require a sense of purpose when embarking on an AI-first strategy. It’s crucial that you take the time to figure out where to use the technology and to what effect. That means developing scenarios in which AI can boost efficiency, productivity, and process optimization through AI-enabled data analytics and automation. 

For this exercise, use cases and their purpose should be as specific as possible. For instance, a manufacturer that places sensors on equipment to capture data should have a plan for that data. Is it to keep systems running at peak performance through predictive maintenance? Will the data have other uses? 

The data may deliver insights on environmental conditions such as temperature and humidity, which affect machine health and productivity. It may also reveal use patterns that can lead to the optimization of both machinery and the staff that oversee the equipment.

A successful approach typically targets cost savings or new revenue, in order to assist in building the value case for broader roll-out of AI use cases.

Ultimately, achieving AI-first status means transforming your company holistically, but that doesn’t just happen overnight. It’s always preferable—and safer—to start small, implementing one use case at a time, while thinking about how the sequence complements those use-cases that may follow. 

For each use case, draw up an exhaustive list of the processes and tasks you want to automate, as well as the pain points that AI can address. Use cases should be prioritized based on your company’s most immediate needs—or the areas where AI implementation will have the biggest impact. 

In a retail operation, for instance, a company may have synced its POS and inventory systems, but still require staff to place orders to restock inventory. With AI, those reorders can be automated based on historical patterns and ongoing sales data; the system learns when to restock a product and what quantities to order. 

No AI-first strategy can truly succeed without a well-defined data management strategy. After all, AI and it’s practice of machine learning (ML), use algorithms to accomplish tasks. Those algorithms require high quality  data to deliver meaningful results. Data, whether structured, unstructured, or partly structured, comes in from various sources and needs to be sorted and analyzed with a data management platform.

With smart use of a data management platform, you can start to identify the datasets and features to leverage for specific AI tasks, such as automating operational processes or improving customer-facing systems and services. To get that right, you need to identify the data sources, establish data governance procedures, and have a process to clean and sort the data according to predefined parameters as to what constitutes good data and what should be excluded.