How the AI Factory Cuts Through the Tech Hype

These days, Artificial Intelligence is all over the news. The world is witnessing the promise, opportunity, and speed of AI’s capabilities. At RevGen, we’ve seen this type of hype before (Remember the .com era? The early days of mobile apps? The Metaverse? Google Glass?).  

We prefer to focus on how to use AI technologies to generate immediate and measurable value for our clients. We use specific technologies such as price optimization or intelligent process automation to target applications such as call center coaching, revenue enhancement, and financial organization efficiency where our clients see immediate benefit to the bottom line. 

The Artificial Intelligence Factory

As we’ve worked to cut through the hype, we’ve settled on an analogy that clarifies things for us and our clients: a factory. Like a physical factory, the AI factory takes raw materials such as data, business knowledge, and strategy, and uses tools and processes like predictive modeling, data engineering, and automation to produce outputs valuable to the business. These outputs can include improved customer experiences, increased employee efficiency, and new or accelerated insights.  

The factory analogy helps to guide questions about investment and value, too. Nobody invests time and money into building factories just because they are in the headlines. We start the conversation by asking:  

• What is the value of the goods the factory will produce?   
• How much will it cost to build the factory?   
• How much energy will it take to keep the factory running to produce the goods?   

Asking these questions is a good first step towards evaluating whether and how AI can bring value to your organization. 

Raw Materials

If we embrace the analogy of a factory, how does that guide our thinking around the inputs, processes, and products created? Let’s start with the raw materials. 

We think of train cars bringing in quantities of raw materials, iron, coal, wood, and the like to the factory to be processed into finished goods. But other ingredients are involved in the production as well: plans and designs dictate what will be made, expertise is required to operate the machines, and energy is required to power the whole enterprise.   

Data is the primary raw material used in our factory: customer, sales, pricing, inventory, and other business data are ingested into the factory. However, like an actual factory, other ingredients are involved – business strategies and goals guide what is being produced, while knowledge of the business dictates how it is applied. 

Processes

Next, it is important to think about the tools used in our factory. Just like a physical factory uses conveyor belts, presses, welders, and other manufacturing processes to move, shape, combine and refine the raw materials, our AI factory uses different tools to manipulate, compare, and examine the data it ingests.  

Data engineering moves, processes, and refines data. Augmentation processes enhance the data and fill in gaps while predictive modeling creates forecasts. Just like the tools in a factory, some might be automated while others are used manually, however each step builds towards the end goal, guided by both the vision and the business understanding. 

Producing Results

Finally, we need to consider the output of the factory. All the raw materials used, all the processes employed, and the energy used to run the factory must produce something of value.  

For example, that may be an enhanced pricing strategy to increase revenue or improvements to your customers’ experience. It could be pinpointing threats or opportunities and the key actions needed to mitigate or leverage them. Sometimes, it takes the shape of the insights and coaching your employees need to sell more effectively or retain at-risk customers. Or it might be improved efficiency that allows your organization to scale quickly or reduce costs. 

Whatever the benefit, RevGen recommends that you start with the end in mind. Just as a microchip factory looks very different than an automobile factory, the shape of your AI factory, the raw materials used, and the processes applied will differ based on your goals. 

How RevGen Can Help

At RevGen, we have helped many of our clients build AI value factories and we have experts who are ready to talk to you about how your organization can leverage the promises of AI to build pragmatic, sustainable solutions. By cutting through the hype, we can help you identify needs and opportunities, build buy-in across the organization by developing a business case, build and operationalize a solution that generates measurable value, and develop the AI capabilities of your organization. 

Visit our site to learn more about the Artificial Intelligence services we offer or contact us for a more in-depth conversation. 

Noah Benedict leads RevGen’s Digital Enablement practice.  He is passionate about using technology to advance business and empower his clients to embrace new opportunities.