In my last post about AI, I wrote about how many implementations will be organizationally proprietary. Much of the value captured for organizations will be in their ability to leverage AI specifically for themselves and with their data. In addition to AI being proprietary, it will also be process specific.
Organizations will have AI implementations in sales, marketing, support, and other specific operational areas. Multiple AI implementations might support small and narrow processes and tasks within functional areas. For instance, in sales, an organization might have AI help to identify under-valued customers, while another AI service is identifying at-risk customers.
A good reference for how process-specific AI implementations will take shape is microservices in software engineering. Microservices are an architectural style used in software development to design and build applications as a collection of loosely coupled and independently deployable services. Unlike traditional monolithic applications, where all functionality is bundled, microservices advocate for breaking down an application into smaller, specialized components that communicate with each other over well-defined APIs (Application Programming Interfaces). Each microservice is responsible for a specific business capability or function. Process-specific AI will be of value to organizations, such as microservices bring value to software engineering.
Process-specific AI will help organizations to avoid the pitfalls of overhyped and under-utilized generic software tools such as CRMs and ERP’s. Generic software tools provide generic value until they are configured or customized to align with the processes and operations of a particular organization. Generic AI implementations also won’t offer much, if any, value to organizations unless aligned and implemented to specific processes. The advantage organizations have now is that they know generic technology tools will provide low value, and they have an opportunity to implement process-specific AI from the beginning.
Open-source models and the API infrastructure around commercial models will help organizations to leverage and/or access models created by others. This will allow organizations to initially not have to develop models while leveraging their data and aligning their AI efforts with the processes and needs. Process-specific AI products are also being created for most operating areas of organizations. There are already tools around facilitating and improving email, chat, and phone support with customers, for example. The AI product and tool race is just getting started. The products and tools will become better and less expensive as time passes. They will also become even more process-specific and niche. Existing products and tools will implement AI capabilities that will make them easier to use and provide higher value. It is still being determined whether most companies will give the AI capabilities included in existing subscriptions or whether they will charge more for AI functionality and modules. The products with the best AI capability and process alignment will have a significant advantage over competitors that need to get it right.
Most organizations will have a mix of AI implementation approaches and tools, but the result is very use-case and process specific to receive maximum value from their AI efforts. Generic AI will not move the needle for most organizations. Well-thought-out and implemented process-specific or micro AI will.