Published on June 10th, 2020 | by Emergent Enterprise0
The Dumb Reason Your AI Project Will Fail
Working on a proof of concept (POC) can cause a workplace team to work in a vacuum of sorts. This is a primary error in an AI adoption process according to an article at Harvard Business Review by Terence Tse, Mark Esposito, Takaaki Mizuno and Danny Goh. It’s not uncommon for a Grand Idea to cause a team to focus on the horizon and not take into account where they are standing. In other words, they need to determine how the Grand Idea is going to exist in their very real existing technological and physical environment. Reality can be very unforgiving – unless you prepare for it.
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Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt — the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated.
What explains the disappointing end? Well, it’s hard — in fact, very hard — to integrate AI models into a company’s overall technology architecture. Doing so requires properly embedding the new technology into the larger IT systems and infrastructure — a top-notch AI won’t do you any good if you can’t connect it to your existing systems. But while companies pour time and resources into thinking about the AI models themselves, they often do so while failing to consider how to make it actually work with the systems they have.
The missing component here is AI Operations — or “AIOps” for short. It is a practice involving building, integrating, testing, releasing, deploying, and managing the system to turn the results from AI models into desired insights of the end-users. At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. Evolved from a software engineering and practice that aims to integrate software development and software operations, it is the key to converting the work of AI engines into real business offerings and achieving AI at a large, reliable scale.
Start with the Right Environment
Only a fraction of the code in many AI-powered businesses is devoted to AI functionality — actual AI models are, in reality, a small part of a much larger system, and how users can interface with them matter as much as the model itself. To unlock the value of AI, you need to start with a well-designed production environment (the developers’ name for the real-world setting where the code meets the user). Thinking about this design from the beginning will help you manage your project, from probing whether the AI solution can be developed and integrated into the client’s IT environment to the integration and deployment of the algorithm in the client’s operating system. You want a setting in which software and hardware work seamlessly together, so a business can rely on it to run its real-time daily commercial operations.
A good product environment must successfully meet three criteria: