Imagine you conceive an idea which will save your company millions of dollars, reduce workplace injuries, and increase sales. Now imagine company executives dislike the idea because it seems difficult to implement, and the implementation details are not well understood. Despite the stated benefits of saving money, reducing injuries, and increasing sales your idea hits a brick wall and falls flat.
Welcome to the world of artificial intelligence (AI) and machine learning (ML), where the struggle is real.
At some point in your career, you have experienced a failed project. If not, don’t worry, you will. Projects fail for all sorts of reasons. Unclear objectives. Unrealistic expectations. Poor planning. Lack of resources. Scope creep. Just to name a few of the more common reasons.
When it comes to projects with AI/ML at the core, all those same reasons apply, plus a few new ones. AI/ML is perhaps the most important piece of general-purpose technology today, which means we are bombarded with AI/ML solutions to solve random or ill-defined problems in much the same way we are bombarded by blockchain solutions for tracking fruit trucks or visiting the dentist.
The overhype of AI/ML has left people skeptical regarding the promises made through project proposals. Even if you manage to get a project funded, the initial results produced by your model may be difficult to explain, leading to apprehension about deploying solutions which cannot be understood. Nobody wants to blindly follow the decisions and predictions produced by machine learning models no one understands.
It is clear the business world needs a way to build, deploy, and maintain AI/ML models in a consistent manner, with a higher rate of success than failure, and completed on time and within budget.
bizML
Thankfully, there exists a modern approach to AI/ML projects. It is called bizML, and it is the core subject inside the new book by Dr. Eric Siegel – The AI Playbook.
For any project, not just AI/ML projects, to succeed there must be a rigorous and systematic approach for real-world deployments. Every successful project has similar characteristics – measurable goals, stakeholder involvement, risk management, resource allocation, fighting scope creep, effective communication, and monitoring project progress before, during, and after deployment.
The AI Playbook breaks this down into digestible sections for anyone with business experience to understand. It outlines bizML as a six-step process for guiding AI/ML projects from conception to deployment: define, measure, act, learn, iterate, and deploy. Using stories from familiar companies such as UPS, FICO, and various dot-coms, Dr. Siegel leans on his experience to help the reader understand how and why even the best ideas often fail.
I don’t want to give away the surprise ending, so I will just say the real secret behind bizML is starting with the end state in mind. Many projects fail due to stakeholders not aligned with the reality of deployment versus expectations. bizML attempts to remove this roadblock by getting everyone aligned with what the end state will look like, and then build towards the agreed upon state.
I read through the book in less than a couple of days, absorbing the material as fast as possible. The use of personal stories was easier to read as opposed to a purely technical book focusing on code and examples. I cannot emphasize enough how this book is not a technical manual, but a business guide for business professionals, executives, managers, consultants, and anyone else wanting to learn how to capitalize on AI/ML tech and collaborate with data professionals.
Summary
As AI/ML solutions continue to gain traction in the market, this book provides the right framework (bizML) for successful AI/ML deployments at the right time. Anyone, or any company, looking to deploy (or has deployed) AI/ML projects should buy copies of this book for all stakeholders.
I’m putting this onto my bookshelf and 15/10 would recommend.
