Capturing a customer’s imagination is the ultimate ambition of any salesperson. Whether you are selling pens or courses, you hit the sweet spot when you create an emotion and a desire for the solution that the product will enable.

The acceleration of digital transformation across all industries has disrupted our work, as we all know and are constantly being told. In an incredibly short amount of time, organizations are leveraging the power of data-driven tech to increase employee productivity, improve business performance management, enhance customer experience, increase and optimize process automation, and develop truly innovative products and solutions. However, this also places a burden on organizations to upskill or acquire not only employees with the right technical skills to build these solutions ethically and securely, but also employees who can create the business case, lead the projects, and market and sell the solutions. All these employees need to have a solid understanding of the concepts of the most prevalent emerging technologies—notably, Artificial Intelligence (AI), Data Science (DS), and the Internet of Things (IoT).

This is all well and good, but if you as an Account Executive in a training organization are trying to sell courses on IoT, DS, and AI, there is a fairly high chance that stakeholders at your customers’ organizations are not fully up to speed on these technologies either. “What’s the difference between Machine Learning and Artificial Intelligence?” is a question we often get. (For those of you who are reading this who are not technical but have been on an AI Awareness course— I know that will make you chuckle!)

What we all know now when speaking to end customers is that:

• They collect data.

• Their data is likely unstructured, and it needs to be cleaned and ethically engineered in order to make it useful.

• They have great ideas to transform their business if they could develop and implement AI/ML solutions.

However, it can be a challenge to explain how data is collected using IoT devices, what the tasks of a data scientist are, how coding an algorithm works, or what it takes to develop an AI model to use the data. Sometimes you need an analogy and some storytelling if you’re selling to a non-technical person.

To tie these pieces together, we use the analogy of the process that has gone into the tasty seafood meal that you’re served in a restaurant. What actually happened before this food ended up on your plate? Sounds crazy? Just bear with me.

Imagine a fishing trawler far out at sea. Fishermen cast the net (the IoT device) into the sea in order to capture anything in its path (the data). When they bring the net onboard and empty the contents on the deck, the fishermen (data scientists) have to rummage through and categorize objects (data) in unique piles: fish
over there, car tires in another pile, plastic in another pile, etc.

When they are finished, they go back to shore to pass on their catch to the Executive Chef (Senior AI Specialist) in a restaurant where the cooks (developers) utilize their cooking (coding) skills to make the meal (the solution) ready for customers—without burning or cutting themselves (secure coding).

If you then imagine all the other people and their roles in the restaurant, you can quickly understand the level of knowledge that these people need in order to sell the solution…or in this analogy, make an appetizing meal and leverage an appealing menu to the customers.

Kitchen Staff / Back Office

• Executive Chef / Senior AI Specialist

  • Needs vendor-neutral technical knowledge with multiple specializations in applying a technology to a specific vendor technology.

• Kitchen manager / Senior Data Scientist / Ethical Emerging Technologist

  • Has data scientist qualifications and is ombudsman of corporate ethical framework. Supervises the developers and other data scientists, coordinates work, and ensures that the output is done in an ethical manner.

• Sous-chef / Machine Learning Specialist and DevOps Engineer

  • Needs vendor-neutral technical knowledge. Works closely with the Senior AI Specialist and has an overview of the work of the data scientists and developers. Coordinates with project managers in the front office.

• Cooks / Developers

  • Need coding skills with an emphasis on secure coding practices, in addition to the application of code in the context of building a solution.

Front of House / Front Office

• Head Waiter, Wait Staff, Bar Staff, Host / VP Sales, Inside Sales, Client Services, Project Managers

  • Need AI/DS/IoT awareness-level knowledge and the ability to describe the concept of the technology and the solution.

• Sommelier / Technical Sales Consultant

  • Needs technical knowledge and the ability to describe how the solution was built, what the solution is, and the experience of the solution.

So while this new digital era is daunting in some respects, storytelling and creating analogies can help to enhance understanding and create those “eureka” moments and most importantly, emphasize the importance of training.

What analogies and storytelling do you use when selling to an audience unfamiliar with the process of developing a solution?

The next version of the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) course is currently under development, and I’m excited to give you a preview of how the course has evolved since the initial release of CAIP-110 in 2019. 

Just like the original course, CAIP-210 is driven by a high-stakes, vendor-neutral certification exam. The CAIP-210 exam—due out later this year—was updated to account for various changes in the industry, as well as to align better with the job roles that machine learning practitioners are likely to fill. 

The new CAIP-210 blueprint condenses the exam domains from six to four, with more emphasis on the overall machine learning workflow: formulating the problem; preparing data and engineering features; training, tuning, and evaluating models; and putting those models into operation. Most CAIP-210 subject matter is not dramatically different from CAIP-110. The new Domain 4.0, however, goes into much more depth on an important aspect of machine learning that the original exam and course covered only briefly: Operationalizing ML Models. 

The changes to the exam have brought corresponding changes to the course structure, flow, and content. For example, the course no longer teaches statistical and data visualization concepts, as students are expected to bring this as prerequisite knowledge. Instead, learners will spend more time applying various data preparation techniques, including those for use on unstructured data such as text and audiovisual media. 

Like before, the first few lessons in the course set the stage for students to follow a machine learning workflow, whereas the “meat” of the course is all about building many different models from many different algorithms. The algorithms in CAIP-110 appear again in CAIP-210, with the addition of forecasting algorithms as well. 

Instead of ending the course with a lesson about ethics in AI, the ethical risks and strategies for mitigating those risks are sprinkled throughout CAIP-210 within specific contexts. To support exam domain 4.0, the new course concludes with two lessons that let students operationalize their machine learning models by deploying an automated pipeline and integrating it with existing software environments. This MLOps approach is an improvement over CAIP-110, in which students built models in an ad hoc fashion, but didn’t put them into production. 

The course also features general content improvements, including better explanations of certain concepts, a new instructor slide deck, and more robust activities. 

For more certification information and a copy of the CAIP-210 exam blueprint, click here. For an early working draft of the CAIP-210 course outline, email assist@logicaloperations.com, or contact your Logical Operations account representative.   

I hope this high-level overview has been helpful and that you’re looking forward to the CAIP-210 exam and courseware as much as I am! 

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