This is the second article in our “AI for Career Acceleration” series – be sure to read the first installment (and watch the video on that page).
We had a lot of feedback from the first article in this series. I’m glad to see these ideas resonating with so many of our readers and subscribers. The survey responses from our first article were insightful, and I’ll be addressing many of them in the upcoming release of our “Getting Started with AI” report.
In this second article, I’ll be going further into ways that nontechnical professionals can advance their careers in the era of AI.
This article focuses on one main question:
“How can nontechnical professionals add value to an AI project?”
The most succinct answer to this question is:
“By asking the right questions.”
Throughout this article, I’ll share how I learned this lesson myself (as someone with no previous experience in AI), and how any nontechnical professional can add value to the early AI conversations in their organization — and be part of the more exciting, transformative aspects of their industry.
The AI Career Gap – an Opportunity Even for Martial Arts Teachers
I mentioned the idea of the “AI Career Gap” in my previous article in this series. I hope you’ve read the first article, but I’ll reiterate just in case – the AI Career Gap idea goes something like this:
- Nontechnical professionals who understand how AI works, what AI does, and how to apply AI will be pulled into early AI conversations and plans for their company
- These same professionals who understand AI, are likely to be pulled into early AI projects
- An understanding of AI adoption plus actual experience with hands-on AI projects will be extremely valuable in the job market
- These nontechnical professionals with early AI experience will have a huge advantage for their career opportunity, prestige, and income – while many of their peers will remain stuck in “legacy” business processes outside of AI transformation
I also made the following claim:
Within the next 5 years, having practical experience applying AI in the enterprise (and understanding best-practices in doing so) will be worth 5x more than an MBA.https://t.co/Ou8a9iGYrd pic.twitter.com/EqVDs9uYeI
— Daniel Faggella (@danfaggella) November 13, 2019
This idea applies even to the most unlikely nontechnical folks, myself included. As a reminder, this is my not-so-fancy career path:
In 2012 I decided to start what is now Emerj – but I had literally no idea how I would do it.
How could I become involved in the opportunities of AI business transformation (and the big-picture ethical concerns of AI) with absolutely no coding experience, no consulting experience, and no
By learning a lot of lessons the hard way – and by interviewing hundreds of AI experts for many years (on the AI in Industry podcast) – I gradually learned how I could be valuable to businesses.
Starting as a martial arts teacher in a 4000-person town, today I’m paid to travel and speak about AI, and Emerj works with large and powerful organizations like the World Bank, and multi-billion dollar retail and insurance firms.
After years of bumbling (aimlessly at first) for AI insights, I was the guy with AI tech background but who understood context on problems and use-cases… and became the guy holding the marker at the AI strategy whiteboard.
My experience isn’t that unique and essentially anyone in the following positions will have all the same opportunities:
- Managers and VPs who want to improve their career prospects (or build job security) in the AI era
- IT or strategy consultants who want to be part of the massively lucrative “AI transformations” across the enterprise
- Functional business leaders who want to be part of AI opportunities in their business or industry
I’m working on finalizing a report that will flesh out all of the critical lessons I’ve learned in this journey to an AI strategy career – but today I’ll get into one specific slice of this insight. Namely: How I first learned to add value to an AI strategy conversation as a nontechnical person.
Frameworks of AI Success – for Nontechnical Professionals
So – how can nontechnical professionals add value to an AI project?
Asking the right questions is a good start – but different situations call for different questions.
Today I’ll focus on two of the most important situations that nontechnical professionals might find themselves in.
I developed these questions based on dozens of interviews with AI project leaders in companies like Facebook, Airbnb, and others – as well as AI leaders and consultants in traditional enterprise sectors (oil and gas, banking, retail, etc). Here are some of my favorites:
1 – Prioritizing AI Project Ideas
When a company first begins laying out their ideas for initial AI projects, they often make the mistake of seeing AI projects as “plug and play” applications. They aren’t. AI is built on a set of critical capabilities (which I’ll be exploring in more depth in the third installment in this article series, going out next Tuesday) – and rolling out AI projects based on a succinct plan is essential to developing those company-wide capabilities.
Questions to begin with:
- What is the relative weight we’d like to give to the following AI project factors?
- Ease of deployment
- Data science talent requirement
- Data requirements
- (Note: Deciding on the criterion with which to assess AI projects, and how each factor is weighed, is critical to achieving an AI ROI, and rolling out a successful AI strategy.)
- Which of these AI project ideas have a known, successful case study that we know of?
- (Note: Without any known precedents of use, it is very hard to know whether or not AI is the right tool for the job. Most initial AI projects should be relatively proven use-cases that we can follow along with to gain initial experience with enterprise AI – before venturing into uncharted territory.)
2 – Starting an AI Project
Once an initial project (or in large organizations, set of projects) is determined, it’s important that data scientists and nontechnical experts ask the right questions before allocating resources or even talking to vendors.
Questions to begin with:
- What is the measurable ROI we would hope to achieve from this project, and do we have a benchmark to achieve that ROI?
- (Note: It’s important to ground your assumptions about AI ROI from real known use-cases. If the largest and most powerful companies in your sector were unable to achieve a measurable ROI – it is likely unrealistic that you’ll be the first to determine a new and novel AI use-case, as very few companies can stomach the R&D cost of being the first.)
- What subject-matter experts will be assigned to work with the data science team on this project?
- (Note: Companies learn the hard way that it is naive to assume that nontechnical subject-matter experts will automatically collaborate with data scientists to bring an AI project to life. They are paid to do their “normal job”, and unless their job description changes and they understand that collaborating and working with data scientists is “their job”, they’ll ignore that responsibility and potentially make the project impossible to move forward with.)
There’s a lot more for both of the situations above, but these are excellent starter questions, and just asking them makes it clear that you understand the realistic challenges ahead, and the way that AI projects have to be rolled out in order to have a solid chance at a strong ROI. “Getting it” in terms of AI adoption is what took me from aspiring AI advisor to someone with a growing book of business and a continuous lineup of speaking engagements.
As I mentioned in my first article in this series (which I recommend reading if you still haven’t), these strategic, practical AI considerations potentially pay much better than being an actual data scientist. It will be the AI strategists (i.e. the people who guide and direct the programmers and engineers) who will reap the greatest AI career benefits in the years ahead.
Every Profession Will be Impacted
AI transformation isn’t limited to Silicon Valley, to the USA, or to fast-adopting sectors.
The fact of the matter is that:
- Some professionals will be more valuable in the job market in this current AI era, some will be less valuable
- Some professionals will have more job security and valuable skills, some will have less
- Some professionals will see more opportunity and prestige in their careers in the AI era, some will have less
The purpose of this series on AI for career advancement is to make sure that my readers and subscribers end up on the right side of AI disruption.
There are professionals who don’t believe that this AI Career Gap transformation is something that they can take advantage of, but essentially all professionals will be impacted. Here are some of the common arguments I hear:
- “But my company isn’t using AI now.”
- Most companies aren’t! That’s perfectly fine – the point is to have AI context and insight now, so that you’re part of those early planning conversations and early projects – giving you a massive career opportunity advantage in the years ahead.
- “My company isn’t even thinking about AI – at all.”
- If you’re within a large enterprise, it is nearly inevitable that they will be thinking and planning around AI soon – and at the very least – you should develop AI insights yourself so that you can be valuable on the job market.
- If you’re within a small business, AI will impact your workflows and processes as the tools become more accessible (see my article on the AI Zeitgeist about AI accessibility).
- “But I don’t have any AI certificates.”
- I personally have no such credentials, and when the United Nations asks me to present my research – or a large organization asks for AI strategy guidance – they don’t ask for one. Plenty of my Emerj subscribers who have used our articles and podcasts to advance their career also have no such certification. Practical knowledge blows away a certification any day – and that’s what will matter as you begin being pulled into early AI conversations within your company.