Three Ways to Leverage Industry Expertise for an AI Career – A Guide for Non-Technical Leaders

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Three Ways to Leverage Industry Expertise for an AI Career

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI’s capabilities to important business workflows and objectives.

Whether you’re aiming to become an AI consultant or an enterprise AI project leader – pivoting your career into AI doesn’t involve starting from scratch. It involves leveraging your past experience in the most productive way.

Aspiring AI catalysts should consider three types of experience “assets”:

  1. Understanding Drivers of Success (in industries and workflows)
  2. Understanding Pain Points and Motives (for roles and types of companies)
  3. Personal and Professional Network

If you aim to become an enterprise AI project leader: Use this self-assessment as an opportunity to find areas where you might add value to existing AI projects – or where you might be a part of early AI projects at your current firm, or with a future employer.

If you aim to become an AI consultant or advisor: Use these sections to inventory your own experience, and consider how these assets can help to land you new business, or refine your services, value proposition, or go-to-market approach.

In the sections below we’ll explore each of these factors in depth.

1. Understanding Drivers of Success

AI projects require much more than an understanding of data science. They require a rich understanding of how the business operates, the content in which it operates, and how to define “success.”

A machine learning PhD will have a lot to say about the data quality or a bank’s fraud operation, and the actual algorithms that might be the best fit to find anomalies in payment or money transfer data but they’ll know almost nothing about the common patterns of fraud, the data points that most commonly lead to fraud already, and the way a fraud operation’s success is measured. 

Non-technical subject matter experts are almost always one of the most crucial elements of the success of AI project selection, or to the success of any individual enterprise AI project.


If you have rich experience in a specific industry – or set of industries – that knowledge will almost certainly provide an upper hand in competing in the consulting ecosystem.

Bankers, retailers, defense contractors, etc all feel comfortable doing business with people like them – people who understand their problems, their language, their unique constraints.

For example:

  • If you worked in CPG for years, you might bring with you a robust contextual knowledge of how CPG firms generate revenue and manage manufacturing and supply chain operations. You might understand the ins and outs of how departments interact, how the most successful companies differentiate themselves from the rest of the industry, and more.
  • If you worked in retail banking for years, you might have a strong grasp of their overall operations and various departments. You might know how banks make money on various products, and how they compete in the market for both customers and talent. None of these insights should be taken for granted, and almost no data science PhDs will enter the job market with the remotest understanding of these factors.


Some workflows and business processes are industry specific (for example, underwriting in insurance), but others are transferable to almost any industry.

Even industry specific workflows often have many transferable lessons. Someone with a rich understanding of insurance underwriting workflows may be able to find new efficiencies or opportunities in a loan underwriting workflow, or other paperwork-heavy processes. Deep workflow familiarity can provide non-technical AI consultants with another kind of differentiation or relative advantage.

Examples of workflow experience might include:

  • Call center customer service
  • Email marketing
  • Account management
  • Financial forecasting
  • Etc… 

Action Steps

Take inventory of the industry and workflow areas where you have the most experience. Create a list for each respectively.


  • Should I brand my services specifically towards the industries (i.e. insurance) or workflows (i.e. call center operations) where I have the strongest experience?
  • Should I brand my services to a broader set of prospects (beyond my workflow or industry knowledge), but focus my early marketing and sales efforts on those industries and workflow areas where I have the most familiarity?
  • Which kinds of prospects would value and benefit most from the industry and workflow experience that I’ve accrued over the years?

Find the Right AI Use-Case, Faster 1200x200@2x

2. Understanding Pain Points and Motives

Making sales is about solving problems and alleviating pain. An understanding of motives and pain points allows a consultant to appeal to those motives in their market messaging and branding – and in their sales presentations.

Types of Companies

Think about the kinds of companies you’ve worked in, and the dominant pains and motives that you experienced within them.

Examples might include:

  • National telcos are motivated by anything that will improve market share
  • Investment banks are inordinately concerned with avoiding regulatory fines and punishments
  • Manufacturing firms are constantly experiencing pain from overproduction and underproduction
  • Etc… 


The same goes for roles. If you consider the roles that you had the most exposure to in your professional career, you’ll be able to understand the pain points and motives that drive them as well.

Representative examples might include:

  • Anti-money laundering leaders in banking are focused entirely on “checking the boxes” for regulatory compliance, not 
  • Heads of marketing in eCommerce are pained by attribution problems, and in finding the marketing channels that are genuinely responsible for growth
  • Etc…

Action Steps

Write down the most important pain point and motive lessons you’ve discovered based on the companies you’ve worked in and the roles you’ve interacted with frequently.

Ask yourself:

  • Can AI address any of the pain points I’m familiar with? Is there evidence of AI delivering strong value to alleviate these pain points?
  • What kinds of AI applications or opportunities align with the motives that I understand? 
  • Is it possible to mold my brand’s overall message around the motives and pain points of the target audiences I understand well?
  • Is it possible to mold some of my initial marketing campaigns specifically around the motives and pain points of the target audiences I understand well?

For aspiring AI consultants – like those which we coach one-on-one in our Catalyst Advisory Program – understanding industry and business pain points is often the best leading insight to determining your services and business model. Readers with an interest in non-technical AI business models may benefit from reading our article titled Five Non-Technical AI Services Business Models. Our recent LinkedIn post about this topic garnered some interesting comments and additions from the Emerj community:


3. Personal and Professional Network

My experience meeting with AI consulting firm founders (including our many interviews on The AI Consulting Podcast) leads me to believe that something like 80-90% of AI consultants sell their first projects through their network.

In the early days of an AI consulting firm, there are generally two benefits to reaching out to one’s network:

Referrals and Recommendations

Some percentage of the contacts in your rolodex might be prospective customers, or (more likely) have an ability to introduce you to a buyer directly.

For example, if you’re interested in offer AI strategy services to mid-sized financial services firms – your acquaintances who work in leadership in this space might themselves be potential customers.

Ideas and Perspective

Some contacts might not have the ability to buy, but they operate within an industry of functional business area that is relevant.

For example, you may have contacts who have procured consulting services before for their company – and you may ask them about your marketing material to get their first impression, and see if the value proposition is clear and design is professional. Or, you might have a friend who works in the retail sector and you can use their experience to help validate a set of services you were considering offer in the retail sector.

Action Steps

List the relevant contacts in your rolodex, along with a column for what you could do for them (introduce them to someone to further their career, help them with a specific business problem, etc), and what they could do for you (provide feedback on your business model, introduce you to a potential prospect, etc).

Ensure this list is complete by thinking through the most likely categories of important contacts:

  • Bosses or managers
  • Coworkers
  • Direct reports
  • Customers

Ask yourself:

  • Given the maturity of my firm, should I be focused more on leveraging my network for feedback and ideas, or for sales?
  • How many contacts can I rotate through per week in order to tap my rolodex fully? (i.e. 150 contacts might be reached with 12 outreach emails per week over 12 weeks)

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