What Does An AI Advisor Do? A Day In The Life Of My Emerging Role.

Quick update. There won’t be any office hours today, but we will meet Monday at 6 pm PT. Tune into my LinkedIn audio event with IBM’s Director of Product Management for Data and AI, Edward Calvesbert, at 9 am PT. We will discuss the problems data lake houses solve for businesses and where they fit in the data and AI strategy.

“What do you do for a living?” I have dreaded that question for over a decade. In 2012, Data Scientist and Big Data weren’t ubiquitous terms. I found out the hard way that saying something vague like, “I’m in tech,” just led to more questions. Explaining what I really did was even worse.

Nothing’s changed between then and now. Complex systems modeling, customer behavioral modeling, and decision support systems were all unknown niches when I worked in each. Today, I am an AI advisor.

It’s apparent that I enjoy working in emerging roles and defining them as I go. As an AI advisor, I am helping to define technical strategy and create a new job category. Step 1 is explaining what an AI advisor does, and “a day in the life” article is probably the easiest way to do that.

What’s An AI Advisor’s Value Proposition, And Why Do People Pay Me?

This is a surprisingly easy question to answer, which is why I’m leading off with it. I work with C-level leaders, board members, and investors to help them understand AI in a way that’s relevant to their roles.

The biggest need is to speak competently to a range of questions about AI. We’ve all seen very intelligent people get tripped up by questions about data, analytics, and AI. They also need enough information to avoid being bamboozled.

People in my target market understand the value of a deeper grasp of the domain. It builds or maintains their credibility and adds weight to their ability to execute in an AI-crazed marketplace. They can also make informed decisions in the face of conflicting or inaccurate information.

Those 3 groups have the least time to dedicate to this endeavor. That’s where I come in. I aggregate information from multiple sources and identify what’s important. I use my experience to add context and forward-looking guidance. I build summaries that explain:

• High-Level Talking Points They Need To Know

• Why Each Is Important

• The Implications And What They Should Be Preparing For

• Specific Opportunities And Threats That May Arise

How Did I Become An AI Advisor, And How Do I Get Clients?

I became a startup advisor for the first time in 2015, which led to VC engagements. I evaluated a startup’s technology and wrote up my findings for them. As the data science and machine learning field grew, I became more of a technical data and AI analyst.

It’s evolved into a two-sided role. Tech companies bring me in to evaluate and provide them with my feedback on their data and AI products. Sometimes they want me to use my social media following to explain how those products deliver value or position them as solutions for business needs. That’s side one.

Side two is closer to the AI advisor role I have transitioned into. My 3 primary customer segments want insights into which products best fit their needs. It began as an extension of my consulting practice. Building data and AI strategies for clients included helping build up the capabilities and infrastructure. Being an insider helped me stay ahead of both and build forward-looking strategy.

That was the setup for my transition into an AI advisor role. As I delivered more forward-looking, prescriptive insights, I started doing the job without realizing it. Many of my clients transitioned me into the role on their own. In my experience, that’s the easiest way to move into the role. Just start doing it for those 3 customer segments, and you will get approached to take on advisory roles.

Just start doing it can take on a few different forms, but every other AI advisor I know has a similar path into the role. Some are conference circuit regulars who speak at 3+ events per month. Their talks were unintentional auditions or job interviews for the role. Our target customer segments are in the audience.

Others were noticed through their content and being a frequent source of quotes or commentary in large media outlets’ content. Spending time as a media analyst or public-facing investment analyst is another avenue I have seen work, but it’s probably the hardest road.

Relationships matter a lot to land this role. Cold outreach doesn’t work, and there’s nowhere to send a resume.

A Day In The Life: Apple’s WWDC

There are days I expect to be chaotic, and this is a good example. I begin a week in advance by providing a brief of what to expect from the event. I am preparing clients to answer the questions, “What are you expecting from Apple this week?” or “What will you be listening for at Apple’s event?” The purpose is to give them points to touch on. My brief covered two items, the AR/VR headset, and AI roadmap.

The keynote dragged on for over an hour before it reached the Vision Pro. It was painful, and my 3 target segments didn’t have the time to sit through it. They need a summary after the event to refer to if questions arise. That’s my next job.

I created a brief but did not cover every announcement. I focused on two, the Vision Pro and the Apple silicon strategy. I provided context around the Vision Pro announcement, hardware platform, and use cases.

The Summary

Apple is presenting its vision for an AR/VR platform. It’s less about the features themselves. The important takeaways were why they implemented each one. Apple’s announcement was them elaborating on a thesis for where the Vision Pro will go.

The platform itself is a developer’s prototype and consumer novelty. Apple’s goal is to provide early adopters with access to the platform so it can gather feedback. The company poses the question, “What would you do with this and build on it?”

The right response is to form a working group to explore use cases and potential threats. The technology is still in the early innings, so the best response is cautious optimism and preparing for the next generation platform’s release. There’s already a lot to invest in this year, so pushing AR/VR into next year’s budget is a fiscally responsible approach.

Apple silicon was the expected AI roadmap presentation. Apple intentionally avoided saying AI as a subtle jab at the hype. Apple instead talked about machine learning supporting key features and experiences. Apple silicon is at the center of the AI roadmap. The company is building chips to support running models on device and supporting data scientists in the model training phase.

The presentation emphasized data security and privacy protection. Apple made it clear that the customer’s data wouldn’t be moving off device unless the customer opted into data collection, which few do. There are two ways to respond.

1. Agree with the approach as an opportunity to build models that operate on device and protect users’ privacy.

2. Express concern over the potential for Apple to create a data monopoly as the only business with access to all that customer data.

The Responses

What comes next is sometimes silence and other times a wave of questions and meeting requests. For me, it was a wave of questions and only one meeting request.

Many questions focused on the sentiment that the Vision Pro was a dud. My response was that it’s too early to say. This version isn’t a finished product, and some things have probably been held back. At the same time, I didn’t hear Apple explain a compelling reason to adopt.

However, the Disney partnership and its CEO, Bob Iger’s, presentation were very compelling. Disney teased customized content and experiences tied into Disney+. They need to define a roadmap and commit to the release schedule, but there’s a lot of potential.

A got a few questions about the immediate impacts of the Apple silicon announcements. My response was to expect a lot of requests for new Macs. Other than that, it’s the long-term trend and approach that matters. There isn’t anything to be done about it other than to decide which camp you want to fall into, aligned or concerned.

The meeting I had was to discuss who should be part of the working group and define its deliverables.

What Happens Next?

Now I watch for responses from Apple’s competitors and partners. I also watch my clients’ competitors. They will all begin dropping hints about what they are doing in response. Some of it’s smoke, but there’s information to be gained from substantive actions and commitments.

An unprepared C-level leader will often give too much away in an interview. Other companies tip their hands in their hiring patterns. My job is collecting the breadcrumbs and assembling the most likely scenarios. For example, the Disney partnership is interesting and worth keeping close tabs on. Disney’s partners will provide a lot of insight into what the company is building.

That’s the high-level overview of an AI advisor’s role. If you’re interested in learning more, drop your questions in the comments or respond to this email.