Starting Your AI Journey

It’s been almost a year since I had the opportunity to go all-in on Enterprise AI; taking a leadership role in IBM’s AI consulting practice where I have the opportunity to work with C-levels and practitioners across complex organization to build and scale AI projects. My day-to-day involves meeting a diverse set of leaders from Fortune 100 companies and connecting AI solutions to business problems. I feel fortunate to be perched at this vantage during this exciting moment in time.

I am frequently asked for advice on building a career in AI. I do think this is something worth considering given the disruptive potential of this technology. As VC Tomasz Tunguz said: "The surface area for innovation is staggeringly large, which means opportunity & career progression : a magnetic pitch to ambitious candidates." Conversely, now is a dangerous time to ignore the rapid innovation. Especially given that up to 30% of hours currently worked across the US economy could be automated by 2030 (McKinsey).

So the question I get is some combination of: "How did you learn AI" “Are you an engineer/data scientist” and "How did you get started?"

The large language models of today are SO complicated that literally no one except 500 PhDs really understand whats going on below the surface. Even the smartest people in the world that are building these things don’t fully understand their applications.

The best we can do is try to keep up by reading research papers and building upon their learnings, but this is the important point: The technical barriers aren't higher, in fact they've never been lower! ChatGPT ushered in an era of incredibly powerful consumer AI technologies that are almost all affordable and accessible. This is the first wave of innovation where consumer tech is at the leading edge. We've been given access to a set of technologies that allow us to ideate, build, test and iterate faster and at a lower cost than ever before.

Does this mean you should quit your job and join or start an AI startup that just raises a $30M seed round pre-revenue? Probably not in most cases. As transformative as the potential is, the technology is still catching up to the vision. In my day job, I see patches of true magic in AI implementations, but most companies are still a long way from the visions and sensations that make headlines. We are still early. A lot of startups that I speak to are solutions in search of problems to solve, and that’s not a great place to be.

What are employers looking for today?

• Passion: Not necessarily for AI, for the blending of AI with expertise on the subject matter that you will be applying it to. Technology is not a substitute for domain and role expertise

• Adaptability: In an ever-changing environment, the ability to continuously reeducate yourself and stay focused

• Soft Skills: Creativity, communication and collaboration have never been more important

• Focus: If used correctly GenAI can make an average coder a great coder, an average writer a great writer, an average lawyer a great lawyer etc. But you do need some base focus discipline and experience to get the most value out of it.

What aren’t employers looking for?

• 10 years of GenAI experience:

Overrated: years of experience, fancy degrees

Underrated: focus and passion

How I started my AI journey

Before I started working in AI full-time, I adopted a full-time mentality in terms of reading, writing, and experimenting with AI tools. I spent hours talking to “Chuck”, learning the contours of consumer AI capabilities. And through this, beginning to understand the underlying technology enough to be mildly dangerous.

I was a VP within the product org at Quantum Metric when I caught the AI bug and didn’t know what to do with it. So I studied the product, and began to brainstorm how large language models could impact various aspects. I built a hypothetical AI roadmap and a deck to “sell” the value of each capability. I then pitched it to the CEO along with a couple recommendations of light POCs we could run to prove out my hypothesis. Finally, I worked with the CTO to organize an AI hackathon, bringing everyone in the company together to build pilots and share ideas. I’m proud to say my team’s hackathon project was a very early and messy iteration of what eventually became Quantum’s Felix.

Reflecting on that unique experience, I learned that you need to understand the technology enough to be able to come up with unique ideas on how it can be applied, call bullshit, partner with technical teams, and tell a compelling story. To position yourself at the intersection of technology and business, use your business expertise to drive value realization of AI.

Getting started today

Step 1: Evaluate if you already have a job in AI. Your AI job of your dreams might be the job you are already in. Your boss or c-suite are likely already sweating about AI's impact on your company. How can you become the change agent that helps the business adapt? Some ideas here:

• Get your manager’s feedback on organizational tolerance and guardrails

• Help your company build an AI Governance counsel for implementing Safe AI within your organization based on its particular needs and risks

• Brainstorm a set of ideas for how AI can be implemented within your current function and associated business impacts

Step 2: Showcase your “Cyborg” superpowers: Within the constraints of your organizational policy, showcase the effectiveness of leveraging AI in your current role and become an internal thought leader. For example, demonstrate how you can reduce the time it takes to complete a repetitive task by X%. And then scale it out to your team or organization.

• Measure everything, show the time it takes to do tasks with and without AI and create internal case studies. AI is coming for many jobs but if you can be the leader, you can benefit from that (further reading: who moved my cheese)

• Build prompt libraries and share ideas with your manager and coworkers

• Create opportunities for innovation like an AI Hackathon where folks from all around the organization brainstorm and present ideas for AI projects

Step 3: Infuse AI into everything you do. Force your exposure to AI until its uncomfortable. Try new technologies, find ways to improve your efficiency. Pay the $20/month for ChatGPT, Claude or Gemini and force yourself to use them multiple times a day. Leverage AI in your personal life, become fluent (see my previous post)

Step 4: Build experience. If your current role restricts you, then take online classes, research, build a portfolio, create a side hustle or jump on networking opportunities. The barrier to entry has been lowered, there is no excuse for not building experience.

If you have ideas about using AI in your current role and want feedback or a thought partner - drop me a line!