“I want to get into AI, but my current company is not using it yet.”
I hear this often as I talk to professionals with a strong desire to join the fast-growing field of artificial intelligence.
So, should you switch companies in order to break into the field?
That depends on who you want to become in this new era of AI.
Do you want to become a machine learning engineer writing code and doing technical implementations most of the time?
If so, unless your current company is reasonably ahead in their AI adoption journey, you will have to get one of the many machine learning engineer roles out there – and that’s perfectly fine.
However, what are your options if you don’t want to be writing code and would rather be a technical leader working in AI strategy, casting corporate vision, collaborating with business leaders, and leading high-performance AI/ML teams?
Are there any such roles out there? Yes, but not many. In fact, as you research AI/ML roles you will find that the vast majority of them require hands-on engineering – even at the senior manager level. I have personally confirmed this by interviewing for senior manager roles at Ernst & Young, Deloitte, Capital One, and others.
Again, this is perfectly fine if you are willing to do 50% technical and 50% non-technical work at best (actually, my research has found that it’s more like 80/20 for most roles, even the ones with the title ‘AI Manager’).
As far as open AI/ML roles, the companies hiring are typically farther ahead in their AI adoption journey. This means they can raise the bar and only accept people with a strong technical, hands-on engineering background for any AI-related roles (AI Project Manager may be an exception which does not require coding, but there are only a few open roles and certainly not enough for everyone).
What else could you do if you really want to get into AI/ML from a leadership/management and strategy perspective? What can you do if you simply don’t want to let the AI wave pass and be left behind?
You could do what I did: Lead AI adoption at your current company – especially if AI is not being used at all yet.
This is a great option for several reasons:
- Your company already trusts you and likes you.
- You already have access to stakeholders and potential champions.
- You don’t have to go through a standard career transition process.
- You have little to no competition if you know what you are doing.
- You can position yourself as the expert to help lead AI adoption in the company.
- You can create the exact AI role you want, instead of depending on open roles.
- You will be the hero that started and led the AI transformation of your company.
- As AI adoption in your company progresses and your team grows, you will naturally grow and be at the top of the hierarchy (e.g. VP of AI or Chief AI Officer).
When I first got into artificial intelligence, the company I was working for (as a technical consultant) was not using AI/ML at all. However, the management team (including the owner) was already discussing their options and potential use cases during business meetings.
They did not want to be left behind, especially as competitors adopted AI, and they had a strong feeling that AI could add immense value to the organization.
However, the biggest issue preventing them from moving forward was a lack of in-house AI/ML expertise, both strategy and technical. The uncertainty in process and results made it easier to ignore AI altogether, at least for the time being.
I was able to discover all of these things as I talked to multiple people in the company. To my pleasant surprise, several people were extremely excited about AI/ML and would love to get a project going. I will expand on this story in a future article, but I want to show you what is possible.
The 3 high-level components that allowed me to create in-house excitement about AI and quickly close a deal for the first project are:
1) Positioning and Technical Competence
2) Opportunity Assessment
I was involved in both AI strategy and technical implementation, but that was a choice. I had freedom and flexibility to structure our AI/ML team in exactly the way I deemed appropriate for the company. I was able to put together an in-house team involving technical and non-technical people and lead all of us to a successful delivery.
In the next blog post, we will expand these 3 components in more detail to help you start thinking about the powerful option of leading AI adoption in your organization.
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at firstname.lastname@example.org and I will be happy to help you.
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