How To Lead AI Adoption In Your Company

Do you have what it takes to lead AI adoption in your company?

Global AI adoption will be driven by a new breed of leaders: Powerful visionaries who step up, seize the opportunity, and embrace the challenge. They have no fear of failure, and they possess an unwavering belief in their ability to lead AI teams successfully.

Building on my previous blog post, whether you are leading AI adoption at your current company, or you decide to get an AI leadership/management role at another company, it is paramount that you keep 3 key factors in mind:

1) Positioning and Technical Competence

The most fundamental aspect of professional success in this new era of artificial intelligence is to gain a strong understanding of AI/ML fundamentals and capabilities.

You must understand enough AI/ML to be able to conceptualize the problems to be solved. Unless you know what can be done, it is hard to know what should be done.

If you are going to lead AI/ML teams successfully, develop AI strategy, create AI/ML product roadmaps, and prioritize use cases, you must first have a sharp understanding of the answers to the following questions:

  • What is AI?
  • How does it work?
  • Why does it work?
  • What are some common use cases?
  • What are some common pitfalls and best practices?
  • How are AI/ML teams structured and why?
  • How does the end-to-end machine learning process work?
  • How do we measure the success of an AI/ML product?
  • (Questions about data)

However, this new knowledge means nothing if nobody knows you have it. Whether you are proposing to bring AI into your current company, or you have a desire to break into the field and get hired at a company that is already implementing AI, you will need to provide certainty that you are the right person for the job.

In other words, you must be perceived as a competent professional they can trust with such a large and important responsibility. Here are some ways you can do this:

  • Share what you learn online and in-person
  • Teach what you learn if you understand it well
  • Take AI/ML courses and display them
  • Share your knowledge with your coworkers and professional connections
  • Offer to speak and/or help at AI/ML events
  • Brand yourself as an AI professional
  • Connect with AI/ML professionals, ask them questions, and learn from them

These help position yourself as a competent professional in this new era of AI, and they also help you get better at it over time.

Your goal is to be able to hold an intelligent conversation about AI adoption, strategy, and implementation. This will be key when you propose AI adoption at your current company, or when you interview for AI/ML positions.

2) Opportunity Assessment

Once you have a strong understanding of AI/ML fundamentals, use cases, and capabilities, the next step is to identify specific opportunities in your company.

The most straightforward method of identifying relatively low risk use cases is to assess what are the use cases that have been implemented successfully by other companies in your industry. Value, difficulty, time-to-delivery, risk, and resource requirements can be identified and properly estimated upfront.

Another method is to identify problems your company is already solving, and find a place where AI/ML could be added to create value. You can look at existing products, services, operations, processes, and employee roles.

This is a great approach because you know exactly how well the current product, process, or person is performing. When you bring in AI/ML, you have a benchmark against which to measure the added value. This will also give you an understanding of what successful AI adoption looks like, what are the challenges, and what how to measure ROI.

Check out my blog post titled, “How To Identify AI Opportunities In Your Company” for an example of how we identified AI use cases for one of our enterprise clients.

3) Communication

Stakeholder management is one of the most crucial elements of AI adoption, especially in ‘non-technical’ AI/ML roles such as AI Product Manager.

You must be able to communicate technical concepts to non-technical audiences and vice-versa. This is already the case in IT and software development, but it’s especially challenging in the era of AI.

Most stakeholders and business leaders do not understand artificial intelligence technology (check out my blog post titled, “The Biggest Weakness Of Corporate Leaders In The Era Of AI” to learn more). Therefore, the ability to translate complicated AI concepts/ideas into business terminology is a must-have. This is also important to keep all stakeholders excited and passionate about the value of AI for the organization. If you are not careful and intentional with your communication, their interest could fade – especially if a project is taking longer than expected to implement.

These are just some of the things to keep in mind as you enter this new era of artificial intelligence. As mentioned at the beginning, the world needs a new breed of leader with a powerful mindset to be able to lead AI adoption successfully.

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 and I will be happy to help you.

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