What are three key factors to keep in mind when you are making 7-8 figure investment decisions for AI adoption and managing enterprise AI/ML projects?
It’s not the machine learning models, ETL processes, containerization, or scalable deployments in production.
It’s about the impact of AI adoption on the organization.
Here are 3 key factors to consider when managing AI/ML projects:
1) Business outcomes and objectives
Above everything else, you should always stay focused on the company’s goals and desired results from AI adoption. You must also understand how they fit into the overall business objectives in the short term and the long term.
This means you should be able to measure the tangible value, the intangible value, and the peripheral benefits of any AI/ML use case on the organization.
Use the following formula for estimating ROI from AI adoption:
(Tangible value) * (annualization) + (intangible value) * (emotional impact) + peripheral benefits = Return on investment (ROI)
If you want your proposals for AI adoption to be taken seriously, you must be able to estimate and articulate the business value clearly to all relevant stakeholders (check out my blog post titled, “How To Get ROI From AI Projects” for more information).
At this point you should have a strong understanding of how the end-to-end machine learning process works, how to generate clean datasets, pitfalls to avoid, and best practices.
If you want to improve your competence and credibility, gain a thorough understanding of your company’s operations, systems, processes, products, and competitive landscape. The more you understand the business itself, the better you will become at identifying opportunities for AI adoption (check out my blog post titled “How To Identify AI Opportunities In Your Company” for more information).
2) Extensive documentation
Make sure you and your AI/ML team are creating extensive documentation throughout the AI adoption journey. You also want business users across the organization to keep clear documentation about the various products, processes, systems, and operations.
Given that successful AI adoption follows a lean methodology, you want to document what works, what doesn’t work, and why. Keep track of all your experiments (such as A/B tests) with their corresponding results.
Without extensive documentation of all AI/ML activities, it will be difficult (and expensive) to make progress in your company’s AI adoption journey.
You should be constantly developing and refining ‘best practices’ documents and what I call your ‘AI adoption manual.’ Be sure to keep track of all your team’s mistakes and failed experiments, as well (including your own).
Even if a given use case has been proven at the industry/sector level, your company has its own unique systems and processes. AI/ML use cases are never developed in a vacuum (if they are, the POCs will fail). These use cases are always integrated into existing products, processes, or systems, and your team needs to learn how to perform these integrations successfully.
Check out my blog post titled, “3 Steps In Your AI Journey” for more information on the enterprise AI adoption journey.
3) Leading AI/ML teams
Leadership is the most valuable skill you can have in this new era of AI. If you master leadership, you will rise quickly in your organization all the way into the C-suite.
As a manager, you will be leading AI/ML teams composed of data scientists, machine learning engineers, deep learning engineers, software engineers, designers, QA testers, and more. The distribution varies depending on the size of the organization and its level of investment into AI adoption.
Take the time to understand these roles and their responsibilities.
You must also know what are the common bottlenecks and pitfalls when implementing an AI/ML use case and how to help your team break through (check out my article titled “What To Do When Your Team Hits A Wall During An AI/ML Project” for more information).
More importantly, however, take the time to understand the people that fill these roles.
Have 1-on-1 meetings with them and learn what they want in life, why they go to work every day, what motivates them, what makes them happy, and where they want to go in their career.
Then, become the ultimate leader through leadership development.
Help them achieve their career goals within your organization by coaching them and showing them the way. As you develop your team’s potential, the company will feel the exponential increase in value.
And nothing goes unnoticed.
If you do this long enough, C-level executives will notice and you will gain unlimited opportunities to grow in your own career.
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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