How To Go From Business Problem To AI/ML Solution

“How do we translate our unique business problems into AI/ML solutions?”

According to machine learning architects at Amazon, many business leaders are struggling to understand how to translate their business problems into IT solutions that somehow incorporate AI/ML.

This is not surprising given that you need a strong intersection of two components to formulate a proper strategy and implement solutions successfully:

  • Subject matter expertise and domain knowledge for your particular business.
  • Strong machine learning engineering skills and knowledge, especially having been applied to similar/related business problems.

The initial phases of AI adoption are crucial to set a strong foundation for your business. You may have only one of these two components at the moment, but you can always acquire the other. Having both firmly in place will maximize your organization’s AI potential and subsequent business results.

Here are 3 things that will help you go from business problem to AI/ML solution:

1) Have a strong foundation of AI business fundamentals

It is paramount that business leaders have a strong intuition of how AI could be applied in their company. This includes a high-level understanding of how AI works, and how that framework could be applied to solve your particular business problems.

You can read about machine learning use cases in your industry, learn how artificial intelligence is being leveraged by competitors, or speak with AI professionals who have developed solutions for similar companies successfully.

This learning does not have to be restricted to your specific industry. Read about AI/ML use cases and applications in other industries. Learn best practices, find similarities, and cross-pollinate ideas from multiple companies and industries. Our clients’ rate of AI innovation is high because of this: We keep finding additional use cases and new ways to apply machine learning internally for further business value.

Having a strong understanding of AI in business will make you a more effective leaders for your AI team(s), and you will be in a position to positively challenge them.

Check out my blog posts titled, “AI Fundamentals For Business Leaders” and “Why Management Teams Need To Understand Artificial Intelligence” for more information.

2) Acquire AI technical expertise

Business is more important than AI. However, AI business strategy is not enough – you need technical implementations. Do you have people in your team(s) with the proper machine learning skillsets to implement your solution(s)?

We know that a combination of subject matter/domain expertise and machine learning skills is essential for successful AI adoption.

One of the most effective ways to bring AI into your organization is to find a senior software engineer who is excited about AI and hungry to learn it (perhaps he/she is already taking AI courses and learning the subject).

Ideally, this person has been with the company for a few years. Why? Because they have a strong understanding of the business domain and the IT infrastructure supporting it. This makes them excellent candidates for in-house AI talent and expertise development.

Check out my blog post titled, “Top 3 Traits To Look For When Hiring AI Talent” for more information. It applies whether you are hiring a new machine learning engineer or choosing an internal employee to participate in AI/ML projects.

Alternatively, you could create a combination of in-house technical talent and one or more strong AI consultants. The best AI consultants possess a combination of strong technical expertise and a deep understanding of your business. This collaboration definitely accelerates AI adoption and ROI. We are doing exactly this with our clients with great success.

Collaborations and teams always involve: business leaders, veteran senior software engineers, and strong AI consultants devoted to serving them and their business. Together, we maximize potential, opportunities, implementation speed, and business value.

3) Be committed to the outcome

AI adoption has short-term components, but most of the value for your organization will be realized over a longer time horizon. It’s important to understand what the AI adoption journey looks like, manage expectations, be willing to learn and iterate, develop in-house technical expertise, and be open to expanding use cases (check out my article titled, “3 Steps In Your AI Journey” for more information).

It’s crucial for business leaders to maintain constant involvement in AI/ML projects. No matter how ‘smart’ AI engineers may be, the business itself will always be more important. AI initiatives cannot succeed in a vacuum detached from the overall organization.

Successful AI adoption in the enterprise requires a strong commitment from senior management to maintain communication, participate in feedback loops, ask questions, challenge the technical team(s), and never lose sight of their vision for the company’s AI transformation.

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 info@carloslaraai.com and I will be happy to help you.

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Client case studies and testimonials: https://gradientgroup.ai/enterprise-case-studies/

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