During the latest GeniusMesh event, Jeremy L. Strickland, Global Strategy, Analytics, & Ops Leader, provided an in-depth look at how businesses can use AI and ML to achieve tangible ROI. Jeremy focused on strategies to get AI/ML initiatives funded, with a strong emphasis on presenting clear business cases tied to cost savings, productivity gains, and throughput improvements.
Key Takeaways: Building a Compelling Business Case for AI/ML
Lead with ROI, Not Technology
Focus on use cases and potential business benefits, rather than leading with AI/ML itself. This approach allows companies to better evaluate how automation can reduce team sizes, drive cost savings, and boost efficiency. For example, automating processes that previously required 100 people down to 10 can significantly lower operating costs while enhancing productivity.
Practical Applications in Marketing, Supply Chain, and Production
The easiest starting points for AI/ML implementation lie in areas like marketing, supply chain, and production. AI can optimize channel mix in marketing, detect anomalies in production, and improve route planning in supply chains. These applications provide quick wins in cost reduction and increased operational efficiency.
Executive Sponsorship and Ownership are Critical
Securing executive sponsorship from leaders outside the typical tech sphere, such as in operations or marketing, is vital for AI/ML project success. Equally important is having a single-threaded owner responsible for steering AI and ML strategies across the organization.
Challenges and Solutions in AI/ML Adoption
A common barrier to adoption is the lack of accessible training resources for non-technical leaders. Most AI/ML courses are either too technical or too broad for business professionals. Courses like AWS’s AI for Business Professionals bridge this gap by focusing on practical applications of AI and ML.
Realizing ROI in AI/ML Projects
Calculating ROI early in AI/ML projects is essential. Time savings from AI-powered tools, such as those used to streamline internal documentation, can be measured and redirected to more critical business initiatives. AI can also boost productivity without necessarily reducing headcount, allowing teams to tackle higher-priority projects.
Hiring for AI/ML Leadership
AI/ML leadership doesn’t require deep technical expertise. At the leadership level, it’s more important to understand how AI can drive business outcomes and integrate within existing workflows. Leaders should focus on identifying key business challenges that AI can address, rather than simply finding AI solutions for the sake of innovation.
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