Dec 3, 2024
3 Min
Aistra
As organizations increasingly adopt artificial intelligence (AI), many face challenges in effectively implementing and scaling this transformative technology.
Neeraj Bhargava and Eric Selvadurai, co-founders of Aistra Labs, an AI adoption company, emphasize that a common pitfall is getting caught up in the hype and technology itself, rather than focusing on the strategic business outcomes organizations aim to achieve.
To address these challenges, they have developed a 3-step framework designed to help organizations set impactful AI goals and metrics. Here’s a closer look:
Step 1: Define Your AI Goal Dimensions
The first step is to align AI initiatives with core business priorities, There are three main dimensions to consider in the goal-setting process:
Productivity & Cost Savings: Determine how AI can enhance the efficiency and cost-effectiveness of operations.
New Revenue Streams: Identify new products, services, or business models that AI can enable.
Enhanced Capabilities & Experiences: Explore ways AI can augment and elevate the experiences of customers, employees, and stakeholders.
Clarity in prioritizing these dimensions is crucial for identifying the right AI use cases that will drive the most significant impact on business objectives.
Step 2: Assess Your Virtualization Ratios For Each Targeted Process
Given goals and priorities, organizations should focus on the "virtualization ratio," which indicates the degree to which a given process or task can be automated or virtualized using AI.
This metric quantifies the level of AI integration possible, as well as the identification of the potential Virtualization Surplus, such as time and cost savings, that can be captured, or new revenue that can be generated. The goal is to maximize the virtualization ratio and surplus while minimizing associated risks.
Step 3: Translate Surplus into Tangible Virtualization Benefits
The ultimate aim is to convert the AI-driven surplus into real business value. This step involves defining specific KPIs and outcomes that the organization wants to achieve.
It’s essential to recognize that there’s not always a direct correlation between the surplus and the ultimate benefits. Organizations must carefully plan how to reinvest that surplus to drive measurable improvements in areas such as revenue, productivity, and customer satisfaction.
An illustrative example involves a CFO who created a parallel "virtual" team to benchmark AI capabilities against human employees. This approach allowed the organization to experiment and learn before scaling AI across the entire finance function.
Conclusion
Effective AI goal setting requires a holistic, organization-wide approach that aligns AI initiatives with core strategic priorities. By following this 3-step framework, organizations can develop a clear roadmap for AI success.
