AI can be a game-changer, but it's crucial to measure its real impact – the "AI performance" – to ensure investments pay off.

Artificial intelligence (AI) has captured the business world's imagination. The rise of generative AI and the tangible results many companies have achieved with AI have fueled high expectations. 

 

The Right Questions to Ask

Embracing AI involves significant investments in skills, technology, and infrastructure. This creates an immediate need to demonstrate value. Measuring AI performance goes beyond calculating ROI; it involves defining clear objectives for each AI initiative and adopting indicators to track progress and effectiveness. Start by asking the right questions:

  • How has customer experience improved?
  • Has employee turnover decreased?
  • Have production costs been reduced?
  • Is AI helping us achieve sustainability goals?
  • Is AI leading to better decision-making?

 

Metrics That Matter: From Adoption to Model Accuracy

In the data-driven era, measuring success means tracking specific performance indicators (KPIs) aligned with your AI goals. Here are some common metrics to consider:

  • Adoption Rate: For internal AI solutions, understanding how widely it's being used is key. Low adoption may signal a need for better change management or improvements to the solution itself.
  • Customer Experience Metrics: When AI is aimed at improving customer relations, metrics like Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and churn rate are vital.
  • Employee Experience Assessment: AI can boost employee morale by automating tasks and providing support. Track metrics like turnover rate, engagement, and absenteeism.
  • AI Model Performance: The accuracy of your AI models is crucial for building trust. Data science metrics like F1 Score and Mean Squared Error (MSE) help evaluate this.

 

ROI: The Key Indicator

Return on Investment (ROI) remains a central metric for measuring the profitability of AI investments. However, calculating AI ROI is challenging due to:

  • Intangible Benefits: AI can lead to improvements in brand reputation or organizational agility that are hard to quantify financially.
  • Evolving Nature of AI: The performance of AI models changes over time, impacting business outcomes and ROI.

Companies often make mistakes when measuring AI ROI, such as not accounting for model uncertainty, evaluating ROI statically, or focusing on individual projects instead of the bigger picture.

 

A Path to Calculating AI ROI

To calculate AI ROI, consider these steps:

  1. Quantify Benefits: Include both tangible benefits (cost savings, revenue increases) and intangible benefits (improved brand reputation, organizational improvements).
  2. Quantify Investment Costs: Factor in development, implementation, consulting, training, and ongoing maintenance costs.
  3. Define a Timeframe: Allow time for AI solutions to adapt and optimize before measuring ROI to avoid unrealistic expectations.

 

The Importance of Measuring AI Performance

The potential of AI is immense, but it's important to approach it with realistic expectations and a clear plan. Integrating AI involves significant costs and requires careful planning, skills, and technology. Define objectives, track progress, and measure benefits to ensure your AI investments deliver real value. Partnering with an expert consultant can make a difference.

The AI landscape is constantly evolving, and performance measurement will become increasingly critical. Companies that equip themselves with the right tools and strategies for measuring AI performance will be best positioned to reap the rewards.