Elevarse Performance Consulting

The pressure to adopt Artificial Intelligence in e-commerce has never been higher. AI is no longer a purely technical task for the data science team; it is an organizational imperative. As MIT Sloan research emphasizes, AI is just a tool—it only creates value when used properly , and that demands the right capabilities and widespread collaboration.

For any operational leader or change agent tasked with successful implementation, the key is moving beyond generating product descriptions and strategically managing the process to achieve financial returns.

Here are the three foundational principles for guiding enterprise AI investments that generate measurable value:

1. Invest in Practices that Build AI Capabilities 

Successful AI use depends on advanced capabilities in data science, data management, data platforms, and acceptable data use.

• The Trust Factor: When dealing with customer data, it is crucial to understand the technology enough to trust it. You need to know how data scientists validate models, even if you don’t know the exact way neural networks work.
• The Data Foundation: AI requires “supercharged” data monetization, meaning your data must be structured to convert into financial returns.

2. Involve All Stakeholders in the AI Journey 

AI is everyone’s business, requiring business users, developers, and solution providers to be considered key stakeholders.

• Operational Success: Imagine an AI model predicting a spike in demand for a specific item. The business user (Marketing/Sales) must provide feedback to the data scientists to ensure the model aligns with sales promotions and inventory reality.
• Fewer Consequences: This collaboration helps all stakeholders understand what AI can do, how much it costs, and how long it takes , leading to models with more positive benefits and fewer negative consequences.

3. Focus on Realizing Value from AI 

The ultimate goal of AI investment must be financial health. Operationalizing AI requires planning out the five steps of the value creation cycle before the project starts.

• The Value Creation Cycle (Example: Inventory Optimization):
  • Data: Gathering information from multiple sources (e.g., electronic health records and current data from medical devices at the bedside). (Substituting for the retail example): Gathering information from multiple sources (e.g., point-of-sale data, warehouse inventory, and customer web clicks).
  • Insight: AI analyzes data to predict high demand and potential stock-outs.
  • Action: Insights inform changes to best practices. Inventory policy is updated to automatically trigger a restock alert for that specific item.
  • Value Creation: Fewer stock-outs, increased customer satisfaction, and reduced loss of sales opportunities.
  • Value Monetization: The changes can be linked to tangible value creation. This manifests as increased revenue from captured sales, reduced inventory carrying costs, or reduced staff time spent manually expediting orders.

The Operational Imperative: Leadership must be prepared for monetization—the most difficult step. If your new automated inventory policy increases customer satisfaction but doesn’t lead to additional revenue or reduced labor costs, the ROI is not achieved.

When planning your next AI initiative, identify the specific financial return you seek, and then work backward to define the required data and actions.