Crossing the AI Chasm: Start with Outcomes, Not Data

Laks Srinivasan

November 13, 2024

FacebookXEmailGoogle+LinkedIn

Organizations are struggling to translate their AI investments into meaningful business outcomes.

This gap between AI implementation and real-world impact is what we call the “AI Chasm.”

In this article, we’ll explore the nature of AI Chasm and why it exists, as well as introduce a framework for successfully bridging this gap. Understanding and applying these concepts ensures that your AI initiatives deliver tangible value to your organization.

The AI Chasm: A Critical Challenge

While AI’s potential is clear, our research at the RoAI Institute reveals a stark reality: most organizations struggle to progress beyond the initial stages of AI implementation. This challenge represents a critical component of the “AI Chasm.”

Our studies show that 90% of AI projects never reach fruition, failing to bridge the crucial gap between generating insights and driving tangible outcomes.

So why does this problem persist?

One of the root causes often lies in a fundamental misalignment of approach. Many organizations embark on their AI journey from the wrong starting point. They begin by focusing on:

  1. The data they already have at their disposal
  2. The AI capabilities they’ve heard about or find intriguing

While these factors are important, they shouldn’t be the primary drivers of an AI strategy. Instead, the most successful AI implementations start with a clear focus on desired business outcomes.

This misalignment leads to AI initiatives that, while technologically impressive, fail to deliver meaningful business impact. They produce insights that, while interesting, don’t translate into the actionable strategies needed to drive real change.

Bridging the AI Chasm requires a shift in mindset. Organizations must reorient their approach, placing business objectives at the forefront of their AI strategy. By doing so, they can ensure that their AI journey leads not just to interesting insights but to transformative business outcomes.

The RoAI Framework: A Solution to Cross the Chasm

To help organizations successfully navigate the AI Chasm, we’ve developed the Return on AI (RoAI) Framework. This innovative approach inverts the traditional AI implementation strategy, starting with desired business outcomes and working backward to identify the necessary data and AI capabilities.

Here’s how the RoAI Framework guides organizations across the AI Chasm:

  1. Define Clear Business Outcomes: Begin by pinpointing specific business challenges you aim to address or goals you want to achieve. These should be directly aligned with your organization’s overarching strategic objectives. This step ensures that every AI initiative has a clear purpose from the outset.
  2. Design the Delivery Mechanism: Determine how your AI-driven insights will translate into real-world actions. This could involve integrating with existing business processes, creating new workflows, or developing user interfaces for decision-makers.
  3. Architect the AI Solution:
    • Design the Diamond: Outline the decision-making processes that will drive your desired outcomes. Consider questions like:
      1. Will decisions be fully automated, human-driven, or a hybrid approach?
      2. What rules, procedures, and ethical guidelines need to be established?
      3. How will you measure and optimize the impact of these decisions?
    • Define the Circle: Now, identify the specific insights and predictions needed to support your decision-making processes. Ask yourself:
      1. What key information is required to make informed decisions?
      2. Which AI models and analytical techniques are best suited to generate these insights?
      3. How will you ensure the ongoing accuracy and relevance of your predictive models?
    • It may be that one needs to iterate back and forth between the Circle and Diamond.
  4. Identify and Source Data: Finally, determine the data requirements to fuel your AI models effectively. Consider:
    • What types of data are essential for your chosen AI approaches?
    • Where will you source this data from (internal systems, external providers, new data collection methods)?
    • How will you ensure data quality, privacy, and compliance with relevant regulations?

Following this outcome-driven approach, the RoAI Framework ensures that your AI initiatives remain tightly aligned with your business goals from conception to implementation. This methodology increases the likelihood of successfully crossing the AI Chasm and maximizes the tangible business value generated by your AI investments.

Case Study: Transforming a Snack Vending Business with the RoAI Framework

To illustrate the power of the RoAI Framework, let’s consider a case study of a snack vending company that successfully transformed its operations. This example demonstrates how starting with business outcomes and working backward can produce tangible results.

  1. Define Clear Business Outcomes: The company identified its primary business challenges:
    • Vending machines frequently running out of stock
    • Unexpected truck breakdowns disrupting operations
    • Lost sales due to inefficient inventory management
    • Their overarching goal was to increase profitability by addressing these issues.
  2. Design the Delivery Mechanism: The company developed a comprehensive transportation management tool. This tool would serve as the interface between AI-driven insights and real-world actions, enabling them to:
    • Optimize delivery routes
    • Predict and prevent truck breakdowns
    • Ensure efficient stocking of vending machines
  3. Architect the AI Solution:
    • Design the Diamond: The company designed decision-making processes to:
      • Automatically generate optimal delivery routes
      • Schedule preventive maintenance for trucks
      • Trigger restocking orders based on inventory levels and predicted demand These processes combined automated decisions with human oversight for complex situations.
    • Define the Circle: To support these decision-making processes, the company developed several AI models:
      • Sales forecasting models for each vending machine location
      • Route optimization algorithms considering factors like traffic, distance, and delivery urgency
      • Predictive maintenance models to identify potential truck breakdowns before they occur
      • Dynamic drive time estimation models accounting for real-time environmental conditions
  4. Identify and Source Data: The company identified and collected various data types to power their AI models:
    • Historical sales data from each vending machine
    • GPS data from delivery trucks
    • Traffic and weather data from external providers
    • Maintenance records and sensor data from delivery vehicles
    • Inventory levels in real-time from IoT-enabled vending machines

Results: By implementing this holistic, outcome-driven AI strategy, the snack vending company achieved remarkable results:

  • Significant improvement in service levels, with machines rarely running out of stock
  • Increased customer satisfaction due to consistent product availability
  • Enhanced operational efficiency through optimized routes and reduced truck breakdowns
  • Ultimately, a substantial increase in overall profitability

This case study demonstrates the power of the RoAI Framework. The company transformed from simply having interesting sales forecasts (Circle insights) to implementing a comprehensive AI-driven system that delivered tangible business impact.

Conclusion: Bridging the AI Chasm

The AI Chasm represents a significant challenge but also an immense opportunity for forward-thinking organizations. By adopting the RoAI Framework, you can transform your organization’s approach to AI:

  1. Start with clear business outcomes, not available data or intriguing AI capabilities.
  2. Design delivery mechanisms that bridge the gap between insights and action.
  3. Architect AI solutions that encompass both the Circle (insights) and the Diamond (decision-making).
  4. Identify and source data strategically to fuel your AI initiatives.

The most successful organizations in the coming years will be those that can effectively bridge the AI Chasm, moving beyond interesting analytics to implement comprehensive, AI-driven systems that deliver real business impact.