Measure Twice, AI Once: A Strategic Approach to Predictive Transformation
In the rapidly evolving world of capital-intensive project management, Artificial Intelligence (AI) has emerged as a game-changing technology. However, most companies focus on the “art of the possible” without prioritizing tangible outcomes, which leads to overcomplicated system design, goal misalignment, and data overload.
The key to successful AI implementation lies in having clear business objectives first, and ending with only the data sources and technology needed to achieve those outcomes. This strategic alignment, or outcome-based approach, ensures efficient resource allocation and measurable success.
Getting Started with AI: The Importance of Data Quality
AI’s effectiveness rests heavily on the quality of data it processes, making data transformation a foundational step in any AI initiative. AI algorithms learn from data, and if the data is flawed, the outcomes and the associated model learnings will also be flawed.
Data needs to be accurate, relevant, and clean. When data quality is compromised, AI systems are at risk of producing inaccurate, biased, or even dangerous results at lightning quick speeds.
The Outcome-Based AI Approach
LoadSpring advocates for an outcome-based AI strategy, which offers several key advantages:
- Focus on Specific Results: This approach aims to achieve predefined, measurable outcomes, such as increasing job site productivity by 20% or reducing operational costs by 15%.
- Alignment with Business Goals: By starting with clear objectives, AI systems can be designed and trained to deliver specific outcomes that align with the company’s strategic goals.
- Improved Evaluation: This method allows for precise measurement of AI’s impact, ensuring that the technology contributes directly to business success.
- Efficiency through Focus: Resources are used more efficiently, minimizing wasted effort on capabilities that do not contribute to desired results and ensuring that time, money, and computation resources are utilized in the most efficient manner.
This approach is in contrast to a capabilities-based AI approach, which focuses on technological features that enhance business capabilities irrespective of a specific outcome. Companies utilizing this approach foster innovation and enhance their feature set but may encounter challenges in scalability, adaptability and misalignment with business needs.
Analyze Backwards, Build Forwards: Download the Methodology
LoadSpring’s 10-Step Predictive Transformation method provides a framework for businesses to “analyze backwards, build forwards” to achieve desired outcomes. This approach involves:
- Defining business goals and strategic objectives
- Working backwards through the steps needed to achieve these goals
- Identifying the base data and sources required for analysis
- Building solutions looking “forward” based on this analysis
I had the honor of presenting our approach and the entire 10-step process at the 2024 Project Controls Expo in Washington, DC, in a session called “Measure Twice, AI Once.” During the session I outlined exactly how you can use AI to solve precise business challenges without spending years or millions of dollars doing so. I hope you find the presentation and methodology helpful.
Final Thoughts
While AI promises significant advancements in project management, companies should be careful not to be swayed by the “shiny AI object.” Instead, careful planning and alignment should take place before embarking on an AI initiative.
By defining clear goals, understanding target outcomes, identifying and cleaning relevant data, and tailoring AI solutions to specific results, companies can avoid common pitfalls and achieve more successful, impactful AI deployments.
In essence, when it comes to AI implementation, it’s crucial to measure twice and cut AI once.
For more information about LoadSpring’s outcome-based AI approach and how to implement it, contact us at info@loadspring.com.
