Low value to high value AI

A useful distinction is not between human vs AI, but between high-value and low-value contributions, regardless of whether they come from people or machines.

Many organizations are approaching AI as a cost-reduction tool. A more sustainable approach is to view AI as a value amplification tool.

From Low-Value AI to High-Value AI

The Problem

Organizations often deploy AI to:

1) Reduce headcount

2) Process more transactions

3) Handle more customer interactions

4) Generate more reports

5) Increase efficiency of repetitive work

These applications can create economic value, but they often fall into what we might call Low-Value AI:

AI that primarily substitutes existing activity without materially improving human outcomes.

Examples:

(i)Automated report generation nobody reads

(ii) Chatbots replacing human interaction with poorer service

(iii)AI-generated marketing content flooding the internet

(iv) Internal productivity gains that do not improve customer outcomes

The world may end up consuming more energy, more water, and more computing resources without becoming substantially better.

Defining High-Value AI

High-Value AI should satisfy at least one of five criteria:

1. Solves Previously Unsolvable Problems

Examples:

- Drug discovery

- Climate modeling

- Protein folding

- Disease prediction

- New material discovery

AI creates value that humans alone could not realistically achieve.

2. Multiplies Human Capability

Examples:

- Doctors diagnosing disease faster

- Engineers designing more resilient infrastructure

- Teachers personalizing education

- Facility managers optimizing thousands of assets simultaneously

The human remains central.  AI acts as a force multiplier.

3. Improves Societal Resilience

Examples:

- Flood prediction

- Earthquake early warning

- Cybersecurity defense

- Critical infrastructure monitoring

- Water management

These applications reduce systemic risk.

4. Creates Net Environmental Benefit

Examples:

- HVAC optimization

- Smart grids

- Water leak detection

- Renewable energy forecasting

- Circular economy optimization

The resources consumed by AI are outweighed by the resources saved.

5. Enhances Human Potential

Examples:

- Personalized learning

- Accessibility tools

- Language translation

- Elderly care support

- Mental wellness support

The objective is human development, not merely efficiency.

A Framework for Selecting AI

Before approving an AI project, organizations could ask:

Question 1,  What problem are we solving?

Poor answer:

Reduce staff costs.

Better answer:

Reduce hospital-acquired infections.

Question 2,   Would society be better if this AI did not exist?

If the answer is yes, perhaps the AI is not creating meaningful value.

Question 3, Does it augment or replace human judgment?

Generally:

Augmentation → Higher value

Blind replacement → Higher risk

The best AI systems usually work alongside people.

Question 4, What is the resource footprint?

Measure:

Energy

Water

Carbon

Rare minerals

Computing resources

Question 5,  What is the net impact?

A simple formula:

Net AI Value = Human Benefit + Environmental Benefit + Knowledge Benefit + Resilience Benefit − Resource Cost − Social Cost

A Value Pyramid for AI

Applying This to the Built Environment

For someone working in facility management, sustainability, and asset management, the most valuable AI applications are unlikely to be chatbots or content generation.

They are more likely to be:

1) Predicting equipment failure before breakdowns

2) Reducing building energy consumption

3) Reducing water losses

4) Improving indoor air quality

5) Extending asset life

6) Improving resilience to climate risks

7) Supporting elderly and vulnerable populations

8) Optimizing renewable energy systems

These applications generate economic value, environmental value, and social value simultaneously.

A New Principle

Instead of asking:

"How many jobs can AI eliminate?"

Organizations should ask:

"How much human potential, resilience, knowledge, and sustainability can AI create?"

The future winners will probably not be those with the largest AI models, but those that deploy AI where it produces the greatest net value to humanity per unit of resource consumed.

That is the distinction between low-value AI and high-value AI. The goal should not be AI-first. The goal should be value-first, human-centered, and sustainability-aligned AI.