From AI Investment to Business Outcomes That Can Be Measured

AI projects often start out with an exciting question:

What can we do with AI?

An even better question when building a production system is:

What measurable outcome will this AI system drive?

Businesses are investing in AI capabilities for automation, analysis, customer experience, forecasting, and operations intelligence. However, putting models into production and using AI tools doesn’t necessarily translate into business value creation.

The implementation itself is just half of the equation.

Start with a Problem, Not a Solution

One error people make is starting out by choosing AI technologies and then looking for a use case.

A better method begins by clearly defining the problem.

Examples include:

• It takes too much time to complete the process.
• People are spending too much time on data analysis.
• Breakdowns in equipment are causing unnecessary downtime.
• Lead qualification results in poor quality leads.
• Processes result in mistakes that could have been avoided.

At that point, the problem can be quantified and the solution to the problem can be measured.

The relationship becomes:

Business Problem
↓
AI Use Case
↓
Process Improvement
↓
Measurable Outcome
↓
Financial Impact

Establish a Baseline Before Deployment

Without understanding the starting point, it is hard to prove improvement.

Before deploying the AI solution, the team needs to make measurements of the baseline that are relevant for the workflow.

These can be any of the following metrics:

• Time required for processing
• Error rate
• Cost per transaction
• Conversion rate
• Manual intervention
• Downtime
• Resource utilization
• Customer response time

The goal is not to measure every possible thing. The aim is to choose the metrics that best represent the problem.

For example, while improving the processing time may have its value, if the solution results in increasing errors or requiring more manual reviews, there might not be much business benefit from such a system.

Model Metrics Are Not Business Metrics

For developers and machine learning engineers, technical metrics are natural to track.

Accuracy, precision, recall, latency, throughput, inference cost, and model drift can all matter.

Yet, the technical performance does not always map to business value.

Take, for example, the AI system that classifies the incoming leads.

Technical assessment of the solution can go like that:

Input -> Model -> Classification

But the business assessment has to consider more:

Input
|
AI Classification
|
Qualified Leads
|
Sales Opportunities
|
Conversions
|
Revenue

Model Performance Matters, But Will Improved Classification Make the Process Better?

Integration is What May Actually Tell You about Real ROI

AI is rarely implemented as a standalone solution.

Production systems include databases, API integration, other enterprise systems, analytics solutions, IoT systems and human workflows.

Even if the information that an AI tool provides is valuable, if people still need to move it manually between different systems, the value of the solution is reduced.

So, the ability to integrate is an important factor of AI implementation.

One could consider a good architecture as follows:

Data Sources
↓
Data Pipeline
↓
AI/ML Layer
↓
Decision or Recommendation
↓
Existing Workflow
↓
Business Result

AI layer is just one piece of a whole solution.

AI + IoT is What Makes Measurement More Interesting

Similar principles are used when implementing AI in physical world.

Industrial devices could continuously provide huge streams of information using sensors, telemetry and connected systems. AI would be able to analyze these data and detect anomalies, do predictive maintenance, optimize inventory or enhance operational visibility.

For example:

Machine Sensors
↓
Real-time Data
↓
AI Processing
↓
Anomaly Detection
↓
Maintenance Recommendation
↓
Action

The benefit of such solution is not in anomaly detection itself.

Don't Forget About the Total Cost of AI

ROI calculations need to consider the full cost of the system's operation.

This can potentially include the following:

• AI Model API cost
• Cloud resources usage
• Storage usage
• Integration development
• Monitoring
• Security measures
• Maintenance
• Human involvement for review
• Training
• Optimization

An experimental system which runs cheaply may turn out to have very different cost structure at the production level.

Accounting of such expenses together with business outcomes will give you a much more realistic picture of the ROI.

Measure Before and After

One useful way to think about it would be measuring key metrics before and after using the technology.

For example:

             BEFORE AI     AFTER AI

Processing Time 30 min 12 min
Error Rate 8% 4%
Manual Review 100% 40%
Cost / Workflow $X $Y

The exact metrics will depend on your application.

Experimentation or pilot groups will help you isolate the effect of AI on your workflow from other factors.

The Human Workflow Remains Crucial

Adoption of AI goes beyond technology.

People need to comprehend how AI-based suggestions tie into their current role. Poorly designed workflows may make employees either ignore alerts, double up on work or become skeptical of the system.

As a result, an excellent AI system in terms of technology may not generate much value if it is poorly aligned with people's workflow.

The most effective implementations take into account:

Data + Model + Integration + Workflow + People + Measurement

instead of being concerned with the model alone.

From AI Experimentation to Business Value Generation

AI experimentation is helpful when finding out potential opportunities. Going live requires a different approach.

Instead of asking:

How many AI applications did we deploy?

Organizations need to ask:

What business problems do we solve? How do we measure that our solutions work?

The change in focus alters the way AI projects are planned, measured, and optimized.

Aperture Venture Studio works on creating AI and IoT businesses tailored for real-world industrial use cases where the connection between technology and business problems becomes key to generating value.

Eventually, AI adoption success does not rely on the widespread use of AI.