How do we achieve AI Productivity Governance in IT?
The way software is developed is changing every day thanks to Artificial Intelligence. Teams are using copilots, agents, automation tools, and intelligent assistants to accelerate different parts of the software development lifecycle.
However, adopting AI does not automatically lead to improved productivity. As a result, many organisations are trying to answer new questions:
How much real value is AI generating in their software development activities?
How can they turn that value into savings, quality improvements, greater efficiency, and better management decisions?
At LedaMC, we help organisations measure, interpret, and govern the real impact of AI in IT. Our approach enables companies to move from promised productivity to proven productivity, using objective and comparable metrics always focused on the software product delivered.
AI changes how software is built. It does not change how much functional software is delivered, nor the need to understand the effort, cost, timeline and quality associated with that delivery.
Our process for turning productivity promises into management decisions
What do we measure?
We must avoid introducing excessive numbers of indicators. The key is measuring the right ones.
FUNCTIONAL OUTPUT
Function Points delivered into production
Why? Function Points represent the unit of software value. They are independent of technology, programming language, or development methodology.
END-TO-END EFFORT
Total effort invested (development, AI-generated code review, testing, corrections)
Why? If AI code review and correction activities are excluded, productivity figures become artificially inflated.
OUTPUT QUALITY
Recorded quality metrics or declarations from teams and vendors
Why? AI can generate code quickly, but it can also generate insecure or difficult-to-maintain code. Without proper control, today’s savings can become tomorrow’s costs.
The LedaMC methodology
Knowing what to measure and how to measure it is just as important as interpreting the results correctly:
1.
Diagnose
We assess the starting point: AI usage, affected services, vendors, tools, processes, and available data.
We identify where improvement potential exists and what information is missing to objectively measure impact.
2.
Measure
We define or adapt a measurement model that enables productivity assessment in AI-assisted environments, measuring delivered product, end-to-end effort, cost, quality, rework, and lead time.
The key is not measuring more indicators, but measuring the right ones.
3.
Segment
AI does not impact every environment in the same way.
We classify services and projects according to technology, initiative type, complexity, vendor, and realistic improvement potential, avoiding the application of uniform expectations to environments that are not comparable.
4.
Compare
We compare results against internal historical data, internal references, market data, and software productivity benchmarks.
This makes it possible to distinguish genuine improvements from temporary variations and identify patterns by vendor, technology, or initiative type.
5.
Govern
We translate measurement into management decisions: contract renewals, productivity clauses, progressive improvement models, gain-sharing agreements, dashboards, and commitment tracking.
The objective is to ensure that the value generated by AI does not remain a promise but can be captured objectively and sustainably.
Benefits of AI Productivity Governance for IT
A qué organizaciones les interesaría el Gobierno de Productividad con IA en TI
This solution is particularly relevant for organisations that develop software extensively and need to understand how AI is affecting costs, vendors, productivity, and quality.
This solution is particularly relevant for organisations that develop software extensively and need to understand how AI is affecting costs, vendors, productivity, and quality.
It is especially valuable for:
