That artificial intelligence is not just a passing trend has become clear in recent months. In many industries, it’s becoming a fundamental tool, changing the way we work. We’ve witnessed the automation of many repetitive tasks, and decision-making has also been enhanced thanks to AI’s analytical capacity. New specialized roles in AI systems are being created, while existing ones are being modified, requiring workers to adapt to these new ways of working.
The ICT sector is not immune to these changes. In software development, AI is becoming a catalyst towards new levels of efficiency and quality. These aren’t empty words; several studies have already detected these changes. Productivity improvements of developers using generative AI tools reach up to 55%. Even in more complex tasks, although more limited at the moment, these developers are 25-30% more likely to complete them within the specified timeframe.
Software testing is one of the areas where artificial intelligence can achieve the highest potential within software development. This is perceived by 90% of DevOps professionals surveyed during a study conducted by TechStrong and Tricentis. By employing machine learning techniques and predictive analysis, AI-driven testing tools can identify areas of risk, prioritize test cases, and suggest defect mitigation strategies. This not only speeds up the testing process but also enhances its effectiveness and coverage, reducing time and associated costs. AI thus becomes a great ally in reducing the routine tasks of the testing process.
The Great Challenge, Sizing the Value of IT
We have all these figures that speak of productivity improvements in developments achieved through artificial intelligence. How do we translate this value to the business? This has always been one of the sector’s great challenges: knowing how to quantify IT’s real contribution to the organization.
It’s crucial to understand that behind the apparent work of the IT area—developing software and implementing systems—there’s much more. Because beneath that premise, we find fundamental aspects that directly affect an organization’s ability to achieve its strategic and operational objectives. Hence, the need to measure and evaluate this value effectively.
One way to quantify this value would be to take as a measure unit the work performed by the IT area, from planning and designing solutions to implementation and maintenance. But stopping there is a rather simplistic view, and is it what really matters to the business?
The next step would be to ask ourselves how these solutions are contributing to achieving the company’s goals: Have we improved customer satisfaction? Is it helping us in our decision-making? Are you contributing to an increase in sales?
Those questions aren’t always possible to answer, but we can measure the amount of software that IT develops to be able to answer them. With that value, we can make comparisons between teams and/or vendors. Who offers me more value for every euro invested? Who can deliver quality results in less time and with fewer defects? Undoubtedly, critical questions that need to be addressed when evaluating and selecting IT service vendors and assessing the performance of our teams.
The Role of AI in Defining IT Value
If artificial intelligence can help us improve team productivity, the quality of developed solutions, and therefore, the time-to-market—key metrics in any development project—can it also help us define IT Value?
The first step is to be clear about what really matters to the business: the functionality it obtains to achieve its objectives. And this functionality comes from the software developed, whether by internal teams or third-party vendors.
Can we measure this functionality? Calculating this value is not a trivial task and requires a solid methodology to back it up. Function points have been established as the de facto industry standard for measuring the functional size of the software product. By being able to calculate the size and complexity of the software, we also get the effort required for its development and the associated cost. However, applying this methodology requires precision and consistency.
Estimating apps like Quanter were already helping us make these estimates up to 3x faster, but now with the integration of OpenAI’s LLM model into Quanter, we’re taking a step towards smart AI estimating. All you must do is enter the requirements in natural language, review the results, and voilà! We get the estimate of hours and costs based on software size metrics, up to 12 times faster than before.
By combining a trusted metric with the capabilities of AI in Quanter, organizations will have the ability to size the value IT brings more accurately to achieving business goals. And this will facilitate strategic decision-making and the efficient allocation of resources, consolidating IT as one of the keys to the company’s success. Are you ready to move towards smart estimating?
