The data will surprise you. Most vendor selection criteria are wrong.
66% of software projects fail, either cancelled, significantly over budget, or failing to deliver core requirements.
45% of software features built are never used by end users.
80% of project cost overruns happen in the first third of the project.
73% of CTOs have experienced the junior army problem. Projects with a single accountable senior technical owner are significantly more likely to be delivered on time and within budget.
89% of CTOs say black box delivery is their number one reason to terminate a vendor relationship early. Elite teams deploy more frequently and restore service faster due to process transparency.
A partner who gives an instant estimate without deep discovery is flagged as high risk by most experienced CTOs.
CTOs, VPs of Engineering, and AI leaders responsible for building, deploying, or governing software and AI systems. It is especially relevant for teams evaluating external partners or trying to move from experimentation to reliable production delivery.
Eval refers to structured evaluation of systems during development. It includes measuring accuracy, reliability, performance, cost, and failure cases continuously rather than only testing at the end. It ensures systems prove their effectiveness before scaling.
The research showed that delivery success is not driven by technical skills alone. It is driven by senior ownership, transparency, scope discipline, and clear accountability. Most failures happen due to lack of visibility and weak execution systems rather than poor coding.
Working demos often succeed in controlled conditions but fail in real world environments. They do not account for scale, edge cases, security, or cost. Without evaluation systems, they create false confidence and lead to long term engineering debt.
Evaluation reduces rework by identifying issues early. Teams that measure outcomes continuously ship faster because they avoid late stage failures, unclear requirements, and unstable releases.
Evaluation frameworks are added into existing CI CD pipelines. They run alongside development and testing processes, measuring outputs at each stage without disrupting current workflows.
More predictable delivery timelines, fewer production failures, better system reliability, and improved confidence in scaling new features or AI systems.
Start by introducing evaluation checkpoints in your current workflow. Define clear success metrics for each release and validate systems continuously. A short trial sprint with defined outputs is the fastest way to implement this approach.
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