Our AI Metrics & Process Improvement services help you create the organisational capability to build AI/ML solutions reliably and cost-effectively. This may be using in-house teams, outsourced development specialists, or a mixture. We are also agnostic about the use of AI/ML packages and platforms vs. bespoke development of your AI/ML solutions.
Our AI Metrics & Process Improvement services cover three main areas:
Building AI/ML solutions is following a similar evolution to software development:
AI and ML could be considered still to be in its equivalent of the first stage. But while that’s probably true for most businesses, it’s already possible to move an individual organisation to the second stage. And not necessarily that difficult.
We help you move to a process-based approach to AI/ML work, allowing you to incorporate new tools, services and packages. We do this through capability, processes and metrics.
Choosing Your Approach to AI/ML Capability
Three key decisions to be made are:
If you don’t have a firm view on these yet, we can help you navigate the factors involved in deciding. Alternatively, we can undertake the work required to make this strategic decision.
AI/ML Processes
If you already have reasonably robust IT development processes and tools, these will form a good basis for your AI/ML work.
Some AI/ML work is relatively standard software development; your current processes will probably be fine for those with relatively little change. Other work is very different in nature, such as algorithm development, so additional processes may be required.
However, one of the trickiest areas is AI/ML work that is superficially similar to modern software development, but in practice quite distinct. For example, many organisations have adopted Agile for their software work, but they way they apply it wouldn’t work well for AI/ML.
AI/ML Metrics
There are two types of metric to consider for AI/ML work:
Some organisations – particularly those with sophisticated IT departments – will track software development metrics such as defects and productivity. Others may only work at a higher level, focusing on product backlogs and feature burn-down rates.
The role of our AI/ML Metrics work is to find the appropriate level of measurement & monitoring for what an organisation is familiar with. As with other types of metric, the human factor is key to whether these are successfully introduced, and whether they end up helping or hindering.