AI needs IA

Too few artificial intelligence (AI) projects succeed.  Many organisations approach AI believing that you can collect data for an algorithm in the hope that it realises the anticipated benefits.  Instead you should look at data and design a system to address a problem, not an algorithm.

Here are some keys to success for adopting AI.

  • Select the right business problem. This must be one for which a team already exists and has the data.  It avoids the pitfall where, “We need to test AI,” results in a deceptively attractive initiative which has low business value and is hard.  For example, a business process is required to collect data.  Nevertheless, there is a conundrum for many organisations that the business case to get the data requires a demonstration of AI.
  • Look at the data. Typically, organisations significantly underestimate the effort needed to orchestrate the data in readiness for AI.  AI needs accurate data, and data cleansing and preparation takes 80% of the effort.  This is a hard engineering problem and requires a sound approach to information architecture, technologies and a range of skills, not just data scientists.
  • Build systems, not algorithms. Many assume that a sequence of steps is sufficient to generate insight and recommendations.  However, feedback is crucial to improving overall accuracy.  It is complex with lots of moving parts and demands a multi-disciplinary approach.

AI must be transparent for the public to trust it.  This is especially significant for the public sector because important decisions must be explainable.  It is essential to understand who trains the AI system, what data was used to train it, and what went into the recommendations made by the algorithm.  This extends the realm of information governance.

In summary, AI needs IA: Information Architecture.

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