In this blog, I briefly look at MP3 technology and analyse the factors behind its success and failure. Based on the analysis and the factors that I've identified, I predict the trajectory that AI will take over the next five years using an example of work that I'm carrying out with a client organisation.
MP3 technology transformed the portable audio player yet failed as a mobile technology. Its success lay in its evolution from cassettes/CD players to digital audio. However, the functionality of MP3 was limited. It was a laborious, time consuming task to transfer audio files from a PC to the device. Early models had a complicated interface, compatibility issues with file formats, lacked connectivity to the internet, limited storage and poor battery life. Apple’s iPod brought significant improvements to these issues with the exception of storage which remain limited. The cost of an iPod increased exponentially with storage size.
The adoption of smartphones with built in audio players led to the eventual decline of MP3 technology. Smartphones provide an enhanced audio player functionality: longer battery life through wired or wireless charging, high-speed mobile and internet connectivity, digital files replaced by music streaming apps, giving the customer access to unlimited music without the need for large storage space for a low subscription fee. The cost of a smartphone is also relatively inexpensive for a device with combined mobile phone, computing and audio player capabilities. AI sparked complementary innovation e.g. personalised music recommendations.
I’m currently working with a client organisation to implement strategic workforce planning (SWP). The organisation has an ambitious mission which requires a highly skilled workforce. SWP is a 1-to-5 year blueprint to ensure workforce optimization: a holistic strategy encompassing recruiting, developing, retaining and deploying the workforce to maximize the effectiveness of the current and future workforce in light of the mission. SWP decisions leverage data about future work and associated workforce supply and demand.
A possible AI solution is Machine Learning that can reliably and efficiently make better decisions from scenarios and FTE trajectory. A Machine Learning solution which can analyse data from multiple sources, detects patterns, insights, makes predictions, judgements or decisions to solve strategic workforce planning problems. This will require the development of an AI algorithm trained on strategic workforce planning data to be deployed in the process.
Supervised Learning and Reinforcement Learning is likely to add the most value. The critical success factors are a clearly defined task and availability of data to train the algorithm. It could radically change the way decisions are made but it must also be technologically feasible. The AI solution may fail because it simply fails to meet expectations: the algorithm fails to find any insightful patterns in the data, makes inaccurate predictions or decisions, the data may be inaccurate or unavailable, and the development of the algorithm may take too long.
AI data insight solutions are available through complementary technologies e.g. Enterprise Resource Planning. My client is using an ERP system therefore it is likely there will be interest in AI within the next 1-3 years. Some challenges may also trigger an interest in AI: lack of resources to analyse data and decision-makers are stretched. Providing my client remains committed to its ambitions, the training of the algorithm may reach acceptable levels within 3 to 5 years and become an integral part of the strategic workforce planning decision- making process.
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