Artificial Intelligence has an adoption problem.
There are plenty of articles about what AI can do for your business, but why isn’t everyone using it?
We are likely seeing AI going into the downward slope of the “Hype Cycle”
The Hype Cycle is a visual representation tool from American Research. It shows how for a new technology people get really excited at first, are disappointed by their inflated expectations, then people slowly start getting real work done by the new tech.
Why does the ‘trough of disillusionment occur’? Many reasons – but often it’s because the other parts of organization are unable to fully support whatever tech is currently being hyped. AI is no different.
AI is often sold as a ‘spot solution’ – this is industry jargon for a specific tool to solve a specific problem. Unfortunately it doesn’t really work that way. A constant flow of relevant and accurate data is required for an AI to learn and improve – there is a reason why “Artificial Intelligence” and “Machine Learning” are used together so much.
So we are seeing organizations struggle to force their current data architecture to support AI frameworks. Unfortunately this often fails and leads to disappointment. Thus resulting in said disillusionment and, worse, abandoning it altogether.
At TheoryLane our architects help dis-entangle your existing data processes to help operationalize machine learning and data science solutions. Our data development patterns construct reusable, governed information objects. Combined, innovative data architecture and development patterns provide reusable streaming context to break the barriers of the hype cycle and create true value added applications!
Contact us for more information.