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Genome sequencing, autonomous vehicles, predicting natural disasters, drug discovery, precision agriculture and advanced manufacturing. Many emerging AI use cases are evidence of science fiction becoming reality, promising to tackle some of society’s biggest problems and transform the world we live in forever.
While it will take a few more years for some of these breakthroughs to go mainstream, AI is already quietly transforming some of vital yet mundane processes that have the potential to make an even bigger impact on our lives.
Not that we should lose sight of longer term advancements – far from it. The last 12 months have shown us the tremendous potential and capabilities of Generative AI. This wouldn’t have been possible without years of dedicated R&D, and we have a lot to look forward to.
However, balancing longer term AI goals with short term productivity and efficiency boosters is essential for any organisation that wants to demonstrate the value of continued AI investment. It’s the application of ‘everyday AI’ that is leading to a quiet revolution across a range of sectors.
Take healthcare for example. While new AI-enabled treatments are rightly being celebrated, the use of AI to radically improve administrative and back office processes could arguably do more to enhance patient care in the short to medium term.
The pandemic highlighted how much pressure healthcare systems face across the globe, with many services and provisions still facing oversubscription and significant patient backlogs. Whether its optimising operating theatre schedules, planning better rotas, or freeing up time for frontline medical staff, AI can help ensure more people receive the right care at the right time.
By integrating AI-powered tools with hospital systems, complex administrative tasks can be fully optimised and automated leading to more effective and efficient public health management. In effect, these tools can reduce the administrative burden on clinicians, giving them more time to treat patients and help improve the quality of care. Better quality data, predictive analysis, and the ability to track a patient’s journey through the health system could also mean earlier interventions that mitigate the need for more resource intensive treatment further down the line.
‘Everyday AI’ is making progress in most other industries, with a plethora of similar examples evident in sectors such as agriculture, manufacturing, transport, and education. However, none of these innovations are possible without an abundance of good quality, highly relevant data.
This makes putting a robust data strategy in place a top priority for any organisation that harbours AI ambitions. Firstly, your data needs to be secure, with the right balance of accessibility and resilience. This should provide the reassurance and confidence to innovate without fear of compromising sensitive data or valuable IP.
Just as importantly, your data needs to be clean and usable. Training AI models on poor quality or unclean data can have severe implications. Put simply, the better the data, the better the results and the sooner it will have demonstrable impact on day-to-day operations.
True watershed moments are rarer than history would lead us to believe, and it can be tempting to let anything other than a ‘big bang’ innovation pass without due recognition. The reality is that most progress is cumulative and incremental, and only by taking the time to look back do our achievements become apparent.
This is certainly true for AI, and it means we should always remember where we started from by celebrating the success of all AI projects – no matter how trivial or unsexy they may seem.
The technology industry is almost universally focussed for the possibilities of a future powered by AI, but we shouldn’t underestimate everything we’ve achieved to date. In particular, we should recognise the innovations and optimisations to the invisible processes that are already improving our day to day lives – and there’s nothing more exciting than that.
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Kirsty Biddiscombe is the EMEA Business LEAD AI, ML and Data Analytics for NetApp.
