The advances in artificial intelligence and automation continue to drive seismic shifts in how organizations operate. AI-powered automation is driving change across major industries like health, finance, manufacturing, construction and (of course) retail. Companies that are faster to embrace these changes in their workflow can remain competitive.
In 2018, PwC projected how AI technologies would impact the global workforce in its report. The study examined 200,000 workers in various fields from 27 OECD countries (Organisation for Economic Co-operation and Development), plus Singapore and Russia to identify three waves of automation in the coming years.
The first wave, which we’re currently experiencing, involves the automation of basic computations and data analysis in finance, information and communications. As the workforce transitions to the second wave in the late 2020s, PwC predicts the automation of repeatable tasks will reach maturity, specifically in tasks like “filling in forms, communicating and exchanging information”. This would then lead to the third wave in the 2030s: full scale autonomation of physical labour and real-world problem solving.
While the third wave may be some time away, forward thinking companies can start preparing for greater automation by integrating AI solutions into their daily workflows. This shift would not only make workplaces faster and more efficient, but smarter too, by opening up potential for thoughtful, meaningful and creative work.
But what does AI-supported workflows look like?
The answer obviously varies based on industry, though generally, it sees businesses adapt tools that dynamically augment existing practices.
For instance, in HR, automated AI-based systems can sift through reams of applicant data to identify resumes, while AI-powered chat-bots can pre-screen potential candidates. In medicine, AI can help doctors diagnose skin lesions as either malignant or benign simply by analysing a digital photograph. In law, these solutions can help sort, review and shortlist documents relevant to a case, assess potential risks in contracts and even predict the outcome of a trial.
Another area seeing great transformation with automation’s computing power is transcription. It’s a staid process that stretches across journalism, law, government, healthcare, academia or any field, really, that depends on audio note-taking or dictation. Transcription traditionally, fails efficiency. Recordings need manual conversion to text, which is painstakingly achieved in-house or through third-party contractors at per minute rates. This loss of time or money (or even both) creates a ripe space for technology to intervene, particularly one that assists Automatic Speech Recognition (ASR).
Today, you can see uses of ASR when you interact with Siri or Google Assistant, but its commercial history spans back to the 1990s with the launch of Dragon Dictate – the first intelligent speech recognition software for consumers. Nuance, the pioneer and leader in conversational AI solutions and owner of Dragon, takes speech recognition further by marrying it to machine learning.
Dragon Anywhere, the company’s professional-grade dictation tool for mobile, allows users to record and transcribe notes right from their mobile phones or tablets. The machine learning engineered into this cloud-based app makes it highly accurate. Users can dictate continuously, with no time or length limits, and because the software learns speech styles and preferences, the app achieves up to 99% recognition accuracy.
Dragon Anywhere integrates with cloud-based apps, so notes can be shared and accessed from any device, which dramatically simplifies workflows for users who aren’t often desk-bound, like field workers.
It’s another example of how automation technology offers real world solutions to day-to-day operations. Instead of threatening to outdo workers, AI and automation take a supportive role that enables professionals to do work that creates value: lawyers can develop their cases, researchers can explore more lines of interest, journalists can investigate more thoroughly – the list goes on.
It’s clear AI and automation are here to stay. It’s no longer a question of whether organizations should adapt AI, but rather when.
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