We've seen an acceleration of technologies and capabilities to enable today's workforce. According to a recent PwC study, 37% of surveyed employees worried their job is at risk from automation. We have noticed the "half-life" of any given job skill is decreasing which only enflames concerns that more frequent re-skilling is crucial and getting the right knowledge for any given workforce is even more crucial. Though some job categories undoubtedly lend themselves to replacement of people with technology, a vast majority can benefit from technology assisting the workforce in their jobs. This has led the the rise in popularity of the augmented workforce concept; a [study of the] blend of human employees and technology working on tasks together. How will technology continue to augment human employees? In this primer we'll dive into the latest approaches and technologies to best answer this and highlight how the most important technologies can enable most any workforce.
1) Learning & KM in the workplace
The new AI driven knowledge management
Because of the changing of job skills, learning in the workplace is not going away. From the same PwC study, 74% of those employees surveyed "are ready to learn new skills or re-train to remain employable in the future". Some jobs will be more susceptible to wholesale machine automation than others. For a great many job roles existing learning vendors will continue to offer specific job training and job skills content. We are seeing a synthesis of knowledge management technologies augmented and AI augmenting current workforce engagement tools. Such as Slack, Salesforce chatter and email are heavily used, and there are now knowledge management technologies using advanced search, curation and natural language understanding of text to be able to on demand, pull relevant citations and answers from this content; days, months or even years after they were written. Additionally, the same approaches can be applied to siloed content such as pdfs, word documents, excel sheets, powerpoints along with video and audio. Not only citations matching our queries can be found, the exact timestamp in a video or audio source can be retrieved instantly giving us the ability to get relevant job knowledge from most any existing content as easily as searching Google. Additionally, any existing content repositories such as MS Sharepoint, OneDrive, Box and others can be accessed for their stored content for immediate access.
Knowledge management and learning
We see the above new AI driven knowledge management (KM) technologies working with existing workplace learning approaches; long form subject matter expert led events and on-demand learning content. Though they've been around for some time, this structured type of learning is critical to set the tone and when combined with advanced knowledge management means a workforce doesn't have to go through the pain of using older time consuming search approaches to recall important material from their initial onboarding and follow-up learning experiences. AI driven KM also critically reduces queries with peers and managers to get job skill answers. We're starting to see not only the "pull" aspect of content consumption through advanced AI search but the "push" aspect of advanced content recommendation as well. By tieing into the analytics and data generated by existing talent management systems such as learning management systems (LMS), performance management and recruiting systems, payroll and HR systems along with KM AI search and content consumption behaviors, deep analysis of all this data and compared with the workforce as a whole can be undertaken. We get an understanding of the content that should be recommended, content that is getting "stale" and alerting a manager to this as well as even predicting performance outcome of any given team member based on their current learning and content consumption trajectory.
More resources - learning
More resources - learning and KM
2) AI content curation is on the rise
Back in 2017, Box.com one of the worlds largest Enterprise Content Collaboration Platforms announced they would be applying NLU (natural language understanding) to all 30 billion files that they manage for their clients. The main methods currently used for content curation: 1) Social rating - what you experience when using social sites like Facebook by measuring “likes”, shares and votes. 2) Collaborative filtering based on users past actions and 3) Semantic Analysis which understands the meaning of small segments of the content and then applying machine learning analyzes relationships between all these segments for future suggestion or recall of specific content. The third approach, semantic analysis will only grow. A successful example of this is Amazon.com. As a shopper on their site we have access to millions of book and product SKUs. Amazon uses a combination of all three techniques to position the right book or product based on our behavior, peer experiences as well as having a semantic understanding of the product page we’re viewing. There’sno reason we can’t have the same experience on workplace learning systems where all viable learning content and company content could be organized and disseminated to each employee for the right time and circumstance.
3) Everyone can participate.
As providing and searching for relevant job skills answers becomes easier, what content that hasn’t been already curated or bought by the learning department teams won’t only be on their shoulders to produce. Team leads and even the rank and file employees themselves can create and share content they wish to contribute. Until the above curation methods came along, this idea had been difficult to act on. There would simply be too much content for any L&D department or business manager to curate and give their thumbs-up to in addition to their other duties.
4) Voice enabled search.
Amazon Echo and Google Home’s voice enabled / AI driven appliances are pointing the way for employees to simply “ask” by voice any question they have and instead of having to search to find the answer to their work query, the answer can be instantly told to them. Even between different languages; Imagine asking a question in English, getting the best answer from a document in Japanese and then having the system automatically translate and read the relevant passage from the document in English!
5) AI and the future of the augmented workforce
All departments in small to large organizations freely admit they are absolutely pressed for time. They are building, buying and curating learning content, managing and running classroom/ group instruction sessions along with administrating their content and learning systems, Sharepoint repositories and intranets and providing reporting and visibility to management. Imagine instead if they could focus only on tweaking and improving the learner experience drawing and working in lockstep with whichever content repository, learning system which itself is able to understand the effect of all questions asked and answered and all content viewed? The futurist Kevin Kelly may sum up best the future role of AI and the augmented workforce in his 2016 TED talk;
“When Deep Blue beat the world's best chess champion, people thought it was the end of chess. But actually, it turns out that today, the best chess champion in the world is not an AI. And it's not a human. It’s the team of a human and an AI. The best medical diagnostician is not a doctor, it's not an AI, it's the team.”
Read more about AI and the augmented workforce in our blog posts below: