Today, the vast majority of enterprise knowledge is impenetrable: as much as 80% of it exists in static documents, emails or some other unstructured form, while 54% takes the form of ‘dark data’ – which is completely undiscoverable digitally.
Until now, that is.
Next-generation AI tools, harnessed via the cloud, now promise to surface and make sense of these previously impenetrable or uncombine-able knowledge resources, helping employers to better target environmental, social and corporate governance (ESG) improvements.
Seeing the unseeable
To do better with ESG, employers need to be able to spot or predict avoidable scenarios such as underrepresented members of the workforce or high-potential people becoming stressed, demotivated and leaving the company. Clues to employee burnout, neglect or unfair treatment may reside within appraisal notes, calendar schedules or sick leave records for instance.
Another ESG goal might be to discover and address environmentally-inefficient use of resources along the supply chain – from new insights into order duplications, logistics mileage, and excessive energy consumption (hidden across purchase orders, invoices and delivery notes).
The challenge is not only to capture and structure all of this intelligence digitally and assign to it rich metadata (to aid its discovery), but also to link it in a meaningful way with associated data, and to then harness the latest AI techniques and tools to monitor, cross-analyse and distil meaningful insights from all of those inter-related knowledge assets.
Multiplying cloud-based AI capabilities
AI technology is very effective in pattern matching, especially now – thanks to a wide range of deep learning capabilities, from visual analysis/image recognition to natural language processing (NLP) – and these can help to precisely identify and capture what the content is. Storing content in a next-generation content management system that incorporates such AI enables continuous scanning and metadata generation, for detailed tagging and indexing of content.
The application of ‘contextual AI’ adds further value, helping companies to understand what the content is about and how it adds to its overall intelligence around a topic. This is about joining the dots between content with related metadata, to capture the context of content and compare/contrast related information over time. This builds the ability to understand correlations, trends and outliers/red flags – or untapped opportunities – on demand. It is through this application of AI that a company might determine the link between a particular manager and colleagues feeling held back or under-developed, for example.
Then there are intelligent content assistants, boosting AI’s role in search and discovery – a kind of ChatGPT equivalent for the workplace; in other words a bot that can query an enterprise’s metadata-enabled content to distil insights such as “Show me high-potential individuals in our employment who are not satisfied/showing signs of restlessness”.
Transforming the experience of modern knowledge workers
Even just transforming the everyday lives of knowledge workers, who typically spend over a third of their day hunting for information to complete a task by our own calculations, can boost employees’ wellbeing – by enabling them to complete their work more effectively.
The key is to keep content infrastructures and platforms flexible, so that new tools and capabilities can be added as cloud-based AI technology continues to advance.