AI’s latest role: shining a light on ESG performance

The challenge for many employers in improving their environmental, social and corporate governance performance is identifying on demand where its weak spots are.

This year, 120bn terabytes of content will be created, captured and consumed by enterprises – growing by a further 50% by 2025. Buried somewhere within that data is an unprecedented opportunity to understand the way those companies behave as employers, and how that affects everyday outcomes, not least their social and corporate governance (ESG) performance.

But there’s one big problem that needs to be cracked first. Currently, as much as 80% of the content in an organisation exists in static documents, emails or some other unstructured form, making it very difficult to find and combine as a source of decision-supporting insight. Worse still, 54% of all enterprise data is thought to be ‘dark data’ – in other words, completely undiscoverable (e.g. locked in people’s heads, or gathering dust in paper form). 

AI now promises to unlock the value of these overlooked sources of business intelligence, and direct them to the mobiles of key decision-makers.

Pre-empting ESG issues

Armed with the right insights, on demand, employers could spot and pre-empt critical 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. 

Or perhaps the immediate priority is to curb environmentally-inefficient use of resources along the supply chain – from new insights into order duplications, logistics mileage, and excessive energy consumption which ordinarily would be dispersed 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 - to support or trigger targeted actions.

Three complementary options for driving new AI insights

There are three stages through which companies can apply different forms of AI to move closer to their ESG and wider business transformation. For instance, through the improvement of stakeholder experiences; a honed product or service vision; and/or improved cost efficiency.

A first role for AI technology is in pattern matching, using any number of deep learning capabilities – from visual analysis/image recognition to natural language processing (NLP) – to identify and capture what the content is, through a process of continuous scanning and metadata generation.

Next comes the application of ‘contextual AI’ to understand what the content is about and how it adds to the company’s intelligence about a given 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.

The third opportunity for AI lies in intelligent content assistants, which is about AI’s role in search and discovery. Think of this as a ChatGPT equivalent for the workplace – 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”.

Employers can only act on what they know

In an ESG context, the opportunity might be to reverse staff attrition and/or enhance employee wellbeing by boosting fair treatment and targeting new development opportunities; or identify new opportunities to limit carbon emissions across the supply chain. More broadly, it could present the chance to enhance the customer experience, or to hone product/service development as new insights are discovered from across helpdesk exchanges, sales/indirect channel feedback, and review forums. 

Even just transforming the everyday lives of knowledge workers, who still on average spend over a third of their day hunting for information to complete a task, 60% of this across more than four different IT systems, according to our own analysis, can contribute significantly to their improved wellbeing - by reducing stress and enabling them to complete their work more efficiently and with greater sense of achievement.

Keeping content infrastructures and platforms as flexible and as open as possible will go a long way in ensuring the organisation can keep embracing the latest AI advances. The rest is down to the company’s vision, and its ability to direct timely insights to the people who can make a difference.

Dr John Bates is a former Cambridge Don who gave up the gown in the year 2000 when he became addicted to starting and growing businesses, often linked to his primary research field of AI, smart algorithms and intelligent analytics. After more than a decade in the US – in Boston and Silicon Valley – he moved back to London where he is now the CEO of SER, a leader and major player in enterprise content management, headquartered in Bonn, Germany. SER specialises in intelligent content automation (ICA) – the convergence of content management, business process automation and AI-powered content understanding. 

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