How artificial intelligence can help to measure long-term value

As companies come under increasing pressure to measure long-term value creation, the main challenge is how to draw meaning from their data.

There was a time when an annual report demonstrated a company’s value to its shareholders via a strong order book, a healthy balance sheet and a history of attractive dividends. But the landscape has changed in recent years, and companies now face the need to express corporate value on a broader level.

There is an ongoing shift from shareholder capitalism (where the overriding priority is to protect value for shareholders) to stakeholder capitalism, where there is a focus on demonstrating to a much wider audience – including customers, employees, investors and regulators – that the company is creating long-term value.

Whether it is caused by the millennial generation forcing a change in values toward a more responsible and socially conscious approach to business, or a consequence of high-profile corporate collapses, the focus has changed. It is no longer just about maximizing profits; transparency, sustainability and inclusion are increasingly part of the overall measure of success.

Failure to factor in these elements when assessing long-term value could impact on investment, recruitment, reputation and, ultimately, productivity and profit. We live in an increasingly engaged climate where stakeholders demand more information across a broader spectrum. And in a digital world, they want this data to be up-to-the minute and readily available.

Levels of data

According to the Embankment Project for Inclusive Capitalism (EPIC), key performance indicators (KPIs) to measure long-term value will increasingly be based around four key areas: talent; innovation and consumer trends; society and the environment; and governance. Stakeholders want clarity on how these KPIs can inform strategic planning, risk management, executive compensation, sustainable operations, business growth and long-term corporate value.

However, one of the challenges facing companies attempting to report on long-term value is the huge amount of data available and how to draw meaning from it. To comprehend the scale of this challenge, consider that data in our global digital universe, is doubling in size every two years¹.

In this context, artificial intelligence (AI) could prove to be the game-changer, with the ability to make sense of this data and identify meaningful indicators. Through AI, the analysis of colossal amounts of information could be both instant and bespoke.

Data-mining can help to analyze large volumes of data and classify information. For instance, it can be used to analyze a large volume of patents and classify them into “qualitative clusters”: incremental, adjacent, disruptive and so on. Predictive analytics could also help to analyze market and consumer behavior and provide trends and forecasts.

The fact that companies should be able to use this technology to measure aspects of their activities such as financial value, production, staff performance, cost management and sustainability in real time represents enormous progress. In the past, it was hugely costly and time-consuming to measure value. Data provided to analysts was of mixed quality and there was often no structure or framework to the process. In future, companies should be able to use real-time KPIs, resulting in real-time outputs. 

AI as enabler

But AI is not the “silver bullet”; it is purely an enabler to support the generation and analysis of new metrics.

Moreover, companies are still at a very early stage of working out their approach to metrics for long-term value. They must strike a workable balance between tactical and strategic KPIs; operational and financial KPIs; and KPIs that effectively capture the moment while anticipating the future. 

As it stands, there is a question as to how well equipped companies are to employ new metrics for corporate governance, consumer trust, innovation, talent and the environment, among other things. Many organizations don’t yet have the appropriate “plumbing” in place, the data sourcing and aggregation capabilities, or the reporting systems required.

An obvious problem is, how do you directly measure aspects of innovation, trust, culture or sustainability? Any effort to do so can be complicated by the unstructured nature of the operational data needed for such nonfinancial reporting. The onus is on every organization to establish the KPIs that are most appropriate and then ensure these KPIs can be traced back to the right source to make them measurable.

Timely interventions

Despite significant challenges, the potential benefits AI offers as an enabler are striking. Take for instance the issue of measuring corporate culture. In the past, this would probably be done via an employee engagement survey that would be conducted once a year. It would typically take months to collate information and the results would only be available after staff reviews had already taken place. With AI, this measurement can be more expansive and more immediate. AI can also suggest types of intervention that might be appropriate; it is not just used to produce reports.

The power of network analytics within HR is certainly one area that is developing at pace. New social mapping platforms are using simple staff surveys and metadata from staff phone calls and emails to see how effectively people are working and communicating. As well as identifying potential misconduct, these platforms (if used properly) can help to improve productivity, identify innovation and foster a more collaborative culture.

Sustainability applications

It will no doubt take time for common practices and standards to emerge across industries, but we are already seeing concrete actions in this direction in some areas. And one of the key advantages of AI is that it can be used to make KPIs more forward-looking rather than retrospective, which has been the case up until now.

In particular, reporting on sustainability in the US and Europe is using AI as a risk identifier. According to the World Economic Forum’s Global Risk Report 2018, four of the top five global risks in terms of impact are related to environmental or social issues2. AI can help to identify and quantify these risks; for example, where a company has suppliers in a particular country or region, AI tools can contribute to the analysis of information and help to predict potential human rights risks.

There are a variety of ways in which AI can help in measuring performance against KPIs in the area of sustainability. For instance, one multinational technology company has created a neural network-based system that is trained (using data from sensors inside and outside its datacenters) to track how various environmental factors affect performance. One datacenter reported a 40% decrease in the amount of energy needed to cool the facility as a result³.

AI can also help to estimate the carbon footprint of large companies by analyzing public data, avoiding the need for extensive data collection. Similarly, by using AI to analyze large quantities of both internal and external data, companies can get a clearer idea of materiality – a key element of sustainability efforts – and thus better understand which sustainability-related areas they should focus on in future. This saves both time and resources.

Looking ahead

As the transition from shareholder capitalism to stakeholder capitalism continues to gather pace, companies need to develop better ways to identify, measure and communicate corporate value, and to accept that a longer-term, prognostic focus can lead to investment and innovation that promotes growth and value creation.

There is still work to be done to identify the most appropriate KPIs to measure long-term value creation. Thanks to its ability to analyze and make sense of large quantities of data and identify meaningful indicators, AI can make a significant contribution to this effort, and looks likely to be an increasingly important element of the corporate toolbox in the coming years.