How AI will enable a better understanding of long-term value

AI will be a vital tool in the next generation of company key performance indicators.

In brief

• The onus is on companies to define KPIs that provide a more holistic view of their activities and their long-term value drivers.
• AI can help to measure KPIs in areas such as: trust, culture, ESG risks, and ESG reporting.
• AI can improve company performance, but it should be used in conjunction with other technologies, including data analytics and blockchain.

The majority of a company’s value is now reflected in the intangible aspects of its business, relating to factors such as innovation, culture, trust, human capital, sustainability and governance. These concepts are difficult to measure, and most of them are not captured by traditional accounting and managerial reporting frameworks, but there is a pressing need to do so as shareholders, capital providers and other stakeholders increasingly focus on how companies create long-term value.

The global COVID-19 pandemic has brought even more attention to bear on companies’ societal roles and responsibilities. As Klaus Schwab, Executive Chairman of the World Economic Forum (WEF), pointed out in an article published in the Financial Times on 25 March 2020: “A short-term economic crisis such as the one induced by the coronavirus outbreak reveals which companies truly embodied the stakeholder model, and which only paid lip service to it, while fundamentally maintaining a short-term profit orientation.”

Prior to the pandemic, there had already been a notable shift from shareholder to stakeholder capitalism. In the field of corporate reporting, the Embankment Project for Inclusive Capitalism has worked on developing new metrics and methods that help businesses measure and articulate the value they create for a broad range of stakeholders.

Measuring the next generation of KPIs

So the onus is now on companies to define the next generation of key performance indicators (KPIs): those that provide a more holistic view of their activities and their long-term value drivers. As these new KPIs and reporting frameworks start to emerge, companies have to address how to measure these new indicators, which can be more complex to evaluate than pure financial indicators and ratios.

The good news is that the advent of big data means there is a broad set of unstructured and external information to tap into. The challenge for business leaders is how to do this effectively – which is where artificial intelligence (AI) comes in. AI will be a vital tool in measuring next-generation KPIs and enabling companies to better demonstrate how they create long-term value.

Trust and financial performance

For example, AI is already being used to measure levels of trust – something that is vital to a company’s value. That’s because the way in which someone trusts a company or brand now will affect how they behave toward it in the future. While trust impacts consumers, suppliers and employees, it also impacts the company’s cost of capital insofar as it influences the views of capital providers and the capital markets. While these implications are clear, they are often difficult to report upon or measure directly.

Advanced analytics and AI can be leveraged to gather and aggregate large quantities of data, including data from multiple sources, and produce “trust scores” for a range of metrics such as integrity, consistency and openness. A variety of tools have been developed that address many versions of these so-called trust analytics, based on different attributes and factors. Companies and capital markets alike are increasingly using these tools to analyze market and consumer sentiment and understand the level of trust in a brand or organization. They can help companies to make managerial decisions, by directing them to areas of the business they need to enhance, while analysts and investors can use them to make investment and credit decisions.

Using these AI-based tools, it is now possible to demonstrate a tangible relationship between behaving in a way that instils trust and, ultimately, delivering better financial returns in both the short and long term. A high trust rating reflects positively on customer and employee retention, price inelasticity and competitive advantage.

Demonstrating this correlation and causality between trust and financial performance also compels asset managers (who have a fiduciary duty to maximize returns for their investors) to look beyond traditional financial metrics.

What about other intangibles such as wellbeing, inclusion, talent, diversity, environmental impact, innovation and corporate governance? These are all important but, to date, have also proved difficult to measure effectively. This is now changing, thanks to the power of AI and the digitalization of business. Below are examples of three areas where AI can make a difference: culture; measuring environmental, social and governance (ESG) risks; and ESG reporting.

Culture and AI

Looking at the culture within a business is nothing new, but companies are increasingly investigating other measurement options beyond the traditional focus groups and staff surveys.

AI can analyze communications across an entire organization (including emails and messages on collaboration platforms), focusing on grammar, syntax, sentiment and keywords, and identify the tone within messages. (Clearly, when performing such analysis, attention needs to be paid to the appropriate privacy standards and regulations.) This helps to identify trends and to evaluate how healthy the culture within an organization truly is.

Measuring ESG risks using AI

Four of the top five global risks in terms of severity of impact are related to the environment or society, according to the WEF’s Global Risks Report 2020. This means the quantification of ESG risk is essential.

Again, AI can help. For example, companies could analyze and predict risks related to human rights issues among suppliers from a certain country or sector. And by screening social networks and news broadcasts, emerging risks could be pinpointed sooner.

ESG reporting and AI

Investors are actively looking for more disciplined and rigorous approaches to evaluating nonfinancial performance, particularly around ESG, as the recent EY Institutional Investor Survey showed. From the perspective of investment analysts and portfolio managers, there are significant advantages in using AI to make an informed judgment on corporate governance and industry-wide standards.

Unlike financial reporting, which follows a set of strict and uniform rules, ESG reporting is flexible and often dependent on what companies choose to disclose. The use of AI could prove beneficial here as ESG reporting becomes more prominent and consistent. In the interim, AI also makes it possible to aggregate the ESG-related information that is currently provided in various reports, to inform comparisons and decision-making by companies, market participants and ratings agencies.

Learning to use AI

It’s clear from these examples that AI is suited to the measurement of long-term value and, in particular, the attributes that drive long-term value in an organization. However, it should be used correctly and in conjunction with other technology. AI can improve the performance of companies, but not in isolation; rather, it should be regarded as part of a pool of new technological resources, alongside big data and blockchain.

There is also a learning curve to contend with. Many companies do not fully appreciate the potential of what can be achieved using AI, and individuals with the expertise to use it most effectively are in short supply. The level of insightful data gathered via AI – for instance, establishing the appropriate correlation between a range of KPIs – will evolve over time as companies become more adept in its application.

But, while it may be early days, companies should be getting to grips with this technology. If they don’t, their competitors will be.