Prof Al Naqvi of the American Institute of Artificial Intelligence outlines how the deployment of intelligent machines will allow companies to overcome the problems that have impeded corporate social responsibility

In his 2011 book charting the history of corporate social responsibility, the academic and futurist Wayne Visser divided CSR into five ages: greed, philanthropy, marketing, management, and responsibility. Each age related to a particular stage for companies: defensive, charitable, promotional, strategic, and systemic. As we enter 2018 I am convinced that a new age of CSR has begun. It is known as the intelligence age and the corresponding stage is the cognitive era, when machines are deployed to radically transform and improve CSR processes.  

While ethics, sustainability, the multi-stakeholder model, and CSR have gained in popularity, problems with the management, effectiveness, need, and efficacy impede progress. Supporters and critics of CSR engage in a seemingly never-ending debate of nuanced rhetoric. These altercations seem to go on at a high conceptual and holistic level, and often under the guise of being “strategic”. Consequently, there is seldom any effort to go beneath the surface and explore the underlying processes that may play a significant role in the alleged failures or problems of CSR.

At its core, CSR can be viewed as a business process, and is therefore in constant need for improvement in its efficiency and effectiveness. That orientation – the resolve to make CSR profoundly more efficient and effective – forms the basis for cognitive CSR.

Where are we going wrong?

The criticism of CSR generally falls in one of the following three broad categories:

Measurement Problems arise when companies fail to measure, measure the wrong things, or measure what is inconclusive or irrelevant, and crop up in both value measurement and materiality measurement. Debates over value measurement include how value is measured, what should be included (eg economic vs other factors), and whether CSR creates or burdens shareholder value. Materiality assessment measures stakeholder priorities and concerns, which become a major input to the design and development of the CSR programmes. Critics argue that such assessments are based upon subjective data and that materiality becomes relative to management agendas and constrained by managers’ sincerity, knowledge, and wisdom.

Al Naqvi

Behavioural This refers to management behaviour that intentionally prevents CSR programmes from achieving their full potential, or undermines the legitimate success or positive impact of CSR programmes. Misalignment of incentives can lead to such behaviour. CSR may be deemed irrelevant or even be subverted for personal gain. Issues from agency conflict to fraud can affect the performance of some CSR programmes.

Strategic and organisational This includes the inability to properly integrate CSR into business strategy, or vice versa, communicating the CSR programme, getting organisational support, and other similar issues. These issues are not driven by intentional deceitfulness of managers, but result from negligence or incompetence.

CSR requires tight integration with strategy. As Michael Porter and Mark Kramer observed, the failure of a company to develop a bridge that tightly integrates company strategy and CSR can lead to sub-optimised programmes and companies may miss out on creating a competitive advantage. Others have pointed out that factors such as lacking support from business units, centralised teams losing touch with reality, and short-lived programmes continue to derail CSR programmes. 

Intelligence and cognitive CSR

The rise of artificial intelligence technology in business is transforming our world. From sales to marketing and finance to supply chain, the impact of AI applications is being felt across the globe. The new machine age is upon us and is unleashing powerful disruptive forces ready to shape a new economic revolution.

In cognitive CSR, the artificial intelligence technology is deployed in a strategic and integrated manner to radically improve the CSR process. Consider the following applications of AI:

  • An intelligent system understands the business value drivers of a firm as well as its ability to generate positive outcomes for multiple stakeholders. It recommends a programme strategy and keeps management honest. By learning the business drivers and CSR goals, it draws out a programme map that can help configure and optimise the CSR programme.

  • The AI technology is used to eliminate or reduce human bias in value measurement and materiality assessment.

  • The AI system provides accurate and multi-dimensional performance measures that not only measure the performance of the programme against management or regulatory standards but also against global standards (eg pollution levels) by dynamically monitoring and tracking emerging global changes. 

  • The technology identifies the incentive misalignment, management bias, insincerity, self-serving, as well as other management issues – makes recommendations, and learns through the process.


Credit: Chombosan/Shutterstock Inc.
  • The AI technology is used for fraud-detection and improving internal controls.

  • The AI technology can help identify the integration points of corporate strategy and CSR initiatives by learning how to integrate CSR strategy with the overall business strategy. 

Developing a cognitive CSR strategy

  • Begin by creating a formal business plan for cognitive CSR. It is important to recognise that it is a fast-emerging and complex field and many consulting firms have only shallow knowledge of this area.

  • While the above examples of AI were fragmented to help clarify concepts, in an actual plan an integrated ecosystem of AI and robotics capabilities will form the architecture. Think holistically about the full-scale cognitive automation and interdependent technologies. A cognitive architecture includes various capabilities including data management, big data, machine-learning, and the use of various AI artefacts and capabilities.   

  • Start with using robotic process automation to automate data-entry type clerical processes. This can give some quick success and can be the starting point of cognitive CSR transformation.

  • Engage and bring other departments to help develop the transformation.

  • Don’t rely exclusively on internal tech departments. Traditional tech departments generally do not possess AI skills. 


Machine learning, a sub-field of artificial intelligence, is helping create autonomous machines that can learn and accumulate experience. Finally, it is important to recognise that the AI technology will itself create many social issues that would need to be addressed as part of the CSR.  

In the age of artificial intelligence, CSR will finally find its zenith. Welcome to the era of cognitive CSR.

Al Naqvi is the pioneer researcher and professor of applied AI in business and strategy at the American Institute of Artificial Intelligence. He is the author of nine upcoming books in applied AI in business. 


This is part of our in-depth briefing on AI. See also:

Can we turn AI into a force for good?

Comment: 'We can't leave Silicon Valley to solve AI's ethical issues'

Machine learning: how firms from Danone to Sodexo are integrating AI

First, do no harm: regulators and tech industry scramble to tame the AI tiger

AI explainer: why machines have an edge

'With AI polluters will have nowhere to hide'

Apocalypse soon? Tech giants warn of risks of 'AI arms race'

Rise of the sewbots: Asian factory workers feel chill winds of automation

'Our problem with automation is a labour shortage, not surplus'


Main image credit: Chombosan/Shutterstock Inc.

Machine learning  AI  Industry 4.0  Ethics  sustainable business 

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