Responsible Business Intelligence - An Emerging Paradigm?

Responsible Business Intelligence (RBI) is an emerging concept that integrates responsible business practices with the application of Business Intelligence (BI) and Artificial Intelligence (AI) technologies. With the continual advance of AI's capabilities, business leaders find themselves flooded with new opportunities and responsibilities for how BI can and should be used to grow business. RBI presents a new paradigm where these growth opportunities can be realised responsibly, to the benefit of all business stakeholders.

Context of Responsible AI

As discussed in a previous newsletter , RBI is closely aligned with the broader concept of Responsible AI. It focuses on how businesses develop and implement AI-driven BI solutions in ways that adhere to ethical principles and societal values. This includes ensuring that AI applications in BI are transparent, accountable, and unbiased.

Context of Responsible Stakeholder Intelligence

In next week's newsletter, I will consider Responsible Stakeholder Intelligence, which sits across all applications of RBI - where responsible businesses are oriented to partner with and serve the interests of all of their stakeholders, including employees, customers, suppliers, shareholders, governments and regulatory bodies, academia and think tanks, trade & industry associations, trade & labour unions, community and NGO groups, and society at large.

Applications of Responsible BI

This week, I am focusing on a number of applications of Responsible BI, considering how RBI extends beyond the technical aspects of BI and AI. RBI encompasses the business models and strategies companies develop, using AI-powered BI solutions, where both the risks and merits of using these technologies are taken into account. Typical examples include:

• Board Intelligence: Where the misjudged application of AI, or reliance on AI, can severely harm a business - whilst being unaware of the summary of the most critical risks facing a business (ie not having good governance through AI) is just as likely to cause harm to the business .
• Reputation Intelligence: Where blind application AI without human insight can lead to critical misjudgement - whilst Co-Intelligence can identify potential reputation risks early allowing for proactive management.
• Risk Intelligence: Where AI itself is the key focus of business risk raising cyber-attack security concerns and regulatory compliance challenges - whilst identifying and quantifying business and third party risks earlier results in better business decisions and outcomes.
• ESG Intelligence: Where AI can introduce unintended bias and drive decisions based on 'black box' systems that lack transparency - whilst ensuring all stakeholders can access reliable information through transparent reporting has become a regulatory necessity.
• Media Intelligence: Where AI algorithms can perpetuate biases, trample on privacy and spread misinformation - whilst achieving deeper insights into real-time stakeholder trends and sentiment leads to better responsible business decisions.
• Human Capital Intelligence: Where AI can harm trust, fairness and morale through privacy, bias and surveillance concerns - whilst improving talent management through data-driven decisions, personalised experiences, improved DE&I, leads to happier, more efficient employees.

There are risks and merits to all applications of Responsible BI, but given the rate of advancement of AI technologies, the alternatives to Responsible BI are not good. Standing still, using old technologies is not an option. Lurching forward, irrespective of the risks, is undoubtedly a mistake. Responsible Business Intelligence is an emerging paradigm. The real question is, how can we ensure RBI is deployed for best benefit of all stakeholders?