Responsible Business Intelligence - It's like Rocket Science

It turns out rocket scientists (aerospace engineers) and brain surgeons are no smarter than the average person, according to The BMJ, based on reasoning, attention, emotional processing, memory and planning abilities. Neurosurgeons and rocket scientists are often great at their jobs, applying knowledge acquired?through?years of study and vocational training, but no more than teachers, lawyers, accountants, nurses, software developers and business leaders to name a few.

The principles of rocket science - I can talk to this one a bit, but only a bit, as an ex Air Force Engineer - are pretty simple. It starts with Newton's third law of motion. It then takes the analysis, and understanding, often in real time, of millions of parameters to ensure the rocket actually does what you intended. Some of these parameters are more complicated than others but, in the end, they can all be explained, encoded and applied. Bringing these millions of parameters together - to perfectly guide a rocket - builds on the historical rocket science knowledge of mankind combined with the continual advances in technology required to apply and deliver this all in real time. That's complicated - but not unfathomable.

Just like rocket science, the concepts behind Responsible Business Intelligence (RBI) are pretty simple:

• The responsible use of Business Intelligence (BI), where BI is a set of strategies and technologies for analysing business information and transforming it into actionable insights that inform strategic and tactical business decisions.
• The responsible use of Artificial Intelligence (AI), where AI - or anyway the Large Language Models (LLMs) that sit behind the generative AI solutions we are typically referring to - is a set of deep learning algorithms and techniques applied to massively large data sets, to understand, summarise, generate and predict new content.
• The application of BI and AI by Responsible Businesses, where Business Leaders want to consider the opinions and concerns of thousands of different stakeholder groups and companies across thousands of different topics and issues - balanced against their own strategic and tactical business objectives - to define, measure, enhance and predict the outcomes of their actions.

For sure, the analysis, and understanding, of 400+ billion parameters to ensure Responsible Business Intelligence actually gives you the right information is complicated - but not unfathomable. In principle, building a Frontier LLM is relatively easy, it 'just' takes a great deal of investment, computing power and data, plus the latest deep learning algorithms and some good AI engineers. And if you don't have those types of resources, then you can build on one or more of the hundreds of significant LLMs (and growing) already out there on the market. There is a lot of choice.

AI is 'data + relationships'. It's not just about the tech, but how we as humans interact with and interpret it. [It's not about] RBI telling business leaders or stakeholders what to think, [it's about] AI-in-the-loop versus human-in-the-loop. We're not replacing human judgment, but augmenting it with richer, more contextual data.

So why are neurosurgeons, rocket scientists, teachers, lawyers, accountants, nurses, software developers and business leaders (to name a few) often great at their jobs? Because they apply knowledge acquired?through?years of study and vocational training....and, thanks to advances in AI, they don't need to acquire all that knowledge and experience on their own. As Bronwyn Kunhardt commented on the RBI Newsletter last week, "AI is 'data + relationships'. It's not just about the tech, but how we as humans interact with and interpret it. [It's not about] RBI telling business leaders or stakeholders what to think, [it's about] AI-in-the-loop versus human-in-the-loop. We're not replacing human judgment, but augmenting it with richer, more contextual data."

Emotional intelligence and critical thinking in the AI era are as foundational as the LLMs used to augment that thinking.

How People Can Create—and Destroy—Value with Responsible Business Intelligence

In September 2023, Fran?ois Candelon , Lisa Krayer, PhD , Saran Rajendran and David Zuluaga Martínez from 波士顿谘询公司 carried out a first-of-its-kind scientific experiment: How People Can Create—and Destroy—Value with Generative AI. This excellent research found that people mistrust generative AI in areas where it can contribute tremendous value, such as creative ideation, and trust it too much where the technology isn’t competent, such as business problem solving:

• When using GenAI for creative product innovation, a task involving ideation and content creation, around 90% of participants improved their performance. People best captured this upside when they did not attempt to improve the output that the technology generated.
• When using GenAI for business problem solving, a task to identify the the root cause of a company’s challenges based on performance data and interviews with executives, people performed 23% worse than those doing the task without GenAI. Even when people were warned about the possibility of wrong answers from the tool, they did not challenge its output.

Creative product innovation task

Thinking about the BCG creative product innovation task from a Responsible Business Intelligence perspective, where a business leader is engaged on a task involving ideation and content creation, which of the following courses of action would be most effective?

1. The business leader presents their own creative ideas only.
2. The business leader seeks input from a wide group of diverse stakeholders and uses these to solely reinforce their own creative ideas.
3. The business leader seeks input from a wide group of diverse stakeholders and draws on the broad base of creative ideas generated.

I think the third course of action would be most effective, much like the BCG consultants allowing the GenAI to do it's unconstrained creative summarisation. One of the benefits of RBI is the opportunity for a business leader to break out of their and/or their company's 'echo chamber' to understand the full breadth of relevant stakeholder inputs and concerns in the context of their own business objectives and constraints.

Business problem solving task

Also thinking about the BCG business problem solving task from a Responsible Business Intelligence perspective, where a business leader is engaged on a task to identify the root cause of a company’s challenges based on performance data and interviews with executives, which of the following courses of action would be most effective?

1. The business leader gives some non-SME analysts the performance data and interview transcripts and asks them to summarise and present their root cause analysis direct to the executive team without the business leader's SME-expert engagement.
2. The business leader gives some non-SME analysts the performance data and interview transcripts and asks them to summarise and suggest root cause analysis. The business leader then ignores the summarisation and root cause suggestions of the non-SME analysts and provides feedback to the executive team based on their own SME-expert analysis only.
3. The business leader gives some non-SME analysts the performance data and interview transcripts and asks them to summarise and suggest root cause analysis. The business leader reviews the summarisation and root cause suggestions of the non-SME analysts and provides follow up questions to the non-SME analysts to further clarify findings and improve future output. The business leader then provides feedback to the executive team based on their own SME-expert analysis augmented by the analysis and findings of the the non-SME analysts.

I think the third course of action would be most effective, much like the BCG consultants taking heed of the guidance to challenge the GenAI's output. Another benefit of RBI is the opportunity for a business leader to rapidly assimilate large amounts of data, provide context to the data based on their expertise, and ask questions of the data within the context of the expertise provided.

Responsible Business Intelligence is like rocket science

Responsible Business Intelligence is like rocket science - that is to say, the principles are pretty simple and whilst the technology behind producing the output is complex, it's not unfathomable. The conclusions are clear for business leaders (and rocket scientists) - it's not about whether emotional intelligence and critical thinking are applied to AI, it's about gaining the experience on how and when this should be done.