Digital Labour is, I’d say, impossible to avoid as a topic in the technology world. This is the latest iteration of a broad narrative, that the ever-present challenge of growing / maintaining / stopping a collapse in (delete as you see fit) productivity, can be solved through the use of technology. Very particularly, in this case of this conversation, digital labour is being brought to us in the form of AI “agents”; leading to the - in my view - awful term, “agentic AI’.

I believe investment in AI involves not just money but also significant amounts in both reputation and effort, creating huge pressure for it to be perceived as successful. This is all multiplied by a fear of failure that these investments create. It is doubtful that AI in its present state will repay this combined investment. Lowering the explosive hype of expectations needs to start now and organisations helped to focus on finding tangible, meaningful value in AI’s current - not future - applications.

With the extended scope of AI-powered outcomes and the possibility of them making wider-ranging and more impactful decisions, the risk of harm presented by the use of personal data in AI is significant. Harm that could be founded on some of our most fundamental personal characteristics.

Together, Purpose and Outcome are more than the sum of their parts, I think of it as being similar to a simple equation. For example, if the purpose is commercial, say tailored advertising, and the outcome’s scope is limited to offering a small discount for a product or service then the result of that equation, the impact, is likely quite limited.

I know I’m not alone in my belief that being an analyst in the technology world means being rigorously independent. But what does being independent mean?

My interest is in a practical, shareable and disclosable perspective that gives a starting point from which people can then make up their own minds.

Human governance or oversight is, in my view, often overlooked yet critical to building and sustaining trust in the development and use of AI.

When trying to explain how something works, transparency is - perhaps obviously - important. The question that needs to be addressed is,

“If an AI solution being developed or adopted is a black box, whether by design or perhaps shielded by complexity, then how is it possible to trust it?”

Many of those in the world of data, business intelligence and analytics are very familiar with the line, “garbage in, garbage out.” This is just as true of artificial intelligence (AI) technologies. In fact, given the broad scope of use cases and outcomes that could have AI applied to them, output may surpass garbage status and become toxic waste.

The AI-TQ does not attempt to impose any specific set of values on organizations using it. Doing so would be to assert the values of a third party. It does assert that values are an important part of any organization’s fabric and sets an expectation that they will be assessed as part of the process.

Bridging The Trust Gap

The Artificial Intelligence Trust Quotient (AI-TQ) is designed to help address some of the gaps AI technologies are exposing in existing assessments of technology. It is my view that the biggest gap being exposed is that of trust.