Julia Handl
It’s been called the biggest breakthrough in the fight against deadly superbugs in decades. Scientists have successfully used Artificial Intelligence (AI) to analyse thousands of chemical compounds and identify a new antibiotic to treat lethal drug resistant bacteria.
The transformational technology has been hailed as a revolutionary force in science and medicine, but it has widespread applications in business too.
Business leaders are focused on two principal areas where AI and data science can be effectively integrated into their operations.
Firstly, ensuring that all available data is being used to inform the decision-making process; AI can efficiently integrate and analyse enormous amounts of data, saving huge amounts of time and resource.
And secondly, applying AI to evolve the products or services that a company is delivering, including rapid prototype development.
However, adopting AI doesn’t come without risk.
There are serious concerns about the potential of AI techniques to perpetuate or exacerbate existing biases in society, and to accelerate the spread of misinformation. This particularly applies to the use of generative AI – otherwise known as the type of AI that can create a wide variety of data, such as images, videos and audio.
Critical thinking has always been an essential skill for business leaders, who will be well-practised in considering the relevance and reliability of multiple information sources before reaching a conclusion based on all the evidence.
But in recent years, the sheer quantity and complexity of data available to decision makers has made it much harder to interrogate.
Data science and AI offers tools to help with scrutinising and streamlining the information intrinsic to this data far more efficiently.
The benefits of this are easy to understand, but it is crucial to bear in mind that AI is by no means a perfect partner.
One of the myths about machine learning is that it is entirely objective and immune from human bias.
That’s not actually the case; AI learns by analysing trends and patterns in historical data, so can inadvertently perpetuate and amplify existing biases in society.
It’s crucial business leaders understand the limitations of these systems, and have an appreciation of inputs and high-level assumptions that have gone into a given system, just as we would with any other information source.
Having blind faith in AI outputs can actually be more dangerous than useful, introducing biases and inadvertently creating disadvantages for a subset of your customer base.
As part of ensuring the responsible use of AI, businesses need to consider AI, not just in in terms of what it can deliver for them as an organisation, but also what it means for all their stakeholders, especially their customers. For example, how would they feel about the ways in which you are using their data in your systems?
Embedded oversight
Embedding AI solutions into a business requires an understanding of these facets, including the conceptual differences between various types of models, their limitations and biases.
Crucially, it means that employing AI in a business doesn't just require technical expertise, it demands an in-depth understanding of all aspects of the organisation too.
So, to create real opportunities out of data science and AI, appropriate understanding of AI must be embedded across different functions. This will allow colleagues with relevant knowledge and experience to support the identification of credible opportunities for the use of AI, and critically analyse AI outputs.
This will likely require a rapid upskilling of colleagues across different department so they can use AI effectively, understand its limitations and appreciate the ethical implications involved.
Business leaders must be equipped with the skills they need to effectively embed best practice across their organisation.
There’s no doubt AI has transformational potential, but it is rapidly evolving, and the full implications of its ultimate impact are hard to predict.
For now, any business wanting to make the most of AI must ensure there is a steady human hand on the steering wheel.
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