Sponsored by KPMG
Amidst the hype following the launch of ChatGPT in 2022, it would have been easy to think that AI was brand-new technology. In fact, it goes back as far as the mid-1950s, says Alan Lavery of KPMG.
The increasing application of complex mathematical processes and ever improving technology capabilities has seen AI move through the realms of data science, big data and, since 2017, into an area of transformer models and neural network architecture, and given rise to modern Large and Small Language Models (LLMs, SLMs).
The hype and surge in excitement and fear over the past 18 months has been largely due to the opening of the technology to consumer markets through the release of the ChatGPT application. This has opened AI consumption to entire organisations and not just specialised data teams.
Both feelings are valid, to a certain extent. AI’s potential is mind-blowing and will undoubtedly have transformative effects on how we live our lives and conduct business. That same power can and will also have negative impacts.
Already we have seen the Screen Actors Guild (SAG) in the US negotiate a clause in new contracts to prevent AI being used to create on-screen ‘characters’ based on data collected from real actors.
Things don’t have to be perfect all the time to be a real threat to humans, just occasionally is enough given the volume of material it can churn out in short time periods.
The pressure to adopt AI at pace
There is enormous pressure on business leaders to adopt the technology as quickly as possible. This is understandable. The speed of movement means that those who do not consider and adopt AI in their business risk being surpassed by those who do.
This is not necessarily always the case. AI adoption should be approached with an open mind but with clear eyes and a degree of caution and control. The starting point is to look inside your business, at the challenges you face, the problems you are trying to solve, and the changes you need to make.
Quite often, when you drill down into challenges you might find they are best addressed through traditional data means (data engineering, management, quality control and governance) and don’t necessarily require AI adoption immediately to solve them. Good data engineering and management still forms the basis of trusted reporting, analytics and insights derived through AI.
In this light, business owners need to ask what AI is and how they can make it work for them. They need to measure its impact and establish an informed business case for it in the same way as any other investment.
Measurable positive impact is key
They must also ensure they get value from the change being implemented. Change for its own sake is a waste of time and resources, and change driven by pressure to use a new technology is worse than pointless.
AI adoption must have measurable positive impacts. Always front of mind must be whether it is tackling the challenge or not.
Some use cases are clear. The development of LLMs and SLMs has given life to massively untapped potential held in unstructured data: files, documents, audio, video, and imagery. Learning from, accessing, querying, retrieving, and generating content based on this data is delivering previously unobtainable and impactful insights straight to the hands of the user, at a pace that has never been seen before. They can examine vast volumes of policy documents, contracts, regulations, operating manuals, and other data and find previously undetected anomalies, areas of risk, trends, and omissions, and generate recommended actions and responses.
Controlling without curtailing innovation
More specialised LLMs are now targeted at specific sectors such as legal, research, and code development, further driving enhancements to these skilled professionals. Their deployment always contributing to enhanced insights, efficiency, and new areas of business value.
Like most things in life, there are risks, of course. Combining AI with facial recognition technology and feeding it CCTV and drone footage to analyse has clear implications on civil liberties. This is before we go anywhere near the topic of intellectual property.
There is a need for tight regulation and the EU AI Act and other legislation around the world will bring control without curtailing innovation, helping businesses, their workforce and customers feel the benefits without the loss of trust in the system.
In the meantime, we must continue to view AI as an early-stage technology with enormous power that needs to be harnessed constructively.
Human oversight remains crucial
Augmentation and enhancement are its principal role at present; helping people and organisations get to the answer faster. It can surface and enhance better insights quicker and help users get the answer in seconds rather than days. It can carry out laborious tasks without getting bored or tired, helping us to be more effective. Ultimately, it’s about making humans and organisations better.
Training is an important consideration, and that’s where humans have to remain in control. Humans must form part of the adoption process throughout. If we are going to trust AI to make important decisions, there needs to be human oversight to ensure that it is making ethical choices and that the decisions are not damaging. AI will never replace human knowledge, connection, empathy and institution in the workplace.
The technology is continuing to advance rapidly, and we need to be open to its current and potential capabilities. By putting the correct governance and controls in place and beginning with low-risk test applications and building from there, organisations can adopt AI safely and obtain real benefits.
Just as the internet and the smartphone changed our lives over time, AI will do the same and create new business models that we cannot even conceive of today. Organisations ready for those changes and who adopt AI in a controlled and safe way will prosper, those that do not may find their viability threatened in the near future.
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