When considering the trajectory of artificial intelligence, it’s worth looking back to see forward, writes Tania Kuklina
Though it’s hard to believe now, following the invention of the World Wide Web in 1989, it took several years for businesses to realise its potential and value.
That journey began, slowly and tentatively, with ‘portals’ providing information for investors and the curious public.
Next came sites assisting job applicants or helping customers to make purchasing decisions.
With Web 2.0, businesses moved towards self-service models, enhancing customer engagement and user experience.
In a clear case of back to the future, what we are currently witnessing in relation to artificial intelligence (AI) is similar, as businesses are only gradually beginning to understand its potential.
Of course, it is already here and in a variety of guises. It provides enhanced search capabilities and supports learning and teaching. It can write, summarise and analyse large documents.
In the realm of computer vision, AI is already being used for context-specific focus tracking in digital cameras.
Despite these advancements, we are still waiting for AI’s first “killer app”, the groundbreaking application that will revolutionise and disrupt the world like the first internet browser on the World Wide Web.
We do not know if this application will be a job killer or a job creator, but what we do know is that, when it comes, it will shape the thinking of employers and employees about AI within their own organisations.
Productivity challenge
At present, we believe AI will replace humans in low-stakes tasks. It is increasingly being used for customer engagement tasks, such as the pop-up web chat screens that sometimes launch when we visit websites.
But as AI becomes more widespread and demystified, and the large language models that power them are cheaper to build, businesses are returning to a fundamental question – what is its value to them?
For businesses ready to look at their processes in a new way, the best way to assess AI’s value is the old-fashioned way – through business case assessment and return on investment.
Opportunities for improvement need to be quantified, processes may need to be redesigned and specific AI applications need to be developed. Total costs, including regulatory compliance, must be measured against potential benefits.
People power
People have a key role to play in assessing AI’s value proposition and making the technology work. As part of this work, several trends have emerged.
First, workers still struggle with basic AI concepts and applications. Many do not grasp what AI implies for their roles, nor question why they should master a technology that might eventually take their jobs. This uncertainty underscores the need for clear communication and education about AI's personal benefits and potential.
It is also increasingly clear that the success of generative AI (GenAI) technologies and the ability to realise their value depends on the ability of the workforce to adopt and apply them effectively.
Despite this, many organisations are pushing for rapid adoption before their teams are fully equipped.
As GenAI features evolve constantly, providing employees with consistent, stable and coherent learning experiences will prove difficult. With an ever-changing curriculum, Gen AI learning must be broad-based and continue to keep pace with change.
Employees also need abundant structured opportunities to apply and practice what they are learning. Yet AI is not well enough democratised – not every employee has access to it, or support.
This could lead to the ‘Matthew effect’, which is the phenomenon wherein those with pre-existing advantages accumulate more advantages over time. If access to GenAI is unevenly distributed, it could exacerbate existing disparities.
AI has already started to extend our cognitive abilities, enabling us to access, understand and process more information than ever before.
Highly skilled individuals find that when they explore and figure out how to use AI to support their work, it enhances and extends their capabilities without diminishing their hard-earned skills.
However, for novices, an over-reliance on AI tools may limit their ability to develop essential skills such as problem-solving and subject matter expertise.
So, while Gen AI requires traditional methods of evaluating investment and return on investment, in the training and people space, we need to reconsider learning approaches.
This includes incorporating data-driven measurements such as tracking understanding and perceptions of GenAI, engagement levels and sustained versus lapsed adoption. KPMG has been actively developing and supporting these initiatives for clients, including through our GenAI Academy.
Get it right, now
Recognising the central role people play in the AI journey is crucial. It is also important to consider the medium and long-term impacts on skills, roles, learning, and culture.
Investing in workforce upskilling is the cornerstone of how organisations show their commitment to putting humans at the centre of AI transformations.
We may reach a point in the future where AI can be trusted to work autonomously. We may see a digital workforce of bots emerge as our co-workers. For now, however, AI adoption is a journey in which employee engagement, participation and support are vital.
Tania Kuklina is a Director at KPMG
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