The rise of AI
Artificial intelligence (AI) has become the hot topic these days. From governments shifting entire economies around development projects, to individuals trying to choose between the latest assistance apps, AI is swiftly becoming embedded into every aspect of our lives.
Recent figures show “the UK now has more than 1,300 AI companies- a 600% increase in the number of firms over the last decade – with a collective turnover of almost £1.47 billion.” In November 20224, the Chancellor Rachel Reeves presented the Autumn Budget which included plans for the Artificial Intelligence Opportunities Action Plan- a “roadmap to capture the opportunities of AI to enhance growth and productivity.” This drive reflects Gartner’s research which showed “seventy-nine percent of corporate strategists said that technologies such as analytics, artificial intelligence (AI) and automation will be critical to their success over the next two years, according to a survey by Gartner, Inc.”
As a result, developing transferable technological skills is already becoming a key consideration point in employers’ hiring decisions, and employees’ long term career growth options. According to Resume Builder, “Ninety-one percent of companies using or planning to start using AI in 2024 will hire new employees next year, and 96% say it will be very beneficial (65%) or somewhat beneficial (31%) for candidates to have experience with AI… Additionally, 83% of this same group say employees who have AI skills will definitely (45%) or probably (38%) have more job security at their company than those who do not.” Clearly then, at the very least, familiarity with AI tools is going to be of long-term benefit.
The consensus seems to be that artificial intelligence is set to become an intrinsic part of normal existence, the impact of which is already being seen and felt.
So what does artificial intelligence mean for mentoring?
From an operational perspective there are clear benefits to using AI within the mentoring process, in much the same way as they are becoming standardised in other areas of business services. Examples include using them for various admin tasks, such as automated session reminders for programme participants, or note taking assistance tools. When evaluating the progress of a mentoring programme, artificial intelligence can provide rapid data analysis of specified metrics (such as engagement, participant feedback scores, company profit growth etc) and generate insights based on the overall goals of the organisation. There is even the possibility of AI helping to match mentors and mentees at the start of their participation based on identifying complementary goals and experiences.
This level of automation and augmentation can play an important part in streamlining many parts of the background workings of mentoring programmes. Indeed, this could even help serve to make such initiatives more attractive to businesses where logistics and implementation had previously been barriers. The greater support capability provided by such systems has the potential to improve overall standards of mentoring practices by offering better oversight, clarity and control.
But could artificial intelligence overtake, or even replace, this type of human interaction?
Our short answer would be no.
One of the key fundamentals for effective mentoring practice is establishing a sense of trust and safety (see the 5 Pillars of Mentoring). However, Forbes Advisor research found “59% of Brits have concerns about the use of artificial intelligence” with 38% specifically citing fears around privacy and data security. McKinsey took this further, commenting “as businesses begin to see the benefits of gen AI, they’re also recognising the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.” As with any digital system, there is always the possibility of data breaches, whether malicious or accidental. With mentoring sessions delving into personal information, including private fears, barriers, and challenges, participants may feel an understandable sense of wariness around truly opening up and sharing, for fear of their disclosures later being made public.
At the same time, for all the vast scope of GenAI capabilities, at their core they are designed and trained to fulfil a specific purpose. They are fed data and prompts in order to extrapolate and provide answers. To a degree that exists with humans as well- we draw on our own experiences to inform our choices, but we are able to think laterally and challenge our own assumptions. It is true that the more a programme consumes, the wider the range of responses which it can give, yet their options are still chosen and influenced by the specific end goal of their function. AIs, in essence, simply relay information as opposed to empathising with experiences. Conversely, humans can draw lessons from what, on paper, may appear to be completely unrelated topics or situations, but when experienced provoked reactions, challenges, and outcomes which later become relevant and applicable in unexpected ways. A human being has an entire lifetime of experience to draw upon, tempered by emotional intelligence and understanding. True familiarity with an experience allows for a far greater depth of empathy and meaningful support than can be generated from a set of anticipated prompts and responses, however large the databank they are drawn from.
Even when given access to a vast range of information, it is important to note that artificial intelligence does not (yet) have a genuine understanding of the content it has consumed. This limitation was made abundantly clear in the recent issues regarding AI chatbots attempting to summarise news reports. A BBC investigation revealed AI reporting had “significant inaccuracies” as “51% of all AI answers to questions about the news were judged to have significant issues of some form.” The AI programmes “struggled to differentiate between opinion and fact, editorialised and often failed to include essential content”. Such results raise significant question marks over the use of AI at the frontline of mentoring provision. Simply feeding a programme every piece of literature written about mentoring would not enable it to fulfil the role of a mentor. With some dedicated development training the accuracy of responses would improve, however that would still not meet the requirements of an effective mentoring relationship.
Mentors need to be able to take into account a vast array of factors in order to follow best practice, including a mentee’s identity, environment, experiences, values and beliefs (see our Mentoring and DEI article for a closer look at this). They then need to draw on their own experience and knowledge to provide guidance and insight in a way which is accessible and impactful for their mentee. It is a nuanced role, requiring ongoing adaptability and authenticity from both parties. As such, it would seem that AI programming still has a long way to go.
That desire for authenticity, particularly around emotionally vulnerable situations, is increasingly becoming evident in reaction to the spread of artificial intelligence. One example of this can actually be seen in recent reactions to Pinterest. As a platform which was built to share ideas and inspiration between people from all walks of life, the recent influx and promotion of AI generated content is resulting in a backlash from users. When reporting on this, Social Media Today observed “[this] highlights the dichotomy of AI in social media, in that the platforms themselves are encouraging AI use, which runs counter to the actual human connection pillars that these apps were founded up.” Going so far as to warn of a future “AI content collapse”. Clearly then, there is a definite need to preserve space for real interaction and connection, made even more valuable to the perception of its growing scarcity.
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