Uses Of Generative AI: Pros, Cons & The Effect on the Human Workforce
By Helena Carnell, Junior Account Executive
If anything has been made abundantly clear over the last few months, it is that the rise of generative AI in the workplace is inevitable.
The launch of OpenAI’s ChatGPT has inaugurated Artificial Intelligence to the front of worldwide conversation, garnering mixed responses of excitement and concern and accumulating a global market value of over $136bn.
As advanced AI becomes increasingly integrated into business processes, discussions must be had to ensure seamless synchronicity between humans and AI to ensure maximum benefits are achieved and to assure current employees that they aren’t facing redundancy.
This blog will explore how generative AI can be augmented alongside the human workforce to ensure increased productivity, profitability and potential.
Generative AI benefits
Generative AI advancements have limitless potential to transform employees’ day-to-day lives and restructure business processes. Here are the four main ways AI can augment work for the better:
1. Enhanced efficiency and productivity
Generative AI’s simple interface can enable employees to ask questions and receive almost immediate feedback. Not only does this reduce research time, but subsequently improves the employee experience, freeing up significant time for workers to focus on more complex and exciting tasks. In a world where companies must do more with less, AI is proving irresistible for this reason.
2. 24/7 availability
Organisations may struggle to maintain excellent customer service if the assisting team only works 9-5 hours, or if they serve a global customer base. Generative AI’s Q& chatbot functionality can bolster the customer experience by providing instant answers for simple queries, ensuring that more difficult questions can be dealt with much faster by human counterparts. Remember, automated phone systems and chatbots can provide instant solutions but humans will always be more trusting of other humans than technology.
3. Improved communication and collaboration
Within companies that have a global team of representatives, communication and collaboration can be challenging, especially when navigating language or time zone barriers. AI has the potential to enable seamless communication, save time, enhance translation quality, and facilitate faster global collaboration.
4. Personalisation and performance
Organisations that take the time to train AI on specific industry documentation will unbolt further potential – improving the overall accuracy of the technology so that it can soothe varying pain points experienced by different industries. Gen AI can also be used to accelerate personalised and applied learning, enriching the overall experience by creating engaging, tailored learning environments to suit any employee’s needs.
However, whilst these benefits are undeniable, questions still remain regarding AI’s useability and risks including, how advanced is generative AI?
Potential Generative AI risks
As with any new technology, generative AI is clouded with uncertainty. Disseminating true concerns with media fear-spiralling is far from easy, especially around AI’s potential to affect or displace the human workforce. Here are some of the biggest concerns around generative AI right now:
1. Job Displacement (or Replacement?)
The headlines are providing employees with cause for concern, highlighting that as many as 300 million jobs could be diminished by AI. Whilst this is a worrying figure, arguably it shouldn’t be too terrifying. After all, jobs chop and change frequently as innovation reshapes and moulds different industries. Where many jobs may be lost, many new ones will emerge. GenAI will enable individuals to upskill more quickly, so whilst it may disrupt traditional roles, it will also help employees navigate new career paths. Not to mention, soft skills such as leadership, problem-solving and teamwork, will become even more important for employees as they embrace AI in the workplace.
2. Inaccuracy – how accurate is this assumption?
It is widely understood that AI can hallucinate and be inaccurate from time to time. Recent studies have even suggested it’s becoming increasingly inaccurate, dropping to 2% accuracy on some maths questions. However, as mentioned previously, organisations can train AI on industry-specific tasks, slowly improving the technology for the better. Ultimately – both humans and AI must be allowed some inaccuracy leeway – no one or nothing is perfect all the time!
3. Data privacy and security – even technology requires a bit of handholding to avoid trip-ups
AI’s reliance on data can raise several questions regarding privacy and secure handling of sensitive data. But arguably, risk is in the hands of the beholder. Organisations that provide AI with appropriate guardrails, hand-holding and human oversight can avoid most adverse consequences.
4. Regulation and ethical concerns: How fast is AI advancing in comparison to regulation?
The speed at which AI is advancing presents risks on its own. Whilst teething problems can be expected and mistakes to be had, the world is racing to get risk management and compliance decisions made, with the UK hosting the Global AI Safety Summit this November. Once a decision has been made, organisations will be in a much stronger position to safely and ethically implement AI into their long-established programmes and processes.
Striking the right balance: How can generative AI and humans work together?
Clearly, generative AI tools like ChatGPT will be game-changers, but not solutions in themselves; any services they perform rely on replicating existing processes humans have already implemented. Realistically, they are more a ‘copy, combine and paste job’ rather than something that will ever be able to drive creative business innovation forward. Currently, we are witnessing generative AI and humans working together across all industries. Here are a few examples of how:
Cybersecurity – Millions of developers are utilising Github copilot to generate code, forever altering the code creation process. Analysts that tend to receive an overwhelming number of alerts each day, are utilising AI and machine learning-based analytics to free up teams, allowing them to fortify organisation’s cybersecurity defences.
Finance – Generative AI is being used to complete the time-consuming and repetitive tasks that fill up everyone’s, but most significantly juniors’, day-to-day. This allows these employees to acquire new skills that deliver more excellent value and support the wider business.
Data Centre Industry – The offering of generative AI within ‘data centre infrastructure management’ (DCIM) is its ability to take flagged details and advise on what should be done with this information. Previously, legacy systems would send a message to shut down the entire system whereas AI can now absorb the info and provide sophisticated solutions.
Marketing and PR – Generative AI is proving to have significant potential within these professions, increasing speed, creating inspiration and filling any needed gaps. Yet, on the other hand, AI has yet to adopt a human flair, may eventually be subjected to penalties from search engines and could have a detrimental effect on brand reputation. Don’t take the person out of personality.
Ultimately, organisations across all industries can find use cases for generative AI, but those wishing to succeed must understand and decide which tasks are ripe for automation, and which are best left to the human professionals.
As we look into the future, where these technologies will be increasingly present throughout all aspects of work, get in touch with us today if you are curious how your tech enterprise could benefit from a human approach to your AI strategy, or if interested in wider marketing and pr tips or how to create brand awareness.