Propelling productivity growth through generative AI

The next generation of technological advancements – generative Artificial Intelligence – is poised to transform and boost the performance of not only individual roles, but of entire functions.

Generative AI is rapidly filtering into the working world thanks to its broad utility and ease of use for certain routine tasks. By now, most people will have heard of applications such as ChatGPT, GitHub Copilot, and Stable Diffusion, and a growing number will have used them in some capacity.

Stakeholders are increasingly aware of having to manage it and trying to understand how it could impact their particular part of business, as well as their industry as a whole, but with very little historical context to base their understanding off.

Stepping into the unknown?

We’re at the beginning of a constantly developing journey to understand generative AI’s power. The potential for value creation is clearly there for multiple business areas, across almost every industry; in truth, the scale and scope of potential transformation is unknown.

And there lies a key challenge – many people feel we are stepping into the unknown.

Where will businesses accrue value from generative AI? What are the knock-on effects for their workforces? How will the mix of occupations and skills needed be transformed over the coming years? We don’t know.

A full realisation of its benefits will take time, and leaders will undoubtedly have to overcome challenges as the technology develops further; from managing risks, and determining skills the workforce will need, to rethinking and adjusting their core business processes.

As we all know, technology adoption does not happen quickly. But with the unrelenting pace of change in generative AI, organisations should be mindful of moving quickly enough to capture the potential value at stake.

Adding value through boosting productivity

According to research by McKinsey, generative AI could add trillions of dollars in value to the global economy – and even more if it is widely embedded into existing software.

This will come from all different areas of organisations, driven by the revolution of internal knowledge management systems where people can quickly and easily access relevant information. The ability of generative AI to digest data and draw conclusions offers insights that enhance knowledge work.

As a result of this newly-found freed-up-time, professionals are therefore able to think more strategically to make better-informed decisions and develop suitable strategies.

Changing work…forever?

Generative AI enhances an individual worker’s capabilities by automating certain activities – ultimately saving time.

Whilst the workforce has already undergone pretty widespread changes in the last decade, this has the potential to accelerate workforce transformation like nothing we’ve seen before. But transitions carry risk, and risks need to be properly managed.

If organisations are going to reap the benefits of AI’s full efficacy, appropriate investments will have to be made; their people need to be upskilled and properly equipped to use the tools.

Where could it have impacts?

Its full reach is still unknown, but there are a select few sectors where positive impacts have been predicted. For the below sectors, generative AI can deliver…

• Banking: Expertise to improve banking employees’ performances, as well as code acceleration which can reduce tech debt and deliver the desired software faster.
• Life sciences: Improved levels of automation in preliminary screening, and enhanced indication finding.
• Retail and consumer packaged goods: Adjustment of customer interaction patterns, better customer care through rapid resolution and insights in customer care, and innovation.

And in terms of professions, the following have been identified by McKinsey as areas in which generative AI could produce operational benefits – either as a ‘virtual expert’ or a ‘virtual collaborator’.

Disclaimer, there are plenty more – there is just more substantial research around these four!

Software engineering

Generative AI can act as a coding assistant to increase each developer’s efficiency levels.

The most intriguing use case in software engineering is through training large language models (LLMs). Off the back of this, applications can be developed to receive a natural language prompt that describes what a piece of code should do, and then generate code based on that instruction.

This acceleration of the coding process could significantly elevate the skill sets and capabilities of developers as it opens them up to focus more on code and architecture design, rather than actually writing the code itself.

Customer operations

Through digital self service, generative AI can help improve the customer experience through its ability to automate interactions using natural language prompts. It can also enhance the skills and productivity of customer service agents by freeing up their time.

To put it into context, McKinsey cited research from one company where AI increased issue resolution by 14% every hour, and reduced the amount of time taken to come to a solution by 9%. From a workforce perspective, the same research revealed a reduction in attrition of customer service agents and 25% less requests ‘to speak to a manager’.

Key things for leaders to consider

We’ve already seen AI change the way we live and work, but mostly it has been taking part behind the scenes. Now, through generative AI, it is truly stepping into the spotlight. Claiming a place on centre stage.

The exponential development of generative AI will only enhance the impact of AI in its entirety. It’s clear that there is an enormous amount of potential in terms of positively impacting businesses and the wider economy, but it carries unique challenges.

Leaders need to be hyper aware of the risks, not only to their particular function, but to society as a whole. The ethical considerations are still being debated, so it’s important to proceed with an element of caution.

As the technology develops further still – which it will do on a daily basis – leaders will have to continually think about how they manage not only the positives of generative AI, but the potential negatives as well.

What is your stance on generative AI? Want to know more about ours? Get in touch with Greg Milsom, Managing Director at Org to find out more!