• Cherie Gartner, Leadership |

How may generative AI be a game changer?

There is a very good possibility that you or your colleagues have heard of at least one of the numerous iterations of generative artificial intelligence (AI) that have rapidly pervaded the modern workplace. This sudden influx of AI technology has gained so much traction that the World Economic Forum in Davos recently acknowledged the importance of generative AI and its possible effects on society as a whole.

Zeroing in specifically on the field of modern work, AI has the capacity to transform how we go about our day-to-day tasks. While the emerging technology that supports creative functionality is still in early, exploratory phases, organizations and employees have begun to tap into the opportunities afforded by weaving generative AI throughout common workplace practices. Many use cases have been presented, ranging from summarizing text to more advanced capabilities like the generation of naturally flowing written answers to emails, or supplying full modules of code. With AI now a steadfast contributor to aid productivity, and often already integrated in day-to-day computing, generative AI is distinguished through its ability to produce new content with relatively simple input. 

The modern workplace can now be supported by AI capable of generating new data based on a set of learned patterns, principles, and rules. Images, text, and music are all within the capability of generative or “creational” AI, bolstering the production of creative output. Employees now have added help on their side to help spark creativity and productivity. 

Many organizations (including KPMG) are already exploring how they can leverage this technology. Microsoft's recent investment in OpenAI and the subsequent announcements about Microsoft 365 Copilot are merely starting points exhibiting how this technology is becoming more thoroughly incorporated in day-to-day occurrences in the workplace. 

How can generative AI revolutionize the future of work?

Generative AI already exhibits the potential to transform the current workplace into one geared towards increased productivity by automating tasks that can help free up employees’ time, allowing them to focus on more complex, higher-value work. In turn, organizations can improve their performance and drive business results by fostering innovation and working collaboratively with AI to optimize usage.

Looking deeper at Microsoft’s recent decision to embed OpenAI technology into their core set of products – namely Microsoft 365 Copilot and Viva Sales – organizations are able to leverage opportunities to boost productivity. For example, asking the AI to generate a sales email to customers using real-time, live data about prices and stock information from back-office systems can help save time and effort. Taking advantage of the full opportunity will require guidance and support as this a transformational era for the modern workplace.

Going forward, there are deeper exploratory usages of generative AI that are important to consider as businesses begin implementing this technology.

1. Augmenting the workforce

By asking a generative AI tool to draft a letter or summarize existing content, there is intrinsic value in freeing up time that would otherwise be spent completing general administrative tasks. Given the current level of quality output, generative AI is capable of assisting workers in their day-to-day routines; that said, at this stage, human review is still necessary and mandatory, fostering a collaborative environment between man and machine.

Human creativity remains the essential core of creative output, with generative AI providing enhancements to already-promising ideas. Generative AI can assist with a range of options that act as starting points for human creativity, but that human element is necessary to curate and fine-tune the output.

Despite the increasingly widespread practice of embedding AI into major workplace technologies, responsible use and ethical judgement is imperative.

2. Democratizing AI

Generative AI is also often referred to as “prompt AI,” where you only need a simple query input to generate quality results – no tweaking of models or parameters required. This ease of use supports the potential to more broadly leverage the AI that was once typically reserved for coders exclusively. Generative AI could be compared to the first low-code platforms, but with the added elements of user-friendliness and high-quality output, making it more accessible than its predecessors.

Generative AI will likely reach deeply into the workforce for many different use cases, from frontline workers to back-office staff. With free/low-cost models, developers, researchers and entrepreneurs may leverage the power of these models without having to invest in expensive hardware and data sets.

Looking ahead, the ongoing democratization of AI will require continued investigation and heightened focus on the underlying issues of data privacy and security and ethics.

3. Impact across industries and business functions

Preparing the workforce to work more closely with generative AI is less a question of “if” and more a matter of “how and when.” Use cases are broad, covering various industries and business functions. Generative form of AI has already begun appearing through:

  • Sales – permitting improved communication between workers using real-time data from back-office systems about clients and products.
  • Marketing & Communications – producing content from copywriting to videos that address customers more personally (i.e., via writing style and tonality).
  • Research and development (R&D) – supporting to identify creative new combinations as a new R&D starting point for product creation.
  • Risk and Legal – providing suggestions to complex questions more quickly, pulling from vast amounts of legal documentation, and drafting and reviewing annual reports.

The list of use cases continues to expand as our understanding of generative AI capabilities evolves, especially since programs such as ChatGPT have already begun changing the way we learn, code and generate content.

"Generative AI will likely change the way our clients run their businesses in the future. It can allow us to automate repetitive tasks, make more informed decisions and ultimately help drive growth and efficiency in the workplace. It's not about replacing human jobs; it’s about augmenting teams and creating new opportunities for businesses to thrive." – Dr. Sven Röhl, Global Alliance Lead – Microsoft Modern Work, KPMG International

Will you be an integrator or an innovator?

To stay up-to-date, organizations should consider assessing the art of the possible for their respective industries and functions and their own business, deciding whether to focus on integration or innovation as a starting point

Systematic evaluation

Businesses should be starting to identify tasks where generative AI may benefit and enhance day-to-day processes by experimenting and testing new fields within their internal innovation circle. This can help them decide if integrating generative AI in the workplace can make enough of a difference or if creating new solutions or approaches will be cutting edge in the market. For Marketing and Sales oriented functions, leveraging widely available models could assist in boosting productivity, while for R&D related topics, models may require more training with and using internal data.

Integrate into the workplace

With Microsoft investing in OpenAI, various generative AI components begin to proliferate the Microsoft product suite, and companies choosing to leverage and implement them have the chance to get ahead of the curve. For example, in Microsoft Teams Premium, it will be possible to generate meeting summaries with intelligent recap leveraging OpenAI. Standard software combined with generative AI models available via the internet will be a powerful tool, adding another potential productivity gain.

As generative AI is being commoditized through widespread availability, it’s imperative to inform and train the workforce about its responsible use, including but not limited to sensitivity around the usage of confidential data that should not be “shared” with publicly available models to protect intellectual property and other sensitive information.

When provided with a solid understanding of the limitations of AI, education (including high-level overviews about how the technology works) can assist employees with the knowledge and parameters needed to navigate and leverage it more successfully. KPMG professionals’ expertise comes into play here, where we can assist with the transformation process by efficiently applying our technology and industry experience.

“To be a responsible business leader, we must ensure that our use of generative AI aligns with our core values and ethical principles. By implementing safeguards and regularly assessing the impact of our AI initiatives, we can harness the power of this technology to drive innovation while also safeguarding against unintended potential consequences.” – Marco Casalaina, Vice President of Product Management for Microsoft Cognitive Services

Innovating and applying new solutions

The competitive edge of generative AI can be achieved through the creation of new solutions that use custom data. The public availability of OpenAI models through Microsoft Azure has the capacity to meld new solutions together with existing cloud solutions. While an AI like ChatGPT trained on the corpus of much of the public internet is already a powerful tool, a company’s internal R&D data may provide the insight to develop new pharmaceuticals or investment strategies when a generative AI model is also trained on select or highly specific data.

This specificity, however, comes at a cost – such as increased requirements in terms of funding, resources and capabilities – an inevitability for some industries. KPMG’s five guiding pillars for ethical AI can help in assessing and establishing an appropriate level of governance framework for companies who need to develop their own solutions.

Naturally, as with many emerging technologies, there is a thorough level of assessment to be done when determining how to better integrate generative AI into the modern workplace. The constraints – or lack thereof – considerations and challenges of generative AI have already been called into question and discussed across many business circles. However, I am confident that these uncertainties can be overcome through careful quality assurance and controls, and by creating a symbiotic relationship between human and machine

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