Experts predict generative AI’s business
impact over the next 5 years
Since it burst onto the scene in November 2022, ChatGPT has ignited widespread interest and debate about generative AI.
Generative AI is artificial intelligence capable of creating text, images, videos, or other data via “large language” learning models that respond to prompts or instructions from humans. By learning from vast amounts of training data, these models can generate new content and provide recommendations for users at great speed.
Advocates of generative AI highlight its potential to boost the global economy and increase workforce productivity, among other benefits. However, questions persist about the ethical implications and risks associated with its use across various industries, such as the arts, entertainment, and education.
From a business perspective, generative AI is far from a passing trend – it’s a powerful tool that will continue to redefine how businesses operate, innovate, and grow. To explore this idea further, we called on experts from The University of Queensland’s Business School and School of Economics. They share their insights on how generative AI will impact different sectors over the next 5 years and offer strategies for business leaders and organisations to navigate this evolving landscape.
Impacts of Generative AI on...business leaders
Insights from Professor Victor Callan AM
A 2024 survey by Oxford Economics found that 61% of C-suite executives see AI as a “game changer”. However, nearly the same percentage report a lack of confidence in their own and their leadership teams’ AI skills or knowledge to respond effectively to the potential impacts of AI on their businesses.
In other recent surveys of CEOs and senior executives, they report confidence levels in their executive teams to be at their lowest point in the past 3 years. A key reason linked to this low confidence is AI’s rapid evolution and the uncertainties and challenges in responding to its impact.
Curious and ambitious business leaders can take some clear actions to prepare their businesses for the AI world, particularly as tools such as generative AI become more integrated into their day-to-day operations.
First, if they haven’t already at an organisational level, they must continue to embrace diversity and inclusivity in their workforces.
In more inclusive business cultures, employees are more willing to share their diverse experiences with AI, feel encouraged to engage in experimentation and innovation, and want to seek out cross-team collaboration about how to apply recent technologies. Relatedly, all team members should have access to training and support that motivates them to take risks in adopting new technological solutions for existing and emerging business challenges, such as using generative AI platforms to automate processes or visualise data.
Second, at a more personal level, senior leaders need to be enthusiastic learners about advancements in generative AI and AI more broadly. Leaders should reinforce their positions on the visionary impact of this technology by keeping up with industry trends through regular conversations, conferences, and meetings with tech-savvy colleagues. They should also stay connected and personally support their own business teams that are searching out new ways to innovate by using AI applications such as generative AI.
Impacts of generative AI on…small business and entrepreneurship
Insights from Associate Professor Frederik von Briel
In the near future, generative AI will unlock an abundance of opportunities for entrepreneurs and small businesses. It will enable faster innovation, cost reduction, and more effective personalisation of customer experiences.
For example, generative AI will automate much of the product development process, allowing entrepreneurs and small businesses to validate and iterate on new ideas more rapidly and cost-efficiently.
Early adopters of generative AI will gain a significant competitive advantage, helping them to increase their market share, profitability, and overall bottom line.
However, as generative AI adoption becomes more widespread and AI-driven practices become standard, the initial competitive advantage enjoyed by early adopters will diminish. Everyone will have the same foundation to identify and seize new opportunities, leading to increased competition and potentially reduced profit margins. As such, to remain profitable, entrepreneurs and small businesses will need to increasingly focus on niche markets and highly customised offerings that allow them to differentiate themselves from competitors.
High-quality, proprietary data will become an increasingly strategic asset for differentiation. It will enable entrepreneurs and small businesses to continue leveraging generative AI for unique value propositions and personalised customer experiences.
Moreover, as generative AI takes over more routine tasks, the value of human creativity, critical thinking, and emotional intelligence will also increase. Entrepreneurs and small businesses that find ways to effectively combine human skills with generative AI capabilities will be better positioned to offer unique, human-centred products and services that stand out in the market.
So, in the mid- to long-term, access to high-quality, proprietary data and the ability to synergise human skills and generative AI will be important for entrepreneurs and small businesses to create unique value propositions, build strong customer relationships, and ultimately be able to command premium prices.
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Impacts of generative AI on...finance and accounting
Insights from Associate Professor Min Zhu
Generative AI has the potential to transform almost every industry, including finance and accounting. In particular, generative AI could be revolutionary for financial advisers.
According to a report by Rainmaker Information, the number of financial planners has declined by 43% since December 2018 following the Hayne Royal Commission into the banking and financial services sector. Meanwhile, the demand for advice is expected to rise due to a projected 17% increase in the number of pre- and post-retirees (individuals aged 55 to 84 years) over the next decade. This situation has placed financial advisors on the government’s skills shortage list in 2023.
Additionally, the complexity of the Australian superannuation and tax systems makes financial planning decisions daunting for many households.
Low-cost financial advice is a massive, underserved market in Australia, and AI can help address that.For financial planners, generative AI offers the potential to automate mundane tasks, allowing them to take on more clients. Over the next 5 years, we can anticipate an increase in the popularity of hybrid models that combine AI with human financial planning.
At the high end, generative AI could provide fully automated and individualised financial advising. While the current robo-advisory market is growing rapidly, these platforms mainly provide simple portfolio construction advice. With generative AI, robo-advisors can develop more domain-specific expertise and tailor their services to a user's unique situation, helping clients understand their financial goals and find attainable solutions.
Integrating human elements into generative AI will usher in a new era of financial advising. A finance-specific AI can effectively convey and interpret investment and risk management concepts to a wide range of individuals, making financial advice more accessible and inclusive.
While the case for increased generative AI adoption in financial advice is strong, several key barriers hinder its widespread implementation. AI must demonstrate accuracy and reliability, build trust, adhere to users' moral and ethical standards, and comply with regulatory guidelines. These challenges apply universally to all potential applications of generative AI. The financial advising industry should embrace these challenges and opportunities, working to incorporate and improve AI-driven financial advice and build trust with the general public.
Impacts of generative AI on…the customer service experience
Insights from Professor Janet McColl-Kennedy
Generative AI is poised to change many aspects of core business functions over the next 5 years.
In the customer service experience, massive amounts of unstructured data like images, emails, and text files of customer complaints and suggestions are generated at each key touchpoint in the customer journey – such as the customer’s first interaction with a business or at the point of purchase.
The collected customer data can vary widely in format, sequence, and frequency, posing significant challenges to businesses in determining what it all means and how to use these insights to improve the customer experience and the business overall.
Thanks to advances in generative AI, businesses potentially have access to innovative methods and techniques to analyse the data collected across the customer journey and generate new data. These new methods allow for more sophisticated data interpretation, helping businesses gain deeper insights and creating more personalised customer service interactions.
While generative AI tools such as ChatGPT may offer suggestions for relatively simple tasks such as crafting social media posts, the real game changer is customised Large Language Models (LLMs) that can use very large and diverse data sets to provide evidence-based, data-driven recommendations to a business.
LLMs can simultaneously work with textual data from various sources, such as customer blogs, customer chat groups, product review websites such as Yelp or TripAdvisor, industry and government reports, and the business’s own social media posts, survey and interview data.
Ultimately, these advancements should also drive significant improvements in employee productivity, business operations and strategic decision-making.
In particular, 5 important business functions could benefit from advancements in generative AI over the next 5 years:
- Performing customer service duties, including responding to customer inquiries through various channels such as call centres, chatbots, emails, ticketing solutions and social media platforms.
- Preparing draft business and marketing reports, summarising content, and generating brand and promotion copy.
- Crafting posts for social media channels based on discussion points and threads among key target markets.
Problem solving and handling customer complaints by generating options personalised to the customer and recommending tailored recovery actions.- Maximising sales through understanding and recommending which offer(s) could be most relevant to customer segments and how to communicate these offers effectively.
To navigate the shift towards generative AI, service organisations should:
- Invest in understanding the specific benefits that generative AI tools can offer them
- Provide access to training for their staff
- Be clear about the challenges of generative AI implementation in their particular business
- Develop a plan to address the key challenges.
All of this will require careful planning and sufficient time and resources. Working with staff across the organisation to co-create an implementation plan designed to realise the benefits and address the challenges of generative AI will be crucial.
Leaders should develop frameworks, guidelines and guardrails to identify problems before they arise, and implement rules to avoid AI generating inappropriate text or tone. Building trust with staff will be vital, demonstrating how humans and generative AI can work together harmoniously to enhance customer experience, increase staff satisfaction, and drive overall business success.
Impacts of generative AI on…income inequality, the labour market, and recruitment processes
Insights from Dr David Smerdon
Generative AI will significantly reshape the labour market in both predictable and unexpected ways, with substantial implications for societal inequality.
While there are fears of job losses, history shows that new technology often creates as many jobs as it displaces. Generative AI will likely automate specific tasks within jobs rather than eliminating entire roles, allowing many workers to focus on more complex, creative, and interpersonal tasks and enhancing job quality. However, the impact will vary across sectors.
Clerical and administrative roles, highly exposed to automation, are likely to shrink substantially. This reduction will disproportionately affect women in these sectors, especially in high-income countries.
Another significant challenge is the potential effects on income inequality. Generative AI will boost demand for high-skilled workers, such as data scientists, who can work alongside these new technologies. However, unlike historical productivity shocks that mainly hurt low-income workers, such as the industrialisation of agriculture, generative AI is expected to impact middle-income earners the most.
Research indicates that about a quarter of clerical positions are highly exposed to AI, with two-thirds considered moderately exposed. This trend could lead to an overall shrinking of income inequality for most of the population, even though the top 1% are unlikely to be negatively affected and may even pull further ahead.
Generative AI will also transform job searches. As AI tools become commonplace for writing CVs and cover letters, the usefulness of these documents will decline, making it harder for employers to distinguish between candidates. Moreover, employers will likely use generative AI to filter applications, reducing the effectiveness of traditional productivity signals in job applications. This shift will increase the importance of other signals, such as job interviews or personal references, for hiring decisions.
The unpredictability of traditional job searches will lead employers and applicants to focus on social networks and personal connections for interviews. This change could exacerbate inequality as job opportunities increasingly go to those within established networks, disadvantaging less-connected individuals.
To address these challenges, businesses should focus on redeploying and retraining affected workers, particularly those in clerical and administrative roles, to new positions that require human interaction and creativity.
Early investment in training programs to develop digital literacy, data analysis, and other relevant, future-proofed skills will be crucial.
Implementing practical skills assessments and simulations during the hiring process can ensure a fair evaluation of candidates' abilities. Additionally, diversifying recruitment channels and using structured interview processes will help mitigate inequalities and create a broader talent pool.
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Meet the experts
Professor Victor Callan
Professor Victor Callan AM is a Professor of Management and Leadership at UQ Business School and the UQ MBA program. He’s also a member of the Future of Health and Service Innovation Alliance research hubs.
Associate Professor Min Zhu
Dr Min Zhu is an Associate Professor in Finance at UQ Business School. Her research interests include asset management, empirical asset pricing and fintech.
Dr David Smerdon
Dr David Smerdon is a Senior Lecturer at the UQ School of Economics. He primarily works in behavioural and development economics.
Associate Professor Frederik von Briel
Dr Frederik von Briel is an Associate Professor in Strategy and Entrepreneurship and the Program Leader of the Master of Entrepreneurship and Innovation at UQ Business School.
Professor Janet McColl-Kennedy
Professor Janet McColl-Kennedy is a Professor of Marketing and Co-Lead of the Service Innovation Alliance Research Hub at UQ Business School. She’s also the Lead of the Innovation Pathways Program within the Food and Beverage Accelerator (FaBA).