Why global leaders see generative AI as a game-changer
Across industries, we’re continuing to see unprecedented levels of interest and excitement about generative AI and large language models. In fact, my team recently tracked those interest levels amongst 2,300 global leaders.
Our survey found an overwhelming 97% who say generative AI will be transformative for their company and their industry.
It’s a breathtaking statistic. In fact, it’s difficult to think of any other technology that has captured the attention of both business leaders and the public as quickly and comprehensively.
That's why I recently sat down with Lan Guan, Global Lead for Accenture's Generative AI Center of Excellence, to dig into the findings and understand what makes generative AI different.
Generative AI is ushering in a new era in which machines can generate accurate content and insights, in simple everyday language, at scale, almost instantly.
As we explained in our report, A new era of Generative AI for everyone, this is triggering a complete reinvention of the way we think about getting work done.
And it’s happening fast. Over the next decade, we expect generative AI to be a mega-trend, with Accenture’s research indicating it will transform around 40% of all working hours across economies.
But what do business leaders themselves think? We looked at some other important insights from our recent survey.
Virtually all respondents (97%) believe generative AI is a “game-changing” technology worthy of long-term investment. And the same number have a keen interest in exploring the investment opportunities within this calendar year.
Executives see the high potential in areas like IT Operations, customer service, R&D, product development, coding and software development and content generation.
This shows the immense potential the technology holds for rapidly driving innovation across all parts of the business.
Of course, it takes investment to see real results. Generative AI is intricately connected to other technologies within the digital core. The good news is that all but a handful of organizations (95%) are planning to increase their technology spending as a proportion of revenue over the next year.
This reflects the fact that, as Accenture’s leader Julie Sweet has said, “all roads lead to technology” for today’s enterprises.
Whether it’s cloud, data or AI, investing in the digital core underpins all the other strategic needs of the enterprise.
And, in fact, the survey indicates that data and AI is an area where two-thirds (67%) of organizations will be upping their spending in 2023.
That investment is sorely needed. Business leaders themselves recognize the challenges they face on this journey.
Consider that 56% of respondents to our survey see a lack of data readiness as the top obstacle in adopting AI.
It’s clear that companies are still grappling with longstanding issues around data quality, accessibility and governance.
Resolving these issues won’t be easy. But it’s a crucial step in harnessing the potential of AI at scale. It calls for new strategies, operating models, business cases and digital core architectures capable of capitalizing on AI innovation.
This is a key reason Accenture is making a multi-billion-dollar investment in our data and AI capabilities.
We’re investing in assets, industry solutions, ventures, acquisitions, talent and ecosystem partnerships. And we’re doubling the size of our data and AI practice.
We’ve also developed new tools like our AI Navigator for Enterprise. It’s a generative AI-based platform that will help clients define business cases, choose architectures and make other key business decisions as they navigate their AI journeys.
These capabilities are designed to enable our clients to harness generative AI’s full potential to reshape their strategy, technology and ways of working.
Business leaders are understandably eager to get started. Over half (56%) say their companies are organizationally ready to scale up generative AI.
But they also recognize the need to navigate this journey securely and responsibly. Interestingly, 93% of executives surveyed express support for some level of government regulation around AI.
This underscores the fact that many of the ethical and societal implications are yet to be fully understood, yet alone solved. Companies recognize the sheer pace of change is creating uncertainty and are looking for guidance in how to manage it.
It’s another reason why creating or updating enterprise responsible AI frameworks is essential. This is something that Accenture has long championed. In fact, we incorporated responsible AI principles into our own core business values as far back as 2016.
Given Accenture’s view that generative AI will reinvent around 40% of all working hours, the impact on the workforce is potentially massive. This does not mean that 40% of work will be eliminated, but rather, it means that that some tasks will change, with some being automated and others being augments. This will change the way work will be done and how value is created.
Indeed, one of the most exciting things about generative AI is how broadly applicable it is. There are countless use cases across countless human activities across countless industries that will be transformed.
So, it's encouraging to learn that over 70% of organizations have specific training programs planned for 2023 to ensure their workforces are well-prepared to use generative AI tools.
This proactive approach to upskilling is the right strategy. It reflects the fact that, across huge swathes of workplace tasks, generative AI is a tool that will largely augment, rather than replace, human employees.
The survey puts hard data behind insights we’ve gathered from nearly a thousand conversations with clients:
The key now is to develop scalable solutions that deliver real value in day-to-day work. But turning experiments into something that can reinvent entire business workflows at scale is not a trivial exercise.
It means building the business case. Understanding how and where to apply generative AI. And getting the digital core in shape.
It means assessing which ecosystem partners and models to use. Rewiring business processes for AI. And upskilling people with new ways of working.