AI for everyone
ChatGPT’s explosive global popularity has given us AI’s first true inflection point in public adoption. Finally, everyone, everywhere can see the technology’s disruptive potential for themselves.
The large language models (LLMs) and foundation models powering these advances in generative AI are a significant turning point. They’ve not only cracked the code on language complexity, enabling machines to learn context, infer intent, and be independently creative, but they can also be quickly fine-tuned for a wide range of different tasks.
This technology is set to fundamentally transform everything from science, to business, to healthcare, for instance, to society itself. The positive impact on human creativity and productivity will be massive.
Companies will use these models to reinvent the way work is done. Every role in every enterprise has the potential to be reinvented, as humans working with AI co-pilots becomes the norm, dramatically amplifying what people can achieve. Generative AI will impact tasks, not occupations. Some of those tasks will be automated, some will be transformed through AI assistance, and some will be unaffected.
We can also expect a large number of new tasks for people to perform, such as ensuring the accurate and responsible use of generative AI systems. It’s why organizations that invest in training people to work alongside generative AI will have a significant advantage.
ChatGPT has woken up the world to the transformative potential of generative AI, capturing global attention and sparking a wave of creativity.
Imagine every employee in your company had an assistant that "knew" everything your organization had ever known—the entire history, context, nuance and intent of the business and its operations—and could process, analyze and use that information in a matter of seconds, in infinitely repeatable ways.
We're at a phase in the adoption cycle when most organizations are starting to experiment by consuming foundation models "off the shelf." However, the biggest value for many will come when they customize or fine tune models using their own data to address their unique needs.
Generative AI and LLM applications are ready to consume and easy to access. Companies can consume them through APIs and tailor them, to a small degree, for their own use cases through prompt engineering techniques such as prompt tuning and prefix learning.
To increase the value of generative AI and foundation models in specific business use cases, companies will increasingly customize pretrained models by fine-tuning them with their own data—unlocking new performance frontiers.
of global executives agree AI foundation models will play an important role in their organizations’ strategies in the next 3 to 5 years.
of all working hours can be impacted by large language models (LLMs) like GPT-4.
The coming years will see outsized investment in generative AI, LLMs and foundation models. What’s unique about this evolution is that the technology, regulation, and business adoption are all accelerating exponentially at the same time.
Each layer of the generative AI tech stack (applications, fine-tuning, foundation models, data and infrastructure) will rapidly evolve – as the technology matures and as the compute demands grow exponentially. Cost and carbon emissions are central considerations in adopting energy-intensive generative AI.
ChatGPT raises important questions about the responsible use of AI. The speed of technology evolution and adoption requires companies to pay close attention to any legal, ethical and reputational risks they may be incurring. They will have to answer key questions on intellectual property, data privacy and security, discrimination, product liability, trust and identity.
Companies must reinvent work to find a path to generative AI value. Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people. Ultimately, every role in an enterprise has the potential to be reinvented, once today’s jobs are decomposed into tasks that can be automated or assisted and reimagined for a new future of human + machine work.
LLMs will impact every category, ranging from 9% of a workday at the low end to 63% at the high end.
Companies will have thousands of ways to apply generative AI and foundation models to maximize efficiency and drive competitive advantage. But they’ll need to reinvent work to find a path to business value from this technology. Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people.
To get started, consider the following adoption essentials:
Organizations must take a dual approach to experimentation. One, focused on "low-hanging fruit" opportunities using consumable models and applications to realize quick returns. The other, focused on reinvention of business using models that are customized with the organization's data. A business-driven mindset is key to define, and successfully deliver on, the business case.
Focus on people as much as on technology, ramping up talent investments to address both creating AI and using AI. This means developing technical competencies like AI engineering and enterprise architecture and training people across the organization to work effectively with AI-infused processes.
Foundation models need vast amounts of curated data to learn and that makes solving the data challenge an urgent priority for every business. Take a strategic and disciplined approach to acquiring, refining, safeguarding and deploying data. Ensure the organization has a modern enterprise data platform built on cloud with a trusted, reusable set of data products.
Consider requirements for infrastructure, architecture, operating model and governance structure in order to leverage generative AI and foundation models—keeping a close eye on cost and sustainable energy consumption.
Access resources and expertise needed to build and scale AI applications. Take advantage of industry best practices and insights offered by ecosystem partners—big tech players, start-ups, professional services firms and academic institutions.
Urgently assess whether the company's responsible AI governance regime is sufficiently robust before scaling up generative AI applications. Build in controls for assessing risks at the design stage and embed responsible AI principles and approaches throughout the business.
Language tasks account for 51% of the total time employees work.
of companies want to make a large investment in ChatGPT in 2023.
Moments like this don’t come around often. We’re at the start of an incredibly exciting era that will fundamentally transform the way information is accessed, content is created, customer needs are served and businesses are run.
Embedded into the enterprise digital core, generative AI and foundation models will optimize tasks, augment human capabilities and open up new avenues for growth. In the process, these technologies will create an entirely new language for enterprise reinvention.
But reimagining how work gets done, and helping people keep up with technology-driven change, will be essential in realizing the full potential.
Companies need to invest as much in evolving operations and training people as they do in technology.
Now’s the time for companies to use breakthrough advances in AI to set new performance frontiers—redefining themselves and the industries in which they operate.
The TQ is how we build and demonstrate our understanding of transformative technologies and how they deliver on the promise of technology and human ingenuity. From the C-suite to the front line, employees at all levels will need to develop a TQ to drive successful reinvention.
We invest in continuous training across Accenture, and each employee receives an individual TQ score. Accenture’s TQ learning series is a simple and effective way to ensure every team member learns about technology, how it’s applied, why it matters and how it works with other technologies.