A new era of generative AI for everyone

/ By Hina Patel, Managing Director within Strategy and Consulting business for Accenture in Africa /

Large language models (LLMs) are both a type of generative AI and a type of foundation model. With ChatGPT being the most popular, LLMs have woken up the world to the transformative potential of artificial intelligence (AI), capturing global attention and sparking a wave of creativity rarely seen before. Its ability to mimic human dialogue and decision-making has given us AI's first actual inflexion point in public adoption.

Accenture found that LLMs like GPT-4 can impact 40% of all working hours, as language tasks account for 62% of employees' total time. Therefore 65% of that time can be transformed into more productive activity through augmentation and automation. Business leaders recognise the significance of this moment, as they can see how LLMs and generative AI will fundamentally transform everything from business to science to society itself, unlocking new performance frontiers.

Welcome to AI's new inflexion point

Generative AI is designing, building and deploying AI following clear principles to empower businesses, respect people, and benefit society. ChatGPT raises essential questions about the responsible use of AI, and it's critical that generative AI technologies, including ChatGPT, are accountable and compliant by design. AI systems need to be "raised" with a diverse and inclusive set of inputs to reflect the broader business and societal norms of responsibility, fairness, and transparency.

Companies must reinvent work to find a path to generative AI value by decomposing jobs into tasks, investing in training people to work differently and reskilling people. Generative AI will disrupt work as we know it today, introducing a new human and AI collaboration dimension. Organisations that take steps now to decompose jobs into tasks and invest in training people to work differently alongside machines will define new performance frontiers and have a leg up on less imaginative competitors.

Embrace the generative AI era: Six adoption essentials

1. Dive in with a business-driven mindset

AI is essential for organisations to experiment with and learn about innovations.

Companies should take a dual approach to experimentation, focused on low-hanging fruit opportunities using consumable models and applications, and reinvention of business, customer engagement and products and services using customised models with the organisation's data. A business-driven mindset is critical to defining and successfully delivering on the business case. Companies will learn more about which types of AI are most suited to different use cases, how to test and improve their approaches to data privacy, model accuracy, bias and fairness, and when "human in the loop" safeguards are necessary.

2. Take a people-first approach

Companies should invest in talent to address the two challenges of creating and using AI: creating AI and using AI. It means building skills in technical competencies and training people to work effectively with AI-infused processes. Independent economic research indicates that companies are underinvesting in helping workers keep up with advances in AI, which require more cognitively complex and judgment-based tasks. Companies should start by decomposing existing jobs into underlying bundles of tasks and assess the extent to which generative AI might affect each task.

3. Get your proprietary data ready

AI has revolutionised how businesses acquire, grow, refine, safeguard and deploy data. Companies need a strategic and disciplined approach to obtaining, developing, refining, safeguarding and deploying data, specifically a modern enterprise data platform built on the cloud with cross-functional features. These platforms allow data to break free from organisational silos and be democratised. All business data can be analysed together in one place or through a distributed computing strategy.

4. Invest in a sustainable tech foundation

Companies must consider the technical infrastructure, architecture, operating model and governance structure of LLMs and generative AI to meet high computing demands while keeping a close eye on cost and sustainable energy consumption. To reduce carbon emissions, companies need a robust green software development framework that considers energy efficiency and material emissions at all stages of the software development lifecycle. Additionally, AI can help make businesses more sustainable and achieve ESG goals, with 70% of companies successfully reducing emissions in production and operations.

5. Accelerate ecosystem innovation

AI stands for Artificial Intelligence and is a type of machine learning that uses artificial intelligence (AI) to improve computer systems. It is becoming increasingly popular, with investments from cloud hyperscalers, big tech players, and startups. These partners bring best practices and can provide valuable insights into using foundation models efficiently and effectively in specific use cases. The right network of partners, including technology companies, professional services firms, and academic institutions, will be vital in navigating rapid change in AI.

6. Level up your responsible AI

Responsible AI must be CEO-led, focusing on training and awareness, followed by execution and compliance. It should include principles and governance; risk, policy and control; technology and enablers and culture and training. Accenture was one of the first to take this approach, with a CEO-led agenda and now a formal compliance program.

The future of AI is accelerating

The most critical details in this text are that generative AI and foundation models have revolutionised how we think about machine intelligence for several years and that ChatGPT has woken up to the possibilities this creates. Artificial general intelligence (AGI) remains a distant prospect, but the development speed is breathtaking. Companies are right to be optimistic about the potential of generative AI to radically change how work gets done and what services and products they can create. Still, they also need to invest as much in evolving operations and training people as they do in technology. Companies need to invest as much in developing operations and training people as they do in technology to realise the full potential of this step-change in AI technology.