A new era of generative AI for everyone – Accenture

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Synthèse Beesnest : 5 ways to use AI into business

ChatGPT has demonstrated the disruptive potential of AI technology, marking a turning point in public adoption. The model’s unprecedented growth reached 100 million users in just two months post-launch. Accenture’s research suggests that 40% of all working hours across industries can be influenced by LLMs, given that language tasks account for 62% of employees’ total work time.

Advising: AI models serve as an “AI co-pilot” for every worker, enhancing productivity and providing personalized intelligence. In customer service, for example, LLMs could deal with about 70% of complex communication, delivering more accurate and quality responses.

Creating: Generative AI will be a crucial creative partner, providing innovative approaches in fields like design, copy generation, real-time personalization, and content creation. For instance, systems like DALL·E can generate realistic images based on text descriptions.

Coding: Generative AI can enhance software development productivity, facilitating tasks such as automatic code writing, documentation generation, and problem prediction. For example, Accenture is piloting LLMs to automatically generate technical documentation.

Automating: With its comprehensive understanding and predictive intelligence, Generative AI can drive a new level of efficiency and personalization in both the back and front office operations, revolutionizing business process automation. Some banks are already using AI to manage post-trade processing emails.

Protecting: Generative AI will support enterprise governance and information security, protect against fraud, and improve regulatory compliance by drawing cross-domain connections and identifying risks. However, in the short term, organizations should also be aware of potential misuse, such as the creation of malicious code or phishing emails.

6 adoptions essentials :

1- Dive in, with a business-driven mindset

  • Organizations should adopt a dual approach of experimenting with off-the-shelf generative AI models for quick wins and customizing models with their own data for reinvention and long-term benefits.
  • Through experimentation, organizations can understand better which types of AI are suitable for various use cases, develop better data privacy practices, and establish necessary “human in the loop” safeguards.

2- Take a people-first approach:

  • Organizations need to invest in building AI technical competencies and training people across the organization to work effectively with AI-infused processes.
  • Companies should assess the impact of generative AI on various job tasks and roles, and prepare for entirely new roles like AI quality controllers, AI editors, and prompt engineers.

3- Get your proprietary data ready

  • Modernizing data architecture is essential as foundation models need vast amounts of curated data. Organizations need a strategic approach to data acquisition, refinement, and deployment.
  • A modern enterprise data platform built on the cloud can help break data free from organizational silos and democratize its use across the organization.

4- Invest in a sustainable tech foundation

  • Organizations must ensure their infrastructure can meet the high compute demands of generative AI and Large Language Models (LLMs) while being cost-effective and environmentally sustainable.
  • As the use of AI increases, companies need a robust green software development framework that considers energy efficiency and emissions. AI can also aid in achieving broader sustainability goals.

5- Accelerate ecosystem innovation

  • Creating a foundation model is a complex task and may be beyond the means of most companies. Leveraging a network of partners, including tech companies, professional services firms, and academic institutions can provide valuable insights.

6- Level-up your responsible AI

  • The rapid adoption of generative AI necessitates robust responsible AI compliance regimes, including risk assessments at the design stage and embedding responsible AI practices throughout the business.
  • This effort should be CEO-led and include training and awareness, execution and compliance, and must adapt to the fast pace of changing technology. Companies need to transition from a reactive to a proactive compliance strategy for responsible AI.

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Notation de l'étude

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