By Edy Liongosari

The following is a condensed summary of select sections from Accenture’s publications: “Reinvention in the Age of Generative AI” and A New Era of Generative AI for Everyone.”)

In the next 12 to 24 months, companies will face a crucial test to determine if their investments in digital transformation have positioned them to leverage generative AI for leap-frogging competitors and reinventing their businesses and industries, thereby setting new performance standards. Generative AI stands out from other technological innovations due to its potential to reinvent every aspect of an organization. This technology is expected to lead to the emergence of new Reinventors and possibly displace current ones, with an overall increase in their number. Empirical evidence from work in this field indicates that this trend is already underway, particularly as generative AI rapidly disrupts various industries.^1,7

Reinventors, Transformers and Optimizers

Based on our latest survey of 1,500 C-suite executives, a small group of companies, referred to as “Reinventors” (9%), have successfully built the capability for continuous reinvention, with technology at the core of their strategy. This group has grown slightly from 8% last year, but a more significant increase is observed among the largest companies with revenues over $50 billion, where the number of Reinventors has quadrupled. Unlike the digital revolution, large companies are taking an early lead in leveraging generative AI by building on their substantial digital core investments and resources as part of their digital transformation journeys.²

The majority of organizations (81%) are classified as “Transformers,” indicating they are at the beginning of their reinvention journey and are taking steps towards it but may lack the sustainable capabilities for continuous reinvention and the speed and cost efficiencies of a connected strategy. Reinventors are pulling ahead in financial performance, with the remaining 10% of organizations, known as “Optimizers,” not prioritizing reinvention at this time.

Reinventors are anticipated to surpass all others in terms of revenue growth. Accenture’s analysis revealed that between 2019 and 2022, Reinventors experienced a revenue increase that was 15 percentage points higher than the rest of the survey respondents. It is projected that the disparity in revenue growth between Reinventors and the rest will expand by 2.4 times, reaching 37 percentage points by 2026. This widening performance gap underscores the urgency for other organizations to identify innovative methods to further accelerate their reinvention efforts.¹

Generative AI and Reinvention

Generative AI represents a technological revolution unlike any other in recent decades, with the potential to impact every aspect of a company—hence the connection between generative AI and reinvention. When fully deployed, this technology allows a complete overhaul of processes and talent, guided by responsible AI principles and supported by a digital core – the heart of digital transformation — with a data and generative AI backbone. While some companies, particularly the Reinventors, are proactively harnessing this potential, most organizations are still focusing on safe, no-regret applications of generative AI, such as content generation and customer care, rather than exploring strategic, transformative uses.

Reinventors understand that generative AI can redefine the entire value chain, driving both productivity and growth in unprecedented ways. They recognize that to fully leverage this technology, it must be integrated with other technologies and connected to broader organizational changes, including process redesign and talent management. This holistic approach is what sets them apart and allows them to redefine their performance frontiers.

Generative AI is increasingly recognized as a key lever for organizational reinvention, with 98% of organizations identifying technology as their top lever and 82% specifically highlighting generative AI as one of their main levers. The year 2023 marked a period of education and experimentation with this technology, while 2024 is expected to be a year of solidifying the foundation for generative AI and scaling its value. A vast majority of executives (97%) believe generative AI will transform their enterprises and industries in the next three to five years, with 99% planning to increase their investment in AI initiatives.^3,5

The adoption of generative AI is poised to accelerate the need for reinvention across industries, enabling organizations to redefine their ambitions and disrupt their sectors. Industries such as software and platforms, banking, communications, media, and life sciences are leading in the application of generative AI for various functions, including content creation, software development, knowledge retrieval, and customer service. Reinventors are pushing the boundaries further by applying generative AI to strategic business areas, introducing new capabilities at an unprecedented pace.

Adoption Essentials for Generative AI

To harness the transformative power of generative AI, organizations must navigate a complex landscape of technological, ethical, and operational challenges. Here are six essentials for successful generative AI adoption.⁴

1. Dive in, with a business-driven mindset – Experimenting with generative AI helps organizations overcome diffusion challenges and initiate transformation by creating early successes and change agents. A dual approach to experimentation—focusing on quick wins with consumable models, and reinvention with customized models—enables companies to identify suitable AI types for different use cases and refine their approaches to data privacy, accuracy, bias, fairness, and human oversight.
2. Take a people-first approach – To succeed with generative AI, companies must focus equally on people and technology by significantly increasing investment in talent for creating and using AI. This includes developing technical skills and training all employees to work with AI-infused processes. Independent research suggests companies are currently underinvesting in worker training for AI-related tasks. Furthermore, new roles such as linguistics experts and AI quality controllers will emerge, and companies should analyze existing jobs to determine how generative AI can automate or augment specific tasks.¹⁰
3. Get your proprietary data ready – To customize foundation models for AI, companies must modernize their data architecture and adopt a strategic approach to managing domain-specific data. This involves using a cloud-based enterprise data platform that supports cross-functional use and analytics, allowing data to be democratized across the organization. Addressing the data challenge is crucial, as foundation models require vast amounts of curated data to learn effectively.
4. Invest in a sustainable tech foundation – Companies must ensure their technical infrastructure can support the demands of large language models and generative AI while considering cost and sustainability. They need to evaluate the cost-effectiveness of these technologies for specific use cases. As AI usage grows, its carbon footprint will also increase, making it essential for companies to adopt energy-efficient green software development practices. Additionally, AI can be a key tool in achieving sustainability and ESG goals, with a significant portion of companies using AI to reduce emissions in their operations.⁶
5. Accelerate ecosystem innovation – Creating a foundation model is a complex and costly process, often beyond the means of all but the largest companies. However, a growing ecosystem of cloud hyperscalers, big tech players, and startups, with over $50 billion invested in AI startups in 2023, offers support.⁸ These partners bring best practices and insights for using foundation models efficiently in specific use cases. Having the right network of technology companies, professional services firms, and academic institutions is crucial for navigating rapid change.
6. Level-up your Responsible AI – The rapid adoption of generative AI highlights the urgent need for organizations to establish robust responsible AI compliance regimes, including risk assessment controls and embedding responsible AI approaches. However, most companies are lagging, with only 6% having a fully robust responsible AI foundation.⁹ It’s crucial for responsible AI to be CEO-led and integrated into governance structures for effective risk management and compliance. Organizations should transition from reactive to proactive responsible AI development, utilizing a comprehensive framework that encompasses principles, governance, risk, policy, control, and technology.

Closing

To succeed with reinvention, companies must embrace Generative AI, recognizing it as a critical technology for staying competitive. CEOs and their teams must objectively assess their current position and systematically execute a reinvention strategy, focusing on value, talent, and a digital core to harness Generative AI. Organizations must fluently navigate a complex landscape of technological, ethical, and operational challenges to truly embrace continuous reinvention and build the necessary capabilities.

References

¹Azagury, J., et.al, Reinvention in the Age of Generative AI, Accenture, retrieved Feb 26, 2024, www.accenture.com/us-en/insights/consulting/total-enterprise-reinvention
²Accenture Research conducted a survey of 1,500 C-suite executives across 10 countries and 19 industries in October-November 2023. They were asked about their organization’s approach to business transformation and reinvention strategy, as well as about their specific programs and success factors. Of the 1,500 respondents, 136 were identified as Reinventors and 1,210 as Transformers.
³Accenture Research, Pulse of Change Quarterly C-suite Survey, October 2023
⁴Daugherty, P., et. al. A new era of generative AI for everyone, Accenture, retrieved Feb 26, 2024, www.accenture.com/us-en/insights/technology/generative-ai
⁵Accenture Research, Pulse of Change Quarterly C-suite Survey, February 2023
⁶Daugherty, P., et. al., Uniting technology and sustainability, Accenture, retrieved Feb 26, 2024, www.accenture.com/us-en/insights/technology/uniting-technology-sustainability
⁷Rosenbaum, E., MIT research on ChatGPT shows ‘Industrial Revolution-level large’ leap for workers, says AI CEO, CNBC, May 20, 2023, retrieved Feb 26, 2023, www.cnbc.com/2023/05/10/mit-data-show-industrial-revolution-level-leap-for-workers-using-ai.html
⁸Mentinko, C. Artificial Buildup: AI Startups Were Hot In 2023, But This Year May Be Slightly Different, Crunchbase News, Jan 9, 2024, news.crunchbase.com/ai/hot-startups-2023-openai-anthropic-forecast-2024/
⁹Eitel-Porter, R., From AI compliance to competitive advantage, Accenture, retrieved Feb 26, 2024, www.accenture.com/us-en/insights/artificial-intelligence/ai-compliance-competitive-advantage
¹⁰Brynjolfsson, E., Rock, D., Syverson, C., The Productivity J-Curve: How Intangibles Complement General Purpose Technologies, American Economic Journal: Macroeconomics, Vol 13, No. 1, Jan 2021, pp.33-72, https://www.aeaweb.org/articles?id=10.1257/mac.20180386