Generative AI in Business: AI-Led Processes Drive Growth, Accenture Finds

The proof of the pudding is in the eating, as the old saying goes. When we try to gauge the effectiveness of Generative AI in business investments in process improvement, productivity growth, and firms' bottom line, our best shot is to examine the tangible results these technologies deliver in real-world applications. Mere theoretical models or projections don’t serve the purpose. 

The Landscape of AI Investments in Enterprises 

Recently, Accenture surveyed 2,000 executives across 12 countries and 15 industries to assess the progression of business operations maturity across four criteria: Reinvention-ready, Insight-driven, Automated, and Foundational. Each of these criteria is supported by more advanced methods of working with data, automation, common AI, and Generative AI in business. The key finding is that companies embracing AI-led process improvement are not just keeping pace with their peers but are significantly outpacing them. 

The Landscape of AI Investment 

According to the research, about 1 in 3 companies (74%) have reported that their investments in generative AI and automation have met or exceeded their expectations.  

This optimistic outlook around GenAI investment is prompting 63% of these companies to ramp up their efforts in AI and automation by 2026. This signals a robust commitment to harnessing futuristic technologies for organizational growth.  

One of the most striking findings of the survey is that there is a steep growth in the number of companies that have fully modernized their processes with AI. Accenture’s “Reinventing Enterprise Operations with Gen AI” report shows the number of organizations categorized as having “AI-led” processes has nearly doubled from 9% in 2023 to 16% in 2024.  

This transformation is yielding impressive results: these advanced companies experience 2.5 times higher revenue growth, 2.4 times greater productivity, and an astonishing 3.3 times more success in scaling generative AI use cases compared to their less advanced counterparts. 

Challenges and the road ahead for companies  

Despite these encouraging numbers, the report highlights a significant divide between those reaping the benefits of AI and those still grappling with operational challenges.  

For instance, 64% of organizations are struggling to adapt their operational models, often due to inefficiencies in their data infrastructure. 61% of these companies admit their data assets aren’t yet ready for generative AI, and 70% find it quite hard to scale projects utilizing proprietary data. 

Moreover, the report highlights one critical aspect that many companies overlook while implementing GenAI is the human element. A staggering 82% of companies at the early stages of operational maturity have not implemented a talent reinvention strategy. This gap poses a risk, especially as 78% of executives believe that the pace of AI advancement is outstripping their organization’s ability to train and upskill their workforce. 

As we have witnessed in earlier waves of technological adaptation, acquiring and developing talent is a key aspect of ensuring smooth and effective implementation.  

According to Arundhati Chakraborty, group chief executive of Accenture Operations, “Generative AI is more than the technology. It is a driver of a mindset change that impacts the entire enterprise. It requires organizations to have a strong digital core, data strategy, and a well-defined roadmap to change the way they operate. Additionally, an end-to-end perspective leveraging talent, leading practices, and effective collaboration between business and technology teams is essential for intelligent operations”. 

Key Actions to Advance AI-Led Processes in Enterprises

insights from industry leaders 

One key challenge is that even though most executives understand the urgency of reinventing with generative AI, in many cases, their enterprise operations are not ready to support large-scale transformation. Another critical aspect that needs to be highlighted is that GenAI is not just about technology; it represents a fundamental shift in organizational mindset and operations. 

To bridge the gap, the report outlines four essential actions that business leaders should consider: 

1. Centralized Data Governance: Implement a domain-centric approach to data modernization. Ensuring that data is structured and standardized will enable AI tools to operate effectively across the organization. 

2. Talent-First Strategy: Rethink workflows and processes to identify where generative AI can enhance customer service, support staff, and drive business outcomes. Investing in talent and skills development is key to this transformation. 

3. Collaborative Reinvention: Encourage collaboration between business and technology teams. A joint ownership model of assets and platforms will foster innovation and maximize the benefits of AI capabilities. 

4. Leading Processes: Utilize cloud-based process mining to visualize and address operational inefficiencies. By benchmarking against internal and external standards, organizations can better identify opportunities for improvement. 

   
   

Final words 

As we navigate a fast-evolving technological landscape, organizations must embrace the transformative potential of AI and automation. Accenture's findings serve as a clarion call for businesses to reevaluate their operational strategies and prioritize investments in Generative AI in business. 

By fostering a culture of collaboration, establishing a strong data foundation, and focusing on talent development, companies can position themselves for sustainable growth and innovation. 

FAQs 

1. What is Generative AI in business? 

Generative AI refers to AI technologies that can generate new content, automate processes, and drive decision-making. In business, it improves operations, boosts productivity, and drives innovation. 

2. How does AI-led process improvement benefit companies? 

AI-led process improvement streamlines operations, increases productivity, and drives revenue growth by leveraging automation and advanced AI tools. 

3. Why is AI automation important for enterprises? 

AI automation in enterprises helps optimize processes, reduce costs, and enhance efficiency. This gives companies a competitive advantage in their respective industries. 

4. What challenges do companies face when scaling Generative AI? 

Companies often struggle with outdated data infrastructure, a lack of skilled talent, and difficulties in scaling projects, which are essential for successful AI-led transformations. 

5. How can businesses leverage Generative AI for growth? 

To leverage Generative AI, businesses should focus on data governance, talent development, and collaboration between business and technology teams.