New Accenture research finds that companies with AI-Led processes outperform peers

New research from Accenture indicates that 79% organisations in India have seen investments in generative AI and automation meet or exceed expectations, with 64% planning to increase their efforts and further strengthen these capabilities by 2026.

According to the report, ‘Reinventing Enterprise Operations with Gen AI’, globally, the number of companies that have fully modernised, AI-led processes and have achieved intelligent operations has nearly doubled from 9% in 2023 to 16% in 2024.  Compared to peers, these organisations achieve 2.5x higher revenue growth, 2.4x greater productivity and 3.3x greater success at scaling generative AI use cases. In what is a key highlight, the research found that in India the corresponding number of companies that have fully modernised, AI-led processes, and intelligent operations has tripled from 8% in 2023 to 25% in 2024.

Findings also assessed that these ‘reinvention-ready’ companies are moving faster and are amplifying the impact of generative AI across the business. Enabled by a digital core, in India, these organisations have already developed generative AI use cases in finance (76%), IT & security (65%), customer service (63%), and other core functions.

While the research indicates that some companies are reinvention-ready—moving to the highest level of operations readiness—a majority, 64% globally, and 58% in India, still struggle to change the way they operate. The reasons for this are universal.

• Globally, organisations lag behind on building a robust data foundation. For example:

• • 61% report that their data assets are not ready for generative AI yet
  • 70% find it hard to scale projects that use proprietary data.

• Across the world, the deep dependency on people is often overlooked:

• • 82% of companies at the early stage of operations readiness, have not applied a talent reinvention strategy, planned to meet workforce needs, or acquired new talent or training to prepare workers for generative AI-led workflows.
  • In fact, many executives (78%) indicate that AI and generative AI are advancing too fast for their organisation’s training efforts to keep pace.

The findings are an outcome of an Accenture survey of 2,000 executives, across 12 countries and 15 industries. This included 200 senior executives (81% CXOs) from companies headquartered in India.

“Most executives understand the urgency of reinventing with generative AI, but in many cases their enterprise operations are not ready to support large scale transformation,” said 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 organisations 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.”

The report highlights four key actions business leaders should take to advance their operations maturity:

• Implement a centralised data governance and domain-centric approach to data modernisation. Connect processes and tools across functions to ensure people have a clear understanding of how to create, handle and consume data, which should be structured in a standardised way to be accessed by AI tools across the business.
• Embrace a talent-first reinvention strategy. Reinvent work and rethink processes and entire workflows to gain a clear view of where generative AI can have the most impact in serving customers, supporting people and achieving business outcomes.
• Ensure business and tech teams co-own reinvention. Collaboration drives innovation as both teams jointly own how assets, platforms and products are developed to leverage the full capabilities of generative AI, enterprise wide.
• Adopt leading processes to drive business outcomes. Apply cloud-based process mining to calibrate internal and external benchmarks so it’s easier to visualise process gaps and get clear insights into operational inefficiencies or opportunities for improvement.