Accenture: Accelerating AI Scaling for Digital Leadership

Accenture's latest research reveals a striking trend among enterprises: while a majority are exploring AI, only a minority have successfully extended their AI ventures across the organisation to foster real business change.

According to the study, which examined insights from 2,000 executives across nearly 2,000 companies with revenues above US$1bn, only 8% of these organisations stand out as “front-runners.” These companies have managed to scale several AI strategies effectively.The "front-runners" demonstrate an average of 34% scalability in their industry-specific AI projects.

“Today, our clients need more value faster and Accenture is their reinvention partner of choice,” says Julie Sweet, Chair and CEO, Accenture. 

“We are writing the playbook for how to be the most AI-enabled, client-focused professional services company in the world.”

Authors of the report, Accenture's Global Lead for Data & AI Senthil Ramani, Lan Guan, its Chief AI Officer (CAIO) and Philippe Roussiere, Global Lead for Innovation and AI at Accenture Research, say: “For businesses, securing a sustained advantage over competitors was long the Holy Grail – a coveted, yet elusive prize. 

“Today, however, Gen AI and other forms of AI have flipped the script, bringing the previously unattainable within reach.”

Accenture’s three company categories

Research conducted during June and July 2024 spanned nine sectors such as banking, energy and life sciences, to explore what defines AI scaling.

Accenture describes successful scaling as integrating AI throughout a business to achieve broad outcomes, including optimising processes and improving performance metrics.

Organisations fall into three categories based on their AI maturity:

• 42% are “experimenting with AI” 
• 43% are “progressing with AI”
• 15% achieve “AI reinvention-ready” status
  • Within the 15%, 8% classified as front-runners have scaled multiple strategic bets

Strategic bets, according to Accenture, are substantial, long-term investments in Gen AI that target crucial areas of the core value chain.These strategic bets stand apart from “table stakes” investments, which refer to essential, foundational AI deployments such as customer support chatbots — solutions that provide incremental improvements rather than true transformational value. 

The authors highlight that leading organisations excel in what the firm defines as “new data and AI essential capabilities for Gen AI.”

These encompass maturity in large language model operations (LLMOps), advanced approaches to data management and targeted initiatives in specialised talent development.

The research further identifies 105 strategic bets across the nine industries. In the utilities sector, companies are concentrating on optimising workforce operations and improving generation forecasting. Banking institutions, by contrast, are directing their efforts toward fraud management and enhancing cards and payments systems.

Meanwhile, life sciences firms are focusing on accelerating time to market and expediting clinical trials.

Financial performance gap widens between leaders

Financially, the leading AI implementers outshine their peers, achieving a 7% revenue growth edge over those engaging in AI experimentation.

They boast a 4% higher return on invested capital and a 6% increased shareholder return between 2019 to 2024.

Accenture reports that companies scaling even a single strategic AI initiative are nearly three times more likely to surpass expected returns from Gen AI investments compared to those who haven’t scaled.

Moreover, front-runners anticipate boosts across several metrics post AI rollout, including a 13% rise in productivity, 12% revenue growth and 11% improvement in customer experience.

The essence of success lies in "agentic architecture", a network of AI agents dedicated to orchestrating business workflows.

A third of surveyed businesses already employ AI agents to enhance innovation, with 70% recognizing robust data infrastructure as crucial for AI scaling success.

“The future belongs to those who can scale AI not just in pilots – but across every domain of the supply chain,” says Kristalyn Warren Mumaw, AI Evangelist at Accenture.

However, low data readiness remains a primary obstacle, particularly regarding unstructured data utilisation.

Outdated IT systems and insufficient worker access to AI tools and training also present significant barriers.

The strategic investment patterns emerging

Front-runners allocate 51% of their technology expenditures to cloud and AI initiatives, according to Accenture, showing a trend of superior executive sponsorship compared to mere 5% in AI-ready but not yet scaled companies.

Centralised operating models are also emphasised, with leading organisations frequently employing centres of excellence for AI, a practice only seen in 16% of fast-followers.

In terms of data usage, front-runners often utilise zero-party, second-party, third-party and synthetic data more effectively than their peers.

The adoption of advanced practices like retrieval-augmented generation (RAG) is more prevalent among front-runners, highlighting their capability to enhance model performance significantly.

The industry leaders emerging

Life sciences firms represent the highest proportion of front-runners at 12%, focusing on time-to-market acceleration and product value maximisation.
Conversely, the retail sector lags at just 2% achieving front-runner status.

In insurance, fraud detection represents the most scaled strategic bet at 23% of companies, followed by call assistance at 13% and claims intake at 12%. 

Meanwhile, banking institutions focus primarily on fraud management and cards and payments, each scaled by 29% of firms. 

Five imperatives defining the success path

Accenture details five imperative strategies that enable successful strategic bet scaling.

The framework begins with the imperative to “Lead with value,” which calls for proactive involvement from CEOs and boards — setting clear value targets and focused, disciplined investment priorities for AI initiatives.

The second imperative, “Reinvent talent and ways of working,” centres on broad AI upskilling, agile workforce models and fostering human-agent collaboration.

Accenture underscores the importance of attracting and retaining specialists — including AI strategists, AI architects and computational scientists — alongside cultivating university partnerships to strengthen the talent pipeline.

According to the report, the third imperative is to “Build an AI-enabled, secure digital core,” emphasising the need for modernised data ecosystems, integrated AI models and agentic architectures that directly support business objectives.

Companies are also encouraged to develop “cognitive digital brains”— centralised AI intelligence hubs designed to deliver real-time enterprise decision-making by processing live data streams.

The fourth imperative, “Close the gap on responsible AI,” urges organisations to go beyond compliance by building trust, enhancing product quality and delivering customer value.

With AI-driven incidents rising 32% in 2023, establishing robust responsible AI practices is vital for sustainable growth and risk management.

Completing the framework, “Drive continuous reinvention” acknowledges that enterprise transformation is an ongoing journey.

This requires embedding change within organisational culture, maintaining financial discipline, monitoring ROI and reallocating resources to sustain momentum and unlock lasting value.

Implementation challenges persist across categories

Despite varying AI maturity levels, Accenture finds that companies face similar scaling challenges with different intensities. 

Building and maintaining multi-disciplinary teams remains the foremost challenge for both leading and experimental companies, while developing comprehensive, end-to-end data foundations emerges as the principal hurdle for organisations trying to advance.

Further challenges rounding out the top five include foundation model customisation, proving tangible ROI and addressing security and privacy risks — concerns prevalent across all company catagories.

Front-runners are four times more likely than fast-followers to prioritise cultural transformation and organisations with mature change management capabilities are more than twice as likely to achieve successful enterprise reinvention.

Accenture’s research applies hierarchical clustering with Gower’s distance to categorise companies according to 10 critical capabilities.

These range from foundational aspects — including data and AI strategy, platform maturity and talent development — to emerging essentials such as LLMOps maturity and foundation model customisation.

The clustering approach was validated against latent class analysis, revealing 85% overall concordance and 95% agreement specifically among AI reinvention-ready firms.

This strong alignment underlines the robustness of the methodology and confirms the unique, traceable traits of front-runner organisations.

Looking ahead, the consultancy stresses that AI’s role has shifted from simply boosting efficiency to driving full-scale enterprise reinvention.

To enter the front-runner category, organisations must embrace the five imperatives and develop both foundational and new essential capabilities essential for sustained AI scaling success.

The authors conclude: “When used to its full potential, AI is now something far greater: an unstoppable force for enterprise reinvention, allowing companies to grow faster and innovate better than rivals.”