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As AI moves from experimentation to real-world deployments, enterprises are determining best practices for what actually works at scale.
Multiple studies from various vendors have outlined the core challenges. According to a recent report from Vellum, only 25% of organizations have deployed AI in production with even fewer recognizing measurable impact. A report from Deloitte found similar challenges with organizations struggling with issues of scalability and risk management.
A new study from Accenture, out this week, provides a data-driven analysis of how leading companies are successfully implementing AI across their enterprises. The “Front-Runners’ Guide to Scaling AI” report is based on a survey of 2,000 C-suite and data science executives from nearly 2,000 global companies with revenues exceeding $1 billion. The findings reveal a significant gap between AI aspirations and execution.
The findings paint a sobering picture: only 8% of companies qualify as true “front-runners” that have successfully scaled multiple strategic AI initiatives, while 92% struggle to advance beyond experimental implementations.
For enterprise IT leaders navigating AI implementation, the report offers critical insights into what separates successful AI scaling from stalled initiatives, highlighting the importance of strategic bets, talent development and data infrastructure.
Here are five key takeaways for enterprise IT leaders from Accenture’s research.
1. Talent maturity outweighs investment as the key scaling factor
While many organizations focus primarily on technology investment, Accenture’s research reveals that talent development is actually the most critical differentiator for successful AI implementation.
“We found the top achievement factor wasn’t investment but rather talent maturity,” Senthil Ramani, data and AI lead at Accenture, told VentureBeat. “Front-runners had four-times greater talent maturity compared to other groups. Leading by executing talent strategies more effectively and directing talent-related spending to the highest-value uses.”
The report shows front-runners differentiate themselves through people-centered strategies. They focus four times more on cultural adaptation than other companies, emphasize talent alignment three times more and implement structured training programs at twice the rate of competitors.
IT leader action item: Develop a comprehensive talent strategy that addresses both technical skills and cultural adaptation. Establish a centralized AI center of excellence – the report shows 57% of front-runners use this model compared to just 16% of fast-followers.
2. Data infrastructure makes or breaks AI scaling efforts
Perhaps the most significant barrier to enterprise-wide AI implementation is inadequate data readiness. According to the report, 70% of surveyed companies acknowledged the need for a strong data foundation when trying to scale AI.
“The biggest challenge for most companies trying to scale AI is the development of the right data infrastructure,” Ramani said. “97% of front-runners have developed three or more new data and AI capabilities for gen AI, compared to just 5% of companies that are experimenting with AI.”
These essential capabilities include advanced data management techniques like retrieval-augmented generation (RAG) (used by 17% of front-runners vs. 1% of fast-followers) and knowledge graphs (26% vs. 3%), as well as diverse data utilization across zero-party, second-party, third-party and synthetic sources.
IT leader action item: Conduct a comprehensive data readiness assessment explicitly focused on AI implementation requirements. Prioritize building capabilities to handle unstructured data alongside structured data and develop a strategy for integrating tacit organizational knowledge.
3. Strategic bets deliver superior returns to broad implementation
While many organizations attempt to implement AI across multiple functions simultaneously, Accenture’s research shows that focused strategic bets yield significantly better results.
“C-suite leaders first need to agree on—then clearly articulate—what value means for their company, as well as how they hope to achieve it,” Ramani said. “In the report, we referred to ‘strategic bets,’ or significant, long-term investments in gen AI focusing on the core of a company’s value chain and offering a very large payoff. This strategic focus is essential for maximizing the potential of AI and ensuring that investments deliver sustained business value.”
This focused approach pays dividends. Companies that have scaled at least one strategic bet are nearly three times more likely to have their ROI from gen AI surpass forecasts compared to those that haven’t.
IT leader action item: Identify 3-4 industry-specific strategic AI investments that directly impact your core value chain rather than pursuing broad implementation.
4. Responsible AI creates value beyond risk mitigation
Most organizations view responsible AI primarily as a compliance exercise, but Accenture’s research reveals that mature responsible AI practices directly contribute to business performance.
“Companies need to shift their mindset from viewing responsible AI as a compliance obligation to recognizing it as a strategic enabler of business value,” Ramani explained. “ROI can be measured in terms of short-term efficiencies, such as improvements in workflows, but it really should be measured against longer-term business transformation.”
The report emphasizes that responsible AI includes not just risk mitigation but also strengthens customer trust, improves product quality and bolsters talent acquisition – directly contributing to financial performance.
IT leader action item: Develop comprehensive responsible AI governance that goes beyond compliance checkboxes. Implement proactive monitoring systems that continually assess AI risks and impacts. Consider building responsible AI principles directly into your development processes rather than applying them retroactively.
5. Front-runners embrace agentic AI architecture
The report highlights a transformative trend among front-runners: the deployment of “agentic architecture” – networks of AI agents that autonomously orchestrate entire business workflows.
Front-runners demonstrate significantly greater maturity in deploying autonomous AI agents tailored to industry needs. The report shows 65% of front-runners excel in this capability compared to 50% of fast-followers, with one-third of surveyed companies already using AI agents to strengthen innovation.
These intelligent agent networks represent a fundamental shift from traditional AI applications. They enable sophisticated collaboration between AI systems that dramatically improves quality, productivity and cost-efficiency at scale.
IT leader action item: Begin exploring how agentic AI could transform core business processes by identifying workflows that would benefit from autonomous orchestration. Create pilot projects focused on multi-agent systems in your industry’s high-value use cases.
The tangible rewards of AI maturity for enterprises
The rewards of successful AI implementation remain compelling for organizations in all stages of maturity. Accenture’s research quantifies the expected benefits in specific terms.
“Regardless of whether a company is considered a front-runner, a fast follower, a company making progress, or a company still experimenting with AI, all the companies we surveyed expect big things from using AI to drive reinvention,” Ramani said. “On average, these organizations expect a 13% increase in productivity, a 12% increase in revenue growth, an 11% improvement in customer experience, and an 11% decrease in costs within 18 months of deploying and scaling gen AI across their enterprise.”
By adopting the practices of front-runners, more organizations can bridge the gap between AI experimentation and enterprise-wide transformation.
