5 strategies that separate AI leaders from the 92% still stuck in pilot mode

As the AI ​​moves from experience to the placement of a real world, the enterprises are in fact, determine the best practices as they work on a scale.

More than one job from various vendors announced the main problems. According to Last Report Only 25% of the organizations, only 25% of organizations, in a lesser known way, placed the EU in production. One Report from Deloitte There were similar problems with the organizations fighting measurement and risk management.
A new study MamurOutside this week, it ensures how to manage the EI successfully in the enterprises of leading companies. “Pre-employers’ guide to scale AI“The report is based on a survey of 2000 C-Suite and data sciences with about 2,000 global companies. Croats are a significant gap between AI requests and execution.

Findings take a picture a drawing picture

The report for the enterprise provides critical concepts of the regulated Ai, which stresses the importance of strategic betting, talent development and information infrastructure, strategic betting development and information infrastructure.

Here are five keys to the owner of the screening of the screenture.

1. Talent adulthood is invested as the main scale factor

Many organizations first turn on technological investment, Accenture’s research, which is the most critical distinctive to the use of talent development in fact, the application of successful AI.

“We have not invested in the highest achievement factor, but we found talent,” Senthil Ramani, information and EU accounting, Ventürebeat said. “The front athletes were four times larger than other groups compared to other groups.

The report shows that the front staff distinguished themselves through human-centered strategies. Compared to other companies, they pay four times more attention to cultural adaptation, three times more and the talent adaptation is three times more and the structured training programs are exercised by the opponent’s exchange rate twice.

IT leader action product: To develop a comprehensive talent strategy that applies to both technical skills and cultural adaptation. Create a centralized AI Perfection Center – 57% of the front employees are used in 57% of this model compared to only 16% of this model.

2. Data infrastructure violates or breaks EI scale efforts

Perhaps the most important obstacle of the implementation of the AI ​​in the enterprise is the preparation of information. According to the report, 70% of the companies in the survey agreed that the strong information fund was needed while trying to be scale on a scale.

“The biggest problem for most companies trying to scalize the EU is the development of the correct information infrastructure,” Ramani said. “97% of the frontages developed three or more new information and more new information and AI opportunities compared to only 5% of the companies that practice with AI.”

These important opportunities, developed data management methods such as search expanded generation (dwarf) use various data (26% – 3%), as well as various data between the zero side, second party, third party and synthetic sources.

IT leader action product: Explore a comprehensive information preparation assessment that clearly focuses on the EI executive requirements. Along with the structured information, prioritize construction capabilities to manage unstructured information and develop a strategy for the integration of the crowning organizational knowledge.

3. Strategic betting advice on broad implementation

While many organizations are trying to implement the AI ​​between many functions at the same time, Accenture studies show that the strategic betting of strategic betting has gave significant good results.

“C-Suite leaders first expressed exactly how increasingly growing and achieved it,” Ramani said. “In the report, we called a significant, long-term investment in ‘strategic betting bets’ or Cener EU.

This oriented approach pays dividends. Companies with at least one strategic betting scale are about three times more, which has predictions compared to Gen AI.

IT leader action product: Identify the special strategic AI investment of 3-4 industry directly affecting your main value chain, rather than extensive application.

4. Responsible AI creates value outside the risk reduction

Most organizations see a responsible AI primarily as compliance exercises, but Accenture’s research contributes to the operational AI experience directly.

“Companies” explained “Ramani” to visualize as a liability to recognize the business value as a strategic provider in terms of a strategic provider of responsible AI. “

The report not only reinforced the EU not only risls, but also strengthens customer confidence, the quality of the product and the bolsters are obtained and contributed to financial activities.

IT leader action product: Develop a comprehensive responsible AI management that goes beyond the compliance checkboxes. Exercise active tracking systems that constantly appreciate the risks and effects of AI. Consider developing the principles of the AI ​​than to develop your direct development processes.

5. Front athletes embrace agent AI architecture

The report contains a transformative trend between the front staff: “Agentic Architecture” – networks of AI agents who autonomous all work flows.

Preliminary athletes show more maturity in the placement of autonomous AI agents in accordance with industrial needs. In the report, 65% of this capacity, 65% of this capacity, with one-third of the AI ​​agents to strengthen innovation shows Excel with 50% of these abilities.

These intelligent agent networks represent a key turn from traditional AI applications. The scale provides complex cooperation between AI systems that dramatically improve quality, productivity and cost efficiency.

IT leader action product: Start exploring how the EU can change the workflows to benefit from an autonomous orchestra of the EU. Create pilot projects aimed at multiple agent systems in cases of high-valuable use of your industry.

Material awards of AI adulthood for enterprises

Successful AI Application Awards remain compelling to organizations at all stages of maturity. Accenture’s research measures the amount of benefits expected in certain conditions.

“A company that a company is a progress of a company, a company, a company, which is experimenting with the EU, is practicing by a company to use AI to reinvention,” Ramani said. “On average, these organizations expect a 13% increase in productivity, increasing the increase in income, 11% of customer experience and accommodation of the Generous AI in the enterprises and reduced the Generous AI.”

By mastering the practices of the front athletes, more organizations can eliminate the gap between AI experience and enterprise width.